Agile Science in Cancer Implementation Research: Accelerating Evidence into Practice

Abstract

This article explores the transformative role of agile science methods in cancer implementation research, addressing a critical need for more adaptive and efficient approaches to integrating evidence-based interventions into routine care. Tailored for researchers, scientists, and drug development professionals, it provides a comprehensive framework spanning foundational concepts, practical methodologies, and optimization strategies. The content synthesizes current applications—from national cancer control planning to personalized treatment protocols—and examines validation frameworks like PRISM and RE-AIM. By addressing common implementation barriers and emphasizing iterative, stakeholder-engaged processes, this guide aims to equip professionals with the knowledge to enhance the reach, equity, and sustainability of cancer care innovations.

What is Agile Science? Redefining Research for Complex Cancer Challenges

Agile Science is an innovative research paradigm that applies the principles of agile methodologies, originally developed for software engineering, to the complex challenges of scientific inquiry, particularly in health and behavior change research. Drawing from Merriam-Webster's definition of agile as "having a quick resourceful and adaptable character," Agile Science embodies a nimble, iterative approach to knowledge generation and implementation [1]. This methodology stands in contrast to traditional linear research models, emphasizing instead rapid-cycle development, continuous optimization, and early-and-often sharing of resources and findings [1]. Within cancer implementation research, this approach offers promising frameworks for accelerating the translation of evidence-based interventions into real-world practice, ultimately aiming to reduce the persistent gaps between scientific discovery and patient care delivery.

The transition of agile principles from software development to health contexts represents a significant evolution in research methodology. In software development, agile methods such as extreme programming (XP) utilize "sprints" to create "minimal viable products" (MVPs) that are rapidly released to stakeholders for feedback and refinement [1]. These principles have now been adapted for health research, where they enable more responsive and contextually appropriate intervention development. This paradigm shift is particularly relevant for cancer control, where the slow pace of translating research into practice has been a longstanding barrier to reducing cancer burden and addressing disparities [2] [3].

Conceptual Framework and Core Components

Foundational Principles

Agile Science is built upon three core principles derived from both software development and emerging practices in health research. First, it embraces iterative development and ongoing optimization that carefully studies the "fit" between individuals, context, and interventions for producing desired outcomes [1]. This represents a fundamental shift from the traditional four-phase biomedical model (discovery, pilot, efficacy, effectiveness) toward a more dynamic, non-linear process that better accommodates the complexity of behavior change and implementation in real-world settings.

Second, Agile Science emphasizes the deconstruction of complex interventions into meaningful modules. Rather than treating multi-component interventions as monolithic entities, Agile Science identifies the smallest, meaningful, self-contained, and repurposable elements of an intervention [1]. This modular approach enables more precise understanding of active ingredients and facilitates adaptation across different contexts and populations.

Third, the paradigm prioritizes the development of decision algorithms for personalization. By creating algorithms that support matching interventions and modules with specific individuals in context, Agile Science enables more precise implementation strategies [1]. This focus on personalization aligns with the broader movement toward precision medicine and precision implementation in cancer care [4].

The Three Core Products of Agile Science

Agile Science targets three specific products that collectively advance implementation research:

• Behavior Change Modules: These are the fundamental units of intervention - the smallest, meaningful, self-contained components that can be repurposed across different interventions and contexts [1]. In cancer screening implementation, examples include patient reminder systems, small media education tools, or provider assessment and feedback mechanisms [5].

• Computational Behavioral Models: These models define the interaction between modules, individuals, and context [1]. They provide the theoretical framework for understanding how different intervention components work synergistically and how their effects may vary across different implementation contexts.

• Personalization Algorithms: These are decision rules that guide intervention adaptation for specific individuals, populations, or settings [1]. In cancer implementation research, these algorithms might help determine which combination of evidence-based interventions is most appropriate for a particular clinical setting based on organizational capacity, patient population characteristics, and available resources.

Table 1: Core Products of Agile Science and Their Applications in Cancer Implementation Research

Product	Definition	Example in Cancer Research
Behavior Change Modules	Smallest, meaningful, self-contained intervention components	Patient reminders, provider assessment/feedback, structural barrier reduction [5]
Computational Behavioral Models	Frameworks defining module-individual-context interactions	PRISM/RE-AIM guiding multilevel data collection for CRC screening [5]
Personalization Algorithms	Decision rules for intervention adaptation	Rules for matching EBIs to FQHC capacities and patient characteristics [5] [1]

Agile Science in Practice: Methodologies and Experimental Protocols

Key Research Methodologies

Agile Science employs several specific methodological approaches that enable its iterative, optimized approach to research:

The Multiphase Optimization Strategy (MOST) provides a framework for continuous optimization of behavior change interventions by iteratively evaluating the efficacy of intervention components [1]. This approach uses factorial and fractional factorial designs to efficiently examine main effects and interaction effects among multiple intervention components simultaneously.

Sequential Multiple Assignment Randomized Trials (SMART) represent another key methodology, enabling researchers to test decision rules within adaptive interventions [1]. This is particularly valuable when dealing with scenarios where initial non-response to intervention requires subsequent adaptation of strategy.

Micro-randomized trials combine the logic of N-of-1 trials and factorial designs to model the effectiveness of treatment components over time [1]. These trials can test proximal main effects of components, time-varying moderation, and support the development of idiographic computational models.

Protocol for an Agile Evaluation in Digital Screening

A recent study demonstrating agile evaluation methodologies examined the uptake of a digital cardiovascular screening service, providing a clear template for similar approaches in cancer screening [6]. The protocol involved four sequential studies conducted within a four-week period, inviting 1,700 participants and employing multiple rapid randomized controlled trials ("A/B tests") to iteratively optimize invitation and reminder systems.

Table 2: Agile Evaluation Protocol for Digital Screening Uptake [6]

Study Sequence	Experimental Focus	Participants	Key Finding
Study 1	Testing 6 different SMS invitation variants	1,700 recipients	Shortest message had highest uptake (20% vs. 12% standard)
Study 2	Baseline effect of single SMS reminder	1,129 non-responders	+3.1% increase in uptake from single reminder
Study 3	SMS vs. postal reminder comparison	1,076 non-responders	Postal reminder twice as effective as SMS (+7% vs. +3%)
Study 4	"Final reminder" SMS effect	983 non-responders	"Final reminder" wording generated +7% response

The specific experimental workflow for this agile evaluation can be visualized as follows:

Protocol for PRISM-Guided Implementation Mapping

The Project FACtS (FQHCs Assessing Colorectal cancer Screening) study provides another exemplary protocol for Agile Science application in cancer implementation research [5]. This study utilized the Practical, Robust Implementation and Sustainability Model (PRISM) - a contextually expanded version of the RE-AIM framework - to guide partner-engaged data collection on processes, resources, facilitators, and barriers for colorectal cancer screening.

The methodological sequence involved:

• Introductory Meetings: Initial 60-90 minute in-person meetings with each FQHC team to develop priorities, elicit feedback on study design, and set guiding principles [5].

• Agile Science Workshop: Collaborative sessions with site-based coordinators, quality improvement specialists, and academic teams to inform data collection processes and conduct preliminary mapping of CRC screening at each FQHC [5].

• Secondary Data Collection: Gathering internal clinic variables (clinical characteristics, screening rates) and external influences (policies, accrediting requirements) [5].

• Online Surveys: Collecting data on patient-level barriers, referral processes, and existing clinic relationships [5].

• In-Depth Interviews: Gathering detailed information on emerging critical issues identified through previous data collection methods [5].

This iterative, multi-method approach generated insights that led to the development of process maps guiding the selection of implementation strategies to support evidence-based interventions for CRC screening [5].

Application in Cancer Research: Case Studies and Examples

GBM AGILE: An Adaptive Clinical Trial Platform

The GBM AGILE (Glioblastoma Adaptive Global Innovative Learning Environment) initiative represents a groundbreaking application of agile principles in cancer clinical research. As the world's first "adaptive clinical trial platform," GBM AGILE simultaneously evaluates multiple therapies for newly diagnosed and recurrent glioblastoma patients [7]. This innovative design identifies effective new treatments for subtypes of this tumor based on patients' biological characteristics in a rapid manner.

The adaptive nature of GBM AGILE means that the number of patients recruited and their allocation within the study are continuously adjusted based on emerging results [7]. This approach is considered more efficient than traditional clinical trial designs and requires significantly fewer patients, particularly for control arms. The platform functions as a "master protocol" that allows for testing of more than one investigational drug, creating a dynamic system for the discovery and testing of various experimental drugs [7]. This agile framework has attracted significant interest and support from clinicians, industry, and regulatory agencies due to its potential to accelerate treatment development for this devastating disease.

Agile Science in Colorectal Cancer Screening Implementation

The previously mentioned Project FACtS study demonstrates the application of Agile Science principles to implementation challenges in colorectal cancer screening, particularly within federally qualified health centers serving predominantly low-income, publicly insured, or uninsured Hispanic/Latino patients [5]. This research applied the PRISM model to assess relevant internal and external environments and their fit with characteristics of evidence-based interventions for CRC screening.

The project focused on implementing United States Community Preventive Services Task Force evidence-based interventions, including clinic-level structural barrier reduction, provider assessment and feedback, and patient reminders with one-on-one education [5]. The agile approach enabled the research team to gather implementation-relevant information through consecutive and iterative data collection approaches, with each method informing subsequent ones to reduce participant burden and produce partner-driven results.

The conceptual framework for this project integrated multiple elements: (1) CPSTF evidence-based interventions; (2) NCCRT implementation strategies; (3) multi-level outcomes measured at clinic, provider, and patient levels; and (4) RE-AIM dimensions (reach, adoption, implementation, maintenance) [5]. The bidirectional relationships among these elements created a dynamic system for continuous refinement of implementation strategies.

Digital Cancer Screening Implementation

The agile evaluation methodology applied to digital cardiovascular screening [6] provides a directly transferable template for cancer screening implementation. The rapid sequential testing of invitation and reminder modalities demonstrates how agile approaches can efficiently optimize uptake strategies for cancer screening programs, including mammography, cervical cancer screening, and colorectal cancer screening.

The key finding that shorter invitation messages, multi-modal reminders, and specific wording (e.g., "final reminder") significantly impact uptake rates has immediate practical implications for cancer screening implementation [6]. Furthermore, the finding that a postal reminder was twice as effective as an SMS reminder highlights the importance of multi-modal approaches and the value of agile methodologies for identifying these implementation insights.

Implementation science research requires specific conceptual tools and frameworks to effectively study and optimize the integration of evidence-based interventions into routine healthcare. The following table details key "research reagents" essential for conducting Agile Science in cancer implementation research.

Table 3: Essential Research Reagents for Agile Science in Cancer Implementation Research

Tool/Resource	Function/Application	Example Use Case
PRISM/RE-AIM Framework	Guides multilevel contextual assessment and implementation outcome evaluation [5]	Assessing organizational and patient factors in CRC screening implementation [5]
Agile Science Workshop Protocol	Facilitates collaborative data collection planning and preliminary process mapping [5]	Engaging FQHC partners in mapping current screening processes [5]
Implementation Strategy Taxonomy	Categorizes and defines specific implementation strategies [5]	Selecting CPSTF EBIs and NCCRT strategies for CRC screening [5]
Sequential RCT (A/B Testing) Design	Enables rapid iterative testing of implementation variants [6]	Optimizing SMS invitation content for screening uptake [6]
Adaptive Trial Platform	Allows simultaneous testing of multiple interventions with dynamic allocation [7]	GBM AGILE evaluating multiple therapies for glioblastoma [7]
Process Mapping Methodology	Visualizes current workflows to identify implementation barriers/facilitators [5]	Developing process maps of CRC screening at FQHCs [5]
Evidence-Based Cancer Control Program (EBCCP)	Repository of evidence-based programs and implementation materials [3]	Accessing proven cancer control programs for implementation

The workflow for applying the PRISM model in implementation research can be visualized as follows:

Agile Science represents a transformative approach to cancer implementation research that addresses the critical challenge of translating evidence into practice more rapidly and effectively. By adapting principles from software development and emphasizing iterative, responsive methodologies, Agile Science offers practical frameworks for addressing the persistent gaps in cancer care delivery, particularly for underserved populations [5] [2].

The applications of Agile Science in cancer research - from adaptive clinical trial platforms like GBM AGILE to implementation studies optimizing cancer screening - demonstrate the versatility and potential of this approach [5] [6] [7]. As the field advances, the integration of Agile Science with emerging priorities like precision implementation and health equity will be essential for reducing the burden of cancer and improving outcomes for all populations [4] [3]. The methodologies, protocols, and resources outlined in this article provide researchers and implementation practitioners with essential tools for advancing this innovative approach to cancer research and care delivery.

Application Note: Strategic Stakeholder Engagement in Cancer Research

Effective stakeholder engagement is a cornerstone of agile science, ensuring that research remains relevant and applicable to end-users. The "7Ps of Stakeholder Engagement" framework provides a systematic taxonomy for identifying key partners throughout the research lifecycle [8].

Table 1: The 7Ps Stakeholder Framework for Cancer Implementation Research

Stakeholder Category	Description	Example Roles in Cancer Research
Patients & Public	Consumers of cancer care, their families, and advocacy organizations.	Provide input on patient-centered outcomes, trial burden, and dissemination materials.
Providers	Individuals and organizations providing patient care.	Oncologists, nurses, clinic administrators; inform feasibility and clinical workflows.
Purchasers	Entities underwriting healthcare costs (e.g., employers).	Employers, government; provide perspective on economic impact and sustainability.
Payers	Entities responsible for reimbursement of care.	Insurers, Medicare; inform on reimbursement structures and value assessment.
Policy Makers	Governmental and non-governmental policy-making entities.	Regulators, public health officials; guide research aligned with policy priorities.
Product Makers	Drug and device manufacturers.	Pharmaceutical and biotech companies; contribute to therapy development.
Principal Investigators	Researchers and their funders.	Academic and clinical scientists; ensure methodological rigor and scientific value.

Engagement should be a bi-directional relationship, moving beyond token inclusion to active collaboration [8]. The External Stakeholder Advisory Group (ESAG) model from the SWOG S1415CD (TrACER) trial demonstrates this principle in practice, integrating ten patient partners, payers, pharmacists, guideline experts, and providers from the planning phase through to dissemination [9]. This model led to tangible improvements in trial endpoints, patient surveys, and consent form clarity [9].

Experimental Protocol: Establishing a Multi-Stakeholder Advisory Group

Objective: To formally integrate diverse stakeholder perspectives into all stages of a cancer implementation research project.

Materials: Stakeholder identification matrix, communication platform (e.g., email, web-conferencing), compensation framework.

Methodology:

• Identification: Use the 7Ps framework to identify potential stakeholders from each relevant category, ensuring representation of diverse and potentially competing interests [8]. Patient partners should have experience navigating the healthcare system as advocates, patients, or caregivers [9].
• Recruitment & Onboarding: Clearly define roles, time commitments, and compensation. Adhere to compensation frameworks, such as those from PCORI, to financially compensate patient partners for their time and expertise [9].
• Structuring Engagement:
  • Frequency: Conduct a mix of in-person meetings, web conferences, and targeted email discussions annually [9].
  • Agendas: Focus meetings on collaborative problem-solving. For patient-specific meetings, provide study briefings that explain complex clinical or statistical concepts [9].
  • Feedback Integration: Implement a structured process for reviewing stakeholder suggestions (e.g., a two-week comment period), determining feasibility, and reporting back on final decisions [9].
• Evaluation: Administer an annual satisfaction survey to assess stakeholders' satisfaction with communication, collaboration, and respect, using the feedback to refine engagement strategies [9].

Application Note: Quantitative Iteration in Intervention Development

Iteration in agile science is driven by quantitative feedback loops that allow for continuous refinement. This is exemplified in both educational interventions and preclinical research.

Project ECHO (Extension for Community Healthcare Outcomes) utilizes a virtual telementoring model to address cancer-related knowledge gaps among healthcare professionals. Quantitative evaluations of American Cancer Society (ACS) ECHO programs demonstrate their effectiveness, with data showing an average increase in participant knowledge (+0.84 on a 5-point scale) and confidence (+0.77 on a 5-point scale) [10]. Furthermore, 59% of participants reported plans to use the presented information within a month, indicating high readiness for implementation [10].

In preclinical chemical biology, iteration is central to quantifying drug response. The half-maximal inhibitory concentration (IC50) is a critical parameter for rank-ordering compound efficacy and understanding mode-of-action [11]. Robust IC50 determination relies on a quantitative framework using a 4-parameter logistic (4PL) nonlinear regression model to fit dose-response data [11].

Table 2: Quantitative Outcomes from ACS ECHO Cancer Programs (2023-2024)

Program Metric	Program A	Program B	Program C	Program D	Aggregate
Unique Participants	195	45	59	132	431
Number of Sessions	4	7	9	7	27
Average Participants/Session	Information missing	Information missing	Information missing	Information missing	20.15
Avg. Change in Knowledge (5-pt scale)	Not collected*	Collected	Collected	Collected	+0.84
Avg. Change in Confidence (5-pt scale)	Not collected*	Collected	Collected	Collected	+0.77
Participants Likely to Use Info	Information missing	Information missing	Information missing	Information missing	59%

*Program A was a public program and used only post-session surveys, unlike the private programs (B, C, D) that used pre/post assessments [10].

Experimental Protocol: Determining IC50 for Anti-Cancer Compounds

Objective: To quantitatively determine the potency of a chemical inhibitor on cellular viability (a phenotype-based assay).

Materials:

• Patient-derived cancer cell lines
• Chemical compound(s) for testing
• 96-well or 384-well cell culture plates
• Cell Titer-Glo (CTG) or similar viability assay reagent
• Plate reader capable of measuring luminescence

Methodology:

• Plate Seeding: Seed cells at an optimized density in culture plates and allow to adhere.
• Compound Dosing: Prepare a serial dilution of the inhibitor to create a minimum of 8-10 concentration points, spaced equally (e.g., 1:3 or 1:4 dilutions). The concentration range should be broad enough to ensure that half the data points are above and half below the eventual IC50 value [11]. Add compounds to cells, ensuring the solvent concentration is constant across all wells.
• Incubation: Incubate cells with the compound for a predetermined period (e.g., 72 hours).
• Viability Quantification: Add CTG reagent to lyse cells and generate a luminescent signal proportional to the amount of ATP present, which correlates with viable cell mass [11].
• Data Analysis:
  • Normalize luminescence data: Set the signal from vehicle-treated (DMSO) cells to 0% inhibition and the signal from a well with no cells to 100% inhibition.
  • Fit the normalized dose-response data to a 4-parameter logistic (4PL) model using statistical software (e.g., GraphPad Prism) [11].
  • The IC50 is the concentration at which the fitted curve shows 50% inhibition of maximal cell viability. The maximum % inhibition should be >50% to be considered a valid IC50 [11].

Key Criteria for Success:

• Use a minimum of three biological replicates per data point.
• Ensure well-defined top (minimum inhibition) and bottom (maximum inhibition) plateaus in the data.
• Maintain a constant enzyme/cell concentration throughout the experiment [11].

Application Note & Protocol: Adaptive Design with the PRISM Framework

Adaptive design involves using contextual feedback to iteratively refine implementation strategies. The Practical, Robust Implementation and Sustainability Model (PRISM) provides a structured framework for this process, combining multi-level contextual assessment with the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) outcomes framework [5].

Project FACtS (FQHCs Assessing Colorectal cancer Screening) applied PRISM in Federally Qualified Health Centers (FQHCs) to improve colorectal cancer (CRC) screening. The study engaged partners through an iterative sequence of data collection methods, each increasing in specificity [5]. Insights from PRISM domains were used to develop process maps that directly guided the selection of implementation strategies for evidence-based interventions [5].

Experimental Protocol: Iterative Contextual Assessment Using PRISM

Objective: To guide the selection and adaptation of implementation strategies through an iterative, multi-method understanding of context.

Materials: PRISM framework guide, access to community and clinical partners, qualitative and quantitative data collection tools.

Methodology:

• Foundational Relationship Building: Prior to formal data collection, establish trust and mutual capacity with partners over months or years, applying principles of community engagement [5].
• Iterative Data Collection Sequence: Execute consecutive data collection phases, where findings from each phase inform the next [5].
  • Introductory Meetings: Hold meetings with executive and clinical leadership (e.g., CEO, CMO, physician champions) to develop shared priorities and provide feedback on study design [5].
  • Agile Science Workshop: Conduct workshops with site coordinators and quality improvement staff to discuss feasible strategies and conduct preliminary process mapping of the clinical workflow (e.g., CRC screening) [5].
  • Secondary Data Collection: Gather internal clinic data (e.g., clinical characteristics, screening rates) and data on external influences (e.g., policies, funders) [5].
  • Surveys: Deploy surveys to a broader group of stakeholders (e.g., providers, clinic managers) to gather data on specific barriers and processes identified in earlier phases [5].
  • In-Depth Interviews: Conduct interviews with key informants to gather detailed information on critical issues that emerged from surveys [5].
• Analysis and Implementation Strategy Selection: Synthesize data from all phases through the lens of PRISM domains (e.g., organizational and patient perspectives, implementation infrastructure). Use this synthesis to create detailed process maps and collaboratively select evidence-based implementation strategies that fit the specific context [5].

The Scientist's Toolkit: Essential Reagents & Frameworks

Table 3: Key Research Reagent Solutions and Frameworks

Tool / Framework	Type	Primary Function in Agile Cancer Research
Cell Titer-Glo (CTG)	Research Reagent	Quantifies viable cells based on ATP content, serving as a key readout for phenotypic drug response (e.g., IC50) assays in chemical biology [11].
4-Parameter Logistic (4PL) Model	Analytical Tool	Fits sigmoidal dose-response data to quantitatively determine key parameters like IC50 and EC50, enabling rank-ordering of compound efficacy [11].
PRISM/RE-AIM Framework	Implementation Science Framework	Guides the multi-level assessment of context and outcomes to adapt and select implementation strategies for evidence-based interventions in real-world settings [5].
7Ps of Stakeholder Engagement	Taxonomic Framework	Provides a systematic approach to identifying and categorizing key stakeholders to ensure comprehensive and representative engagement throughout the research lifecycle [8].
ACS ECHO / iECHO Platform	Technological Platform	Facilitates virtual telementoring and collaborative learning to disseminate cancer-specific knowledge and build confidence among healthcare professionals [10].

The Urgency for Agility in Cancer Implementation Research

The global burden of cancer continues to rise, with significant disparities in outcomes and access to evidence-based interventions across different healthcare systems. Despite advances in cancer research, a persistent gap exists between the development of effective interventions and their successful integration into routine clinical practice [12]. This gap is particularly pronounced in resource-constrained settings and for complex, medically challenging cancers [13] [14]. Implementation science has emerged as a critical discipline to address this challenge, focusing on methods to enhance the adoption, implementation, and sustainability of evidence-based interventions [15]. However, traditional implementation approaches often lack the flexibility and adaptability required for dynamic cancer care environments.

Agile science represents an innovative paradigm shift in implementation research, emphasizing iterative testing, rapid-cycle evaluation, and adaptive strategies to accelerate the translation of evidence into practice [15]. Drawing inspiration from agile methodologies successfully applied in software engineering and other fields, agile science offers promising approaches for addressing complex implementation challenges in oncology [16] [17]. This article explores the theoretical foundations, practical applications, and future directions of agile methods in cancer implementation research, providing researchers and drug development professionals with actionable frameworks and protocols to enhance the efficiency and effectiveness of cancer care delivery.

Theoretical Foundations of Agile Science in Implementation Research

Core Principles and Definitions

Agile science in implementation research represents a methodological approach that emphasizes iterative development, rapid testing, and adaptive strategies to accelerate the translation of evidence into practice [15]. This approach contrasts with traditional linear implementation models, offering instead a flexible framework that can respond to evolving contexts and emerging barriers. The conceptual foundation of agile science borrows from agile methodologies originally developed in software engineering, which prioritize individuals and interactions, working solutions, customer collaboration, and responsiveness to change [16].

Implementation strategies form the core "how" of implementation science—the specific methods and techniques used to enhance the adoption, implementation, and sustainability of evidence-based interventions [15]. These strategies can target multiple levels of the healthcare ecosystem, from individual clinicians and patients to organizational systems and policies. The Expert Recommendations for Implementing Change (ERIC) project has systematically compiled and defined 73 discrete implementation strategies, which have been further categorized into nine cohesive clusters [15].

Table 1: Key Agile Science Concepts in Cancer Implementation Research

Concept	Definition	Application in Cancer Research
Iterative Testing	Repeated cycles of strategy implementation and evaluation	Rapid refinement of cancer screening programs based on continuous feedback
Rapid-Cycle Evaluation	Short assessment periods to quickly determine effectiveness	Monthly evaluation of patient navigation programs for GI cancer screening
Adaptive Strategies	Implementation approaches that evolve based on context	Tailoring lung cancer screening programs to specific Asian healthcare systems
Mechanism Mapping	Identifying how strategies produce effects	Understanding how facilitation improves colorectal cancer screening rates
Causal Pathway Diagramming	Visualizing relationships between strategies and outcomes	Mapping how training leads to improved breast cancer early detection

Agile Values in Cancer Care

The translation of agile values from software engineering to cancer care has demonstrated significant potential for enhancing patient-centered outcomes. Recent research has identified and validated agile values specifically for breast cancer treatment, including (1) prioritizing patient and family satisfaction through early and continuous delivery of effective, safe treatment; (2) welcoming changing requirements even late in treatment; (3) delivering working treatment plans frequently; and (4) emphasizing collaboration between patients and healthcare professionals [16] [17]. These values align with the growing emphasis on personalized medicine and patient-centered care in oncology, recognizing the dynamic nature of cancer treatment and the importance of adapting to evolving patient needs and clinical evidence.

