Optimizing Cancer Control: A Comprehensive Guide to Implementation Science Methods and Applications

Abstract

This article provides a comprehensive analysis of implementation science (IS) methods for optimizing cancer control, tailored for researchers, scientists, and drug development professionals. It explores the foundational principles of IS and its critical role in bridging the gap between evidence-based interventions and real-world practice across the cancer care continuum. The content examines methodological applications, including frameworks for clinical trials and electronic health records, and addresses key troubleshooting strategies for common barriers like stakeholder engagement and health system capacity. Finally, it validates approaches through portfolio analysis of funded research and comparative studies, offering a synthesized pathway for achieving equitable, feasible, and scalable cancer control outcomes.

Bridging the Gap: Foundational Principles of Implementation Science in Cancer Control

Defining Implementation Science and Its Critical Role in Oncology

Implementation science is defined as a scientific approach that bridges the gap between research and practice, enabling the systematic integration of evidence-based interventions into routine healthcare and policy to improve patient and population health outcomes [1]. In oncology, this translates to ensuring that proven cancer control interventions—from prevention and screening to treatment and survivorship care—are effectively delivered in diverse real-world settings, including low-resource environments [1]. The National Cancer Institute recognizes implementation science as vital for maximizing the impact of cancer research investments and for addressing critical disparities in cancer care and outcomes [1].

The core challenge implementation science addresses in oncology is the persistent gap between knowledge and action. For instance, evidence-based interventions could reduce cervical cancer deaths by 90%, colorectal cancer deaths by 70%, and lung cancer deaths by 95% if they were widely and effectively implemented [2]. However, the adoption of these interventions into routine practice is often suboptimal, slow, and inequitable [2]. This gap represents a critical impediment to improving the health of cancer patients and survivors and underscores the necessity of implementation science as a dedicated field of study [1].

The Imperative for Implementation Science in Cancer Control

The optimization of cancer control demands more than just the discovery of effective interventions; it requires that these interventions are successfully delivered to all populations who need them. Implementation science provides a structured framework to achieve this, particularly by enabling realistic goal setting and benchmarking against regional and global standards [3]. This is especially crucial in resource-constrained settings, where competing health priorities, a larger burden of disease, and limited resources make efficient and context-appropriate planning essential [3].

A recent scoping review of National Cancer Control Plans and strategies from low and medium Human Development Index countries revealed significant gaps in current planning approaches. While many plans incorporated elements like stakeholder engagement and impact measurement, these were often inconsistently applied [3] [4]. Crucially, the review found that none of the plans assessed health system capacity to determine readiness for implementing new interventions, and stakeholder engagement was typically unstructured and incomplete [3]. These findings highlight the potential for implementation science principles to strengthen cancer control planning and policy development globally, leading to more equitable and feasible cancer control policies [3].

Core Applications and Methodological Frameworks

Implementation science employs a variety of methodological frameworks and theories to guide its application. The Consolidated Framework for Implementation Research (CFIR) is one widely used framework that helps identify multi-level determinants (i.e., barriers and facilitators) that influence implementation success [2] [5]. Another key resource is the Expert Recommendations for Implementing Change (ERIC) compilation, which provides a standardized set of 73 defined implementation strategies [3] [2]. These frameworks bring structure and consistency to the process of moving evidence into practice.

Table 1: Key Implementation Science Frameworks and Their Application in Oncology

Framework/Resource	Primary Purpose	Example Application in Oncology
Consolidated Framework for Implementation Research (CFIR) [2] [5]	Identifies and classifies multi-level implementation determinants (barriers & facilitators).	Used pre-implementation to understand barriers to cancer screening uptake in primary care clinics [5].
ERIC (Expert Recommendations for Implementing Change) [3]	Compiles and defines a standardized list of implementation strategies.	Guides policymakers in selecting strategies (e.g., audit & feedback, patient education) for a national cancer control plan [3].
RE-AIM Framework [5]	Evaluates the public health impact of an intervention across five dimensions: Reach, Effectiveness, Adoption, Implementation, and Maintenance.	Used to evaluate the real-world impact and sustainability of a patient navigation program for colorectal cancer screening [5].

Illustrative Application: The OPTICC Center's Three-Stage Approach

The Optimizing Implementation in Cancer Control Center employs a structured, three-stage approach to optimizing the implementation of evidence-based interventions (EBIs) [2] [6]. This methodology provides a clear protocol for researchers and practitioners.

• Identify and Prioritize Determinants: This initial stage involves using mixed methods to identify context-specific barriers and facilitators to implementation. The OPTICC center addresses the critical challenge of moving from a long list of determinants to a prioritized few that have the greatest potential to impact implementation success [2].
• Match Strategies to Determinants: In this stage, implementation strategies are matched to the high-priority determinants. A significant barrier in the field is the incomplete knowledge of implementation strategy mechanisms—the processes through which strategies produce their effects. Matching strategies without understanding their mechanisms is often guesswork [2] [6].
• Optimize Strategies: Before costly large-scale trials, this stage uses methods like the Multiphase Optimization Strategy to refine multi-component implementation strategies. It aims to determine which components are essential, how they interact, and the most cost-effective and potent way to deliver them [2].

The following workflow diagram illustrates this iterative process:

Detailed Experimental Protocols in Oncology Implementation Science

Protocol 1: Evaluating Integrative Nursing in Inpatient Oncology

Objective: To investigate the acceptance, feasibility, and contextual conditions of implementing Integrative Nursing in inpatient cancer care, evaluating perceptions and impact from multiple stakeholder perspectives [7].