The agile approach in cancer implementation research fundamentally shifts the focus from rigid, pre-defined implementation plans to flexible, responsive strategies that can accommodate the complexity and variability of real-world healthcare settings. This is particularly relevant in oncology, where rapid advances in precision medicine, immunotherapy, and diagnostic technologies require implementation approaches that can keep pace with scientific discovery [18].

Current Landscape and Urgent Needs

Gaps in Conventional Implementation Approaches

Systematic analyses of implementation research for common cancers in Asia reveal significant limitations in current approaches. A comprehensive review covering publications from 2004 to 2024 identified only 11 studies that specifically investigated implementation strategies for lung, breast, and colorectal cancers in Asian populations, despite these being the most prevalent cancers in the region [13]. This scarcity of implementation research underscores the critical need for more efficient and adaptable methodologies to accelerate the integration of evidence-based interventions into routine cancer care.

The analysis of National Cancer Control Plans (NCCPs) and strategies from low and medium Human Development Index (HDI) countries further highlights systematic gaps in implementation planning. While many NCCPs incorporated elements such as stakeholder engagement and impact measurement, these were often inconsistently applied and rarely explicit [14]. Notably, none of the plans assessed health system capacity to determine readiness for implementing new interventions, and stakeholder engagement was typically unstructured and incomplete [14]. These findings illustrate the limitations of conventional top-down implementation approaches and the need for more agile, adaptive strategies.

Disparities in Cancer Implementation

The challenges in cancer implementation are particularly acute in resource-constrained settings. Research indicates that approximately 65% of cancer deaths occur in low- and middle-income countries (LMICs), where barriers such as inadequate infrastructure, limited access to palliative and preventative care, and lack of education and awareness exacerbate the cancer burden [12]. These contextual challenges require implementation strategies that are not only evidence-based but also flexible, culturally appropriate, and responsive to local constraints and opportunities.

Even in well-resourced settings like the Veterans Health Administration (VA) in the United States, significant implementation gaps persist. Screening for gastrointestinal cancers—specifically colorectal cancer (CRC) and hepatocellular carcinoma (HCC)—is often inadequately and inequitably implemented, leading to preventable morbidity and mortality [19]. These disparities across different healthcare contexts highlight the urgent need for agile implementation approaches that can be tailored to specific settings and populations.

Agile Methodologies and Protocols

Structured Framework for Agile Implementation

Agile implementation science employs structured yet flexible frameworks to guide the process of translating evidence into practice. The Strategic Implementation Framework represents one such approach, encompassing three progressive stages: (1) setting the stage, (2) active implementation, and (3) monitoring, supporting, and sustaining change [12]. Each stage involves specific strategies, such as identifying champions, developing educational materials, and implementing performance metrics, with continuous iteration and adaptation based on ongoing evaluation.

The Getting To Implementation (GTI) framework provides another agile approach, adapting the evidence-based Getting To Outcomes (GTO) program for cancer screening implementation. GTI guides users through a seven-step process to select context-specific strategies with the help of a manualized playbook, training, and external facilitation [19]. This iterative approach enables implementation teams to systematically address local barriers while maintaining fidelity to core evidence-based interventions.

Experimental Protocols for Strategy Comparison

Recent research protocols exemplify the application of agile principles in comparative effectiveness trials for cancer implementation. A large cluster-randomized implementation study is comparing the effectiveness of two evidence-based implementation strategies—external facilitation versus patient navigation—for improving liver and colon cancer screening completion rates among Veterans [19]. This study employs a hybrid type 3 trial design, which simultaneously assesses implementation outcomes while examining the intervention's effects, representing an efficient approach characteristic of agile science.

Table 2: Implementation Strategy Comparison in GI Cancer Screening Trial

Strategy Component	Implementation Facilitation (IF)	Patient Navigation (PN)
Primary Target	Provider-facing support	Patient-facing support
Core Activities	Tailored problem-solving, data provision, education	Veteran outreach, education, scheduling assistance
Duration & Intensity	Bi-weekly virtual meetings for 6 months + maintenance calls (~20 hours/site)	Introductory call + monthly progress discussions
Key Tools	GTI 7-step playbook, facilitation manual	Patient Navigation Toolkit, tracking reports
Measured Outcomes	Reach, adoption, appropriateness, feasibility	Reach, acceptability, sustainability, cost
Theoretical Basis	Consolidated Framework for Implementation Research (CFIR)	Patient-centered care models

The protocol includes rigorous evaluation of multi-level implementation determinants using CFIR-mapped surveys and interviews with both Veteran participants and healthcare providers at baseline and post-intervention [19]. This comprehensive, mixed-methods approach allows for a nuanced understanding of how each implementation strategy operates within specific contexts and for different populations, facilitating future tailoring and optimization.

The Scientist's Toolkit: Research Reagent Solutions

Implementation scientists require a diverse set of "research reagents"—conceptual tools and methodological approaches—to effectively design, execute, and evaluate agile implementation studies. The following table details essential components of the implementation scientist's toolkit, adapted from established implementation science frameworks and recent research in cancer care [15].

Table 3: Essential Research Reagent Solutions for Agile Implementation Science

Tool/Resource	Function/Purpose	Example Applications in Cancer Research
ERIC Compilation	Standardized taxonomy of 73 implementation strategies	Selecting and specifying strategies for colorectal cancer screening programs
Causal Pathway Diagrams	Visual mapping of strategy-mechanism-outcome relationships	Hypothesizing how audit and feedback improves breast cancer screening rates
Consolidated Framework for Implementation Research (CFIR)	Multilevel framework identifying implementation determinants	Assessing barriers to lung cancer screening in primary care settings
RE-AIM Framework	Evaluating reach, effectiveness, adoption, implementation, maintenance	Measuring overall impact of patient navigation for hepatocellular carcinoma screening
Systems Analysis and Improvement Approach (SAIA)	Cascade analysis combining systems engineering tools	Identifying and addressing bottlenecks in cervical cancer screening programs
Implementation Mapping	Step-based process for selecting and tailoring strategies	Developing context-specific interventions for BRCA testing implementation
Mechanism Mapping	Identifying how strategies achieve effects through specific mechanisms	Understanding how educational materials influence patient adherence to chemotherapy

These conceptual tools enable researchers to systematically address the complex challenges of implementing evidence-based cancer interventions across diverse settings and populations. By providing a common language and structured approaches, these "research reagents" facilitate replication, comparison, and synthesis of findings across studies, accelerating the development of effective implementation strategies for cancer control.

Visualization of Agile Implementation Pathways

Understanding the causal pathways through which implementation strategies produce their effects is essential for agile science. Causal Pathway Diagrams (CPDs) provide a visual representation of the hypothesized relationships between implementation strategies, mechanisms of change, contextual factors, and outcomes [15]. The following diagram illustrates a generic agile implementation pathway for cancer screening interventions:

This causal pathway diagram illustrates the complex relationships between implementation strategies, their mechanisms of action, and resulting outcomes. The diagram highlights how multiple strategies can operate through different mechanisms to influence both provider and patient behaviors, ultimately contributing to improved cancer outcomes. The dotted lines represent the moderating influence of contextual factors, emphasizing the importance of adapting implementation approaches to specific settings and conditions.

Future Directions and Implementation Agenda

The application of agile science methods in cancer implementation research represents a promising frontier for addressing persistent challenges in cancer control. Looking ahead, several key priorities emerge for advancing this field. First, there is a critical need to develop and validate streamlined approaches for classifying and tailoring implementation strategies to specific contexts and populations [15]. Such approaches would enable more efficient matching of implementation strategies to local barriers and resources, particularly in low-resource settings where the cancer burden is rapidly increasing [14] [12].

Second, future research should prioritize understanding the mechanisms through which implementation strategies produce their effects. Methodological approaches such as mechanism mapping and causal pathway diagramming offer promising approaches for elucidating these relationships, enabling more precise and effective implementation approaches [15]. This mechanism-focused research will be particularly important for adapting implementation strategies to new cancer technologies and treatments, such as the next generation of precision oncology therapies, cancer vaccines, and antibody-drug conjugates currently in development [18].

Finally, building capacity for agile implementation science across diverse global contexts represents an essential long-term goal. This includes training researchers and practitioners in agile methodologies, developing infrastructure for rapid-cycle evaluation, and fostering collaborations between implementation scientists and cancer researchers across disciplines [12]. By embracing these priorities, the cancer research community can accelerate progress toward reducing the global burden of cancer through more efficient, equitable, and effective implementation of evidence-based interventions.

Behavior Change Modules for Patient Support and Aftercare

Digital Behavior Change Interventions (DBCIs) are structured modules designed to promote positive health behaviors, such as increased physical activity, among cancer survivors. These modules are crucial for managing long-term health and improving quality of life after cancer treatment.

Application Note: Digital Behavior Change Interventions (DBCIs) for Breast Cancer Survivors

A 2025 systematic review and meta-analysis of 29 randomized controlled trials (n=2,229 participants) demonstrated the effectiveness of DBCIs in improving specific health outcomes for breast cancer survivors [20].

• Intervention Design: DBCIs were primarily delivered at the interpersonal level and incorporated established behavior change techniques (BCTs), including:
  • Instruction on how to perform the behavior
  • Demonstration of the behavior
  • Action planning
  • Problem-solving
  • Social support
• Quantitative Outcomes: The meta-analysis revealed significant improvements in several physical and psychosocial measures. The table below summarizes the key findings, with effect sizes reported as Standardized Mean Difference (SMD).

Table 1: Effectiveness of Digital Behavior Change Interventions for Breast Cancer Survivors [20]

Outcome Measure	Effect Size (SMD)	P-value	Statistical Significance
Shoulder Flexion	2.08 (CI: 1.14-3.01)	< .001	Yes
Shoulder Abduction	2.32 (CI: 1.35-3.28)	< .001	Yes
Internal Rotation	2.98 (CI: 1.08-4.87)	.002	Yes
Finger Climbing Wall Height	1.65 (CI: 1.35-1.95)	< .001	Yes
Upper-Extremity Function	-0.96 (CI: -1.50 to -0.42)	< .001	Yes
Quality of Life	1.83 (CI: 0.44-3.22)	.01	Yes
Pain	-0.58 (CI: -0.93 to -0.22)	.002	Yes
Daily Steps	Not Reported	.69	No
Time in Moderate-Vigorous PA	Not Reported	.43	No
Sedentary Time	Not Reported	.18	No

Experimental Protocol: Blended Care Intervention for Cancer Aftercare

The following protocol outlines a methodology for integrating DBCIs into standard care, aligning with agile principles by using a flexible, co-designed approach with stakeholders [21].

• Study Design: Randomized Controlled Trial (RCT) with a parallel-group design. Randomization occurs at the General Practice Center (GPC) level, with participants nested within GPCs.
• Participants: Adult cancer survivors who have completed primary treatment (e.g., radiotherapy, chemotherapy, surgery) between 6 weeks and 3 years prior.
• Intervention Group: Receives blended care, which combines:
  • The Cancer Aftercare Guide (CAG): An online eHealth program consisting of 8 modules (Physical Activity, Diet, Smoking, Alcohol, Fatigue, Anxiety/Depression, Return to Work, Social Relationships).
  • Face-to-face consultations with a General Practitioner (GP) or Practice Nurse (PN) in a primary care setting.
• Control Group: A waiting list control group that receives care as usual for the study duration.
• Data Collection: Online self-report questionnaires are administered at baseline, 6 months, and 12 months. Measurements include:
  • Self-reported adherence to lifestyle recommendations.
  • Psychosocial well-being.
  • Quality of life (QoL).
• Analysis: Multilevel linear regression analyses will be used to evaluate differences in residual change scores between groups, supplemented by Bayes factor analyses. A cost-effectiveness evaluation is also included.

Computational Models for Drug Discovery & Tumor Analysis

Computational models are in silico tools that simulate cancer biology and drug mechanisms, accelerating discovery and providing deeper insights into complex tumor data.

Application Note: DeepTarget for Drug Target Prediction

A study published in November 2025 introduced DeepTarget, a computational tool that predicts primary and secondary targets of small-molecule cancer drugs [22].

• Model Design: DeepTarget is an open-source tool that integrates large-scale drug and genetic knockdown viability screens with omics data to determine a drug's mechanism of action.
• Performance Benchmarking: The tool was tested on eight datasets of high-confidence drug-target pairs. It outperformed existing state-of-the-art tools (RoseTTAFold All-Atom and Chai-1) in seven out of eight tests for predicting drug targets and their mutation specificity.
• Experimental Validation: In a case study on the drug Ibrutinib, DeepTarget correctly identified that EGFR T790 mutations influence response in BTK-negative solid tumors, demonstrating its ability to uncover context-specific drug mechanisms [22].
• Output: The model has predicted target profiles for 1,500 cancer-related drugs and 33,000 natural product extracts.

Application Note: SMMILe for Rapid Cancer Image Analysis

The SMMILe (Superpatch-based Measurable Multiple Instance Learning) AI tool, developed at the University of Cambridge, analyzes complex digital pathology slides from cancer biopsies [23].

• Training Innovation: Unlike other models that require detailed, time-consuming annotations from pathologists, SMMILe can be trained using slides labeled with simple, patient-level diagnoses (e.g., cancer type or grade).
• Functionality: The tool not only detects cancer cells but also:
  • Predicts tumor lesion locations.
  • Estimates the proportions and spatial distribution of lesions with different subtypes and grades.
• Performance: Testing on 3,850 whole-slide images across six cancer types (lung, kidney, ovarian, breast, stomach, prostate) showed that SMMILe's performance matched or exceeded nine other state-of-the-art AI tools in slide-level classification. It significantly outperformed them in estimating the proportions and spatial distribution of different lesion types [23].
• Agile Advantage: This approach drastically reduces the time and expert effort required for model training, enabling faster iteration and deployment—a key tenet of agile science.

Experimental Protocol: In Silico Modeling for Therapeutic Prediction

This protocol describes a general workflow for using computational models to predict drug responses, a cornerstone of modern precision oncology efforts [24] [22].

• Data Input:
  • Large-Scale Biological Data: Integration of drug sensitivity screens, genetic knockdown (CRISPR) viability data, and multi-omics data (e.g., transcriptomics).
• Model Training & Validation:
  • Benchmarking: The model (e.g., DeepTarget) is tested on known, high-confidence drug-target pairs to establish baseline accuracy.
  • Experimental Case Studies: Predictions are validated in wet-lab experiments using in vitro and in vivo models to confirm biological mechanisms (e.g., effect on mitochondrial function).
• Output & Application: The validated model generates predictive target profiles for a wide array of therapeutic and natural compounds.

Personalization Algorithms for Precision Treatment

Personalization algorithms analyze complex, patient-specific data to guide tailored therapeutic decisions, moving beyond one-size-fits-all cancer treatment.

Application Note: AI-Driven Transcriptomic Analysis for Treatment Selection

A 2024 proposed methodology leverages Deep Learning (DL), a subset of AI, to analyze a patient's unique transcriptomic profile and predict the most effective treatment [25].

• Objective: To overcome the challenges of tumor heterogeneity and imperfect data interpretation by tailoring treatment based on each patient's unique genomic landscape.
• Method:
  • RNA samples from a patient's tumor are sequenced (e.g., via Next-Generation Sequencing).
  • Specifically trained DL models analyze the transcriptomic data to:
    • Identify dysregulated pathways and targeted genes.
    • Recognize molecular biomarkers.
• Output & Matching: The analyzed genomic profile is computationally scanned against an expansive library of FDA-approved and investigational drugs. The algorithm then predicts the response rate of the patient's specific targets to various treatment options, resulting in a statistically ranked list of potential target-drug combinations [25].

Experimental Protocol: Personalized Treatment Matching Workflow

This protocol details the end-to-end process for using AI to move from a patient's tumor sample to a personalized treatment recommendation [25].

• Step 1: Patient Identification and Sampling: Target population includes critically ill cancer patients with aggressive phenotypes. Tumor tissue is obtained via biopsy.
• Step 2: Transcriptomic Profiling: RNA is extracted from the tissue and prepared for comprehensive transcriptomic analysis using Next-Generation Sequencing (NGS).
• Step 3: AI-Based Data Analysis: The raw NGS data is processed by a trained DL model (e.g., a Convolutional Neural Network or CNN) to generate a comprehensive patient-specific genomic report, which includes statistical analyses, dysregulated pathways, and druggable targets.
• Step 4: Drug Matching and Prediction: The genomic report is scanned against a drug library by a prediction algorithm. This generates a output, such as a graph, showing the predicted response percentage for each potential target-drug pair.
• Step 5: Experimental and Clinical Validation: The top-ranked target-drug combinations are validated using in vitro and in vivo experimental models. The most promising validated option is then introduced to the patient.

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Research Reagents and Tools for Agile Cancer Implementation Research

Item Name	Type	Function/Application
Digital Behavior Change Platform	Software	Hosts structured modules (e.g., physical activity, diet) for remote patient intervention and monitoring in survivorship care studies [20] [21].
SMMILe Algorithm	AI Software Tool	Rapidly analyzes whole-slide digital pathology images to map tumor subtypes and spatial distributions without needing pixel-level annotations [23].
DeepTarget Algorithm	Computational Model	Predicts primary and secondary targets of small-molecule drugs by integrating viability screens and omics data for drug repurposing and development [22].
Next-Generation Sequencing (NGS) Kit	Wet-Lab Reagent	Prepares RNA/DNA libraries from tumor samples for comprehensive transcriptomic and genomic profiling, the foundation for personalized therapy algorithms [25].
Validated Implementation Measure	Psychometric Tool	Brief, reliable, and pragmatic surveys used to identify local implementation determinants (barriers/facilitators) and evaluate outcomes in agile implementation studies [26].

The integration of evidence-based interventions into routine cancer care is often complicated by significant contextual variability across healthcare settings and geographical regions, leading to shortcomings in quality and optimal resource allocation [14]. This application note provides a comparative overview of Agile and Traditional Linear research models within the context of cancer implementation science. It details specific protocols, experimental workflows, and reagent solutions to guide researchers and drug development professionals in adopting iterative, adaptive approaches that can accelerate the translation of evidence into practice, particularly in resource-constrained settings.

In cancer implementation research, the traditional linear model, often analogous to the waterfall method, follows a sequential process of planning, designing, developing, testing, and launching without the capacity for mid-project changes [27]. This approach carries significant risks, as mistakes are frequently discovered only at the project's end, leading to costly rework and long development cycles that delay the release of impactful interventions [27]. Conversely, Agile methodologies emphasize flexibility, collaboration, and continuous improvement by breaking projects into small, manageable iterations (sprints), typically lasting one to four weeks [27]. This iterative approach is particularly suited to the complex, dynamic challenges of cancer implementation science, where adapting to evolving evidence and diverse patient needs is paramount.

The table below summarizes the core differences between Agile and Traditional Linear research models as applied to cancer implementation science.

Table 1: Core Characteristics of Agile and Traditional Linear Research Models

Characteristic	Traditional Linear Model	Agile Model
Project Phasing	Sequential, distinct phases (e.g., planning, design, execution) [27]	Iterative, cyclical sprints (1-4 weeks) [27]
Flexibility	Not possible to make changes mid-project [27]	High flexibility; priorities adjusted between sprints [27]
Risk Profile	High risk; issues discovered late, leading to costly rework [27]	Reduced risk; issues identified and resolved early in cycles [27]
Primary Focus	Adherence to a fixed, initial plan [27]	Customer-centric solutions and continuous value delivery [27]
Stakeholder Engagement	Typically limited to initial and final stages [14]	Continuous collaboration via daily stand-ups and sprint reviews [27]
Output Delivery	Single, final delivery at project end [27]	Frequent, incremental deliveries of usable versions [27]
Suited for	Projects with stable, well-defined requirements from the outset [27]	Projects with uncertain or evolving requirements, such as complex health interventions [27]

The application of these models in real-world settings reveals significant gaps. An analysis of National Cancer Control Plans (NCCPs) from low and medium Human Development Index (HDI) countries showed that while many plans incorporated elements like stakeholder engagement and impact measurement, these were often unstructured and inconsistently applied [14]. Furthermore, none of the assessed plans conducted health system capacity assessments to determine readiness for implementing new interventions, a critical step that Agile-style iterative planning could address [14].

Experimental Protocols for Cancer Implementation Research

This section provides detailed methodologies for applying Agile principles in cancer research contexts, from digital health development to policy planning.

Protocol for Agile Development of a Digital Health Intervention

Objective: To develop and refine a mobile health (mHealth) application for supporting head and neck cancer (HNC) caregivers using an iterative, Agile methodology [28].

Workflow: The following diagram illustrates the iterative cycles of data collection, analysis, and app development.

Detailed Methodology:

• Sprint 1: Discovery & Baseline Data Collection (4-week cycle)

  • Concurrent Data Streams:
    • Quantitative: Distribute a 67-item web-based survey to a national panel of oncology dietitians via professional listservs (e.g., Oncology Nutrition Dietetic Practice Group). Use platforms like REDCap (Research Electronic Data Capture) for hosting. Assess perceptions of nutritional challenges, support task importance/difficulty, and resource needs using Likert scales [28].
    • Qualitative: Conduct semi-structured dyadic interviews with HNC survivors (within 6-24 months post-treatment) and their nominated caregivers. Interviews should be audio-recorded, transcribed, and conducted until thematic saturation is reached. Focus on physical, emotional, and social challenges, especially regarding nutritional recovery [28].
  • Sprint Goal: Synthesize data to define core user needs and high-priority app domains.

• Sprint 2: Prototype Development & Validation (3-week cycle)

  • Integrated Analysis: Merge quantitative and qualitative findings using a merging technique. Translate emergent themes (e.g., nutritional challenges, competing symptoms, caregiver distress) into specific app content and features [28].
  • Prototyping: Develop a minimum viable product (MVP) of the mHealth app (e.g., the HEART app). Key features may include an intake tracker, nutrition recovery support, caregiving tips, peer support, and self-care sections [28].
  • Stakeholder Review: Present app screens and workflow to a subset of dietitians, survivors, and caregivers for initial feedback on functionality, relevance, and usability [28].

• Sprint 3: Refinement & Pilot Implementation (4-week cycle)

  • Iterative Refinement: Modify the app prototype based on stakeholder feedback. This may involve adjusting content, user interface elements, or adding/removing features.
  • Pilot Testing: Implement the refined app in a small-scale pilot study with a new cohort of HNC caregivers.
  • Outcome Assessment: Evaluate the app's acceptability, feasibility, and preliminary efficacy using mixed-methods assessments, setting the stage for a larger-scale trial [28].

Protocol for an Implementation Science-Informed National Cancer Control Plan (NCCP)

Objective: To integrate Implementation Science (IS) domains into the development and execution of a National Cancer Control Plan (NCCP) using a structured, iterative pathway [14].

Workflow: The logical flow for integrating IS principles into cancer control planning.

Detailed Methodology:

• Structured Stakeholder Engagement:

  • Move beyond unstructured engagement by purposively identifying and involving IS experts, clinicians, patients, and policymakers throughout the planning process [14].
  • Activity: Conduct a series of structured workshops and Delphi surveys to build consensus on priorities and strategies.

• Comprehensive Situational Analysis:

  • Explicitly analyze the current cancer burden, available resources, and existing gaps using robust local and global data [14].
  • Activity: Map barriers and facilitators against the WHO's health system building blocks to identify key leverage points for intervention [14].

• Explicit Health System Capacity Assessment:

  • Determine the system's readiness to implement new interventions, a step found to be missing in many existing plans [14].
  • Activity: Employ tools like the ERIC (Expert Recommendations for Implementing Change) framework to assess capacity and select feasible implementation strategies [14].

• Activity-Based Economic Evaluation:

  • Develop a costed plan using activity-based costing approaches to ensure financial feasibility and resource optimization [14].
  • Activity: Create a detailed budget that links costs to specific activities and outcomes outlined in the plan.

• Integrated Impact Measurement:

  • Define clear Key Performance Indicators (KPIs) and establish mechanisms with responsible entities to achieve targets [14].
  • Activity: Implement a monitoring and evaluation framework that allows for periodic review and adaptation of the plan based on performance data, embodying the Agile principle of continuous improvement.

The Scientist's Toolkit: Research Reagent Solutions

The following table details key resources and tools essential for conducting Agile cancer implementation research.

Table 2: Essential Research Reagents and Tools for Agile Implementation Science

Item/Tool	Function/Application	Specific Example in Context
Implementation Science Frameworks	Provides a structured approach to understand and/or overcome barriers to implementation in specific contexts [14].	Using the ERIC (Expert Recommendations for Implementing Change) framework to shape research questions and select implementation strategies for a new cancer screening program [14].
Mixed-Methods Research Design	Facilitates gathering and integrating quantitative and qualitative perspectives from multiple stakeholder groups for a comprehensive understanding of the problem [28].	Concurrently collecting survey data from dietitians and qualitative interview data from survivor-caregiver dyads to inform a mobile app's development [28].
Data Collection Platforms	Enables efficient and secure collection of quantitative survey data from distributed participants.	Using REDCap (Research Electronic Data Capture) to host a 67-item web-based survey for a national panel of oncology dietitians [28].
Agile Project Management Tools	Tracks the status of tasks and sprints, providing visibility into project progress and facilitating daily stand-ups and sprint planning [27].	Using a Kanban board (with "To Do," "In Progress," and "Done" columns) to manage the development tasks for a digital health intervention across multiple sprints [27].
Stakeholder Engagement Protocols	Ensures that the perspectives of end-users and experts are systematically incorporated into the research and development process, increasing relevance and uptake [14].	Conducting structured consultations with IS experts and policymakers to refine a pathway for integrating IS into a National Cancer Control Plan [14].

Frameworks in Action: Applying Agile and Implementation Science Methods

The PRISM and RE-AIM Frameworks for Sustainable Implementation

In the field of cancer implementation research, the Practical, Robust Implementation and Sustainability Model (PRISM) and the RE-AIM framework (Reach, Effectiveness, Adoption, Implementation, Maintenance) are complementary tools designed to enhance the translation of evidence-based interventions into sustainable routine practice [29]. These frameworks are particularly valuable in agile science methods, where iterative, context-responsive approaches are necessary to address complex challenges in oncology and drug development.

RE-AIM was originally developed to improve the reporting of research results and to emphasize essential program elements that improve the sustainable adoption and implementation of evidence-based interventions [30]. It provides a structured approach to evaluate five key dimensions: Reach, Effectiveness, Adoption, Implementation, and Maintenance. PRISM extends RE-AIM by systematically incorporating contextual factors that interact with interventions and implementation strategies to produce RE-AIM outcomes [29]. This integration is particularly crucial in cancer research, where interventions must be adaptable across diverse clinical settings and patient populations.

The synergy between these frameworks offers researchers and practitioners a comprehensive methodology for planning, implementing, evaluating, and sustaining complex interventions in dynamic healthcare environments, with particular relevance to cancer control and treatment implementation challenges.

Theoretical Foundations and Framework Integration

The RE-AIM Framework Components

RE-AIM's five dimensions operate across multiple ecological levels, providing a comprehensive approach to evaluating implementation success [30] [31]:

• Reach refers to the absolute number, proportion, and representativeness of individuals willing to participate in a given initiative, intervention, or program. Assessment involves both the percentage of participants based on a valid denominator and characteristics of participants compared to non-participants [31].

• Effectiveness is the impact of an intervention on important outcomes, including potential negative effects, quality of life, economic outcomes, and variability across subgroups. This dimension emphasizes the importance of measuring broader outcomes and differential effects across patient characteristics [30].

• Adoption encompasses the absolute number, proportion, and representativeness of settings and intervention agents (staff) willing to initiate a program. This can be measured at multiple nested levels (e.g., staff, clinics, systems) and should include characteristics of adopting versus non-adopting settings and staff [31].

• Implementation refers to intervention agents' fidelity to various elements of an intervention's protocol, including consistency of delivery, time, cost, and adaptations made during delivery. Assessment includes fidelity, adaptations, and resources required across different settings and staff [30].

• Maintenance measures the extent to which a program becomes institutionalized in organizational practices and policies (setting level), and the long-term effects on outcomes after program completion (individual level). The specific timeframe for assessment varies across projects [30].

The PRISM Contextual Domains

PRISM incorporates four key contextual domains that interact with interventions and implementation strategies [29]:

• Perspectives on the Intervention: Includes organizational and staff perspectives (e.g., readiness, perceived evidence strength, compatibility with workflow) and patient perspectives (e.g., patient-centeredness, barriers, estimated impact).

• Characteristics of Implementers, Settings, and Recipients: Encompasses organizational characteristics affecting behavior change capability, patient characteristics (age, gender, culture, social needs), and implementer characteristics across different levels.

• External Environment: Includes relevant policies, market forces, regulatory environment, and community resources that influence implementation.

• Implementation and Sustainability Infrastructure: Comprises characteristics such as adopter training and support, dedicated implementation teams, ongoing audit and feedback processes, and resources for sustainability planning.

Integration of PRISM and RE-AIM

PRISM and RE-AIM are not separate frameworks; rather, PRISM incorporates RE-AIM outcomes [29]. As shown in Figure 1, the PRISM contextual domains interact with the intervention and implementation strategies to produce RE-AIM outcomes. This integrated approach allows researchers to not only measure outcomes but also understand the contextual factors influencing those outcomes.

Table 1: Key Differences Between RE-AIM and PRISM

Aspect	RE-AIM	PRISM
Primary Focus	Outcome evaluation across five dimensions	Contextual factors influencing outcomes
Framework Scope	Evaluation framework	Deterministic and evaluation framework
Core Elements	Reach, Effectiveness, Adoption, Implementation, Maintenance	Contextual domains + RE-AIM outcomes
Temporal Application	Planning and evaluation phases	Pre-implementation through sustainment phases
Equity Emphasis	Representativeness across all dimensions	Explicit structural drivers of health inequities

Figure 1: PRISM and RE-AIM Integration. PRISM contextual domains interact to influence RE-AIM implementation outcomes.

Application in Cancer Implementation Research

Agile Science Approaches

Agile science emphasizes iterative, rapid-cycle methods that are highly compatible with PRISM/RE-AIM application. In cancer implementation research, this involves:

• Iterative Assessment: Using PRISM domains during pre-implementation, implementation, and post-implementation phases to guide adaptations and create action plans [29]
• Stakeholder Engagement: Actively involving patients, caregivers, clinicians, and community partners throughout the research process to understand contextual needs [32]
• Co-Creation Approaches: Working with diverse partners to align intervention core functions and forms with contextual characteristics [32]

Health Equity Integration

PRISM provides specific guidance for addressing health equity in cancer implementation research [29] [32]:

• Representativeness Assessment: Evaluating equity across all RE-AIM dimensions, not just reach, including examining subgroup differences in effectiveness, adoption, implementation, and maintenance
• Structural Drivers: Identifying and addressing structural drivers of health inequities through contextual analysis
• Iterative Equity Assessment: Conducting ongoing assessment of RE-AIM outcomes to identify and address equity gaps throughout implementation

Table 2: Health Equity Applications in PRISM/RE-AIM

Equity Action	PRISM Domain	RE-AIM Application
Representation in Planning	Perspectives on Intervention	Engage diverse participants in planning phases
Co-Creation/Adaptation	Characteristics of Recipients	Adapt interventions to enhance equity and local fit
Structural Driver Assessment	External Environment	Assess policies, resources, and structural barriers
Subgroup Equity Analysis	All PRISM Domains	Assess representativeness of all RE-AIM outcomes
Iterative Assessment	Implementation & Sustainability Infrastructure	Use ongoing evaluation to identify equity gaps

Practical Application Protocols

Pre-Implementation Assessment Protocol

Purpose: To identify contextual factors that may influence implementation success and guide adaptation of cancer interventions.

Methodology:

• Stakeholder Mapping and Engagement

  • Identify key stakeholders across multiple levels (patients, frontline staff, middle management, leadership, community partners)
  • Conduct introductory meetings (60-90 minutes) to develop priorities, elicit feedback on study design, and set guiding principles [5]
  • Establish community-academic partnerships with defined roles and responsibilities

• Contextual Assessment Using PRISM Domains

  • Organizational Perspectives: Assess readiness for change, compatibility with workflow, perceived evidence strength through surveys or interviews
  • Recipient Characteristics: Document patient demographics, health literacy, social needs, cultural factors
  • External Environment: Identify relevant policies, payment structures, regulatory requirements, community resources
  • Implementation Infrastructure: Evaluate existing resources, staff roles, monitoring systems, sustainability capacity

• Data Collection Methods

  • Mixed-methods approach combining quantitative and qualitative measures
  • Agile Science workshops to discuss feasible site-specific strategies and conduct preliminary process mapping [5]
  • Secondary data collection on clinical characteristics, screening rates, and external influences
  • Surveys and in-depth interviews with key stakeholders

Implementation Evaluation Protocol

Purpose: To monitor implementation progress and guide adaptations using RE-AIM metrics.

Methodology:

• Reach Assessment

  • Define the setting where patients are assessed and identified
  • Count unique patients seen during specific period
  • Calculate tobacco use assessment rate (patients screened/total patients)
  • Determine number of current smokers (denominator for reach)
  • Among current smokers, count those engaged in evidence-based treatment (numerator for reach) [33]

• Effectiveness Measurement

  • Primary outcome: 30-day point prevalence abstinence at 6-months post-engagement
  • Broader outcomes: Quality of life, economic outcomes, potential negative effects
  • Subgroup analysis: Differential results by patient characteristics

• Adoption Tracking

  • Setting level: Percentage of settings approached that participated, characteristics of participating versus non-participating settings
  • Staff level: Percentage of staff invited who participated, characteristics of participating versus non-participating staff

• Implementation Fidelity and Adaptation

  • Consistency and adherence of intervention delivery across settings and staff
  • Documentation of adaptations made to intervention and implementation strategies
  • Assessment of implementation costs (time, money, resources)

• Maintenance Indicators

  • Individual level: Long-term effects on outcomes after program completion
  • Setting level: Institutionalization into routine practices, sustainability plans, organizational commitment

Figure 2: Agile Implementation Workflow. Iterative process for applying PRISM and RE-AIM in cancer implementation research.

Data Collection Tools and Methods

PRISM Assessment Tools:

• iPRISM Tool: Publicly available web-based tool to make assessment of and feedback on PRISM issues efficient and confidential [29]
• PRISM Tools: Four specialized tools to measure RHIS performance, processes, and determinants in various countries have produced consistent and valid results [34]

RE-AIM Measurement Strategies:

• Electronic Health Record Integration: Standardized EHR enhancements to improve identification of target populations and track referrals [33]
• Mixed-Methods Approaches: Combining quantitative implementation data with qualitative insights on contextual factors
• Pragmatic Measures: Low-burden, actionable measures sensitive to change and broadly applicable to diverse settings

Case Applications in Cancer Research

Cancer Center Cessation Initiative (C3I)

The National Cancer Institute's C3I implemented tobacco treatment programs across 42 NCI-Designated Cancer Centers using RE-AIM for evaluation [33]:

• Reach: Defined as proportion of current smokers seen in cancer care who engaged in evidence-based tobacco treatment, requiring consistent EHR documentation of smoking status and treatment engagement
• Effectiveness: Measured as 30-day point prevalence abstinence at 6-months post-engagement through varied data collection approaches
• Adoption: Assessed through characteristics and proportion of targeted cancer care settings and clinicians engaged in cessation service delivery
• Implementation: Evaluated by examining delivery of tobacco screening assessments and intervention components across sites, and provider-level implementation consistency
• Maintenance: Identified whether tobacco treatment services continued after implementation and documented sustainability plans

Project FACtS: Colorectal Cancer Screening

Project FACtS (FQHCs Assessing Colorectal cancer Screening) applied PRISM to guide partner-engaged data collection on processes, resources, facilitators, and barriers for CRC screening in Federally Qualified Health Centers [5]:

• Used PRISM to assess relevant internal and external environments and their fit with characteristics of evidence-based interventions for CRC screening
• Conducted iterative data collection including partner introductory meetings, Agile Science workshops, secondary data collection, surveys, and in-depth interviews
• Developed process maps from PRISM domain insights to guide selection of implementation strategies
• Addressed multilevel contextual factors including service recipients, implementation infrastructure, and external environment

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Research Materials for PRISM/RE-AIM Implementation

Tool/Resource	Function	Application Context
iPRISM Web Tool	Automated assessment of PRISM contextual domains	Pre-implementation contextual analysis
RE-AIM Metrics Toolkit	Standardized measures for five RE-AIM dimensions	Implementation evaluation across sites
Stakeholder Engagement Guides	Structured approaches for partner identification and engagement	Community-academic partnership development
Electronic Health Record Enhancements	Standardized data extraction for reach and adoption metrics	Pragmatic implementation in clinical settings
Adaptation Documentation Tools	Systematic tracking of intervention modifications	Monitoring fidelity-adaptation balance
Sustainability Assessment Instruments	Evaluation of organizational capacity for maintenance	Long-term sustainability planning

Implementation Protocol for Cancer Research

Iterative Alignment Protocol

Based on systems thinking principles, this protocol enables ongoing intervention-context alignment [32]:

• Intervention Function-Form Analysis

  • Identify core functions (what the intervention does) and forms (how it's delivered)
  • Assess alignment with PRISM contextual domains
  • Engage diverse partners in co-creation processes

• RE-AIM Outcomes Cascade Monitoring

  • Track interdependencies between RE-AIM outcomes
  • Identify "where things go wrong" in implementation pathways
  • Use reflective micro-cycles for continuous improvement

• Equity-Focused Iterative Cycles

  • Assess representativeness across all RE-AIM dimensions
  • Identify and address structural barriers to equity
  • Monitor for unintended consequences that may exacerbate disparities

Sustainability Planning Protocol

• Infrastructure Assessment

  • Evaluate existing implementation and sustainability infrastructure
  • Identify resource gaps and capacity needs
  • Develop plans for dedicated implementation teams and support systems

• Organizational Integration

  • Assess compatibility with organizational culture and workflows
  • Develop strategies for institutionalization into routine practices
  • Secure organizational commitment and resource allocation

• Long-term Monitoring

  • Establish systems for ongoing audit and feedback
  • Plan for adaptation to changing contexts
  • Develop sustainability metrics aligned with RE-AIM maintenance dimension

The PRISM and RE-AIM frameworks provide comprehensive, practical guidance for addressing the complex challenges of implementing and sustaining evidence-based interventions in cancer research. By integrating contextual assessment with outcome evaluation, these frameworks enable researchers and practitioners to develop contextually responsive implementation strategies that maximize reach, effectiveness, adoption, implementation, and maintenance while promoting health equity.

The complexity and heterogeneity of breast cancer demand treatment approaches that are dynamic, responsive, and tailored to individual patient characteristics. In the rapidly evolving landscape of oncology, the incorporation of agile values and principles adapted from software engineering offers a promising framework for enhancing patient care delivery. This paradigm shift emphasizes adaptability over rigid planning, patient collaboration over standardized protocols, and responsive treatment modifications over fixed pathways. Originally codified in the Agile Manifesto for software development, these concepts are now being translated to breast cancer treatment through multidisciplinary research, creating a novel approach that prioritizes continuous adaptation to each patient's unique disease characteristics, preferences, and evolving condition [16] [17].

Agile methodology represents a fundamental mindset change from traditional linear approaches, focusing on iterative development, early delivery of value, and responsiveness to change. In breast cancer treatment, this translates to treatment plans that evolve based on continuous feedback from patient responses, biomarker data, and emerging research evidence [35]. The core strength of agile approaches lies in their ability to navigate complex, unpredictable environments – precisely the conditions that characterize breast cancer management, with its varying subtypes, stages, genetic profiles, and patient treatment responses [16] [36].

Agile Values and Principles in Breast Cancer Treatment

Foundational Agile Values for Breast Cancer Care

Research has successfully adapted four core agile values from software engineering to breast cancer treatment, creating a shared framework for multidisciplinary teams to collaborate effectively while adapting to changing circumstances [16] [36]. These values provide the philosophical foundation for agile cancer care delivery.

Table 1: Four Agile Values for Breast Cancer Treatment Adapted from Software Engineering

Software Engineering Agile Value	Adapted Breast Cancer Treatment Agile Value	Clinical Application
Individuals and interactions over processes and tools	Patient and team collaboration over rigid protocols	Multidisciplinary tumor boards, shared decision-making with patients
Working software over comprehensive documentation	Effective treatment delivery over exhaustive documentation	Focus on actual patient outcomes rather than administrative completeness
Customer collaboration over contract negotiation	Patient collaboration over treatment prescription	Active patient involvement in treatment decisions and preference integration
Responding to change over following a plan	Adapting treatment over adhering to initial plan	Treatment modification based on response data and emerging evidence

Of these four values, three were validated as fully conforming to the concept of agility in breast cancer treatment, while documentation, though necessary, was recognized as supporting rather than constituting agility [16]. This distinction highlights how agile values prioritize direct patient care activities while acknowledging the supporting role of necessary documentation systems.

Expanded Agile Principles for Clinical Implementation

Beyond the core values, twelve detailed principles provide specific guidance for implementing agility in breast cancer treatment. These principles were elicited through interviews with oncologists and validated through literature review, creating a robust framework for clinical practice [17].

Table 2: Validated Agile Principles for Breast Cancer Treatment

Principle Number	Core Focus	Agility Conformance	Key Components
1	Patient and family satisfaction	Meets agility	Early/continuous delivery of effective safe treatment
2	Welcoming changing requirements	Meets agility	Late treatment cycle adaptation for patient benefit
3	Frequent delivery of effective treatment	Meets agility	Weeks/months timeframes with preference to shorter timescale
4	Patient-Professional collaboration	Meets agility	Daily cooperation between patients and cancer professionals
5	Motivated individuals and support	Meets agility	Trust environment with needed tools/support
6	Face-to-face conversation	Hybrid (Partially meets)	Most efficient information transfer method
7	Effective treatment as primary measure	Meets agility	Progress measured by treatment effectiveness
8	Sustainable treatment development	Meets agility	Consistent pace indefinitely for patients/care team
9	Continuous attention to effectiveness	Hybrid (Partially meets)	Technical excellence and good design enhance agility
10	Simplicity in essentiality	Hybrid (Partially meets)	Maximizing amount of work not done is essential
11	Self-organizing teams	Hybrid (Partially meets)	Best requirements emerge from self-organizing teams
12	Regular reflection and adjustment	Hybrid (Partially meets)	Team effectiveness adjustment to improve behavior

Seven of these twelve principles fully meet agility standards, while the remaining five demonstrate hybrid conformance, partially meeting agility concepts while retaining some traditional elements [17]. This distribution reflects the balanced approach required in healthcare, where some structured processes remain necessary for patient safety and quality assurance.

Application Notes: Implementing Agile Frameworks

Agile Protocol Design for Phase 2 Clinical Trials

The phase 2 clinical trial environment presents particularly compelling opportunities for implementing agile methodologies. These trials operate in what the Cynefin framework classifies as the "complex" domain, where cause-and-effect relationships are only understandable in retrospect, and emergent patterns require iterative probing and adaptation [35]. This stands in stark contrast to traditional "complicated" domain thinking, which assumes that expert analysis can predict all outcomes in advance.

Implementation Protocol: Agile Phase 2 Trial Framework

• Frequent Reflection Cycles: Schedule biweekly data review sessions with cross-functional stakeholders, including sponsors, CROs, and site staff [35].
• Broad Stakeholder Inclusion: Engage clinical operations, biostatistics, data management, and patient representatives in review processes [35].
• Adaptive System Infrastructure: Implement technology platforms that enable rapid protocol amendments and data flow integration [35].
• Hypothesis Testing Approach: Treat patient population selection as testable hypotheses rather than fixed parameters, allowing for pivoting based on interim data [35].
• Visual Collaboration Tools: Utilize virtual whiteboards and other collaborative technologies to facilitate remote stakeholder engagement and real-time feedback integration [35].

This approach requires three fundamental mindset shifts: recognizing that change is not failure, that collaboration transcends organizational boundaries, and that transparency serves understanding rather than control [35]. The GBM AGILE trial for glioblastoma exemplifies this approach, functioning as a seamless Phase II/III adaptive platform that simultaneously evaluates multiple therapies and dynamically adjusts patient randomization based on emerging efficacy data [37].

Quantitative Framework for Agile Treatment Decision-Making

Agile treatment approaches require robust quantitative frameworks to support rapid, data-driven decision making. Quantitative chemical biology provides essential tools for modeling drug response and optimizing therapeutic strategies [11].

Experimental Protocol: IC50 Determination for Treatment Prioritization

Objective: Quantify compound sensitivity through half-maximal inhibitory concentration (IC50) determination to prioritize candidate therapies for agile treatment protocols.

Materials & Reagents:

• Patient-derived cell lines or purified enzyme targets
• Compound library of investigational therapeutics
• Cell Titer-Glo or other viability assay reagents
• High-throughput screening instrumentation
• 4-parameter logistic (4PL) regression modeling software

Methodology:

• Experimental Setup: Plate cells in 384-well format with 8-10 concentration points for each compound, spaced equally across a logarithmic scale [11].
• Concentration Range: Utilize a broad concentration range (typically 0.1 nM - 100 μM) based on prior knowledge, with subsequent narrowing for precise IC50 determination [11].
• Response Measurement: Quantify cellular viability after 72-96 hour compound exposure using ATP-based viability assays [11].
• Replication: Perform minimum of three biological replicates per data point to ensure statistical robustness [11].
• Data Analysis: Fit concentration-response data using 4-parameter logistic nonlinear regression to determine IC50 values [11].
• Quality Control: Ensure well-defined top (maximum response) and bottom (minimum response) plateaus with sufficient data points above and below IC50 values [11].

Interpretation Criteria:

• Maximum inhibition should exceed 50% for reliable IC50 determination
• Hill slope values inform mechanism of action (standard inhibition vs. cooperative binding)
• IC50 values are benchmarked against known standard-of-care therapies
• Response patterns inform combination therapy strategies

This quantitative framework enables rapid prioritization of candidate therapies based on their potency and efficacy profiles, supporting agile treatment adaptation when initial approaches prove suboptimal [11].

Figure 1: Quantitative Framework for Agile Treatment Decision-Making

Implementation Strategies and Tools

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Research Reagents and Platforms for Agile Cancer Research

Tool/Platform	Primary Function	Application in Agile Implementation
Patient-Derived Cell Lines	In vitro tumor models	Maintain patient-specific characteristics for personalized therapy testing
High-Throughput Screening Systems	Rapid compound evaluation	Enable simultaneous testing of multiple therapeutic candidates
Cell Titer-Glo Viability Assay	ATP-based viability quantification	Standardized metric for treatment response assessment
4-Parameter Logistic Regression	Dose-response curve fitting	Quantitative IC50 determination for treatment prioritization
iECHO Platform	Virtual telementoring community	Facilitate rapid knowledge dissemination among providers
Patient Decision Aids (PDAs)	Structured decision support tools	Enhance patient comprehension and engagement in shared decision-making
Firebase Progressive Web App	Interactive patient platform development	Enable accessible, cross-platform decision support tool deployment

Implementation Facilitation Framework

Effective agile implementation requires structured support systems. The Getting To Implementation (GTI) framework provides a manualized intervention that guides users through context-specific strategy selection [19]. This approach includes:

Implementation Protocol: Facilitation Strategy

• External Facilitator Engagement: Pair sites with clinical and evaluation experts for biweekly virtual meetings over six months [19].
• Barrier Identification: Conduct systematic assessment of implementation challenges using CFIR-mapped surveys and interviews [19].
• Iterative Improvement Cycles: Implement rapid tests of change with continuous process refinement [19].
• Stakeholder Engagement: Engage patients, providers, and healthcare system representatives in collaborative design [19].

This facilitation approach has demonstrated effectiveness in supporting the implementation of evidence-based cancer screening programs, including colorectal and hepatocellular carcinoma screening [19].

Patient Decision Aid Development Protocol

Agile implementation emphasizes patient collaboration, which requires tools to support shared decision-making. Trial-specific Patient Decision Aids (tPDAs) address this need by transforming complex trial information into accessible formats [38].

Development Protocol: Patient Decision Aid Creation

Objective: Develop and validate tPDA to enhance patient comprehension and engagement in clinical trial decision-making.

Phase 1: Technical Development

• Utilize Progressive Web App architecture with Firebase backend [38]
• Implement front-end using JavaScript, HTML5, CSS3, and JSON manifest [38]
• Ensure cross-platform compatibility for desktop and mobile access [38]

Phase 2: Multistakeholder Validation

• Computer Scientist Review (n=17): Assess technical functionality, information presentation, and visual design [38]
• Clinician and Medical Student Review (n=18): Evaluate content accuracy, usability, and comprehensibility [38]
• Patient Validation (n=6): Test with eligible trial participants for real-world applicability [38]

Evaluation Metrics:

• System Usability Scale (SUS) scoring (target >75 indicating good usability) [38]
• Completion time assessment (majority under 30 minutes) [38]
• Qualitative feedback on comprehension and decision-making support [38]

This structured development approach has demonstrated success, with implemented tPDAs achieving mean SUS scores of 79.4, indicating good usability, and significantly improving patient understanding of complex trial information [38].

Figure 2: Agile Implementation Strategy Framework

Discussion and Future Directions

The integration of agile values into breast cancer treatment represents a paradigm shift from traditional linear approaches to dynamic, patient-centered care models. This transition aligns with broader movements in implementation science toward "rapid" and "agile" implementation approaches that emphasize speed, efficiency, and adaptability without sacrificing rigor [39]. The core innovation lies in reconceptualizing cancer treatment as a complex adaptive system rather than a mechanical process, acknowledging the nonlinear, interdependent nature of factors influencing patient outcomes.

The quantitative frameworks and implementation protocols outlined provide concrete methodologies for operationalizing agile principles in both research and clinical settings. As the field advances, several key areas require further development: (1) refined metrics for quantifying agility in treatment pathways, (2) standardized platforms for continuous data integration from multiple sources, and (3) educational frameworks for training oncology professionals in agile methodologies.

Agile implementation in breast cancer treatment ultimately represents a commitment to creating learning healthcare systems that continuously adapt based on emerging evidence, patient responses, and evolving scientific understanding. By placing patient collaboration, adaptive response, and iterative improvement at the center of care delivery, these approaches promise to enhance both the efficiency and effectiveness of cancer care while maintaining the humanistic values essential to healing relationships.

Stakeholder Engagement and Co-Creation in National Cancer Control Plans

National Cancer Control Plans (NCCPs) represent complex public health strategies designed to reduce the cancer burden through coordinated prevention, diagnosis, treatment, and survivorship support. The translation of these evidence-based plans into practice faces significant implementation challenges, particularly the 17-year average time lag between research discovery and routine clinical application [39]. The emerging discipline of agile science addresses this implementation gap through iterative, rapid-cycle evaluation methods that emphasize flexibility and adaptability in real-world settings [1]. This approach recognizes that effective implementation requires moving beyond traditional linear models to embrace continuous optimization that studies the "fit" between interventions, individuals, and contexts [1].

Stakeholder engagement and co-creation represent fundamental components of agile implementation methodology, ensuring that NCCPs remain responsive to evolving community needs and system constraints. The WHO Global Initiative for Childhood Cancer exemplifies this approach, strategically engaging patients, families, and healthcare providers to advance childhood cancer as a public health priority [40] [41]. This protocol outlines systematic approaches for stakeholder integration within NCCP development and implementation, providing researchers and drug development professionals with practical frameworks for accelerating cancer control through collaborative science.