Methods Overview: A convergent parallel mixed methods approach guided by the Consolidated Framework for Implementation Research (CFIR) [7]. The evaluation consists of five substudies reflecting multiple perspectives:

• Substudy 1: Single-arm pre-post questionnaire with patients.
• Substudy 2: Semi-structured interviews with patients.
• Substudy 3: Cross-sectional survey of relatives.
• Substudy 4: Semi-structured interviews with healthcare professionals.
• Substudy 5: Analysis of project-related documentation.

Intervention: The IMPLEMENT-UKU project provides an IN consultation service. Patients are referred by ward staff for symptoms like restlessness, pain, or treatment side effects. An integrative nurse conducts an initial consultation, followed by patient-tailored interventions guided by a symptom-driven catalog [7]. Interventions include compresses, therapeutic baths, and embrocations using substances like yarrow tea, thyme oil, or lavender bath milk, all delivered by specially trained nurses [7].

Table 2: Integrative Nursing Intervention Catalog for Cancer Symptoms [7]

Symptom	Prescribed IN Intervention
Sleep Disorders	Heart compress with aurum-lavandula ointment; Hand/foot bath with lavender bath milk; Embrocation with lavender oil.
Exhaustion & Weakness	Hand/foot bath with rosemary bath milk; Liver compress with yarrow tea or oil; Therapeutic wash with citrus bath milk.
Respiratory Insufficiency	Sternum compress with thyme oil or plantago bronchial balsam; Embrocation with solum oil or plantago bronchial balsam.
Appetite Loss	Liver compress with yarrow tea or oil.

Protocol 2: Comparing Strategies for GI Cancer Screening

Objective: To compare the effectiveness of two implementation strategies—External Facilitation versus Patient Navigation—on improving the reach of hepatocellular carcinoma and colorectal cancer screening within the Veterans Health Administration [5].

Trial Design: Two hybrid type 3, cluster-randomized trials (24 sites for HCC trial; 32 sites for CRC trial) where sites are randomized to an implementation strategy arm, and the primary focus is on testing the strategy while also observing clinical outcomes [5].

Implementation Strategies:

• Facilitation Arm: Sites participate in "Getting To Implementation," a manualized intervention involving external facilitators who guide local teams through a seven-step process for barrier identification, strategy selection, and iterative tests of change via bi-weekly virtual meetings over six months [5].
• Patient Navigation Arm: Sites receive a "Patient Navigation Toolkit" and support focused on three core activities: using dashboards to identify eligible Veterans, conducting patient outreach, and documenting navigation activities, with monthly progress check-ins [5].

Outcomes: The primary outcome is Reach, defined as the proportion of eligible patients who complete guideline-concordant screening. This is measured using the RE-AIM framework, which also guides the assessment of implementation outcomes like Adoption, Implementation, and Maintenance [5].

The structure of these complex, multi-site trials is visualized below:

For researchers designing implementation science studies in oncology, a core set of "research reagents" and resources is essential. The following table details these key tools and their functions.

Table 3: Essential Research Reagents and Resources for Implementation Science in Oncology

Tool/Resource	Category	Function in Research
CFIR Interview Guides [2] [5]	Data Collection	Semi-structured guides to systematically assess barriers and facilitators across intervention, inner/outer setting, individual, and process domains.
ERIC Strategy List [3] [2]	Strategy Specification	A standardized taxonomy of 73 implementation strategies to ensure clear reporting and replication of methods.
RE-AIM Framework Evaluation Matrix [5]	Evaluation	A planning and evaluation tool to measure public health impact across Reach, Effectiveness, Adoption, Implementation, and Maintenance.
Implementation Outcomes Measures [2]	Measurement	Validated and pragmatic measures of key constructs like Acceptability, Appropriateness, Feasibility, and Fidelity.
Facilitation Manuals [5]	Strategy Delivery	Structured guides for external facilitators to support sites through processes of change, problem-solving, and data use.
Patient Navigation Tracking Tools [5]	Fidelity & Reporting	Standardized data collection forms to document navigation activities, patient outreach, and outcomes for fidelity monitoring.

Quantitative Data Synthesis from Current Evidence

The application of implementation science in oncology is generating a growing body of quantitative data on both the gaps in current practice and the outcomes of implementation studies.

Table 4: Synthesis of Quantitative Findings in Oncology Implementation Science

Data Source / Study	Key Quantitative Finding	Implication for the Field
Scoping Review of NCCPs [3]	0% of 33 reviewed NCCPs from low/medium HDI countries included a health system capacity assessment.	Highlights a critical, nearly universal gap in cancer control planning, indicating a major area for improvement via IS.
Scoping Review of NCCPs [3]	4 low HDI and 9 medium HDI countries had costed plans, typically using an activity-based approach.	Suggests variable, and potentially insufficient, financial planning for cancer control in resource-constrained settings.
NCI Impact Estimates [2]	Widespread implementation of EBIs could prevent 90% of cervical, 70% of colorectal, and 95% of lung cancer deaths.	Quantifies the tremendous potential population health impact of closing the research-practice gap through IS.
GI Cancer Screening Protocol [5]	The VA trials will measure Reach (% of eligible Veterans completing screening) as the primary outcome.	Provides a concrete, patient-centered metric for evaluating the success of implementation strategies in a real-world system.

Implementation science provides the critical methodological backbone needed to translate oncological discoveries into equitable, real-world impact. By applying structured frameworks like CFIR and ERIC, employing rigorous mixed-methods designs, and focusing on optimizing strategies for specific contexts, the field addresses the persistent "know-do" gap [3] [2] [1]. The detailed protocols and synthesized data presented herein offer researchers a roadmap for conducting robust implementation research. As the field evolves toward a future of "precision implementation," its continued application is essential for ensuring that all populations, regardless of setting or resources, receive the full benefit of evidence-based cancer care [1].

Quantitative Scope of the Evidence-Practice Divide

Research investigating evidence-practice gaps in cancer care has grown significantly, yet remains predominantly descriptive rather than interventional. The table below summarizes the volume and focus of research output on evidence-practice gaps in cancer care across three time points.

Table 1: Research Output on Evidence-Practice Gaps in Cancer Care

Publication Year	Total Eligible Papers	Data-Based Papers	Descriptive Studies	Intervention Studies	Randomized Controlled Trials
2000	25	Not specified	Not specified	Not specified	Not specified
2005	Not specified	Not specified	Not specified	Not specified	Not specified
2010	100	Not specified	Not specified	Not specified	Not specified
Total (2000-2010)	176	160	150	10	1

Analysis of this research output reveals that nearly one-third of all data-based studies focused specifically on breast cancer care, indicating a potential area of concentrated research interest relative to other cancer types [8].

Experimental Protocols for Assessing and Addressing Evidence-Practice Gaps

Protocol for Identifying and Prioritizing Implementation Determinants

Purpose: To systematically identify and prioritize barriers and facilitators to implementing evidence-based cancer interventions.

Methodology:

• Stakeholder Engagement: Engage a multidisciplinary group of stakeholders including clinicians, administrators, patients, and policymakers through structured workshops and interviews [3].
• Situational Analysis: Conduct a comprehensive analysis of the current cancer care landscape using quantitative metrics (cancer registry data, quality indicators) and qualitative assessments (clinical workflow analysis, resource availability) [3].
• Determinant Identification: Employ mixed methods including surveys, focus groups, and observational studies to identify implementation determinants using established frameworks such as the Consolidated Framework for Implementation Research (CFIR) [2].
• Determinant Prioritization: Use a modified Delphi process with expert panels to prioritize determinants based on their potential impact on implementation success and feasibility of addressing them [2].

Protocol for Testing Implementation Strategies

Purpose: To rigorously evaluate the effectiveness of strategies designed to reduce evidence-practice gaps in cancer care.

Methodology:

• Strategy Selection: Select implementation strategies from expert-compiled taxonomies (e.g., ERIC compilation) matched to prioritized determinants [2].
• Optimization Approach: Utilize a three-stage optimization process:
  • Stage I: Identify and prioritize determinants through systematic assessment
  • Stage II: Match implementation strategies to address prioritized determinants
  • Stage III: Optimize strategy components using multiphase optimization strategies (MOST) and user-centered design [2]
• Evaluation Design: Employ randomized controlled trials or stepped-wedge cluster randomized designs to evaluate strategy effectiveness on implementation outcomes (adoption, fidelity, sustainability) and clinical outcomes (cancer screening rates, treatment adherence, survival) [8] [2].
• Mechanism Investigation: Conduct process evaluations to elucidate the mechanisms through which implementation strategies produce their effects [2].

Visualization of Implementation Science Framework

The diagram below illustrates the three-stage approach to optimizing evidence-based intervention (EBI) implementation in cancer control.

Research Reagent Solutions for Implementation Science

Table 2: Essential Methodological Tools for Cancer Implementation Research

Research Tool	Function	Application Example
Consolidated Framework for Implementation Research (CFIR)	Provides a comprehensive taxonomy of implementation determinants across multiple domains	Identifying barriers to implementing colorectal cancer screening programs [2]
Expert Recommendations for Implementing Change (ERIC)	Compilation of 73 defined implementation strategies for healthcare settings	Selecting strategies to address specific barriers to HPV vaccination in primary care [3] [2]
Implementation Laboratory (I-Lab)	Network of clinical and community sites for conducting implementation studies	Testing optimized implementation strategies across diverse healthcare settings [2]
Multiphase Optimization Strategy (MOST)	Efficient experimental approach for optimizing multicomponent interventions	Identifying essential components of audit and feedback for cancer screening improvement [2]
Implementation Outcome Measures	Assesses key implementation success indicators (adoption, fidelity, sustainability)	Evaluating the implementation of evidence-based lung cancer screening protocols [2]

Application Notes & Protocols for Implementation Science in Cancer Control

Stakeholder Engagement

Application Notes

Stakeholder engagement is a critical, yet often understructured, component of effective implementation science in cancer control. It involves the meaningful inclusion of individuals who are affected by or have a connection to the cancer care topic, including patients, clinicians, caregivers, knowledge users, and decision-makers [9]. In the context of national cancer control plans (NCCPs), while many plans describe stakeholder engagement, it is frequently unstructured and incomplete, limiting its effectiveness [3]. Successful engagement moves beyond tokenism to establish respectful partnerships where all perspectives are represented and participants are empowered to have equal voices [9]. This is particularly vital for research involving distinct cancer populations, such as head and neck cancer survivors, who have unique functional needs that empirically influence how engagement should be operationalized [9]. The core function of stakeholder engagement is to increase the relevance and impact of research by ensuring that investigations address questions important to patients, employ patient-centered methods, and facilitate the successful dissemination and implementation of findings [9].

Experimental Protocol: A Structured Model for Stakeholder Engagement

The following protocol, derived from a pragmatic trial in head and neck cancer (the PRO-ACTIVE trial), provides a detailed methodology for engaging stakeholders throughout the research lifecycle [9].

Objective: To guide the systematic engagement of stakeholders as research partners in a clinical trial, ensuring their perspectives inform the study's design, conduct, and dissemination.

Core Principles:

• Representation: Include perspectives from all groups impacted by the trial results.
• Meaningful Participation: Empower participants to have equal voices.
• Respectful Partnership: Include stakeholders in all trial phases as respected partners.
• Accountability: Demonstrate to stakeholders how their input influences decision-making.