Theoretical Foundation: Agile Implementation and Stakeholder Integration

Agile implementation science represents a significant paradigm shift from traditional research approaches, drawing inspiration from systems engineering and software development methodologies [39]. Where conventional implementation often follows linear pathways, agile science embraces iterative development through rapid-cycle testing and refinement. This approach is particularly suited to complex adaptive systems—such as healthcare networks—characterized by multiple semiautonomous actors connected in nonlinear ways [39].

The theoretical underpinnings of agile stakeholder engagement emerge from several complementary frameworks:

• Community-Based Participatory Research (CBPR) leverages collective knowledge and resources to develop culturally-relevant, community-prioritized interventions [42]
• Comprehensive Participatory Planning and Evaluation (CPPE) provides a five-step, action-oriented approach to project planning and evaluation [42]
• Penta-helix frameworks recognize the essential roles of five sector groups in successful implementation: public sector, academia/research, private sector, media, and civil society [43]

The OPTICC Center (Optimizing Implementation in Cancer Control) exemplifies this integrated approach, utilizing a three-stage optimization process: (I) identify and prioritize determinants, (II) match strategies, and (III) optimize strategies through a transdisciplinary team [26]. This methodology addresses critical implementation barriers, including underdeveloped methods for determinant identification, incomplete knowledge of strategy mechanisms, underuse of optimization methods, and poor measurement of implementation constructs [26].

Table 1: Core Principles of Agile Stakeholder Engagement

Principle	Definition	Application in NCCPs
Representation	Inclusion of all perspectives impacted by research findings or programs	Purposive sampling across all sectors affected by cancer control policies [44]
Meaningful Participation	Empowerment of participants to have equal voices	Combination of homogeneous brainstorming panels and heterogeneous advisory boards [44]
Respectful Partnership	Inclusion of stakeholders as respected partners in all research phases	Engagement from planning through dissemination with fair compensation [44]
Accountability	Demonstrated impact of stakeholder input on decision-making	Continuous feedback loops and reporting on how input influenced decisions [44]

Stakeholder Mapping and Analysis: A Penta-Helix Approach

Effective stakeholder engagement begins with systematic identification and characterization of relevant actors across the cancer control ecosystem. The penta-helix framework provides a comprehensive structure for mapping stakeholders according to five distinct sectors, each contributing unique resources and perspectives to NCCP development and implementation [43].

Recent research examining European CPP initiatives revealed distinctive roles and motivations across sectors [43]. The public sector is predominantly viewed as the main driver of change and most influential in both Western and Eastern Europe, with responsibilities centered on strategy and operational engagement. Academia and research institutions contribute scientific credibility and knowledge generation, though challenges exist in translating research beyond laboratory settings. The private sector brings essential resources and operational capacity, though its profit orientation necessitates careful attention to ethical considerations. Media stakeholders provide dissemination capacity and public reach, while civil society organizations offer proximity to affected communities and advocacy functions [43].

Table 2: Stakeholder Roles and Contributions in Cancer Control

Sector	Primary Role	Key Activities	Motivations
Public Sector	Strategy and governance	"Looking after citizen's health," "Making the system work," policy development [43]	Public health improvement, regulatory compliance, resource allocation
Academia/Research	Knowledge generation	"Scientific credibility," "Diversity of approaches," evidence generation [43]	Scientific advancement, knowledge dissemination, research funding
Private Sector	Resource mobilization	"Profit-oriented," "Resources and operational activities," service delivery [43]	Market opportunities, corporate social responsibility, sustainable business models
Media	Dissemination and awareness	"Capacity to reach people," "Information and dissemination," public communication [43]	Public engagement, content value, audience reach
Civil Society	Engagement and advocacy	"Proximity to people," "Advocacy and voice," community representation [43]	Community benefit, issue representation, member services

Regional differences in stakeholder engagement have been observed, with Eastern European countries emphasizing the role of multiple sectors in cancer primary prevention more strongly than Western counterparts [43]. This highlights the importance of contextual adaptation in stakeholder engagement protocols rather than one-size-fits-all approaches.

Diagram 1: Penta-Helix Stakeholder Framework for NCCPs

Engagement Protocols and Methodologies

Structured Advisory Group Model: The SWOG S1415CD Protocol

The External Stakeholder Advisory Group (ESAG) model implemented in the TrACER study (SWOG S1415CD) provides a structured protocol for longitudinal stakeholder engagement throughout the research lifecycle [9]. This pragmatic clinical trial assessing guideline-based colony stimulating factor standing orders engaged 21 national leaders including patient partners, payers, pharmacists, guideline experts, providers, and a medical ethicist [9].

The engagement structure incorporates:

• Annual in-person meetings complemented by web conferences and targeted email discussions
• Patient partner-specific meetings with study briefings and explanations of clinical/statistical concepts
• Two-week comment periods for stakeholders to offer recommendations on study issues
• Annual satisfaction surveys assessing communication, collaboration, and areas for improvement

This model demonstrated substantial impact on trial design and implementation, including refinement of endpoints, consent processes, patient surveys, and recruitment strategies [9]. The research team maintained a continuous feedback loop, discussing feasibility of ESAG suggestions and reporting back on final decisions.

Community-Academic Advisory Board: Rural Appalachian Model

A Community-Academic Advisory Board (CAB) in rural Appalachia demonstrated effective stakeholder engagement for addressing cancer disparities in underserved regions [42]. This mixed-methods case study employed a Comprehensive Participatory Planning and Evaluation (CPPE) process through which stakeholders prioritized four cancer control needs: human papillomavirus vaccination, tobacco control, colorectal cancer screening, and lung cancer screening [42].

The protocol featured:

• Four half-day in-person meetings annually with dedicated time for CPPE process
• Sub-groups meeting regularly outside main CAB meetings via conference calls
• Website development for sharing meeting minutes and resources
• Stratified sampling to ensure representation across stakeholder sectors

Longitudinal evaluation demonstrated significant improvement across all "Nine Habits of Successful Comprehensive Cancer Control Coalitions," including communication, shared decision making, trust, and satisfaction (all p < .05) [42].

PRO-ACTIVE Trial Engagement Protocol for Head and Neck Cancer

The PRO-ACTIVE trial for dysphagia interventions in head and neck cancer patients developed a specialized engagement protocol operationalizing four core principles [44]:

• Representation through purposive sampling of all impacted groups
• Meaningful Participation through homogeneous brainstorming panels and heterogeneous stakeholder advisory boards
• Respectful Partnership through engagement across all trial phases with independent facilitation
• Accountability through continuous feedback loops and demonstrated impact on decisions

This protocol specifically addressed power imbalances through professional facilitators and research training for less experienced stakeholders, while compensating all participants fairly for their contributions [44].

Diagram 2: Iterative Stakeholder Engagement Lifecycle

Innovative Engagement Methods: Storytelling and Digital Approaches

Storytelling Workshop Protocol for Childhood Cancer Initiatives

The Pan American Health Organization (PAHO) implemented a innovative storytelling workshop utilizing film to engage stakeholders in childhood cancer control initiatives [41]. This protocol employed a six-step process:

• Audience Definition: Ministries of health, pediatric oncology specialists, nonprofit organizations, hospital directors
• Goal Definition: Emotional engagement and compelling action toolkit provision
• Storyline Development: Film selection ("How I Live") documenting challenges in low-resource settings
• Theoretical Framework: Guided by Socioecological Model of Health and Theory of Change
• Interactive Design: Group exercises including stakeholder analysis and prioritization matrices
• Dissemination Plan: Summary reports, word clouds, qualitative responses, and strategic activity matrices

This approach recognized storytelling's capacity to humanize complex medical concepts and create emotional connections, particularly valuable in populations with strong cultural connections to oral tradition [41]. The workshop produced concrete outputs including stakeholder analyses and prioritization matrices for country-level strategic activities.

Agile Science and Rapid Implementation Methods

Agile implementation incorporates principles from the Agile Manifesto originally developed for software development, including valuing customer satisfaction, responding to change, and reducing time to delivery [39]. These approaches show particular promise for addressing the temporal challenges in implementation science.

Key methodological considerations include:

• Adaptive Trial Designs: Basket or umbrella trials utilizing Bayesian decision rules to discontinue ineffective approaches [39]
• Multiphase Optimization Strategy (MOST): Continuous intervention optimization through component evaluation [1]
• Micro-randomized Trials: Sequential factorial designs for modeling component effectiveness over time [1]
• Problem-Oriented Approaches: Strategic planning techniques to gather multi-stakeholder feedback rapidly [39]

These methods enable more responsive implementation approaches while maintaining scientific rigor, potentially reducing the traditional 17-year translation gap [39].

Evaluation Frameworks and Outcome Assessment

Robust evaluation is essential for assessing both stakeholder engagement processes and their impact on implementation outcomes. The Nine Habits of Successful Comprehensive Cancer Control Coalitions provides a validated framework for evaluation, encompassing [42]:

• Communication and trust development
• Priority work plans with clear roles and accountability
• Shared decision making and value-added collaboration
• Empowered leadership and diversified funding
• Participant satisfaction

Mixed-methods approaches combining quantitative surveys with qualitative interviews offer comprehensive insights into engagement effectiveness. In the Appalachian CAB case study, all nine habits showed significant improvement from Time 1 to Time 2 (all p < .05), with most remaining significantly higher at Time 3 [42].

Table 3: Engagement Evaluation Framework

Evaluation Domain	Methods	Metrics
Engagement Process	Annual satisfaction surveys, meeting artifact analysis, participation tracking	Communication quality, meeting structure, respect and value, frequency of interaction [9]
Intermediate Outcomes	Pre-post surveys, semi-structured interviews, document analysis	Trust development, shared understanding, priority alignment, capacity building [42]
Implementation Impact	Implementation fidelity measures, cost-tracking, adoption rates	Strategy effectiveness, adoption breadth, sustainability, cost-effectiveness [26]
Health Outcomes	Clinical outcome tracking, health equity assessment	Cancer screening rates, survival improvements, disparity reduction [40]

Implementation science emphasizes the importance of causal mechanism identification through methods like causal pathway diagrams, which map relationships between strategies and outcomes including moderators and preconditions [15]. These approaches help elucidate how stakeholder engagement contributes to implementation success.

Table 4: Research Reagent Solutions for Stakeholder-Engaged Implementation Science

Tool/Resource	Function	Application Context
Expert Recommendations for Implementing Change (ERIC)	Compilation of 73 implementation strategies grouped into 9 clusters	Selecting and specifying implementation strategies based on contextual barriers [15]
Causal Pathway Diagrams	Visual mapping of relationships between strategies, mechanisms, and outcomes	Hypothesis development about how engagement strategies produce effects [15]
Stakeholder Feedback Surveys	Structured assessment of engagement satisfaction and perceived impact	Longitudinal tracking of engagement quality and identification of improvement areas [9]
Stakeholder Advisory Boards	Structured governance mechanism for ongoing stakeholder input	Providing diverse perspectives throughout research lifecycle from design through dissemination [9]
Storytelling Platforms	Narrative development and sharing tools for experiential knowledge	Humanizing complex health issues and creating emotional connections with stakeholders [41]
Multi-level Implementation Frameworks	Conceptual models depicting macro-micro level influences on implementation	Guiding comprehensive engagement across structural, organizational, provider, patient, and innovation levels [39]

Stakeholder engagement and co-creation represent essential methodologies for advancing agile implementation in cancer control. The protocols and frameworks presented demonstrate that meaningful stakeholder integration across the penta-helix sectors can enhance the relevance, effectiveness, and sustainability of NCCPs. The emerging emphasis on agile science approaches—characterized by iterative development, rapid-cycle evaluation, and continuous optimization—offers promising pathways for accelerating implementation and reducing the documented translation gap in cancer control.

Future directions should focus on mechanism testing to better understand how and why engagement strategies work, adaptive design applications for more responsive implementation, and equity-centered approaches that ensure engagement processes reduce rather than exacerbate cancer disparities. As implementation science continues to evolve, stakeholder engagement methodologies will play increasingly critical roles in ensuring that National Cancer Control Plans achieve their full potential for population health impact.

Application Note: Implementing Agile Science in Implementation Research

Agile Science represents a methodological approach within implementation research that emphasizes iterative, partner-engaged processes to optimize evidence-based interventions (EBIs) in real-world settings. This case study examines the application of Agile Science methods to improve colorectal cancer (CRC) screening rates in Federally Qualified Health Centers (FQHCs), which serve medically underserved populations. The project, known as Project FACtS (FQHCs Assessing Colorectal cancer Screening), utilized the Practical, Robust Implementation and Sustainability Model (PRISM) to guide data collection and implementation strategy selection [5] [45]. PRISM provides a contextually expanded version of the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework, enabling researchers to account for multilevel contextual factors influencing implementation success [5].

The urgent need for improved CRC screening in FQHCs is demonstrated by significant screening disparities. While the American Cancer Society National Colorectal Cancer Roundtable has set a screening target of 80%, FQHCs have notably lower CRC screening rates, averaging approximately 41% [5]. Some individual FQHCs report rates as low as 35% [46], creating a significant health equity gap for vulnerable populations. Project FACtS addressed this challenge through an established clinical-academic partnership with three San Diego County FQHCs serving predominantly low-income, publicly insured, and Hispanic/Latino patients [5].

Quantitative Outcomes and Performance Metrics

The implementation of Agile Science methodologies yielded both quantitative and qualitative outcomes across participating FQHCs. The table below summarizes key quantitative metrics observed during the Project FACtS implementation:

Table 1: Colorectal Cancer Screening Metrics in FQHC Implementation Studies

Metric	Baseline Performance	Post-Implementation Performance	Data Source
Overall CRC Screening Rates	Varying baseline rates (37%, 51%, 66%) across FQHCs [45]	7.4% average improvement with standard facilitation; 2.8% with CoachIQ [47]	Project FACtS, OPTICC Center
Screening Rate Disparities (Latinx Patients)	Not specified	9.1% average increase with standard facilitation; 2.1% decrease with CoachIQ [47]	OPTICC Center
FQHC National Screening Average	41% [5]	Healthy People 2030 goal: 70.5% [48]	National aggregates
Patient Understanding of FIT Instructions	Standard text-based instructions	100% understanding and preference for wordless instructions [49]	STOP CRC Program

The implementation approach also addressed specific evidence-based interventions (EBIs) at multiple levels. At the patient level, these included reminders, small media, and one-on-one education. For providers, interventions included assessments and feedback on CRC screening performance and reminder/recall systems. Clinic-level EBIs focused on reducing structural barriers through expanded clinic hours and improved access to tests and results [5].

Experimental Protocols and Methodologies

Agile Science Workshop Protocol

The Agile Science workshop served as a core component of the implementation approach, following a structured six-step process conducted over a seven-month period [45]. This protocol enabled the research team and FQHC partners to collaboratively map CRC screening processes and identify intervention points.

Table 2: Agile Science Workshop Protocol Components

Phase	Duration	Participants	Key Activities	Outputs
Align & Explore	Pre-workshop	Research team, Agile Science consultants	Define workshop goals, align study outcomes with Agile Science concepts	Goal statement, outcome measures
Pre-Workshop Interviews	20-30 minutes each	7 FQHC staff members	Six telephone interviews to inform workshop design	Interview notes, preliminary data
Workshop Implementation	4 hours	6 FQHC staff, Agile Science team, research team	Process mapping, intervention point identification	Draft process maps, notes
Reflect & Evaluate	Post-workshop	All participants	Evaluation survey administration	Satisfaction data, refinement areas

Procedural Details:

• Pre-Workshop Preparation: The Agile Science team (external consultants to the research team) conducted six individual telephone interviews with seven FQHC staff members to inform the final workshop design. Interviews focused on current CRC screening processes, challenges, and potential improvement opportunities [45].
• Workshop Activities: The 4-hour workshop built upon pre-workshop interviews and involved initial mapping of CRC screening processes using visual aids and collaborative exercises. Participants worked to identify specific intervention points within existing workflows at each FQHC [45].
• Process Mapping: Workshop activities generated visual representations of CRC screening processes, similar to the sample process map for CRC screening by FOBT/FIT developed at one FQHC, which outlined each step from identifying eligible patients to documenting results [45].

Multi-Method Data Collection Protocol

Project FACtS employed consecutive, iterative data collection approaches guided by PRISM to gather implementation-relevant information from each FQHC. The multi-method approach progressed from broader exploratory methods to more specific, targeted data collection, with findings from each phase informing subsequent phases [5] [45].

Table 3: Multi-Method Data Collection Protocol

Method	Participants	Primary Objectives	Data Collection Tools
Introductory Meetings	Clinic coordinators, QI specialists, CEO, CMO, physician champions, lab personnel, data analysts	Develop priorities, elicit feedback on study design, set guiding principles, decide on outcome measures	Meeting notes, compiled research team observations
Secondary Data Requests	Site-based coordinators	Collect internal clinic variables (characteristics, screening rates) and external policy influences	HRSA Uniform Data Systems, policy documents
Online Surveys	CEO, medical director, primary care providers, clinic managers, referral specialists, QI specialists, gastroenterologists	Assess patient-level barriers, referral processes for diagnostic colonoscopy, existing GI referral relationships	Qualtrics software platform
In-Depth Interviews	CEO, medical director, clinic manager, lab manager, QI specialist	Gather detailed information on critical issues identified in previous data collection methods	Semi-structured interview guides, audio recordings, transcripts

Implementation Notes:

• Iterative Refinement: Each data collection method increased in specificity, with earlier findings shaping subsequent approaches. This iterative design reduced participant burden by focusing later surveys and interviews on the most relevant topics [5].
• Partner Engagement: The protocol emphasized meaningful engagement with diverse FQHC partners, following the Principles of Community Engagement. This included becoming knowledgeable about each FQHC, establishing relationships and trust, respecting FQHC self-determination, and working toward long-term commitment [45].
• Contextual Assessment: The data collection was structured around PRISM domains, assessing both internal factors (service recipients, implementation infrastructure) and external factors (community resources, policy impacts) [5].

Digital Health Implementation Protocol

Recent implementations have incorporated digital health platforms to enhance CRC screening in FQHCs. One protocol describes using the mPATH-CRC digital platform in a hybrid type I trial to assess both effectiveness and implementation processes [50].

Trial Design:

• Setting: The protocol is implemented in rural southeastern North Carolina FQHCs serving predominantly low-income populations, with patients primarily at or below 200% of federal poverty guidelines [50].
• Participants: Patients aged 45-73 at average CRC risk, overdue for screening, with at least one clinic visit in the past 12 months. Exclusion criteria include history of CRC, adenomas, total colectomy, family risk of CRC, or inflammatory bowel disease [50].
• Randomization: Patients are identified through EHR queries and randomized 1:1 within strata of appointment status and race/ethnicity to either usual care or mPATH-CRC intervention [50].
• Intervention Components: The mPATH-CRC platform identifies patients due for screening, provides text messages about screening, multi-media education on options, and either mailed FIT kits or assistance scheduling colonoscopies [50].

Visualizations of Workflows and Methodologies

Agile Science Workshop Workflow

The following diagram illustrates the six-step Agile Science workshop process used in Project FACtS to develop and refine CRC screening implementation strategies:

PRISM-Based Implementation Framework

The conceptual model for Project FACtS integrates multiple components to guide implementation efforts, as depicted in the following diagram:

CRC Screening Process Map

Based on the Agile Science workshop outputs, the following diagram represents a generalized CRC screening process at participating FQHCs:

Research Reagent Solutions and Essential Materials

The following table details key research reagents, tools, and materials essential for implementing Agile Science approaches for CRC screening in FQHC settings:

Table 4: Research Reagent Solutions for CRC Screening Implementation

Tool/Resource	Function/Application	Implementation Notes
PRISM Framework	Guides multilevel contextual assessment; expands RE-AIM with implementation and sustainability focus	Used to structure data collection across internal/external environments, organizational/patient perspectives [5] [45]
Fecal Immunochemical Test (FIT)	Stool-based CRC screening method; detects occult blood	Preferred in FQHCs due to lower cost; single sample; no dietary restrictions [46] [49]
Wordless FIT Instructions	Pictograph-based instructions for test completion; minimal text	7-word format; improves understanding across literacy levels and languages; preferred by patients [49]
Electronic Health Record (EHR) Systems	Identifies eligible patients; tracks screening status; generates reminders	Epic system used for query development; enables patient identification and outcome tracking [50]
mPATH-CRC Digital Platform	Automated patient outreach; education delivery; screening preference assessment	Delivers text messages, multimedia education, mailed FIT kits; used as in-reach or outreach strategy [50]
Process Mapping Tools	Visualizes current screening workflows; identifies intervention points	Generated through Agile Science workshops; reveals bottlenecks and improvement opportunities [45]
Qualtrics Survey Software	Administers structured assessments to clinic staff and leadership	Collects data on barriers, referral processes, implementation challenges [45]

Implementation Strategy Components

The Agile Science approach incorporated specific evidence-based interventions and implementation strategies tailored to FQHC settings:

Evidence-Based Interventions (EBIs):

• Patient-Level: Reminders (letter, phone, text), small media (brochures, posters), one-on-one education [5]
• Provider-Level: Assessment and feedback on CRC screening performance, reminder and recall systems (e.g., tablet info linked to patient medical record) [5]
• Clinic-Level: Reduction of structural barriers (expanded clinic hours, ease of access to tests and results) [5]

Implementation Strategies:

• Practice Facilitation: Coaches supported primary care practices to identify local barriers and select quality improvement initiatives [47]
• Multicomponent Outreach: Combined mailed FIT instructions with phone-based reminders and patient navigation [50]
• Stakeholder Engagement: Established community-academic partnerships with sustained interaction among clinic-based coordinators, quality improvement leads, and executive teams [5]

The Agile Science methods described in this case study demonstrate the value of iterative, partner-engaged approaches for implementing CRC screening programs in resource-limited FQHC settings. By combining structured frameworks like PRISM with flexible, adaptive methodologies, researchers and practitioners can develop more effective, sustainable implementation strategies that address health disparities and improve cancer screening outcomes for vulnerable populations.

Micro-Randomized Trials and Sequential Multiple Assignment Randomized Trials (SMART) for Intervention Optimization

Agile science methods are essential for developing adaptive, evidence-based interventions in cancer implementation research. Micro-Randomized Trials (MRTs) and Sequential Multiple Assignment Randomized Trials (SMART) represent innovative methodological frameworks that enable researchers to optimize behavioral and digital interventions through sequential decision-making. Unlike traditional randomized controlled trials (RCTs) that evaluate fixed intervention packages, these designs allow for the examination of dynamic treatment regimens tailored to individual patient needs and changing contexts over time [51] [52]. The integration of these approaches is particularly valuable in cancer care, where intervention needs fluctuate throughout the disease trajectory and treatment process.

MRTs are specifically designed to optimize just-in-time adaptive interventions (JITAIs) by repeatedly randomizing participants to different intervention options at critical decision points over the study period. This approach enables researchers to assess the immediate, proximal effects of intervention components and understand how these effects vary based on individual context [52]. SMART designs extend this framework by randomizing participants multiple times to different intervention options throughout the trial, allowing for the evaluation of decision rules that guide how interventions should be adapted over time based on individual response or changing circumstances.

Foundational Concepts and Definitions

Key Characteristics of MRTs and SMART Designs

Micro-Randomized Trials (MRTs) are factorial experimental designs that involve repeated randomizations of an individual to different intervention conditions throughout the study period. These trials are characterized by their focus on proximal outcomes and their ability to investigate effect moderation by context [52]. In an MRT, participants are randomized hundreds or even thousands of times, making this design particularly suitable for mobile health interventions where decisions about intervention delivery need to be made frequently.

Sequential Multiple Assignment Randomized Trials (SMART) are a type of multistage randomized trial design that informs the construction of adaptive interventions. In SMART designs, participants are randomized multiple times at critical decision points throughout the trial, with subsequent randomizations potentially depending on earlier responses or outcomes. This approach allows researchers to answer questions about how to best adapt interventions based on individual needs.