Procedural Steps:

• Stakeholder Identification & Recruitment: Assemble a purposive sample representing the full range of perspectives in the clinical process. For a cancer trial, this includes:
  • Patients and family caregivers.
  • Clinical providers (e.g., physicians, nurses, speech-language pathologists, dietitians, social workers).
  • Hospital administrators, payer groups, and policy groups.
  • Patient advocacy organizations.
  • Stakeholders should be convened from all countries or health systems represented in the trial.

• Structured Engagement Model:

  • Homogeneous Brainstorming Panels: Convene separate groups of like-minded stakeholders (e.g., a patient/caregiver panel, a clinician panel) to allow for free expression of ideas and concerns without power imbalances.
  • Heterogeneous Stakeholder Advisory Boards (SAB): Form advisory boards in each country with representatives from each homogeneous panel. This group is tasked with weighing and prioritizing recommendations from the panels.

• Operationalization Across the Trial Lifecycle:

  • Pre-launch: Engage stakeholders to identify key research questions and priorities and to provide input on proposal design.
  • During Data Collection: SABs meet regularly to review trial progress and provide feedback on implementation challenges.
  • Data Analysis & Post-analysis: Involve stakeholders in interpreting results and advising on dissemination strategies to ensure findings are actionable.

• Logistics & Evaluation:

  • Compensation: Provide fair compensation for stakeholders' time and expertise.
  • Facilitation: Engage professional, independent facilitators to lead meetings, manage group dynamics, and draw consensus.
  • Training: Offer training in research concepts to stakeholders with less research experience to support meaningful participation.
  • Feedback Loop: Establish a continuous feedback mechanism, such as a quarterly newsletter, to report on how stakeholder input has influenced trial decisions.

Table 1: Core Principles of Stakeholder Engagement

Principle	Rationale	Operational Method
Representation	Results impact a wide range of groups; all voices must be heard.	Purposive sampling of patients, caregivers, all clinical team members, administrators, payers, and advocates [9].
Meaningful Participation	Tokenism undermines engagement; participants must be empowered.	Use of homogeneous brainstorming panels and heterogeneous advisory boards. Offer research training to stakeholders [9].
Respectful Partnership	Stakeholders are valuable partners, not just subjects.	Involvement from the pre-launch phase through to dissemination. Fair compensation and professional facilitation [9].
Accountability	Trust is built when stakeholders see their input in action.	A continuous feedback loop managed by facilitators and reported in communications like a trial newsletter [9].

Situational Analysis

Application Notes

Situational analysis is a prerequisite for developing resource-appropriate strategies to advance cancer control in any setting [10]. It involves a baseline assessment of the existing breast health infrastructure, workforce capacity, patient pathways, existing practices, accessibility, and costs before implementing evidence-based guidelines [10]. Within NCCPs, while many incorporate elements of situational analysis, these are often not explicit or consistently applied [3]. The core function of a situational analysis is to assess the breast health care delivery system within the broader structural, sociocultural, personal, and financial contexts in which it operates, thereby enabling more informed policymaking [10]. This context is vital because significant variability in the delivery of high-quality, evidence-based cancer care often stems from contextual differences across healthcare settings and geographical regions [3]. Frameworks like those from the Breast Health Global Initiative (BHGI) and the International Atomic Energy Agency's Programme of Action for Cancer Therapy (imPACT) provide tested tools for conducting these assessments [10] [11].

Experimental Protocol: Conducting a Comprehensive Situational Analysis of a Breast Health Care System

This protocol is adapted from the BHGI methodology for assessing breast health care capacity to identify strengths and weaknesses and prioritize evidence-based, tailored improvements [10].

Objective: To perform a systematic assessment of a breast health care system across six core domains of service delivery to inform the development of a context-specific and resource-appropriate cancer control plan.

Domains of Assessment:

• Breast cancer early detection practices.
• Breast cancer awareness programs.
• Availability of breast cancer surgery.
• Availability of pathology services.
• Availability of radiotherapy services.
• Availability of systemic therapy services.

Procedural Steps:

• Preparation and Tool Selection:
  • Select a validated assessment tool, such as the BHGI situational analysis tools or the WHO/IAEA self-assessment tool used in imPACT reviews [10] [11].
  • Translate the tool into the local language, if necessary, as was done for the imPACT mission in Iran [11].

• Data Collection:

  • Desktop Review: Collate existing data from national health statistics, cancer registry reports, policy documents, and previous assessments.
  • Stakeholder Workshops & Surveys: Convene local experts and stakeholders from relevant ministries (e.g., Ministry of Health), national cancer research networks, and clinical service providers to complete the assessment tool and provide qualitative insights [11].
  • Site Visits: If resources allow, organize visits by a delegation of international and local experts to key cancer care facilities to validate data and conduct in-person evaluations [11].

• Data Analysis and Reporting:

  • Gap Analysis: Analyze the collected data to identify the relative strengths and critical gaps in the health system across the six domains.
  • Recommendation Development: Based on the gap analysis, generate a set of prioritized, actionable recommendations for advancing cancer care. The imPACT mission in Iran, for example, yielded 31 recommendations across categories like planning, registration, prevention, early detection, diagnosis, treatment, and palliative care [11].
  • Report Drafting: Compile findings and recommendations into a comprehensive report to provide a foundational view for updating and strengthening the national cancer control program [11].

• Integration into Planning:

  • Present the report to key decision-making bodies (e.g., the Ministry of Health) for endorsement.
  • The most critical outcome is often the recommendation to establish a strong, multi-sectoral NCCP committee to oversee the implementation of the findings [11].

Diagram: Situational Analysis Workflow

Impact Measurement

Application Notes

Impact measurement in implementation science involves quantitative summative evaluation to characterize and quantify the effects of an implementation strategy [12]. This differs from clinical intervention research, which focuses on patient-level health outcomes; instead, implementation research focuses on system-level outcomes related to how an evidence-based practice is adopted and delivered [12]. In NCCPs, while all plans include some form of impact measures, such as key performance indicators, some lack mechanisms for engaging stakeholders or responsible entities to achieve the targets [3]. The core function of impact measurement is to help decision-makers understand the overall worth of an implementation strategy and determine whether to upscale, modify, or discontinue it [12]. This requires quantifying key implementation outcomes, such as adoption, fidelity, cost, reach, and sustainment, often using data from administrative records, surveys, and direct observation [12].

Experimental Protocol: Quantitative Evaluation of an Implementation Strategy

This protocol outlines the methods for a quantitative summative evaluation, as applied in an ongoing implementation trial of the Collaborative Care Model for depression management [12].

Objective: To quantitatively evaluate the impact of an implementation strategy on the adoption, delivery, and sustainment of an evidence-based practice (EBP) in a cancer control setting.

Study Design Considerations:

• Between-site designs: Compare outcomes between two or more service system units (e.g., clinics or hospitals), typically testing a novel implementation strategy against routine practice.
• Within- and between-site designs (Rollout trials): Involve a time-based crossover for each unit, such as in a stepped-wedge design where all units eventually receive the implementation strategy [12].

Procedural Steps:

• Define Implementation Outcomes: Select relevant quantitative outcomes from a established taxonomy, such as Proctor et al. (2011) [12]. The focus should be on outcomes salient to the current stage of implementation research (e.g., early-stage vs. late-stage).
• Identify Data Sources: Determine the optimal source for measuring each outcome. Common sources include:
  • Administrative Data: For metrics like adoption, reach, and fidelity.
  • Surveys: For assessing acceptability and appropriateness among providers and consumers.
  • Financial Records: For calculating implementation costs.
• Data Collection & Aggregation: Collect data at the predetermined levels of analysis (e.g., provider, clinic, organization) throughout the study period. In rollout trials, data aggregation is performed at the end of the study to compare outcomes across units and conditions [12].
• Analysis & Interpretation: Analyze the quantitative data to determine the strategy's effectiveness on the specified implementation outcomes. This helps in understanding the extent and variation of change induced by the implementation strategies.

Table 2: Quantitative Metrics for Implementation Impact

Implementation Outcome	Definition	Level of Analysis	Quantitative Measurement Method
Adoption	The initial uptake or intention to try an EBP [12].	Individual provider, Organization	Administrative data (e.g., count of clinics using EBP), Survey, Observation [12].
Fidelity	The degree to which an EBP is implemented as originally intended [12].	Individual provider	Administrative data, Observation, Checklists [12].
Implementation Cost	The cost impact of the implementation strategy [12].	Organization	Financial and administrative data to capture costs of the implementation strategy itself [12].
Reach / Penetration	The integration of an EBP within a service setting; its absolute number of intended users [12].	Organization	Administrative data to calculate participation rates and representativeness [12].
Sustainment	The extent to which an EBP is maintained or institutionalized within a service setting over time [12].	Organization	Administrative data to measure continued use of the EBP after the initial implementation phase ends [12].

The Scientist's Toolkit: Research Reagents & Essential Materials

This table details key tools and resources essential for conducting rigorous implementation science research in cancer control.

Table 3: Essential Research Reagents for Implementation Science

Tool / Resource	Category	Function / Application
ERIC Framework [3]	Methodological Framework	Provides a standardized, pragmatic set of 73 implementation strategies to guide the selection and specification of implementation approaches.
Proctor et al. (2011) Outcome Taxonomy [12]	Measurement Framework	Defines a core set of eight implementation outcomes (e.g., acceptability, feasibility) and provides guidance on how to conceptualize and measure them.
BHGI Situational Analysis Tool [10]	Assessment Tool	A validated instrument to guide the assessment of breast health care capacity across six key domains, helping to identify strengths and weaknesses in a system.
IAEA imPACT Review Tool [10] [11]	Assessment Tool	A comprehensive, WHO-aligned tool for countries to conduct a self-assessment or host a formal review of their overall capacity for cancer control.
Stakeholder Advisory Board (SAB) Model [9]	Engagement Protocol	A structured model for engaging a diverse group of stakeholders throughout the research lifecycle to ensure relevance, feasibility, and uptake of findings.
Quantitative Evaluation Designs (e.g., Stepped-Wedge) [12]	Study Design	Robust research designs suitable for evaluating implementation strategies at the system level, allowing for comparison and aggregation of data across sites.

National Cancer Control Plans (NCCPs) are strategic documents that outline a country's approach to combating cancer through coordinated actions across the care continuum. The global cancer burden is projected to increase by about 74% from 2022 to 2050, potentially reaching 33 million new cases and 18 million deaths annually without effective interventions [13]. In response, the field of implementation science (IS) has emerged as a critical discipline for bridging the gap between evidence-based cancer interventions and their real-world application, particularly in resource-constrained settings [3]. This scoping review examines the current global landscape of NCCPs through an implementation science lens, identifying strengths, gaps, and methodological approaches to optimize cancer control planning and execution. The findings are especially relevant given that only 39% of countries worldwide cover the basics of cancer management within their financed core health services [13].

Current Global Status of NCCPs

Recent analyses reveal significant disparities in NCCP development and implementation across countries with different development indicators. The second global review of NCCPs, completed in January 2025, provides critical insights into these patterns [14]. A detailed scoping review focused specifically on low and medium Human Development Index (HDI) countries analyzed 33 national cancer control plans/strategies (16 from low HDI countries and 17 from medium HDI countries) available through the International Cancer Control Partnership (ICCP) portal [3] [15].