Comparative Analysis of Trial Designs

Table 1: Comparison of Traditional RCTs, MRTs, and SMART Designs

Design Characteristic	Traditional RCT	Micro-Randomized Trial (MRT)	SMART Design
Randomization Frequency	Once at study entry	Repeatedly throughout study (could be hundreds of times)	Multiple times at critical decision points
Primary Unit of Analysis	Between-participant differences	Within-participant differences across decision points	Sequence of decision rules across stages
Primary Outcome	Distal outcomes (e.g., survival)	Proximal outcomes (short-term effects)	Both proximal and distal outcomes
Intervention Approach	Fixed intervention package	Just-in-time adaptive interventions (JITAIs)	Adaptive treatment strategies
Key Research Question	"Is intervention A better than B?"	"When is intervention component C effective?"	"How should treatment be adapted over time based on patient response?"
Contextual Considerations	Controlled through inclusion/exclusion criteria	Explicitly models context as effect modifier	Considers tailoring variables for adaptation

Methodological Protocols and Experimental Procedures

Protocol for MRT Implementation in Cancer Symptom Management

Phase 1: Assessment and Individual Risk Profiling

• Conduct initial assessment phase (typically 14 days) to establish baseline data and identify individual risk patterns [52]
• Deploy Ecological Momentary Assessment (EMA) to collect real-time data on symptoms, triggers, and contexts
• For cancer symptom management, assess factors such as pain episodes, medication timing, activity patterns, and emotional states
• Use assessment data to generate individual risk profiles by combining timestamps, location data (GPS), and self-reported symptoms
• Identify critical decision points for intervention delivery based on risk patterns

Phase 2: Geofence and Contextual Trigger Establishment

• Create virtual boundaries (geofences) around identified high-risk locations using GPS coordinates [52]
• Establish temporal parameters for intervention delivery based on individual risk patterns
• Program assessment triggers to activate when participants enter geofenced areas during specified time windows
• For cancer applications, relevant geofences might include treatment centers, pharmacies, or locations associated with symptom triggers

Phase 3: Intervention Randomization and Delivery

• Implement within-subject randomization at each decision point using computerized algorithms [52]
• For smoking cessation MRTs, message types might include: distraction (CBT-based), acceptance (ACT-based), or control messages [52]
• For cancer symptoms, intervention options could include: cognitive restructuring, symptom coping strategies, activity pacing, or medication reminders
• Deliver interventions via mobile platform when triggers are activated
• Collect proximal outcomes (e.g., symptom intensity, urge strength) within short timeframes after intervention delivery (e.g., 15 minutes)

Phase 4: Outcome Assessment and Analysis

• Deploy follow-up EMAs to assess proximal outcomes after intervention delivery
• Monitor distal outcomes at predetermined intervals (e.g., 45-day follow-up) [52]
• Analyze data using specialized statistical approaches for intensively longitudinal data
• Estimate causal excursion effects to understand intervention impact across different contexts

Protocol for SMART Designs in Adaptive Cancer Interventions

Phase 1: Initial Randomization and First-Stage Intervention

• Recruit eligible participants and obtain informed consent
• Collect comprehensive baseline assessments including potential tailoring variables
• Randomize participants to initial intervention options (e.g., 2-4 different starting interventions)
• Implement first-stage intervention for predetermined duration or until response assessment point

Phase 2: Response Assessment and Re-randomization

• Assess response to first-stage intervention using predefined criteria (e.g., symptom reduction, adherence metrics)
• Classify participants as responders or non-responders based on established thresholds
• For non-responders, randomize to different intensification or adaptation strategies
• For responders, randomize to maintenance or de-escalation strategies
• Define decision rules based on assessment outcomes

Phase 3: Second-Stage Intervention Implementation

• Implement second-stage interventions according to re-randomization assignments
• Continue monitoring outcomes and potential side effects
• Maintain the adaptive intervention framework throughout the study period

Phase 4: Final Outcome Assessment and Decision Rule Estimation

• Conduct final outcome assessment at study endpoint
• Analyze data to inform optimal adaptive intervention strategies
• Estimate decision rules for when to adapt interventions and which adaptations work best for different patient profiles

Table 2: SMART Design Decision Points for Cancer Symptom Management Intervention

Stage	Tailoring Variable	Intervention Options	Assessment Timeline
Initial Randomization	Baseline symptom severity, cancer type, treatment regimen	Option A: Standard symptom monitoringOption B: Enhanced self-management educationOption C: Proactive clinician outreach	2 weeks post-randomization
Response Assessment	Symptom reduction ≥30%, adherence to monitoring, emergency department visits	Responders: Continue current intensity or step-downNon-responders: Intensify or switch approach	4-6 weeks after initial intervention
Second-Stage Intervention	Early response pattern, side effects, patient preference	Non-responder options: • Add pharmacological management• Increase coaching frequency• Switch to different behavioral approach	Ongoing through study period
Final Outcome Assessment	Cumulative symptom burden, quality of life, healthcare utilization	Comparison of embedded adaptive interventions	12 weeks from study entry

Quantitative Data Analysis Methods for Intensive Longitudinal Data

Statistical Approaches for MRT Analysis

Primary Analysis Method: Causal Excursion Analysis Causal excursion analysis is the primary statistical method for analyzing MRT data, enabling estimation of the marginal mean of proximal outcomes under specific intervention decisions. This method accounts for the complex dependence structure in intensively longitudinal data and allows for investigation of effect moderation by time-varying contextual factors [52].

The model specification includes:

• Proximal outcome (Y_{t+Δ}) measured within a short time window after decision point t
• Treatment indicator A_t representing the randomized intervention assignment at decision point t
• Available history H_t containing all information collected up to decision point t
• Causal excursion effect defined as E[Y{t+Δ} | At = 1, Ht] - E[Y{t+Δ} | At = 0, Ht]

Secondary Analysis: Moderated Treatment Effects Examine how intervention effects vary based on contextual factors such as:

• Time of day, day of week
• Location characteristics
• Recent symptom patterns
• Stressor exposure
• Social context

Sample Size Considerations and Power Analysis

Table 3: Power Analysis Parameters for MRTs in Cancer Research

Design Parameter	Typical Range	Considerations for Cancer Applications
Number of Participants	20-100	Smaller samples feasible due to intensive within-subject data collection
Decision Points per Participant	50-500	Depends on intervention frequency and study duration
Expected Effect Size	d = 0.1-0.3	Smaller effects may be clinically meaningful for symptom management
Intraclass Correlation	0.05-0.30	Accounts for within-person dependency in repeated measures
Availability	60-90%	Proportion of decision points when participants are available for intervention
Statistical Power	80-90%	Standard thresholds for detecting intervention effects

Implementation in Cancer Research Contexts

Applications Across the Cancer Care Continuum

Prevention and Screening MRTs can optimize interventions to increase adherence to cancer screening guidelines. Example: Adaptive text message interventions to promote colorectal cancer screening in Federally Qualified Health Centers, where message timing and content can be tailored based on individual patterns of availability and responsiveness [5].

Symptom Management During Treatment Adaptive interventions for managing chemotherapy side effects using real-time symptom monitoring. Example: Smart Cancer Care platform that enables patients to self-evaluate symptoms and receive staged management guidelines based on symptom severity [53].

Survivorship and Follow-up Care SMART designs can inform adaptive interventions for managing long-term and late effects of cancer treatment, adjusting support based on symptom trajectory and functional recovery.

Integration with Implementation Science Frameworks

The PRagmatic Implementation and Sustainability Model (PRISM) provides a valuable framework for integrating MRTs and SMART designs into real-world cancer care settings. PRISM expands the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) framework by incorporating multilevel contextual factors that influence implementation success [5]. Key considerations include:

• Organizational Context: Fit with clinical workflow, resource availability, and clinic priorities
• Patient Perspectives: Accessibility, burden, and perceived usefulness of adaptive interventions
• Implementation Infrastructure: Staff training, technical support, and monitoring systems
• External Environment: Policy context, reimbursement structures, and regulatory considerations

Research Reagent Solutions and Essential Materials

Table 4: Essential Research Tools for MRT and SMART Implementation

Tool Category	Specific Solutions	Function in Research	Implementation Considerations
Mobile Assessment Platforms	MetricWire Catalyst, PACO (Personal Analytics Companion), MovisensXS	Ecological Momentary Assessment (EMA) data collection, real-time intervention delivery	HIPAA compliance, cross-platform compatibility, offline functionality [52]
Geolocation Services	Google Maps Geofencing API, iOS Core Location	Creation of virtual boundaries for location-triggered interventions	Battery consumption optimization, privacy protections, accuracy in diverse environments [52]
Randomization Engines	Custom R/Python algorithms, Research Electronic Data Capture (REDCap) randomization module	Within-subject random assignment at each decision point	Integration with mobile platforms, audit trail maintenance, reproducibility
Data Analytics Software	R (geepack, wgeesel, MRTSampleSize), SAS PROC GENMOD, Mplus	Intensive longitudinal data analysis, causal inference methods	Handling missing data, computational efficiency for large datasets, visualization capabilities
Patient-Reported Outcome Measures	PROMIS measures, ESAS (Edmonton Symptom Assessment System), NCI PRO-CTCAE	Standardized assessment of symptoms and functional status	Clinical relevance, respondent burden, validation in cancer populations [53]
Implementation Tracking Systems	CONSORT extension for MRTs, PRISM checklist, RE-AIM metrics	Protocol adherence, implementation fidelity, context documentation	Adaptability to diverse clinical settings, comprehensiveness for reporting [5]

Visualizing Adaptive Intervention Decision Frameworks

Navigating Real-World Barriers: Strategies for Effective Implementation

Common Pitfalls in Cancer Control Planning and How to Avoid Them

National Cancer Control Plans (NCCPs) are vital tools for governments to systematically address the growing global cancer burden. Despite significant advancements in cancer research and treatment, a persistent gap exists between evidence and real-world implementation. This article examines common pitfalls in cancer control planning through the lens of agile science and implementation research, providing structured protocols to enhance plan specificity, funding, evidence-based practice, and equity. By applying iterative evaluation and adaptive learning methodologies, planners can develop dynamic NCCPs capable of responding to evolving challenges and ensuring equitable access to cancer services.

The global cancer burden is projected to increase by 60% over the next two decades, with predicted cases rising to 30 million by 2040 [54]. National Cancer Control Plans (NCCPs) provide a strategic framework for governments to address this challenge through coordinated prevention, detection, treatment, and palliation efforts. Evidence indicates that approximately 40% of cancers are preventable, and a further third can be cured through early detection and proper treatment [54]. However, a significant implementation gap undermines the potential impact of NCCPs. A recent review of 156 national plans revealed critical deficiencies in funding, evidence-based recommendations, and equitable implementation [55]. This article applies agile science methodologies to diagnose common planning pitfalls and provides protocols to transform NCCPs into dynamic, responsive tools for cancer control.

Quantitative Analysis of Current NCCP Gaps

Table 1: Documented Gaps in National Cancer Control Plans (2023-2024)

Planning Domain	Percentage of Plans with Gaps	Specific Deficiency
Funding & Resources	73%	No dedicated funding strategy [55]
Evidence-Based Practices	77%	No evidence base with references for strategies [55]
Radiotherapy Access	50%	No radiotherapy strategy included [55]
Essential Medicines	74%	No mention of WHO Essential Medicines [55]
Screening-Treatment Linkage	Not quantified	Delays connecting screening to treatment initiation [55]

The tabulated data reveals systemic shortcomings in plan implementation. The deficiency in funding strategies is particularly critical, as even well-designed plans remain theoretical without dedicated resources. The inadequate integration of radiotherapy strategies affects half of all plans, despite radiotherapy being required in over 50% of cancer cases [55]. Furthermore, the disconnect between screening programs and treatment pathways represents a critical failure point where early detection fails to translate into timely intervention.

Agile Science Framework for Cancer Implementation Research

Agile science provides a methodological approach for creating and curating knowledge for behavior change in real-world implementation [1]. This framework emphasizes iterative development, early-and-often sharing of resources, and continuous optimization to address the complexity of cancer control across diverse contexts.

Core Principles

• Iterative Development: Replace linear planning with continuous feedback loops that allow for plan refinement based on implementation data and changing contexts.
• Modularity: Deconstruct complex interventions into smallest meaningful, self-contained components that can be tested, adapted, and repurposed [1].
• Stakeholder Co-creation: Engage patients, providers, policymakers, and community representatives throughout the planning and implementation process.
• Precision Implementation: Develop strategies that are adoptable, acceptable, and sustainable within specific contexts and populations [4].

Implementation Science Foundations

Implementation science systematically studies methods to promote the integration of evidence-based practices into routine healthcare [3]. The National Cancer Institute (NCI) supports this field to bridge the divide between research and practice, with a focus on equity so that disadvantaged communities benefit from scientific advances [4]. The Consortium for Cancer Implementation Science (CCIS) works to build capacity, increase collaboration, and support implementation science activities across the cancer control continuum [3].

Agile Implementation Cycle

Common Pitfalls and Evidence-Based Protocols

Pitfall 1: Inadequate Funding Strategies

Protocol 1.1: SMART Financing Framework

• Specific: Define exact budget requirements for each plan component using activity-based costing.
• Measurable: Establish financial tracking indicators integrated with health management information systems.
• Achievable: Phase implementation according to prioritized interventions with realistic resource mobilization.
• Realistic: Align with medium-term expenditure frameworks of national finance ministries.
• Time-bound: Create multi-year financing schedules with clear milestones for release of funds.

Table 2: Funding Source Diversification Strategy

Funding Source	Implementation Timeline	Key Performance Indicators
Government Budget Allocation	Year 1-5	Percentage of health budget dedicated to cancer control
Donor & Global Health Initiatives	Year 1-3	Grant funding secured for specific evidence-based interventions
Private Sector Partnerships	Year 2-4	Number of functioning public-private partnerships for service delivery
Health Insurance Integration	Year 1-5	Percentage of cancer services covered under universal health coverage

Pitfall 2: Weak Evidence-Based Foundations

Only 23% of NCCPs include evidence-based recommendations with references [55]. This represents a critical methodological flaw in plan development.

Protocol 2.1: Evidence Integration Process

• Systematic Review: Conduct comprehensive literature review using databases like NCI's Evidence-Based Cancer Control Programs (EBCCP) which contains over 200 proven interventions [3].
• Contextual Adaptation: Modify evidence-based interventions for local cultural, resource, and health system constraints while preserving core elements.
• Stakeholder Validation: Present adapted interventions to clinical experts, community representatives, and patients for feasibility assessment.
• Implementation Mapping: Identify and address barriers to adoption through tailored implementation strategies.

Pitfall 3: Fragmented Screening-to-Treatment Pathways

Many NCCPs fail to establish reliable linkages between early detection programs and treatment access, resulting in diagnostic delays and abandoned care pathways [55].

Protocol 3.1: Continuum of Care Integration

• Navigation Systems: Implement patient navigation programs modeled after the Chicago Chinatown initiative that increased breast and cervical cancer screening in Chinese immigrant women through culturally adapted navigation [4].
• Referral Protocols: Standardize referral pathways with clear timelines and accountability mechanisms.
• Digital Tracking: Utilize health information technologies to monitor patient progress through the care continuum.
• Quality Metrics: Establish performance indicators for each transition point in the cancer journey.

Integrated Care with Navigation

Pitfall 4: Insufficient Stakeholder Engagement

Protocol 4.1: Multi-Stakeholder Governance Framework

• Steering Committee: Establish multi-sectoral governance body with representation from ministry of health, finance, civil society, patient groups, and clinical specialists.
• Technical Working Groups: Create domain-specific committees for prevention, diagnosis, treatment, and palliation.
• Community Advisory Boards: Ensure community voice in planning and implementation, particularly for marginalized populations.
• Implementation Consortium: Develop partnerships with academic institutions for evaluation and with private sector for service delivery innovation.

France's establishment of the National Cancer Institute (INCa) to coordinate all cancer control functions exemplifies effective centralized leadership with multi-stakeholder engagement [54].

Pitfall 5: Rigid Planning Without Adaptation Mechanisms

Traditional linear planning approaches fail to accommodate emerging evidence, changing contexts, or unexpected disruptions like the COVID-19 pandemic.

Protocol 5.1: Agile Evaluation and Adaptation Cycle

• Rapid-Cycle Evaluation: Implement quarterly plan performance reviews using balanced scorecard approach.
• Adaptive Management: Empower implementers to make evidence-informed adjustments based on real-time data.
• Learning System: Establish mechanisms for capturing and disseminating implementation lessons across the health system.
• Resilience Planning: Incorporate contingency strategies for system shocks and emergencies.

The multiphase optimization strategy (MOST) provides a framework for continuous intervention optimization through iterative evaluation of component efficacy [1].

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Implementation Research Resources

Research Tool	Function	Access Point
NCI Research-Tested Intervention Programs (RTIPs)	Repository of evidence-based cancer control programs with implementation materials	https://ebccp.cancercontrol.cancer.gov [3]
Implementation Science Workgroups	Collaborative networks to advance methodology	Consortium for Cancer Implementation Science [3]
Training Institute for Dissemination and Implementation Research in Cancer (TIDIRC)	Capacity building for implementation science	NCI-sponsored training institute [4]
Cancer Control P.L.A.N.E.T.	Portal to data and resources for cancer control planning	NCI-funded gateway to comprehensive data [4]
Micro-Randomized Trial Methodology	Optimize adaptive interventions through sequential factorial designs	Agile science methodological toolkit [1]

Effective cancer control planning requires a fundamental shift from static documents to dynamic implementation frameworks. By applying agile science principles and implementation research methodologies, planners can avoid common pitfalls in funding, evidence integration, care continuity, stakeholder engagement, and adaptive management. The protocols presented provide concrete pathways for developing responsive, evidence-informed, and equity-focused NCCPs capable of reducing the growing global cancer burden. Future success will depend on embracing iterative learning, precision implementation, and sustained political commitment to transform scientific advances into population health impact.

Overcoming Resource Constraints in Low- and Middle-Income Settings

In low- and middle-income countries (LMICs), the burden of cancer is rising rapidly, with projections indicating that by 2030, approximately three-quarters of all cancer deaths will occur in these resource-constrained settings [56]. This growing crisis demands innovative approaches to cancer implementation research that can adapt to limited resources, infrastructure challenges, and diverse healthcare systems. Agile science methods, characterized by flexibility, iterative learning, and context-specific adaptation, offer promising frameworks for addressing these complex implementation challenges.

The concept of agility, adopted from software engineering, emphasizes adaptability and responsiveness to change—principles that are equally vital for cancer care in dynamic, resource-constrained environments [36]. This article presents application notes and protocols for employing agile implementation research methods to overcome resource limitations while maintaining scientific rigor and improving cancer care outcomes across LMICs.

Current Landscape and Research Priorities

Table 1: Cancer Burden and Research Infrastructure in LMICs

Indicator	Current Status in LMICs	Comparison to HICs
Cancer Mortality	Rising mortality rates; expected to double in Africa in <2 decades [57]	Declining rates due to effective screening, diagnosis & treatment [57]
Clinical Trial Representation	Only 8% of global phase 3 cancer trials (2014-2017) [56]	Dominates cancer clinical research enterprise [56]
Registry Coverage	Asia (15%), Africa (13%), South America (19%) population coverage [56]	Comprehensive national registry systems common [56]
Stage at Diagnosis	Frequently diagnosed at advanced stages [57]	Earlier detection through established screening programs [57]
Research Output	Limited implementation research; only 11 studies identified in recent systematic review of Asian cancers [58]	Dominates cancer knowledge generation and publication [56]

Key Research Priorities for LMICs

Five critical research priorities have been identified for addressing cancer in resource-constrained settings [56]:

• Reducing advanced-stage burden through context-specific prevention and early detection strategies
• Improving access, affordability, and outcomes via solution-oriented research
• Advancing value-based care and health economic assessments
• Scaling quality improvement and implementation research
• Leveraging technology to improve cancer control with robust evidence

Agile Implementation Framework for Resource-Constrained Settings

Core Agile Principles for Cancer Implementation

Agile methodologies adapted from software engineering emphasize [36] [59]:

• Individuals and interactions over rigid processes
• Responding to change over following a fixed plan
• Stakeholder collaboration throughout implementation
• Adaptive iteration based on continuous feedback

These principles align with successful implementation strategies for cancer control in LMICs, where fixed, linear approaches often fail due to evolving constraints and limited resources.

Implementation Readiness Assessment Protocol

Table 2: Health System Capacity Assessment Tool

Assessment Domain	Key Indicators	Data Collection Methods	Resource-Adapted Metrics
Workforce Capacity	Oncology specialists per population; task-shifting readiness; training gaps	Staff surveys; facility assessments; HR records	Simplified competency checklists; peer-assessment tools
Infrastructure & Equipment	Essential equipment availability; maintenance systems; utility reliability	Facility audit; equipment functionality testing	Tiered equipment lists (basic/advanced); downtime tracking
Supply Chain	Essential medicine availability; stockout frequency; procurement systems	Supply records; key informant interviews; observation	Core cancer medicine list; stockout impact scoring
Financial Resources	Cancer care funding; out-of-pocket expenditure; insurance coverage	Budget analysis; patient cost surveys; financing mechanism review	Catastrophic health expenditure measures; affordability indices
Information Systems	Data completeness; registry functionality; reporting capabilities	System assessment; data quality audit; user feedback	Simplified minimum dataset; data quality scorecards

Protocol Implementation Steps:

• Stakeholder Mapping (Weeks 1-2)

  • Identify key stakeholders across health system levels using purposive sampling
  • Conduct structured interviews using semi-structured guides
  • Map influence-interest matrices to prioritize engagement

• Situational Analysis (Weeks 3-6)

  • Collect quantitative data using standardized assessment tools
  • Conduct facility audits across representative sample
  • Perform document review of existing policies and reports

• Capacity Gap Analysis (Weeks 7-8)

  • Synthesize assessment findings using predefined scoring system
  • Prioritize gaps based on impact and feasibility of addressing
  • Validate findings with stakeholder working groups

Experimental Protocols for Agile Implementation Research

Protocol 1: Adaptive Stakeholder Engagement

Background: While most National Cancer Control Plans (NCCPs) describe stakeholder engagement, it is typically unstructured and incomplete [14]. Effective engagement is crucial for context-appropriate implementation.

Materials:

• Stakeholder mapping template (digital or paper-based)
• Engagement tracking system (simplified database or spreadsheet)
• Communication materials tailored to different stakeholder groups
• Feedback collection tools (structured forms, mobile data collection)

Methodology:

• Stakeholder Identification

  • Create comprehensive list of potential stakeholders using snowball sampling
  • Categorize by sector (government, clinical, community, patient advocacy)
  • Map according to influence and interest in cancer control

• Engagement Strategy Development

  • Customize engagement approaches for different stakeholder categories
  • Develop tailored communication materials for each group
  • Establish feedback mechanisms appropriate to local context

• Iterative Implementation

  • Conduct initial stakeholder consultations
  • Incorporate feedback into program design
  • Maintain ongoing engagement through regular updates and consultations
  • Adapt engagement strategies based on continuous feedback

Evaluation Metrics:

• Stakeholder representation across categories
• Feedback incorporation rate
• Engagement persistence throughout implementation cycle

Protocol 2: Contextual Adaptation of Evidence-Based Interventions

Background: Cancer control strategies effective in high-income countries often fail when directly transplanted to LMICs due to differences in disease characteristics, health system capacities, and sociocultural factors [56].

Materials:

• Evidence-based intervention protocols from HIC settings
• Context assessment toolkit
• Adaptation framework template
• Local expert consultation roster

Methodology:

• Intervention Deconstruction

  • Identify core components versus adaptable elements
  • Map resource requirements for each component
  • Identify potential barriers in local context

• Context Assessment

  • Evaluate health system capacity to deliver intervention components
  • Assess cultural acceptability of intervention elements
  • Identify resource constraints and potential workarounds
  • Map existing infrastructure that can be leveraged

• Adaptation Process

  • Convene multidisciplinary adaptation panel including local experts
  • Systematically modify intervention components while preserving core elements
  • Develop implementation protocols appropriate to local resources
  • Create training materials for local healthcare workers

• Pilot Testing

  • Implement adapted intervention in controlled setting
  • Collect process and outcome metrics
  • Further refine based on pilot results

Evaluation Metrics:

• Fidelity to core intervention elements
• Resource utilization efficiency
• Adoption rates by local providers
• Patient acceptability and satisfaction

Visualization of Agile Implementation Workflow

Agile Implementation Workflow: This diagram illustrates the iterative process of implementing cancer control strategies in resource-constrained settings, emphasizing continuous adaptation based on stakeholder feedback and evaluation data.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Research Materials and Tools for Implementation Research in LMICs

Tool/Reagent	Function/Purpose	Resource-Adapted Alternatives	Implementation Role
Data Collection Platforms	Capture implementation process and outcome metrics	Open-source platforms (REDCap, ODK); paper-based systems with digital backup	Enable rigorous monitoring and evaluation with limited resources
Stakeholder Engagement Tools	Facilitate structured participation and feedback	Mixed-method approaches (focus groups, interviews, surveys); community advisory boards	Ensure context relevance and build local ownership
Implementation Frameworks	Guide systematic implementation process	Adapted ERIC framework; WHO implementation guide; simplified logic models	Provide structure while allowing local customization
Capacity Assessment Tools	Evaluate health system readiness	WHO health system blocks assessment; simplified readiness checklists	Identify implementation barriers and facilitators
Economic Evaluation Tools	Assess cost-effectiveness and resource use	WHO CHOICE methodology; simplified cost-effectiveness analysis	Inform resource allocation decisions
Digital Health Technologies	Extend reach and improve efficiency	Mobile health applications; telemedicine platforms; SMS reminders	Overcome geographic and workforce limitations

Discussion and Future Directions

Agile implementation science methods offer a promising approach to addressing the complex challenges of cancer control in LMICs. By emphasizing adaptation, stakeholder engagement, and iterative learning, these approaches can help maximize limited resources while maintaining scientific rigor.

The integration of implementation science into national cancer control planning provides a structured framework for achieving equitable and feasible cancer control policies [14]. Future efforts should focus on building local research capacity, strengthening data systems, and promoting context-specific implementation strategies that can evolve with changing needs and resources.

As cancer burdens continue to grow in LMICs, agile implementation approaches will become increasingly vital for developing effective, sustainable, and scalable cancer control strategies that can save lives despite resource constraints.

Building Capacity and Assessing Health System Readiness

Application Note: Conceptual Foundations and Assessment Frameworks

Core Domains of Health System Readiness

Table 1: Multidimensional Readiness Constructs and Their Applications

Construct Domain	Key Components	Measurement Approach	Application Context
Institutional Readiness [60] [61]	Research infrastructure, equitable resource distribution, protected research time, focused research areas [60].	Multidimensional instrument assessing investigator and institutional capacity [61].	Doctoral Universities with High Research Activity (R2) [60].
Implementation & Sustainability Infrastructure [5] [62]	Inner setting (culture, leadership), implementation process (planning, engagement), intervention characteristics [61].	Readiness Building System (RBS) stages: engage, assess, discuss/prioritize, change management [62].	Integrated behavioral health care programs; FQHCs implementing CRC screening [5] [62].
General Coalition Capacity [63]	Leadership, member engagement, operational effectiveness.	Adapted scales via repeated cross-sectional surveys of coalition members [63].	Community coalitions for overdose education and naloxone distribution (OEND) [63].

Quantitative Evidence for Readiness Building

Table 2: Documented Outcomes of Structured Readiness-Building Initiatives

Initiative / Context	Timeframe	Key Quantitative Outcomes	Reference
Readiness Building System (RBS) for Integrated Care [62]	2020-2023 (4 years)	Significant linear increase in organizational readiness scores (b = 0.372, q < 0.001) [62].	Livet et al., 2025 [62]
H.O.P.E. Program Implementation Outcomes [62]	2021-2023	Significant increases in screening rates (OR=1.54), follow-up screenings (OR=2.05), and total referrals (OR=1.65); all q < 0.001 [62].	Livet et al., 2025 [62]
Practice Facilitation for Alcohol Use Screening [63]	12-month intervention	Screening rates increased from ~20% to 50% of adult patients; brief interventions after positive screens rose from 0% to 40% [63].	Jonas et al., 2025 [63]
Coalition Capacity & Partner Engagement [63]	2021-2022	56% increased risk in rate of partners engaged per one-unit change in general coalition capacity (p=0.013) [63].	Freedman et al., 2025 [63]

Experimental Protocols

Protocol 1: Cyclic Readiness Building Using the RBS Framework

Purpose: To provide a structured, repeatable methodology for building organizational readiness over time, suitable for complex interventions in healthcare settings such as integrated care or cancer screening programs [62].

Workflow Diagram: A four-stage cyclic process for building organizational readiness.

Materials:

• Readiness Diagnostic Scale: A validated instrument based on the R=MC² framework (Readiness = Motivation × General Capacity × Innovation-Specific Capacity) [62].
• Data Collection Platform: Secure online survey tool (e.g., Qualtrics).
• Stakeholder Roster: List of all key personnel involved in implementation (clinical and non-clinical staff, leadership) [62].

Procedure:

• Stage 1: Engage Key Personnel. Identify and build relationships with all critical team members, including leadership, mid-level managers, and frontline staff. Secure commitment for the readiness-building process [62].
• Stage 2: Assess Implementation Determinants.
  • Administer the Readiness Diagnostic Scale confidentially to all involved staff.
  • Collect both quantitative (Likert-scale) and qualitative (open-ended) data.
  • Clean and analyze data to identify trends across practices, provider roles, and over time.
  • Prepare an Organizational Readiness Report summarizing findings and generating discussion questions [62].
• Stage 3: Discuss and Prioritize.
  • Present the readiness report to the leadership and implementation team.
  • Facilitate structured discussions to interpret data trends and identify root causes of challenges.
  • Collaboratively prioritize areas for implementation improvement based on assessment findings [62].
• Stage 4: Change Management.
  • Adopt and execute specific strategies to address prioritized needs (e.g., skill-building workshops, workflow refinements, EHR modifications).
  • Formatively evaluate the impact of these practice changes.
  • Cycle Initiation: After a predefined period (e.g., annually), re-initiate the process at Stage 1 to assess progress and address emergent needs [62].

Protocol 2: PRISM-Guided Partner Engagement for Implementation

Purpose: To guide multilevel, partner-engaged data collection in low-resource settings (e.g., FQHCs) for selecting and optimizing evidence-based interventions (EBIs), such as those for colorectal cancer (CRC) screening [5].

Workflow Diagram: An iterative data collection process guided by the PRISM framework.

Materials:

• PRISM Framework Diagram: Guides data collection on internal/external context, recipients, and implementation infrastructure [5].
• Data Collection Tools: Semi-structured interview guides, online surveys, secondary data request forms.
• Partnership Agreement: Outlining roles, resources, and principles of engagement (e.g., resources for clinic-based study coordinators) [5].

Procedure:

• Foundational Engagement: Establish long-term partnerships (3-4 years prior is ideal) based on trust and mutual capacity building, applying Principles of Community Engagement [5].
• Iterative Data Collection: Conduct consecutive and iterative data gathering, where findings from each method inform the next to reduce participant burden and produce partner-driven results [5].
  • Introductory Meetings: Hold 60-90 minute in-person meetings with FQHC teams (e.g., CEO, medical director, clinic coordinators) to develop priorities and elicit feedback on study design [5].
  • Agile Science Workshop: Conduct workshops with site-based coordinators and quality improvement specialists to discuss feasible strategies and conduct preliminary mapping of clinical processes (e.g., CRC screening) [5].
  • Secondary Data Collection: Request internal clinic data (e.g., clinical characteristics, screening rates) and data on external influences (e.g., policies, funders) from site-based coordinators [5].
  • Online Surveys: Deploy surveys to a broad range of stakeholders (CEO, providers, clinic managers) to gather data on patient-level barriers, referral processes, and existing relationships [5].
  • In-Depth Interviews: Conduct interviews with key informants (e.g., medical director, clinic manager) to gather detailed information on critical issues emerging from previous data collection [5].
• Analysis and Strategy Selection: Analyze data across PRISM domains to understand contextual factors. Develop process maps of clinical workflows. Use these insights to collaboratively select and adapt EBIs and implementation strategies with partners [5].

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials and Tools for Health System Readiness Research

Item / Tool	Function / Definition	Exemplar Use Case
Readiness Diagnostic Scale (RBS) [62]	A validated instrument to assess organizational readiness for implementation, measuring dimensions like motivation and general capacity.	Quantifying baseline readiness and tracking changes over multiple years in an integrated care program [62].
PRISM Framework [5]	An implementation science framework that expands RE-AIM to include multilevel contextual factors influencing implementation and sustainability.	Guiding partner-engaged data collection in FQHCs to identify barriers/facilitators for CRC screening programs [5].
Research Readiness Instrument [60] [61]	A multidimensional tool centered on two primary domains: investigator readiness and institutional readiness.	Evaluating a university's capacity for competitive, externally funded health research [60] [61].
PHAB Readiness Assessment [64]	A standardized tool for health departments to self-assess capacity in key areas of public health practice and determine readiness for accreditation.	Assessing health department capacities in areas like performance improvement, workforce, and equity [64].
Consortium for Cancer Implementation Science (CCIS) [12]	A National Cancer Institute (NCI) initiative to build capacity, increase collaboration, and support implementation science activities in cancer control.	Enhancing collaboration across different areas of cancer care to promote the adoption of evidence-based interventions [12].

Strategies for Equitable Implementation and Addressing Health Disparities

Agile science represents a transformative approach to implementation research, drawing on principles from systems engineering and software design to create more responsive and effective health equity interventions. This methodology emphasizes speed, adaptability, and continuous optimization to address the complex challenges of health disparities, particularly in cancer implementation research. Where traditional implementation models often lack an explicit temporal dimension, agile science incorporates rapid iterative cycles and early-and-often sharing of resources to accelerate knowledge accumulation and application [1] [39].

The core premise of agile science for health equity aligns with the definition of health equity itself - ensuring all individuals have the opportunity to reach their highest level of well-being despite differences in social, economic, geographic, or other factors [65]. By applying agile methodologies, researchers can more effectively identify and address disparities, working toward eliminating health and social inequities through precisely tailored interventions.

Key Agile Science Concepts for Health Equity

Foundational Principles

Agile implementation science for health equity is characterized by several core principles that distinguish it from traditional approaches. First, it adopts a problem orientation that identifies issues with existing urgency, thereby increasing stakeholder buy-in for rapid implementation [39]. This problem-focused approach naturally aligns with health equity work, which often addresses pressing disparities requiring immediate attention.

Second, agile science emphasizes modular design of interventions, creating the smallest, meaningful, self-contained, and repurposable behavior change modules [1]. This modular approach enables researchers to efficiently test and adapt intervention components for different populations and contexts, which is essential for addressing the diverse needs within health disparity populations.

Third, the approach utilizes adaptive study designs and personalization algorithms that allow for continuous optimization of interventions based on individual and contextual factors [1]. These methodologies enable researchers to account for the complex adaptive systems in which implementation occurs, particularly important when working with underserved populations who may experience multiple overlapping barriers to care.

Theoretical Framework and Supporting Evidence

The theoretical underpinnings of agile science for health equity draw from multiple disciplines. From precision medicine, it incorporates rapid implementation approaches that adapt methods and trial designs to suit complex study needs [39]. From software development, it embraces the Agile Manifesto principles of responding to change rather than sticking rigidly to plans that aren't working [39]. From behavioral economics, it incorporates insights about human decision-making processes to modify social and physical environments for more successful implementation [39].

Evidence for the effectiveness of these approaches is emerging. The agile implementation model has demonstrated success in reducing central line-associated bloodstream infections in hospital settings and decreasing dementia symptoms in safety net healthcare delivery systems [39]. These applications show promise for adapting similar methodologies to cancer implementation research among disparity populations.

Table 1: Core Components of Agile Science for Health Equity

Component	Definition	Application to Health Equity
Modular Intervention Design	Creating smallest meaningful, repurposable intervention components [1]	Enables cultural and contextual adaptation for diverse populations
Computational Behavioral Models	Defining interactions between modules, individuals, and context [1]	Accounts for social determinants and structural barriers
Personalization Algorithms	Decision rules for intervention adaptation [1]	Tailors interventions to individual needs and resources
Minimal Viable Products (MVPs)	Simplified modules to test assumptions with stakeholders [1]	Rapidly identifies effective elements for underserved groups
Adaptive Trial Designs	Study designs that modify parameters based on interim results [39]	Efficiently tests interventions across multiple populations

Quantitative Data Analysis for Health Disparities Assessment

Standardized Metrics and Comparison Methods

Robust quantitative analysis forms the foundation for identifying and addressing health disparities through agile implementation. Appropriate comparative statistical methods must be employed to detect significant differences between population groups, with careful attention to sampling methods and potential confounding variables [66].

When comparing quantitative variables across different groups defined by health disparity indicators (e.g., race, socioeconomic status, geographic location), researchers should employ comprehensive summary statistics including means, medians, standard deviations, and interquartile ranges for each group [66]. The difference between means and/or medians of comparison groups should be computed, with confidence intervals to indicate precision of estimates.

Table 2: Essential Quantitative Comparisons for Health Disparities Research

Metric Category	Specific Measures	Application Example
Central Tendency	Mean, Median, Mode	Average cancer screening rates by racial group
Dispersion	Standard Deviation, Range, IQR	Variability in treatment adherence across socioeconomic groups
Group Differences	Mean differences with confidence intervals, Effect sizes	Disparities in time to treatment initiation
Frequency Measures	Counts, Percentages, Proportions	Distribution of late-stage diagnoses by geographic region
Stratified Analysis	Cross-tabulation, Subgroup means	Interaction effects between race and insurance status

Data Visualization for Health Equity

Effective data visualization is crucial for identifying patterns in health disparities and communicating findings to diverse stakeholders. For quantitative data comparing health outcomes across disparity groups, the most appropriate visualizations include:

• Boxplots that display distributions, central tendencies, and outliers for multiple groups simultaneously, allowing quick comparison of outcome variables across population segments [66]
• 2-D Dot Charts that show individual data points while maintaining group comparisons, particularly effective for small to moderate datasets where individual values remain important [66]
• Back-to-Back Stemplots suitable for comparing two groups with small amounts of data while preserving original data values [66]

These visualization techniques help researchers identify not only central tendencies but also variations within groups, which is essential for understanding the full spectrum of health disparities and avoiding oversimplification of complex population patterns.

Experimental Protocols for Equity-Focused Implementation Research

Protocol Development for Agile Implementation

Comprehensive experimental protocols are essential for ensuring rigor and reproducibility in agile implementation research. Each protocol should function as a complete "recipe" for executing the study, with sufficient detail that a trained researcher could replicate the entire process without additional guidance [67].

Protocols for equity-focused implementation research should include the following standardized sections, adapted for the specific context of health disparities:

4.1.1 Setting Up Protocols must begin with detailed setup procedures to be completed before participant engagement. This includes equipment preparation, environmental adjustments, and verification of all technical systems. Setup should be completed at least 10 minutes before the first participant arrives, with specific checkpoints for confirming accessibility features for participants with disabilities [67].

4.1.2 Participant Greeting and Consent The protocol must specify exactly how participants will be greeted, with particular attention to creating a welcoming environment for individuals from diverse backgrounds. Consent procedures should be comprehensive yet accessible, with specific provisions for individuals with varying health literacy levels and language preferences. Researchers should emphasize the main points of consent documents verbally and check for understanding [67].

4.1.3 Instructions and Practice Given the importance of clear communication in equity-focused research, protocols should detail how instructions will be delivered to participants. Rather than relying solely on written instructions, researchers should use a combination of verbal explanation, demonstration, and guided practice trials. The protocol should specify how researchers will verify participant understanding before proceeding to experimental trials, with particular attention to potential cultural or linguistic barriers [67].

4.1.4 Data Collection and Monitoring The protocol must explicitly define data collection procedures, including how researchers will monitor participant engagement and record any deviations from standard procedures. For equity-focused research, this should include documentation of any adaptations made to accommodate participant needs or preferences [67].

4.1.5 Post-Data Collection Procedures Protocols must detail exactly how data will be saved, secured, and documented after collection. Additionally, they should specify how participants will be debriefed, compensated, and provided with appropriate resources, especially when the research touches on sensitive health topics or identifies unmet needs [67].

Protocol Testing and Validation

Before implementation, all experimental protocols must undergo rigorous testing to ensure clarity and effectiveness. This process includes:

• Researcher Self-Testing: The primary researcher executes the protocol exactly as written, identifying any gaps in instructions or procedures [67]
• Peer Validation: Another lab member follows the protocol instructions to set up and conduct the experiment, providing feedback on clarity and completeness [67]
• PI Authorization: The Principal Investigator reviews and approves the protocol before any participant testing [67]
• Observed Pilot: A senior lab member observes a complete run with a naive participant to identify any practical issues [67]

This multi-stage validation process is particularly important for equity-focused research, as it helps identify potential barriers or biases that might disproportionately affect participants from vulnerable populations.

Research Reagent Solutions Toolkit

Table 3: Essential Research Resources for Agile Equity Implementation

Resource Category	Specific Tools/Platforms	Application in Equity Research
Protocol Repositories	Protocols.io, Springer Nature Experiments [68]	Access to standardized, reproducible methods for engaging diverse populations
Video Method Libraries	JoVE Unlimited [68]	Demonstration of culturally competent research procedures
Statistical Analysis Tools	R Programming, Python (Pandas, NumPy), SPSS [69]	Analysis of disparities using appropriate statistical methods
Data Visualization Platforms	ChartExpo, Microsoft Excel [69]	Creation of accessible data displays for diverse stakeholders
Implementation Frameworks	Multi-level implementation models [39]	Guiding comprehensive assessment of implementation contexts

Workflow Visualization for Agile Equity Implementation

The following diagram illustrates the core iterative process of agile implementation science applied to health equity research:

Agile Equity Implementation Cycle

Health Equity Implementation Assessment Framework

The following diagram outlines the multi-level assessment process for evaluating implementation context and outcomes across equity dimensions:

Multi-Level Equity Assessment

Application to Cancer Implementation Research

The principles and protocols outlined above have specific relevance to cancer implementation research, where significant disparities persist across the continuum from prevention to survivorship. Agile science methods are particularly suited to addressing these challenges through:

Targeted Population Health Strategies

Healthcare organizations are increasingly focusing on targeted population health strategies to drive equitable outcomes in cancer care. Through sophisticated data analysis, care teams can identify patient groups with specific variables matching the provider's or health plan's capabilities and technology [65]. This approach enables more precise targeting of implementation strategies to populations experiencing cancer disparities.

For example, organizations like Kaiser Permanente are adding new programs focused on vulnerable populations through climate-event interventions and older adult care models [65]. Their efforts emphasize delivering the right intervention at the right place and time, utilizing a tech-enabled, multichannel outreach approach - principles perfectly aligned with agile implementation methodology.

Expanding Non-Clinical and SDOH Data Analytics

Collecting and incorporating non-clinical patient data is emerging as a key strategy for addressing cancer care gaps. Through Annual Wellness Visits and Social Determinants of Health assessments, providers and health plans can act on information that indicates social risk, including food access, housing, transportation, medication access, and personal safety [65].

The incorporation of these data elements enables more nuanced understanding of the contextual factors influencing cancer disparities and provides opportunities for developing precisely tailored implementation strategies. Standardized practices to gather, access, and use non-clinical information are growing in importance for patient engagement and intervention design [65].

Establishing Equity as Foundational

In cancer implementation research, equity is becoming not only a business imperative but also a foundational element of design and strategy [65]. As researchers develop new interventions, create implementation strategies, form partnerships, and invest in technologies, the impact on health equity must be a fundamental consideration.

This approach requires researchers to weigh each decision based on how it could potentially harm or help specific patient populations experiencing cancer disparities. For example, when introducing a new digital tool for cancer screening reminders, researchers must consider how this tool might differentially impact populations with varying levels of digital literacy or technology access.

Agile science methods offer powerful approaches for advancing health equity in cancer implementation research. By incorporating modular design principles, iterative development processes, and continuous stakeholder engagement, researchers can develop and implement more effective, tailored strategies for addressing persistent disparities. The protocols, assessment frameworks, and visualization tools presented here provide concrete resources for applying these methods in practice, enabling more responsive and equitable cancer care implementation.

As the field continues to evolve, the integration of agile methodologies with robust equity frameworks holds promise for accelerating progress toward the elimination of cancer disparities. Future work should focus on refining these approaches through continued application and evaluation across diverse cancer care contexts and populations.

Precision medicine represents a fundamental shift in healthcare, using personal information such as genetic, environmental, and lifestyle data to improve disease prevention, diagnosis, and treatment. This approach is particularly transformative in oncology, where tailoring strategies to individual patient profiles can significantly improve outcomes. However, the rapid pace of innovation—driven by technologies like artificial intelligence (AI), digital twins, and genomic sequencing—has created a significant regulatory challenge. Traditional regulatory frameworks often struggle to keep pace with technological advancement, potentially delaying patient access to breakthroughs.

Agile governance offers a solution to this challenge through adaptive, human-centered policy-making that embraces multistakeholder collaboration. Within this framework, regulatory sandboxes have emerged as a powerful tool to safely accelerate innovation. These are controlled environments where innovators can test new products and services without immediately incurring all normal regulatory consequences [70] [71]. Originally pioneered by the UK Financial Conduct Authority for fintech in 2014, this approach has since been adapted for healthcare globally [71]. For precision oncology, these mechanisms enable the testing of complex, personalized approaches while maintaining rigorous patient safety standards, ultimately creating a more responsive pathway from laboratory research to clinical implementation.

Core Principles and Governance Frameworks

The Conceptual Foundation of Agile Governance

Agile governance provides a framework for regulators to respond effectively to rapid technological change. According to the World Economic Forum, it is defined as "adaptive, human-centered, inclusive and sustainable policy-making" that acknowledges policy development is increasingly a multistakeholder effort beyond just governments [72]. This approach is characterized by its continual readiness to rapidly navigate and embrace change while delivering value to end-users.

The application of agile governance to precision medicine can be understood through two complementary methods. The "design method" identifies and addresses problems as they arise, such as adjusting clinical trial protocols in response to emerging data. In contrast, the "system method" addresses foundational problems by creating robust frameworks capable of solving multiple issues simultaneously, such as establishing cross-national systems for approving personalized therapies [72]. This dual approach enables both responsive adaptation and foundational structural support for precision medicine innovation.

Pillars of Agile Governance

A comprehensive blueprint for agile governance in precision medicine rests on seven key pillars, as shown in the table below.

Table 1: The Seven Pillars of Agile Governance for Precision Medicine

Pillar	Core Objective	Application in Precision Medicine
1. Anticipate innovation and its implications	Use foresight mechanisms to capitalize on opportunities and mitigate risks	Proactively address ethical, safety, and implementation challenges of emerging technologies like digital twins
2. Focus regulations on outcomes	Emphasize achieved results rather than prescriptive processes	Allow flexibility in how precision diagnostics and treatments meet safety and efficacy goals
3. Create space to experiment	Develop regulation alongside the technology it seeks to regulate	Implement regulatory sandboxes for testing AI-driven diagnostics and personalized treatment algorithms
4. Use data to target interventions	Employ data analytics for precise regulatory oversight	Utilize real-world evidence to monitor post-market safety of targeted therapies
5. Leverage the role of business	Harness private sector expertise in governance	Collaborate with biotech and pharma on standards for genomic data sharing and interpretation
6. Work across institutional boundaries	Create "one-stop-shop" approaches to simplify engagement	Coordinate among FDA, NIH, and CMS for coherent precision medicine policy
7. Collaborate internationally	Address shared challenges across borders	Harmonize regulatory standards for multinational precision oncology trials

Based on Signé and Almond's framework as described by the Brookings Institution [72]

Operationalizing Agile Governance Through Regulatory Sandboxes

Regulatory sandboxes represent the practical implementation of several agile governance pillars, particularly creating "space to experiment" and "focusing regulations on outcomes." In healthcare, these sandboxes function as controlled environments where developers can test innovative technologies with real patients under regulatory supervision, but with modified requirements [70]. This model moves healthcare regulation from "permission-based" to "partnership-based" oversight, allowing regulatory agencies to keep pace with rapid technological change while maintaining safety standards [70].

The sandbox approach is gaining significant momentum in precision medicine. The GCC region (Qatar, Saudi Arabia, and the UAE) has been at the forefront of governance developments in AI healthcare regulation, using sandboxes to enable testing of AI-powered genomic diagnostics and precision oncology tools in controlled environments before full-scale deployment [70]. Similarly, the recent U.S. Trump administration AI Action Plan promotes a "try-first" approach with regulatory sandboxes to accelerate innovation in personalized medicine [73].

Experimental Protocols and Implementation Frameworks

Protocol for Establishing a Regulatory Sandbox in Precision Oncology

Objective: Create a structured framework for implementing a regulatory sandbox specifically designed for precision medicine interventions in oncology.

Background: The complex, data-intensive, and rapidly evolving nature of precision oncology requires adaptive regulatory approaches that can accommodate high-velocity innovation while ensuring patient safety.

Methodology:

• Stakeholder Identification and Engagement

  • Establish a multistakeholder oversight committee comprising regulatory officials, oncologists, molecular pathologists, bioethicists, data scientists, patient advocates, and health economists.
  • Implement structured engagement processes, including quarterly review meetings and ongoing working groups for specific therapeutic areas.

• Eligibility Criteria Definition

  • Technology Scope: Define qualifying technologies (e.g., AI-based diagnostic algorithms, digital twins for treatment simulation, NGS-based stratification tools).
  • Development Stage: Require preliminary validation data demonstrating basic safety and technical feasibility.
  • Benefit-Risk Profile: Prioritize technologies addressing unmet needs in defined cancer subtypes with favorable theoretical benefit-risk profiles.

• Sandbox Parameters and Safeguards

  • Testing Duration: Establish defined testing periods (typically 6-24 months) with possible extensions based on interim reviews.
  • Patient Consent: Implement enhanced informed consent processes specifically addressing the experimental nature of sandbox testing.
  • Data Collection: Require comprehensive data collection protocols using standardized endpoints defined in the BePRECISE checklist [74].
  • Oversight Mechanisms: Establish real-time safety monitoring with predefined thresholds for intervention.

• Exit Strategy and Translation Pathway

  • Define clear success metrics for graduation to standard regulatory pathways.
  • Establish post-sandbox evidence requirements for full approval.
  • Create mechanisms for integrating sandbox-generated evidence into traditional review processes.

Diagram: Regulatory Sandbox Implementation Workflow

Protocol for Implementing Collaborative Governance in Cancer Networks

Objective: Establish a collaborative governance framework for implementing precision medicine across distributed cancer networks, based on lessons from the Quebec cancer network case study [75].

Background: Cancer care delivery involves complex coordination across specialized centers, community hospitals, and primary care, creating challenges for implementing consistent precision medicine approaches.

Methodology:

• Principled Engagement Structures

  • Establish interdisciplinary committees at national, regional, and institutional levels
  • Create communities of practice for specific cancer types
  • Implement trajectory-development teams to map patient pathways across the care continuum

• Shared Motivation Mechanisms

  • Articulate a clear, patient-centered vision for precision medicine implementation
  • Develop shared measurement frameworks with common indicators
  • Create formal recognition systems for collaborative achievements

• Capacity for Joint Action

  • Align institutional arrangements and incentives
  • Invest in interoperable data systems and shared platforms
  • Establish clear accountability frameworks with distributed leadership

Table 2: Implementation Framework for Collaborative Governance in Precision Oncology Networks

Component	Key Activities	Success Metrics
Principled Engagement	Regular interdisciplinary forums; Cross-institutional working groups; Patient engagement panels	Participation rates; Diversity of stakeholder representation; Documented integration of patient input
Shared Motivation	Co-developed vision statements; Shared outcome dashboards; Collaborative goal-setting	Alignment surveys; Goal attainment rates; Stakeholder satisfaction measures
Joint Action Capacity	Resource sharing agreements; Standardized protocols; Interoperable data systems	Protocol adoption rates; Data sharing volumes; Reduction in duplicate testing

Adapted from the Quebec cancer network case study [75]

Analytical Tools and Reporting Standards

The BePRECISE Checklist for Precision Medicine Research

The translation of precision medicine from research to clinical practice requires robust reporting standards. The BePRECISE (Better Precision-data Reporting of Evidence from Clinical Intervention Studies & Epidemiology) checklist provides a 23-item framework specifically designed for precision medicine research [74] [76]. This checklist complements existing reporting guidelines like CONSORT and STROBE while adding precision medicine-specific elements.

Key domains of the BePRECISE checklist include:

• Equity and Patient/Public Involvement (Items E1-E4): Requires description of how equity considerations were integrated, including diversity of study participants and involvement of patients and public in research design and implementation.

• Title and Abstract (Items 1.1-1.4): Mandates explicit identification of precision medicine focus, study design, relevant pillars (prevention, diagnosis, treatment, prognosis), and description of study population.