Table 1: NCCP Coverage and Characteristics by HDI Category

HDI Category	Total Countries	Countries with NCCPs	Plans Analyzed	Primary WHO Region	Costed Plans
Low HDI	33	17	16	African Region (AFR)	4
Medium HDI	42	23	17	Mixed (AFR, AMR, SEAR)	9

The analysis identified substantial gaps in how implementation science principles are applied within existing NCCPs. While many plans incorporated elements such as stakeholder engagement and impact measurement, these components were often inconsistently applied or inadequately structured [3] [4]. A critical finding was that none of the 33 plans assessed health system capacity to determine readiness for implementing new interventions, representing a significant methodological limitation in current planning approaches [3] [15].

Global Initiatives to Strengthen NCCPs

In response to identified gaps in cancer control planning, several major international initiatives have been launched:

• Cancer Planners Forum (2025): Hosted by the Union for International Cancer Control (UICC), this first global conference dedicated to NCCPs brought together 44 cancer control planners from 40 countries to share knowledge, experiences, and best practices [13].
• International Cancer Control Partnership (ICCP) Portal: Serves as a central repository for NCCPs globally and facilitates knowledge exchange between member countries [16].
• UICC Connect: An online knowledge-sharing platform launched in 2025 that enables more than 600 member organizations to collaborate on cancer control strategies [13].

Methodology for NCCP Analysis: Experimental Protocols

Scoping Review Framework for NCCP Assessment

The primary methodological approach for analyzing NCCPs employed the Arksey and O'Malley framework for scoping reviews, which involves six distinct stages [3] [15]:

Table 2: Scoping Review Methodology Based on Arksey and O'Malley Framework

Stage	Description	Application to NCCP Review
1. Identifying Research Question	Formulating the primary objective	"How have IS domains been applied in NCCPs and strategies from low HDI and medium HDI countries?"
2. Identifying Relevant NCCPs	Systematic search strategy	Searching ICCP portal for NCCPs from all low and medium HDI countries
3. Study Selection	Applying inclusion/exclusion criteria	Including plans available in English or French; excluding other languages
4. Charting Data	Systematic data extraction	Developing data charting form using MS Excel database
5. Collating and Summarizing	Analyzing and reporting results	Thematic analysis across five implementation science domains
6. Expert Consultation	Validating findings	Engaging six implementation science experts to refine analysis and pathway development

The research team drew on the Expert Recommendations for Implementing Change (ERIC) framework to shape the research question and analysis, focusing on five critical implementation domains: (1) stakeholder engagement, (2) situational analysis, (3) capacity assessment/health technology assessment, (4) economic evaluation, and (5) impact measurement [3].

Implementation Science Center Methodologies

Complementing the scoping review approach, several Implementation Science Centers in Cancer Control have developed rigorous protocols for optimizing evidence-based intervention implementation:

OPTICC Center's Three-Stage Approach:

• Stage I: Identify and prioritize determinants through systematic assessment
• Stage II: Match implementation strategies to address key determinants
• Stage III: Optimize strategies using multiphase optimization strategies, user-centered design, and agile science [2]

Penn ISC3 Methodology:

• Behavioral economics strategies: Testing nudges to clinicians and patients through 4-arm pragmatic cluster randomized clinical trials
• Mixed methods analysis: Applying the Consolidated Framework for Implementation Research (CFIR) to capture multilevel factors
• Rapid-cycle approaches: Adapting innovation methods from other industries to accelerate learning [17]

Diagram 1: Implementation Science Pathway for NCCP Development. This workflow outlines the three-stage approach to integrating implementation science principles into cancer control planning, moving from determinant identification through strategy matching to optimization.

Key Findings: Implementation Science Gaps in Current NCCPs

Quantitative Analysis of IS Domain Application

The scoping review revealed significant variations in how comprehensively implementation science domains were addressed across the 33 analyzed NCCPs from low and medium HDI countries.

Table 3: Application of Implementation Science Domains in NCCPs (n=33)

Implementation Science Domain	Low HDI Countries (n=16)	Medium HDI Countries (n=17)	Overall Findings
Stakeholder Engagement	16 included	17 included	Typically unstructured and incomplete
Situational Analysis	16 included	17 included	Generally present but not explicit
Capacity Assessment	0 included	0 included	No plans assessed health system readiness
Economic Evaluation	4 included	9 included	Activity-based costing approach
Impact Measurement	16 included	17 included	KPIs present but 5 lacked engagement mechanisms

The most striking finding was the complete absence of formal health system capacity assessment in all reviewed plans, indicating a critical methodological gap in current NCCP development practices [3] [15]. Additionally, while all plans described some form of stakeholder engagement, this process was typically unstructured and incomplete, limiting its effectiveness in ensuring comprehensive buy-in and contextual appropriateness [4].

Methodological Shortcomings in Current Planning Approaches

The analysis identified several systematic methodological limitations in how NCCPs are currently developed and implemented:

• Underdeveloped determinant identification: Most plans lacked systematic methods for identifying and prioritizing implementation determinants, relying instead on general frameworks without contextual adaptation [3] [2].
• Incomplete strategy matching: Selection of implementation strategies often followed organizational routine or personal preference rather than being systematically matched to address specific, prioritized barriers [2].
• Limited optimization approaches: The jump from pilot studies to full-scale implementation typically occurred without iterative optimization of strategy components, delivery formats, or dosing [2] [6].
• Measurement gaps: Few reliable, valid, and pragmatic measures existed to assess implementation determinants, mechanism activation, or outcomes, particularly in resource-constrained settings [2].

Research Reagent Solutions: Implementation Science Toolkit

Implementation science research for cancer control planning requires specific methodological tools and frameworks to address the identified gaps in current NCCP development and execution.