• Background and Objectives (Items 2.1-2.2): Requires clear rationale for the precision medicine approach and explicit statement of the precision medicine hypothesis being tested.

The adoption of BePRECISE by researchers, reviewers, and editors facilitates more consistent reporting, enables evidence synthesis across studies, and strengthens claims of clinical utility through benchmarking against contemporary standards [74].

Digital Twins as Cognitive Tools in Precision Oncology

Digital Twins (DTs) represent a transformative technology for precision medicine, serving as dynamic virtual representations of individual patients or their biological systems. In oncology, DTs enable real-time, multiscale simulations that integrate genomics, imaging, wearable sensor data, and clinical records to support predictive, adaptive decision-making [77].

Table 3: Digital Twin Typologies and Applications in Precision Oncology

Model Type	Key Characteristics	Oncology Applications	Considerations
Mechanistic Models	Based on physiological and physical principles; Often interpretable	Finite element models for surgical planning; Cardiac safety assessment of chemotherapies	High biological plausibility; May lack personalization
Data-Driven Models (AI)	Use machine learning to identify patterns from complex datasets; Highly adaptive	Predicting treatment response; Identifying novel biomarker patterns	Potential interpretability challenges; Requires large datasets
Hybrid Models	Combine mechanistic frameworks with AI-based personalization	Personalized treatment simulation; Adaptive therapy optimization	Balances interpretability with personalization; Emerging as preferred approach

Based on classification systems for Digital Twins in healthcare [77]

The implementation of DTs in regulatory sandboxes offers significant potential for advancing precision oncology while managing risks. Sandboxes can provide controlled environments for validating DT predictive accuracy, establishing clinical utility, and developing appropriate regulatory frameworks before widespread clinical deployment.

Diagram: Digital Twin Integration in Precision Oncology Workflow

Table 4: Research Reagent Solutions for Agile Governance and Precision Medicine Studies

Tool/Resource	Function/Purpose	Application Context
BePRECISE Checklist	Standardized reporting framework for precision medicine research	Ensuring comprehensive and comparable reporting of precision oncology studies; Required for regulatory sandbox applications
Digital Twin Platforms	Create dynamic virtual representations of patients or biological systems	Testing treatment strategies in silico; Predicting individual patient responses to therapies
Collaborative Governance Assessment Tools	Measure principles engagement, shared motivation, and capacity for joint action	Evaluating implementation of cancer network models; Assessing multistakeholder alignment
Regulatory Sandbox Application Templates	Standardized formats for proposing and evaluating sandbox testing protocols	Streamlining regulatory review process; Ensuring comprehensive safety planning
Agile Values Assessment Framework	Evaluate adherence to agile principles in care delivery	Measuring flexibility and responsiveness of precision oncology programs; Adapted from software engineering [16]

Agile governance approaches, particularly regulatory sandboxes, represent a transformative shift in how precision medicine can be regulated and implemented. By creating collaborative spaces for innovation while maintaining rigorous safety standards, these frameworks enable the healthcare system to keep pace with technological advancement. For precision oncology specifically, this means potentially faster translation of breakthroughs in genomics, AI, and digital health to patient benefit.

The successful implementation of agile governance requires systematic attention to collaborative structures, shared motivation, and capacity for joint action across institutional boundaries. Frameworks like the BePRECISE checklist and emerging technologies like Digital Twins provide the practical tools needed to operationalize these approaches while generating robust evidence. As precision medicine continues to evolve, agile governance mechanisms offer a pathway to balance innovation with safety, ultimately accelerating progress against cancer while ensuring equitable access to emerging technologies.

Measuring Impact: Validating Outcomes and Comparing Agile Approaches

Application Note: A Metric Framework for Agile Cancer Implementation Science

This document provides a structured framework for evaluating the success of evidence-based interventions (EBIs) in cancer implementation research. Rooted in agile science methods, it outlines specific, actionable metrics and protocols to assess Reach, Effectiveness, and Maintenance across the implementation continuum. The guidance is designed for researchers and drug development professionals operating in dynamic, often resource-constrained settings, emphasizing practical data collection and rapid-cycle learning [14].

A foundational principle of agile science is the use of a balanced set of metrics. Relying solely on outcome metrics provides a historical snapshot but offers limited guidance for future improvement. A robust evaluation plan integrates these with actionable, process-oriented metrics that track team responsiveness and engagement in real-time, enabling continuous refinement of implementation strategies [78].

The following core concepts are essential for this framework:

• Implementation Science (IS): The scientific study of methods to promote the systematic uptake of research findings and EBIs into routine practice [14].
• Agile Science: An approach that emphasizes adaptability, rapid feedback loops, and the use of both historical and forward-looking indicators to guide implementation efforts.
• Metrics Triad: The interconnected evaluation of:
  • Reach: The proportion and representativeness of the target population that participates in an EBI.
  • Effectiveness: The impact of the EBI on key outcomes in the real-world context.
  • Maintenance: The extent to which an EBI is sustained over time and integrated into routine care.

The logical relationship between these phases and the corresponding metrics is visualized in the workflow below.

Structured Metrics for the Evaluation Triad

The following table consolidates key quantitative metrics for assessing Reach, Effectiveness, and Maintenance. These indicators provide a composite view of implementation performance, from initial uptake to long-term sustainability [14] [78].

Table 1: Core Metrics for Evaluating Implementation Success

Metric Category	Specific Metric	Definition & Measurement Method	Benchmark/Target
Reach	Stakeholder Engagement Coverage	Definition: Proportion of key stakeholder groups involved in implementation planning. Measurement: Count of unique stakeholder entities divided by total identified entities [14].	>80% coverage of key groups
	Target Population Penetration	Definition: Percentage of the intended patient population that receives the EBI. Measurement: Number of patients receiving EBI divided by total eligible population.	Target based on local context
Effectiveness	Clinical Outcome Improvement	Definition: Change in relevant clinical endpoints. Measurement: Pre-post comparison of rates (e.g., survival, stage-at-diagnosis) [14].	p-value < 0.05
	Process Metric Adherence	Definition: Adherence to EBIs in routine practice. Measurement: Direct observation or chart audit against a fidelity checklist [14].	>90% adherence
	Mean Time to Acknowledge (MTTA)	Definition: Average time for team to acknowledge a new implementation barrier alert [78]. Measurement: Sum of time between alert and acknowledgment, divided by total alerts.	< 4 hours
Maintenance	Sustained Workflow Integration	Definition: Degree to which EBI is embedded in standard clinical workflows. Measurement: Provider survey or audit of standard operating procedures.	Full integration within 24 months
	Long-term Fidelity	Definition: Proportion of core EBI components delivered as intended after initial implementation. Measurement: Fidelity assessment at 6, 12, and 24 months [14].	>80% fidelity at 24 months
	Mean Time Between Failures (MTBF)	Definition: Average time between recurrences of the same implementation barrier. Measurement: Total uptime divided by number of recurring barrier incidents [78].	Continuous increase over time

Detailed Experimental Protocols

Protocol 1: Measuring Reach via Structured Stakeholder Engagement

1. Objective: To quantitatively and qualitatively assess the reach of implementation planning activities across key stakeholder groups to ensure inclusive and representative input [14].

2. Research Reagent Solutions

Table 2: Essential Materials for Stakeholder Analysis

Item	Function/Explanation
Stakeholder Mapping Template	A structured worksheet (e.g., a matrix of influence vs. interest) to identify all relevant entities.
Structured Engagement Log	A centralized database (e.g., Microsoft Excel or REDCap) to track contact, involvement, and feedback from each stakeholder entity [14].
Semi-Structured Interview Guides	Questionnaires designed to elicit open-ended feedback on implementation barriers and facilitators.

3. Methodology:

• Step 1: Situational Analysis. Conduct a preliminary analysis to identify all potential stakeholder groups, including clinical providers, administrative leadership, patients, and payers [14].
• Step 2: Mapping and Recruitment. Classify stakeholders using a power/interest grid. Develop a purposive sampling strategy to ensure all segments are represented. Document the recruitment process.
• Step 3: Active Engagement and Tracking. Employ mixed-methods: hold structured meetings and conduct interviews. Meticulously log all engagement activities in the Structured Engagement Log, noting the stakeholder group, date, and nature of input [14].
• Step 4: Data Analysis and Calculation. Calculate the Stakeholder Engagement Coverage metric (see Table 1). Thematically analyze qualitative feedback to identify key contextual barriers and facilitators.

4. Data Interpretation:

• A coverage metric below 80% suggests critical gaps in stakeholder representation, which poses a significant risk to implementation success and sustainability [14].
• Qualitative data should inform the tailoring of implementation strategies to address local context and improve reach.

Protocol 2: Evaluating Effectiveness via Leading Indicators

1. Objective: To monitor the effectiveness of the implementation process in real-time using actionable, leading-indicator KPIs that predict long-term success [78].

2. Research Reagent Solutions

Table 3: Essential Materials for Process Evaluation

Item	Function/Explanation
CMMS or Issue-Tracking Software	A Computerized Maintenance Management System (CMMS) or similar platform to log, track, and timestamp all implementation alerts and resolutions [79] [78].
Data Visualization Dashboard	A real-time dashboard (e.g., in Tableau or Power BI) configured to display MTTA and MTTR trends.
Fidelity Assessment Checklist	A standardized tool to audit whether the EBI is being delivered with adherence to its core components.

3. Methodology:

• Step 1: Define and Configure Tracking. Within the CMMS, establish specific alert categories for implementation barriers (e.g., "provider training gap," "supply chain issue," "IT access problem").
• Step 2: Monitor and Timestamp. As alerts are generated, the system automatically records the timestamp of the alert, the acknowledgment by the implementation team, and the final resolution.
• Step 3: Calculate and Review Metrics. Weekly, calculate Mean Time to Acknowledge (MTTA) and Mean Time to Resolution (MTTR). Concurrently, conduct random audits using the Fidelity Assessment Checklist.
• Step 4: Agile Feedback Loop. Present trends in MTTA and MTTR to the implementation team. Use rising times as a trigger for process improvement, such as re-training or resource re-allocation [78].

4. Data Interpretation:

• A rising MTTA indicates team overload or waning engagement. A short Mean Time Between Faults (MTBF) for the same issue suggests the team is addressing symptoms, not root causes [78].
• These actionable metrics allow for proactive adjustments before effectiveness and patient outcomes are negatively impacted.

Protocol 3: Assessing Long-Term Maintenance

1. Objective: To evaluate the sustainability and long-term health of the implemented EBI by assessing its integration into the system and the performance of its support processes [14].

2. Research Reagent Solutions

Table 4: Essential Materials for Maintenance Evaluation

Item	Function/Explanation
Scheduled Fidelity Audits	A calendar of periodic audits (e.g., at 6, 12, 18, and 24 months) to prevent "conceptual drift" from the original EBI.
Longitudinal Cost Tracking Spreadsheet	A tool to document costs associated with the EBI over time, including personnel, supplies, and overhead.
Provider and Patient Surveys	Validated instruments to measure sustained acceptability, appropriateness, and feasibility of the EBI.

3. Methodology:

• Step 1: Establish Baseline and Benchmarks. At the end of the active implementation phase, conduct a full fidelity audit and document the final MTBF/MTTR values as a baseline.
• Step 2: Longitudinal Data Collection. Continue to collect fidelity, cost, and process metric data at pre-specified intervals (e.g., every 6 months) for at least two years.
• Step 3: Analyze Integration. Review policy documents and clinical workflows to confirm the EBI is included. Analyze cost data to identify trends and ensure sustainability.
• Step 4: Synthesize Findings. A successful maintenance phase is characterized by stable, high fidelity, low and non-recurring barrier counts (high MTBF), and stable or decreasing operational costs.

4. Data Interpretation:

• Declining fidelity or a rising number of recurring issues signals that the EBI is not effectively maintained and is at risk of failure, requiring a booster intervention or strategy reassessment.

The Scientist's Toolkit: Key Research Reagent Solutions

The following table details essential tools and materials for conducting rigorous cancer implementation research, as cited in the protocols above.

Table 5: Key Research Reagent Solutions for Implementation Science

Item Category	Specific Name/Example	Function in Implementation Research
Data Management & Analysis	Microsoft Excel / REDCap Database [14]	Used for creating data charting forms, tracking stakeholder engagement, and managing quantitative metrics.
	Statistical Software (e.g., R, Stata, SAS)	Used for performing advanced statistical analyses on clinical outcome data and calculating significance levels.
Implementation Tracking	Computerized Maintenance Management System (CMMS) [79] [78]	Centralizes the logging and tracking of implementation barriers, work orders, and timelines for calculating KPIs like MTTA and MTTR.
Evaluation & Auditing	Fidelity Assessment Checklist [14]	A standardized tool to audit whether the evidence-based intervention is being delivered with adherence to its core components.
	Stakeholder Mapping Template [14]	A structured worksheet to identify and classify relevant entities to ensure inclusive and representative engagement.

Expert Validation of Agile Frameworks in Resource-Constrained Settings

Application Notes

Rationale and Context

In resource-constrained settings, such as low and middle-income countries (LMICs), implementing evidence-based cancer interventions is challenging due to competing health priorities, significant disease burden, and limited resources [14]. Agile frameworks, adapted from software development, offer a structured approach to navigate these constraints through iterative development, continuous feedback, and incremental validation [80]. This approach enables realistic goal setting and benchmarking against regional and global standards, making it particularly valuable for national cancer control planning [14]. The core Agile principles of customer collaboration, continuous feedback, and incremental delivery align well with the needs of implementation science in cancer research, facilitating the adaptation of interventions to specific local contexts [80] [14].

Quantitative Analysis of Current Cancer Planning Practices

A scoping review of National Cancer Control Plans (NCCPs) and strategies from 33 low and medium Human Development Index (HDI) countries revealed significant gaps in the application of implementation science principles, highlighting the need for more structured validation approaches [14].

Table 1: Analysis of Implementation Science Domains in National Cancer Control Plans (NCCPs) from Resource-Constrained Settings

Implementation Science Domain	Inclusion in Low HDI Countries (n=16)	Inclusion in Medium HDI Countries (n=17)	Overall Findings
Stakeholder Engagement	Present but typically unstructured and incomplete	Present but typically unstructured and incomplete	Described in most plans but lacked structured methodology
Situational Analysis	Incorporated but often not explicit	Incorporated but often not explicit	Common but inconsistent in application and depth
Capacity/Health Technology Assessment	Not performed	Not performed	None of the plans assessed health system capacity for new interventions
Economic Evaluation	4 countries included costed plans	9 countries included costed plans	Generally used an activity-based costing approach
Impact Measurement	All plans included impact measures	All plans included impact measures	5 plans lacked mechanisms to achieve targets

Experimental Protocols

Protocol for Validating Agile Implementation Frameworks in Cancer Control

This protocol provides a reproducible methodology for expert validation of Agile frameworks in cancer implementation research, ensuring all necessary information is included for replication [81].

Setting Up

• Timeline: Initiate setup 10 minutes before the scheduled expert panel session [67].
• Materials Preparation: Reboot all computing devices, verify display settings (resolution, color temperature), and arrange the physical or virtual workspace. Ensure all collaboration tools (e.g., digital whiteboards, video conferencing) are functional [67].
• Documentation Distribution: Pre-circulate the following documents to all panel members: the Agile framework prototype, structured questions for validation, and a summary of the relevant NCCP context [14].

Expert Panel Engagement and Consent

• Greeting and Orientation: Meet the panel members, provide a brief orientation to the lab or virtual platform, and direct them to their seats [67].
• Informed Consent: Present the consent document, emphasizing the study's purpose, the voluntary nature of participation, data handling procedures, and the right to withdraw at any time without penalty [67].

Instructions and Calibration

• Task Explanation: Do not rely on participants to read instructions independently [67]. Verbally explain the validation process, which involves reviewing the proposed Agile framework against the five implementation science domains (Stakeholder Engagement, Situational Analysis, Capacity Assessment, Economic Evaluation, Impact Measurement) [14].
• Practice Calibration: Conduct a brief, facilitated calibration exercise using a sample framework element to ensure all experts apply the validation criteria consistently [67].

Monitoring and Data Collection

• Researcher Role: The researcher is "on-call" to clarify procedures but should not influence the experts' assessments. Quiet work is permissible during the independent review phase [67].
• Structured Feedback: Experts provide feedback via a structured form and a facilitated group discussion. The form captures quantitative ratings and qualitative feedback on the framework's applicability, feasibility, and completeness for each implementation domain [14].
• Decision Recording: Document all decisions and the rationale behind them, including any disagreements and how consensus was achieved [14].

• Debriefing: Thank the experts, debrief them on the study's goals, and process any compensation [67].
• Data Securing: Immediately save the collected data (feedback forms, discussion notes, and any digital whiteboard outputs) with a unique, structured filename that includes the session date and panel identifier [67].
• Workspace Shutdown: After the session, securely shut down the workspace, ensuring all data is backed up and any physical materials are stored appropriately [67].

Handling Exceptions and Unusual Events

• Participant Withdrawal: If an expert withdraws consent, delete their data as requested. Compensate them on a pro-rated basis for their time if applicable, rounding to the nearest quarter-hour [67].
• Technical Failures: Document any technical issues and their impact on the session. Have a contingency plan, such as a backup communication channel, to minimize disruption [67].

Diagram: Expert validation workflow for Agile frameworks.

Data Analysis and Framework Refinement Protocol

This protocol details the steps for analyzing qualitative and quantitative feedback to iteratively refine the Agile framework.

• Data Collation: Compile all quantitative ratings and qualitative comments into a single database (e.g., Microsoft Excel) [14].
• Thematic Analysis: Perform a thematic analysis on qualitative feedback to identify recurring themes, barriers, and facilitators related to each implementation science domain [14].
• Quantitative Summarization: Calculate descriptive statistics (e.g., mean, median, standard deviation) for the quantitative ratings of each framework component.
• Iterative Refinement: Use the integrated findings from the thematic and quantitative analyses to make targeted revisions to the Agile framework. This embodies the Agile principle of iterative improvement [80].
• Consensus Validation: Present the refined framework to the expert panel (or a subset) to confirm that their feedback has been adequately addressed and to reach a final consensus on the framework's validity [14].

The Scientist's Toolkit: Research Reagent Solutions

The following table details key methodological components used in the validation of Agile frameworks for implementation research.

Table 2: Essential Methodological Components for Agile Framework Validation

Component	Function in Validation	Example Sources/Tools
Expert Recommendations for Implementing Change (ERIC)	Provides a standardized set of implementation strategies used to shape research questions and define validation domains (e.g., stakeholder engagement, impact measurement) [14].	Implementation Science Literature
Structured Validation Questions	Elicits specific, actionable feedback from experts on the framework's applicability, feasibility, and completeness, ensuring consistent and comparable data collection [14].	Custom-developed for the study
National Cancer Control Plans (NCCPs)	Serves as the real-world context for validating the framework, allowing experts to assess its utility against existing plans and identified gaps [14].	International Cancer Control Partnership (ICCP) Portal [14]
Digital Collaboration Platforms	Facilitates remote expert panels, real-time feedback, and the creation of comparison charts or visual frameworks that can be workshopped by the team [82].	Canva Whiteboards, Miro, Mural [82]
Scoping Review Methodology	Provides a systematic approach (e.g., Arksey and O'Malley framework) to analyze existing NCCPs and identify gaps, forming the evidence base for the framework's development [14].	Scientific Research Guidelines

Visualization of the Integrated Agile Validation Pathway

The pathway below, derived from the expert validation process, integrates Implementation Science (IS) into cancer control planning for resource-constrained settings [14].

Diagram: Agile implementation science pathway for NCCPs.

Comparative Analysis of Implementation Outcomes Across Different Cancer Types

The translation of evidence-based interventions (EBIs) into routine healthcare practice remains a significant challenge in oncology. Implementation research is the scientific study of methods and strategies to promote the systematic uptake of EBIs into standard care to improve population health [4]. This field is crucial for bridging the "know-do" gap in cancer control, ensuring that scientific discoveries achieve their maximum public health impact [13] [4]. Despite clear evidence supporting numerous cancer prevention, screening, and treatment interventions, significant disparities exist in their adoption, implementation, and sustainment across different healthcare contexts and geographic regions [14] [19]. This application note provides a structured framework and methodological protocols for conducting comparative analyses of implementation outcomes across different cancer types, with a specific focus on lung, breast, and colorectal cancers. By employing agile scientific methods, researchers can identify context-specific determinants and strategies that optimize implementation success across the cancer care continuum.

Theoretical Framework and Implementation Outcomes

Implementation science provides numerous frameworks and models to guide research design and evaluation. The Consolidated Framework for Implementation Research (CFIR) is particularly valuable for assessing multi-level determinants, while the RE-AIM framework (Reach, Effectiveness, Adoption, Implementation, Maintenance) offers a pragmatic structure for evaluating implementation outcomes [19]. For comparative analyses across cancer types, researchers should systematically assess core implementation outcomes as defined by Proctor et al. and adapted for cancer care:

• Acceptability: Perception among stakeholders that a cancer intervention is agreeable, palatable, or satisfactory
• Adoption: Uptake and intention to employ the intervention within specific healthcare settings
• Appropriateness: Perceived fit, relevance, or compatibility of the intervention for a particular setting or patient population
• Feasibility: Extent to which the intervention can be successfully used or carried out within a given setting
• Fidelity: Degree to which the intervention was implemented as prescribed in the original protocol
• Implementation Cost: Marginal cost of implementing the intervention compared to usual care
• Penetration: Integration of the intervention within a specific care setting
• Sustainability: Extent to which the intervention is maintained or institutionalized within ongoing operations [13] [4]

Table 1: Core Implementation Outcomes for Comparative Analysis in Cancer Research

Outcome Domain	Definition	Measurement Approach	Relevance to Cancer Type
Reach	Proportion of target population that receives the evidence-based intervention	Electronic health record abstraction, screening registries	Critical for screening programs (e.g., CRC, breast cancer)
Effectiveness	Impact of the intervention on clinical outcomes in real-world settings	Patient-reported outcomes, clinical outcomes, survival data	Varies by cancer stage and type (e.g., early vs. late-stage)
Adoption	Uptake by providers and healthcare systems	Provider surveys, organizational assessments	Influenced by system readiness and resource availability
Implementation	Fidelity and quality of delivery	Process measures, adherence metrics	Affected by workflow compatibility and complexity
Maintenance	Sustainability over time	Longitudinal follow-up, cost analyses	Determines long-term population health impact

Methodology for Comparative Implementation Analysis

Study Designs for Comparative Implementation Research

Comparative implementation research requires robust methodological approaches that can account for contextual factors across different cancer types. Recommended designs include:

• Hybrid Type 3 Designs: Primarily focus on testing implementation strategies while collecting data on clinical outcomes [19]
• Cluster-Randomized Trials: Randomize sites or provider groups to different implementation strategies to minimize contamination
• Stepped-Wedge Designs: Sequential rollout of implementation strategies allows for within-site comparisons over time
• Mixed-Methods Approaches: Combine quantitative implementation outcome data with qualitative insights on contextual barriers and facilitators

Data Collection Methods and Metrics

A multi-method approach to data collection ensures comprehensive assessment of implementation outcomes:

• Quantitative Data Sources: Electronic health records, administrative claims data, clinical registries, structured provider and patient surveys
• Qualitative Data Sources: Semi-structured interviews with key stakeholders, focus groups, observational fieldwork, document analysis
• Implementation Strategy Tracking: Standardized documentation of strategy components, adaptations, and resource utilization

Table 2: Core Metrics for Comparing Implementation Outcomes Across Cancer Types

Metric Category	Specific Measures	Data Sources	Application to Cancer Types
Screening Reach	Proportion of eligible population screened	EHR, cancer registries	CRC (colonoscopy/FIT), breast (mammography), lung (LDCT)
Diagnostic Follow-up	Time from abnormal screening to diagnosis	EHR, administrative data	Critical for all cancers, especially high-mortality types
Treatment Initiation	Time from diagnosis to treatment initiation	EHR, cancer registry data	Varies by cancer urgency (e.g., pancreatic vs. prostate)
Provider Adoption	Proportion of eligible providers using EBI	Provider surveys, EHR data	Influenced by specialty, practice setting, and resources
Implementation Cost	Marginal cost of implementation	Administrative data, time-motion studies	Varies by intervention complexity and setting resources

Experimental Protocol: Cluster-Randomized Implementation Trial

Protocol for Comparing Implementation Strategies Across Cancer Types

This protocol adapts methodology from a published cluster-randomized implementation trial comparing strategies for improving gastrointestinal cancer screening [19].

Title: Cluster-Randomized Trial Comparing Implementation Strategies for Improving Cancer Screening Across Multiple Cancer Types

Objective: To compare the effectiveness of external facilitation versus patient navigation on screening completion for colorectal, breast, and lung cancers across diverse healthcare settings.