Table 4: Essential Research Reagents for Implementation Science in Cancer Control

Research Reagent	Function	Application Context
ERIC Framework	Provides standardized set of 73 implementation strategies	Categorizing and selecting implementation approaches across diverse settings
Consolidated Framework for Implementation Research (CFIR)	Identifies multi-level determinants across intervention characteristics, outer and inner settings, individuals, and process	Systematic assessment of contextual factors influencing implementation success
Arksey and O'Malley Framework	Six-stage methodological approach for conducting scoping reviews	Comprehensive analysis of existing NCCPs and identification of research gaps
Implementation Laboratory (I-Lab)	Coordinates network of diverse clinical and community sites for testing implementation strategies	Real-world evaluation of implementation strategies across different care settings
Multi-Phase Optimization Strategy (MOST)	Efficient approach for optimizing multicomponent behavioral interventions	Identifying which strategy components drive effects and most cost-effective combinations
Behavioral Economics Nudges	Implementation strategies targeting cognitive biases and decision-making processes	Increasing uptake of evidence-based practices through clinician and patient engagement

These research reagents enable systematic approaches to implementation barrier identification, strategy selection, and outcome measurement that are currently lacking in many NCCPs, particularly in resource-constrained settings [2] [17]. The OPTICC Center, for instance, has developed specialized methods to address four critical barriers: (1) underdeveloped methods for determinant identification and prioritization, (2) incomplete knowledge of strategy mechanisms, (3) underuse of optimization methods, and (4) poor measurement of implementation constructs [6].

Integrated Pathway for IS-Informed NCCP Development

Based on the findings from the scoping review and expert consultations, a structured pathway for integrating implementation science principles into national cancer control planning has been developed.

Diagram 2: Stakeholder Engagement Framework for NCCPs. This diagram illustrates the transition from limited stakeholder involvement to a comprehensive engagement approach, addressing one of the most significant gaps identified in current cancer control planning practices.

The proposed pathway emphasizes structured stakeholder engagement throughout the planning process, moving beyond token involvement to genuine collaboration with all relevant parties [3] [4]. This includes systematic assessment of health system capacity to determine implementation readiness, which was absent from all reviewed plans [3] [15]. Additionally, the pathway incorporates explicit situational analysis using established implementation science frameworks like CFIR to identify context-specific barriers and facilitators [2] [17].

Economic evaluation transitions from basic activity-based costing to integrated budgeting approaches that account for implementation strategy costs alongside direct intervention expenses [3]. Finally, the pathway emphasizes continuous optimization through rapid-cycle evaluation methods rather than waiting for end-of-plan assessment, allowing for real-time adjustments based on implementation data [2] [17].

This scoping review reveals both progress and persistent challenges in the global landscape of National Cancer Control Plans. While most countries have developed NCCPs, significant gaps remain in how implementation science principles are applied to ensure these plans are contextually appropriate, feasible, and effective—particularly in resource-constrained settings. The complete absence of health system capacity assessment in all reviewed plans represents a critical methodological limitation that must be addressed in future planning cycles.

The proposed integration of implementation science methods offers a structured framework for developing more equitable and feasible cancer control policies by enabling realistic goal setting, appropriate strategy matching, and systematic benchmarking against regional and global standards. As the global cancer burden continues to grow—with projections indicating a 74% increase by 2050—the systematic application of implementation science to NCCP development and execution becomes increasingly urgent [13]. Future research should focus on validating the proposed pathway through empirical studies and developing more pragmatic measures for assessing implementation outcomes across diverse resource settings.

The National Cancer Institute (NCI) anchors its mission in leading, conducting, and supporting nationwide cancer research to advance scientific knowledge and help all people live longer, healthier lives [18]. This strategic framework is mandated by the National Cancer Act of 1971, which requires the NCI director to deliver an annual Professional Judgment Budget to the President and Congress, reflecting ongoing strategic planning and the funding needed to accelerate progress against cancer [18]. The institute's priorities span research, capacity building, and operational excellence, creating an integrated ecosystem that supports the entire cancer research continuum.

Strategic priority setting at NCI acknowledges that cancer comprises hundreds of diseases with diverse causes and manifestations [18]. This complexity necessitates sophisticated planning and coordination across multiple levels. Research ideas driving NCI's investments originate from multiple sources, including investigator-initiated proposals, NCI leadership, advisory boards, and public health needs such as the Cancer Moonshot initiative [18]. This multi-faceted approach ensures that NCI's strategic support remains responsive to both emerging scientific opportunities and pressing public health challenges.

NCI’s Strategic Goals and Research Priorities

Core Mission Goals

NCI has established definitive goals to deliver on its mission. These goals directly support the eight objectives of the National Cancer Plan and provide a framework for the institute's research, capacity, and operational priorities [18]:

• Advance and disseminate knowledge: Conduct and support basic, translational, and clinical research across the cancer continuum
• Train the next generation: Develop cancer researchers and strengthen the cancer workforce
• Spark innovation: Advance biomedical technology through strategic investments
• Facilitate collaboration: Create meaningful partnerships to accelerate progress against cancer

Annual Research Priority Areas

NCI's Annual Plan and Professional Judgment Budget outlines specific scientific opportunities and research priorities across key domains [18]. The institute enables advances against cancer by investing in a comprehensive portfolio that addresses the entire disease spectrum:

Table: NCI's Annual Research Priority Areas

Research Area	Strategic Focus
Biology of Cancer	Fundamental research on cancer mechanisms and pathways
Cancer Prevention	Risk assessment, preventive interventions, and mitigation of disparities
Detection & Diagnosis	Early screening methods and diagnostic technologies
Cancer Treatment	Therapeutic development and clinical trial optimization
Public Health	Implementation of evidence-based interventions in populations

NCI maintains a steadfast commitment to preventing, understanding, and mitigating cancer disparities, which is systematically reflected across its entire research portfolio [18]. The strategic support of basic, translational, clinical, and public health research drives discoveries that improve outcomes across the cancer care continuum, from risk assessment and prevention through treatment and survivorship.