Site Selection and Eligibility:

• Include healthcare systems with below-national-median screening rates for target cancers
• Ensure diversity in geographic location, patient populations, and resource availability
• Secure leadership commitment prior to randomization

Participant Eligibility:

• Patients: Adults meeting USPSTF screening criteria for target cancers, enrolled in participating healthcare systems
• Providers: Clinical staff involved in cancer screening pathways at participating sites
• Exclusion: Patients with limited life expectancy, prior history of target cancers, or contraindications to screening

Randomization:

• Cluster randomization at site level, stratified by:
  • Patient volume (above/below median)
  • On-site specialty care availability (yes/no)
  • Geographic region
• 1:1 allocation to implementation strategy arms

Implementation Strategies:

• External Facilitation Arm:
  • Manualized "Getting To Implementation" (GTI) framework
  • Bi-weekly virtual facilitation sessions for 6 months
  • Monthly maintenance calls for additional 6 months
  • Focus on provider-facing implementation strategies
  • Tailored barrier identification and problem-solving

• Patient Navigation Arm:
  • Structured patient navigation toolkit
  • Initial training session plus monthly check-ins
  • Patient-facing support including education, barrier resolution, and scheduling assistance
  • Focus on addressing patient-level barriers to screening completion

Data Collection Timeline:

• Baseline (T0): Site characteristics, provider surveys, baseline screening rates
• Implementation Period (T1-12 months): Monthly fidelity tracking, adaptation documentation
• Post-Implementation (T12): Primary outcome assessment, repeat provider surveys, patient interviews
• Sustainment Period (T24): Long-term outcome assessment

Primary Outcome: Reach of cancer screening completion (proportion of eligible patients receiving guideline-concordant screening) at 12 months

Secondary Outcomes:

• Adoption by clinical teams (proportion implementing core components)
• Implementation fidelity and adaptations
• Maintenance of screening rates at 24 months
• Implementation cost and cost-effectiveness

Analytical Approach:

• Generalized linear mixed models (GLMMs) to account for clustering
• Intent-to-treat principles
• Mixed-methods integration to explain quantitative findings

Visualization of Implementation Pathways and Relationships

Implementation Strategy Pathway for Multi-Cancer Screening

Multi-Level Determinants of Implementation Success Across Cancer Types

Table 3: Essential Research Reagent Solutions for Comparative Implementation Studies

Tool/Resource	Function/Purpose	Application in Cancer Implementation Research	Access/Source
CFIR Tools	Assess multi-level implementation determinants	Identify barriers and facilitators across patient, provider, system levels	CFIR Guide [19]
RE-AIM Framework	Evaluate implementation outcomes	Measure Reach, Effectiveness, Adoption, Implementation, Maintenance	RE-AIM.org [19]
ERIC Compilation	Taxonomy of implementation strategies	Select and specify strategies for testing and comparison	Expert Recommendations for Implementing Change [14]
Implementation Strategy Tracking System	Document strategy delivery and adaptations	Monitor fidelity and capture modifications during study	Adapted from Bauer et al.
Stakeholder Engagement Platform	Facilitate patient and provider input	Ensure relevance and appropriateness of implementation approaches	PCORI Engagement Rubric
Health Equity Assessment Tool	Evaluate equity in implementation	Assess differential impact across population subgroups	NIH Health Equity Measures
Implementation Costing Methods	Economic evaluation of strategies	Calculate implementation cost and cost-effectiveness	Costing methods from RAND

Application Notes for Agile Implementation Science

Context-Specific Adaptation Framework

Implementation strategies must be tailored to address the unique characteristics of different cancer types and care settings. The framework below guides context-specific adaptations:

• Assessment Phase: Conduct rigorous multi-level context assessment using CFIR-guided analysis [19]
• Selection Phase: Match implementation strategies to identified barriers using implementation mapping approaches
• Tailoring Phase: Modify strategy components while preserving core elements and functions
• Evaluation Phase: Assess impact of adaptations on implementation outcomes

Comparative Analysis Considerations

When comparing implementation outcomes across cancer types, researchers should account for:

• Disease Trajectory Differences: Screening, diagnostic, and treatment pathways vary significantly by cancer type
• Population Characteristics: Risk factors, comorbidities, and health literacy requirements differ across cancers
• Intervention Complexity: Varies from one-time screenings (e.g., colonoscopy) to repeated surveillance (e.g., HCC in cirrhosis) [19]
• Resource Intensity: Demands on healthcare systems range substantially across the cancer control continuum
• Stigma and Psychological Factors: Vary by cancer type and influence implementation outcomes

Agile Science Methods for Implementation Research

Agile implementation science emphasizes rapid-cycle learning, iterative adaptation, and responsiveness to emerging barriers:

• Rapid Qualitative Assessment: Brief, focused assessments to identify implementation barriers in real-time
• Iterative Strategy Refinement: Ongoing modification of implementation approaches based on process data
• Practical Implementation Trials: Efficient designs that balance rigor with relevance to decision-makers
• Learning Healthcare System Integration: Embedding implementation research within routine care delivery systems [4]

Comparative analysis of implementation outcomes across cancer types represents a promising approach to advancing implementation science in oncology. By systematically examining how implementation determinants, strategies, and outcomes vary across different cancers and contexts, researchers can develop more nuanced, effective, and efficient approaches to implementing evidence-based cancer care. Future directions should include greater focus on precision implementation strategies tailored to specific contexts, increased attention to health equity in implementation, and development of robust methods for rapid-cycle evaluation and adaptation. The protocols and frameworks presented in this application note provide a foundation for conducting rigorous comparative implementation research that can accelerate the translation of evidence into practice across the cancer care continuum.

Validating Agile Values for Multidisciplinary Oncology Teams

The translation of evidence-based interventions (EBIs) into routine oncology practice remains unacceptably slow, often taking 17 years for research findings to be adopted into clinical care [39]. This implementation gap is particularly critical in cancer control, where EBIs could reduce cervical cancer deaths by 90%, colorectal cancer deaths by 70%, and lung cancer deaths by 95% if widely and effectively implemented [26]. Agile science methods, adapted from software engineering and systems design, offer promising approaches for advancing implementation research in oncology by emphasizing speed, adaptability, and patient-centeredness.

Agile implementation recognizes that healthcare systems function as complex adaptive systems—networks of semiautonomous individuals who are interdependent and connected in multiple nonlinear ways [39]. Within multidisciplinary oncology teams, this perspective acknowledges the dynamic interactions between structural, organizational, provider, patient, and innovation-level factors that influence implementation success. This protocol outlines a structured approach for validating agile values within these complex team environments, providing implementation researchers with specific methodologies for assessing and enhancing agility in cancer care delivery.

Agile Values and Principles in Oncology

Theoretical Foundation

The concept of agility in healthcare derives from the Agile Manifesto developed for software engineering in 2001, which emphasizes customer collaboration, responsiveness to change, and delivering working solutions [36] [17]. In oncology contexts, these values translate to prioritizing patient-centered care, adapting to evolving patient needs and scientific evidence, and focusing on interventions that deliver tangible health outcomes.

Recent research has adapted these principles specifically for breast cancer treatment, identifying four agile values and twelve agile principles tailored to oncology settings [36] [17]. These values emphasize patient and family satisfaction, welcoming changing requirements throughout treatment, frequent delivery of effective care, and close collaboration between patients and healthcare professionals [17]. The validation of these principles in oncology represents a significant advancement in applying agile science to cancer implementation research.

Validated Agile Principles for Oncology Teams

Table 1: Validated Agile Principles for Multidisciplinary Oncology Teams

Principle Number	Principle Description	Agile Conformance	Validation Evidence
1	Our highest priority is to satisfy the patient and family through early and continuous delivery of effective, safe treatment	Meets agility	[17]
2	Welcome changing requirements, even late in treatment lifecycle	Meets agility	[17]
3	Deliver working treatment frequently with a preference for shorter timescales	Meets agility	[17]
4	Patients and health professionals must work together daily throughout the treatment	Meets agility	[17]
5	Build treatment plans around motivated individuals and give them the support they need	Partially meets agility	[17]
6	The most efficient and effective method of conveying information is face-to-face conversation	Partially meets agility	[17]
7	Quality of life is the primary measure of progress	Meets agility	[17]
8	Agile processes promote sustainable treatment; all stakeholders should maintain a constant pace indefinitely	Partially meets agility	[17]
9	Continuous attention to technical excellence and good design enhances agility	Meets agility	[17]
10	Simplicity—maximizing the amount of work not done—is essential	Partially meets agility	[17]
11	The best treatment plans and outcomes emerge from self-organizing teams	Meets agility	[17]
12	At regular intervals, the team reflects on how to become more effective, then tunes and adjusts accordingly	Partially meets agility	[17]

Methodology for Validating Agile Values

Research Framework

We propose a structured six-phase methodology for validating agile values in multidisciplinary oncology teams, adapted from Odeh et al.'s work on agile values in breast cancer treatment [36] [17]. This methodology encourages active engagement from researchers across disciplines to produce outcomes that directly impact healthcare delivery.

Phase 1: Identifying Research Problem and Motivation

Objective: Establish the implementation gap and need for agile approaches in oncology care delivery.

Procedure:

• Conduct systematic literature reviews to document translation gaps in cancer control EBIs
• Analyze stakeholder interviews with patients, clinicians, and administrators to identify pain points in current care delivery
• Quantify the time lag between evidence generation and clinical implementation using historical data analysis
• Document cancer burden metrics that could be addressed through more agile implementation (e.g., preventable deaths, patient dissatisfaction)

Outputs:

• Comprehensive problem statement with supporting metrics
• Stakeholder analysis documenting needs across patient, provider, and system levels

Phase 2: Proposing Agile Values and Principles

Objective: Adapt agile values from software engineering to oncology contexts.

Procedure:

• Extract core agile values from software engineering manifestos and frameworks
• Conduct preliminary translation of values to oncology terminology through research team discussion
• Develop draft principles using language accessible to oncology professionals
• Create mapping documentation linking original agile concepts to proposed oncology adaptations

Outputs:

• Initial set of agile values and principles for oncology
• Conceptual mapping between software engineering and oncology domains

Phase 3: Eliciting Values from Oncology Teams

Objective: Refine agile principles through direct engagement with multidisciplinary oncology teams.

Procedure:

• Participant Recruitment: Identify and recruit diverse oncology team members (medical oncologists, surgeons, radiation oncologists, nurses, social workers, etc.) from multiple practice settings
• Interview Protocol: Conduct semi-structured interviews using a standardized guide that presents draft principles and probes for relevance, clarity, and completeness
• Data Collection:
  • Schedule multiple interview sessions (2-3 per participant group) to allow for iterative refinement [36]
  • Audio record and transcribe all sessions for qualitative analysis
  • Collect demographic and practice characteristics to contextualize responses
• Qualitative Analysis:
  • Employ thematic analysis to identify patterns in feedback
  • Use constant comparative methods to refine principles until saturation achieved
  • Document suggested modifications to language, emphasis, and application

Outputs:

• Transcribed interview data
• Refined set of agile principles incorporating frontline clinician input
• Documentation of rationale for modifications

Phase 4: Literature-Driven Validation

Objective: Validate the clinical relevance and evidence base for each proposed agile principle through systematic literature review.

Procedure:

• Develop Search Strategy: Create comprehensive search queries for each principle using MEDLINE, Embase, CINAHL, and implementation science databases
• Conduct Structured Review:
  • Identify existing evidence supporting or contradicting each principle
  • Document empirical studies, clinical guidelines, and theoretical frameworks relevant to each principle
  • Assess quality of evidence using standardized appraisal tools
• Synthesize Evidence:
  • Create evidence tables mapping literature support for each principle
  • Identify gaps in current evidence base
  • Document how existing literature confirms agility conformance

Outputs:

• Annotated bibliography supporting each agile principle
• Evidence gap analysis
• Literature-supported validation statements for each principle

Phase 5: Domain Expert Walkthrough

Objective: Final validation of agile principles through structured expert review.

Procedure:

• Expert Panel Composition: Assemble multidisciplinary panel including:
  • Medical oncologists (various subspecialties)
  • Surgical and radiation oncology representatives
  • Oncology nurses and nurse practitioners
  • Implementation scientists
  • Patient representatives
  • Healthcare administrators

• Walkthrough Protocol:

  • Conduct structured session presenting each principle with supporting evidence
  • Use iterative rating process: "Meets Agility," "Partially Meets Agility," or "Does Not Meet Agility"
  • Collect qualitative feedback on application nuances and contextual factors
  • Facilitate discussion to resolve discrepancies in ratings

• Consensus Process:

  • Use modified Delphi technique to achieve consensus
  • Document dissenting opinions and rationale
  • Finalize classification of each principle's agility conformance

Outputs:

• Final validated set of agile principles with conformance classifications
• Documentation of expert consensus process
• Implementation considerations for different cancer care contexts

Phase 6: Communication and Dissemination

Objective: Share validated agile principles with broader research and clinical communities.

Procedure:

• Develop dissemination materials for different audiences (researchers, clinicians, administrators)
• Publish validated principles in peer-reviewed literature
• Create implementation tools and resources for applying principles in practice
• Present findings at relevant scientific and clinical conferences

Outputs:

• Manuscripts for publication
• Presentation materials
• Implementation toolkit for clinical settings

Experimental Protocols and Assessment Methods

Mixed-Methods Evaluation Framework

Table 2: Multimethod Assessment Protocol for Agile Principle Implementation

Assessment Domain	Data Collection Methods	Metrics and Instruments	Frequency
Team Functioning	Direct observation, Semi-structured interviews	Team Functioning Observation Protocol, Qualitative field notes	Pre-, mid-, post-intervention
Implementation Outcomes	Self-report questionnaires, Administrative data	Implementation Climate Scale (ICS), Adoption rates, Penetration measures	Quarterly for 24 months
Team Resilience	Focus groups, Validated scales	Team Resilience Scale, Psychological Safety Scale	Pre-intervention, 12 months, 24 months
Organizational Outcomes	HR records, Questionnaires	Absenteeism rates, Staff turnover, Organizational Citizenship Behaviors	Quarterly for 24 months
Patient-Centeredness	Patient surveys, Treatment plan review	Patient Satisfaction instruments, Shared Decision Making scales	Pre-intervention, 12 months, 24 months

Agile Storytelling Protocol for Creating Implementation Demand

Based on successful applications in sickle cell disease implementation [83], we have adapted the Agile Storytelling method for oncology contexts:

Design Phase:

• Identify Evidence-Based Solution: Select the cancer control EBI targeted for implementation
• Create Minimal Viable Story: Convert the EBI into a narrative structure with:
  • Hero (patient or clinician facing challenge)
  • Villain (the problem or barrier being addressed)
  • Struggle (dramatic tension in current situation)
  • Resolution (how the EBI provides solution)
• Incorporate Behavioral Economic Principles:
  • Apply framing effects to present information in compelling context
  • Use social proof to demonstrate previous success
  • Create visual imagery to enhance recall
  • Trigger emotional engagement through personal narratives

Testing Phase:

• Map Network Structure: Identify formal and informal influencers within the oncology practice environment
• Conduct Storytelling Sprints:
  • Iteratively test stories with different stakeholder groups
  • Refine narrative based on engagement metrics
  • Target communication to network hubs and bridges
• Measure Demand Creation: Track specific investments of time, social, or financial capital by stakeholders

Application Example: Creating demand for implementing shared decision-making tools in breast cancer treatment through stories of patients achieving better quality of life through personalized treatment choices.

Research Reagent Solutions and Tools

Table 3: Essential Research Materials for Agile Implementation Studies

Tool Category	Specific Instrument/Resource	Function/Purpose	Application Context
Determinant Assessment	Consolidated Framework for Implementation Research (CFIR) Interview Guide	Identify barriers and facilitators to implementation	Pre-implementation planning across multiple clinical settings
Implementation Strategy Specification	Expert Recommendations for Implementing Change (ERIC) Compilation [15]	Standardize naming and definition of implementation strategies	Protocol development and reporting
Team Functioning Assessment	Team Resilience Scale, Psychological Safety Scale	Measure team-level capacities for adaptive functioning	Evaluating impact of agile principles on team dynamics
Implementation Outcomes Measurement	Implementation Climate Scale (ICS), Adoption/Penetration Measures	Assess organizational context and implementation success	Tracking outcomes throughout implementation process
Network Analysis	Organizational Network Analysis Tools	Map formal and informal communication pathways	Identifying key influencers for Agile Storytelling
Qualitative Analysis	Codebook Template based on Agile Principles	Standardize qualitative data analysis across sites	Multi-site studies of agile implementation
Adaptive Intervention Design	Sequential Multiple Assignment Randomized Trial (SMART) Design Protocols	Optimize implementation strategies through sequential adaptation	Strategy optimization studies

Analysis and Interpretation

Data Integration Approaches

The validation of agile values requires triangulation of quantitative and qualitative data across multiple sources and timepoints. We recommend:

• Structural Equation Modeling: Test hypothesized relationships between agile principles, team resilience, implementation outcomes, and patient outcomes using pre-specified path models [84]
• Qualitative Comparative Analysis: Identify configurations of agile principles that consistently lead to successful implementation across different contexts
• Mixed-Methods Integration: Use joint displays to visualize how qualitative themes explain quantitative patterns in implementation success

Interpretation Framework

When interpreting validation results, consider:

• Contextual Moderators: How organizational size, cancer type, resource availability, and team composition influence agile principle effectiveness
• Implementation Stage: Whether different principles show varying importance during initiation vs. sustainment phases
• Principle Interdependencies: How principles function as synergistic bundles rather than isolated components

The validated agile principles provide a structured framework for enhancing implementation effectiveness in multidisciplinary oncology teams, potentially reducing the translation gap for cancer control interventions and improving patient outcomes through more responsive, adaptive care delivery.

The translation of evidence-based interventions into routine cancer care remains a significant challenge, with a persistent gap between research discovery and real-world application. Implementation science has emerged as a critical discipline to address this gap, systematically examining the methods to promote the integration of research findings and evidence into healthcare policy and practice [12]. Within this field, agile science offers a promising, iterative approach that emphasizes rapid-cycle development, continuous optimization, and early-and-often sharing of resources to better accommodate the complexity of behavior change and implementation in dynamic healthcare environments [1].

This article synthesizes evidence from global case studies in cancer implementation research, focusing on the practical application of agile principles. By examining research across diverse settings—from low-resource environments to established healthcare systems—we aim to extract transferable insights and methodologies that can accelerate the implementation of evidence-based cancer interventions. The synthesis is structured to provide researchers and drug development professionals with immediately applicable tools, including quantitative summaries, experimental protocols, and visualization frameworks, to enhance the rigor and impact of their implementation efforts.

Synthesized Findings from Global Case Studies

Analysis of implementation research across global contexts reveals consistent themes and context-specific adaptations. The following table synthesizes key quantitative findings from major case studies and reviews, highlighting implementation outcomes across different resource settings and intervention types.

Table 1: Quantitative Implementation Outcomes from Global Cancer Case Studies

Case Study / Review Focus	Geographic Context	Key Implementation Outcomes Documented	Primary Implementation Strategies Employed
Exercise Oncology Implementation [85]	Australia (Multiple sites)	18 consistent determinants identified; 22 consistent implementation strategies; 11 causal pathways explaining implementation success	Stakeholder engagement, iterative adaptation, workforce support, resource allocation
Cancer Prevention Community Program [86]	Georgia, USA (Community settings)	High implementation scores (5-7) correlated with significantly greater improvements in intention for physical activity (p<0.05), healthy weight (p<0.05), and alcohol limitation (p<0.01)	Educational outreach, partnership with community organizations, program fidelity monitoring
Implementation Research on Common Cancers [58] [13]	Asia (Multiple countries)	Only 11 qualified implementation studies identified from 5,750 initial articles; reach, acceptability, feasibility, adoption, fidelity, cost, appropriateness, and sustainability evaluated	Task-shifting, decentralized screening, cultural adaptation, awareness campaigns
National Cancer Control Planning [14]	Low- and Medium-HDI Countries (33 nations)	4/16 low-HDI and 9/17 medium-HDI countries had costed plans; stakeholder engagement common but unstructured; limited systematic capacity assessment	Stakeholder engagement, situational analysis, economic evaluation, impact measurement

The synthesis of these diverse case studies reveals that successful implementation consistently requires contextual adaptation rather than standardized application of interventions. As demonstrated by the limited number of qualifying studies in Asia despite extensive searching [58], implementation research remains underutilized in many global settings, highlighting an opportunity for more systematic application of agile science principles.

Agile Science Methodologies in Implementation Research

Agile science represents a significant shift from traditional linear implementation approaches, drawing inspiration from agile software development methodologies. The core principles include iterative development, modular intervention components, and continuous stakeholder feedback to better accommodate the complexity of healthcare environments [1].

Core Agile Science Framework

The conceptual framework for agile science in implementation research emphasizes three key products, adapted for cancer control applications:

• Behavior Change Modules: The smallest meaningful, self-contained, and repurposable components of an intervention that can be independently tested and optimized.
• Computational Models: Definitions of the interaction between modules, individuals, and context, enabling prediction of implementation outcomes.
• Personalization Algorithms: Decision rules for intervention adaptation based on individual and contextual factors [1].

This framework facilitates a "pay-as-you-go" approach to implementation, where initial functionality is provided rapidly and then incrementally improved through short development cycles and continuous stakeholder engagement [87].

Experimental Protocol: Multiple Case Study Methodology for Causal Pathway Development

The following protocol details the methodology used successfully in exercise oncology implementation research [85], which can be adapted for studying implementation of other cancer interventions:

Table 2: Experimental Protocol for Implementation Case Studies

Protocol Component	Detailed Specifications	Agile Science Application
Case Selection Criteria	Sites implementing evidence-based interventions for ≥12 months; diversity in organizational structure and service delivery models; explicit sustainment period	Purposeful sampling to maximize learning across different contexts
Data Collection Methods	1. Semi-structured staff interviews2. Document review3. Observational visits4. Validated implementation surveys (e.g., Program Sustainability Assessment Tool)	Mixed-methods design for comprehensive understanding; iterative data collection
Analysis Framework	1. Framework analysis using established implementation frameworks2. Application of Implementation Research Logic Model (IRLM)3. Identification of determinants, strategies, and outcomes4. Development of causal pathways	Systematic approach enabling cross-case comparison and generalizable knowledge
Iterative Refinement	Regular team deliberations; member checking; preliminary findings discussed with implementation partners	Incorporates stakeholder feedback throughout research process

This methodology enables researchers to move beyond isolated descriptions of implementation barriers and strategies to develop explanatory causal pathways that elucidate how and why implementation succeeds or fails in specific contexts [85].

Visualizing Implementation Pathways and Workflows

Effective implementation research requires clear visualization of complex pathways and relationships. The following diagrams illustrate key frameworks and workflows derived from the synthesized case studies.

Causal Pathway for Implementation Strategies and Mechanisms

Implementation Causal Pathway

This diagram illustrates the fundamental causal pathway in implementation research, demonstrating how contextually-informed strategies activate specific mechanisms to produce implementation outcomes, based on the approach used in exercise oncology case studies [85].

Agile Science Development Cycle

Agile Development Cycle

This workflow visualizes the iterative agile science process, emphasizing rapid cycles of development and refinement informed by continuous stakeholder feedback, adapted from agile software development methodologies [1].

The Scientist's Toolkit: Essential Research Reagents

Implementation science requires specific conceptual tools and frameworks to effectively study and improve cancer intervention implementation. The following table details essential "research reagents" for designing and executing implementation studies in cancer research.

Table 3: Essential Research Reagents for Implementation Science

Tool/Framework	Primary Function	Application Context
Consolidated Framework for Implementation Research (CFIR) [86]	Identifies and categorizes implementation determinants across multiple domains	Evaluating contextual factors in community and clinical settings; designing implementation strategies
Implementation Research Logic Model (IRLM) [85]	Links determinants, strategies, mechanisms, and outcomes in causal pathways	Explaining implementation processes; developing testable hypotheses for implementation mechanisms
Standards for Reporting Implementation Studies (StaRI) [58]	Ensures comprehensive reporting of implementation strategies and contexts	Systematic reviews; research manuscript preparation; protocol development
Program Sustainability Assessment Tool [85]	Measures organizational capacity for maintaining evidence-based interventions	Pre-implementation planning; evaluation of sustainment prospects
Expert Recommendations for Implementing Change (ERIC) [14]	Catalog of 73 implementation strategies with definitions	Selecting and specifying implementation strategies; comparing approaches across studies

These conceptual reagents provide the necessary foundation for rigorous implementation research design and execution. Their systematic application enables researchers to generate comparable findings across diverse settings and contributes to the cumulative growth of implementation knowledge.

Discussion and Application Notes

The synthesis of global case studies demonstrates that agile science methods offer a promising approach for accelerating the implementation of evidence-based cancer interventions. Three key insights emerge for researchers and drug development professionals:

First, explanatory causal pathways provide more value than simple descriptions of barriers and facilitators. The multiple case study in exercise oncology [85] demonstrated how systematically linking determinants, strategies, mechanisms, and outcomes creates transferable knowledge that can inform implementation in new contexts.

Second, modular intervention components enable more efficient adaptation and optimization. The agile science approach of creating minimal viable products and testing them in rapid cycles [1] allows for more responsive intervention development that better fits real-world constraints and opportunities.

Third, systematic stakeholder engagement throughout the implementation process is essential for sustainability. The consistent finding that unstructured engagement limits implementation effectiveness [14] underscores the need for deliberate, iterative partnership with all relevant stakeholders.

For drug development professionals, these insights highlight the importance of considering implementation factors early in the development process, rather than after efficacy has been established. Designing interventions with modular components and planning for contextual adaptation can significantly reduce the time from evidence generation to real-world impact.

Global case studies in cancer implementation research provide compelling evidence for the value of agile, context-sensitive approaches. The methodologies, frameworks, and tools synthesized in this article offer researchers and drug development professionals practical resources for enhancing the implementation of evidence-based cancer interventions. By adopting iterative development processes, explanatory research designs, and systematic engagement strategies, the field can accelerate progress toward reducing the cancer burden through more effective and equitable implementation of proven interventions.

Conclusion

Agile science offers a paradigm shift for cancer implementation research, moving from rigid, linear models to dynamic, iterative processes that prioritize real-world context and stakeholder engagement. The synthesis of insights across the four intents reveals that successful implementation hinges on robust frameworks like PRISM, the application of agile values from other disciplines, and proactive strategies to overcome systemic barriers. The future of cancer care delivery depends on our ability to embed these agile principles into research design, policy, and practice. This will accelerate the translation of evidence into equitable, sustainable, and effective cancer care, ultimately improving outcomes for diverse patient populations globally. Future directions should focus on building digital infrastructure for data-driven interventions, developing standardized metrics for agility, and fostering international collaboration to test and scale innovative implementation strategies.

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