Implementation Science in Cancer Control Planning

The Role of Implementation Science

Implementation science (IS) has emerged as a critical discipline for bridging the gap between evidence-based interventions and their practical application in routine cancer care. A 2025 international scoping review analyzed national cancer control plans (NCCPs) from 33 low-income and middle-income countries, revealing that while many plans incorporated IS elements, these were often inconsistently applied [4] [3]. The study found crucial gaps in health system capacity assessment and noted that stakeholder involvement was frequently unstructured, highlighting significant opportunities for enhancing cancer control planning through more systematic application of IS methods [3].

The integration of IS principles offers a structured framework for achieving equitable and feasible cancer control policies, particularly in resource-constrained settings [3]. By enabling realistic goal setting and benchmarking against regional and global standards, IS approaches help maximize the impact of limited resources. Effective application of IS in policymaking depends on how accessible and relevant the frameworks are to end users, though the proliferation of over 33 IS frameworks has created challenges for policymakers in selecting the most appropriate models for their specific contexts [3].

Implementation Science Funding Mechanisms

NCI supports rigorous, cutting-edge implementation science through various funding opportunities, including investigator-initiated research grants and targeted notices of funding opportunities (NOFOs) [19]. The NIH-wide Funding Opportunity Announcement (PAR) for Dissemination and Implementation Research in Health (DIRH) provides support through R01, R21, and R03 mechanisms [19]. A portfolio analysis of NCI grants revealed that between 2000-2012, the institute funded 67 implementation science grants in cancer prevention and control, with prevention grants being most common (49.3%) and treatment grants least common (4.5%) [19].

Table: NCI Implementation Science Funding and Support Structure

Funding Mechanism	Purpose	Review Process
Investigator-Initiated Grants	Support researcher-driven projects based on area of expertise	Rigorous peer review evaluating scientific merit and significance
DIRH NOFO (R01, R21, R03)	Targeted implementation research in health settings	Science of Implementation in Health and Healthcare (SIHH) Study Section
Training Programs	Sustain cancer research workforce at all career stages	Multi-level review from students to established investigators

The Science of Implementation in Health and Healthcare (SIHH) Study Section reviews applications that identify, develop, and evaluate dissemination and implementation theories, strategies, and methods designed to integrate evidence-based health interventions into public health, clinical, and community settings [19]. This specialized review process ensures that funded projects demonstrate strong methodological foundations and potential for meaningful public health impact.

Methodological Approaches and Experimental Protocols

Integrating Implementation Science and Intervention Optimization

A methodological framework published in 2025 proposes integrating principles of intervention optimization into implementation science to improve the adoption, fidelity, and scale-up of evidence-based interventions (EBIs) [20]. This approach addresses the critical limitation of traditional implementation trials, which often test multi-component strategy packages in two-arm randomized controlled trials (RCTs) without determining the individual contributions of components or their potential interactions [20].

The Multiphase Optimization Strategy (MOST) provides a principled framework for developing, optimizing, and evaluating multicomponent implementation strategies [20]. MOST consists of three sequential phases: preparation, optimization, and evaluation. This framework employs resource-efficient experimental designs, such as factorial experiments, to systematically assess the performance of implementation components both independently and in combination [20].

Experimental Protocol: Optimization of Implementation Strategies

The following detailed protocol outlines the application of MOST to optimize implementation strategies for increasing adoption of a smoking cessation intervention, based on the methodological framework described by Implementation Science [20]:

Phase 1: Preparation (Foundation Building)

• Conceptual Model Development: Create a theoretically and empirically derived conceptual model depicting the implementation process. This model serves as the blueprint for how implementation strategies will be built and how they will produce desired outcomes.
• Candidate Strategy Identification: Select four candidate implementation strategies based on implementation mapping and contextual analysis:
  • Training: Educational sessions for clinic staff
  • Treatment Guide: Decision support tool for providers
  • Workflow Redesign: Clinic process reorganization
  • Supervision: Ongoing oversight and support
• Pilot Work: Conduct feasibility testing to refine candidate strategies within the local context and finalize study protocols.
• Optimization Objective Specification: Define how effectiveness will be balanced with implementation constraints (affordability, scalability, efficiency).

Phase 2: Optimization (Experimental Testing)

• Experimental Design: Implement a clustered 2⁴ factorial experiment where each of the k factors (implementation strategies) has two levels (present/absent). Clinic sites serve as the unit of randomization.
• Participant Allocation: Randomly assign clinics to one of 16 experimental conditions representing all possible combinations of the four implementation strategies.
• Outcome Measurement: Collect data on:
  • Primary: Adoption rate of smoking cessation intervention
  • Secondary: Implementation costs, provider time requirements, sustainability indicators
• Data Analysis: Use factorial analysis of variance with effect coding to examine main effects and interactions of implementation strategies.

Phase 3: Evaluation (Effectiveness Assessment)

• Strategy Selection: Identify the optimized implementation strategy package based on results from the optimization RCT, resource requirements, and the predefined optimization objective.
• Experimental Testing: Conduct a traditional two-arm RCT comparing the optimized implementation strategy package against a control condition.
• Impact Assessment: Measure effects on implementation outcomes (adoption, fidelity, sustainability) and intervention effectiveness (smoking cessation rates).

Research Reagent Solutions and Essential Materials

Implementation science research requires specific methodological "reagents" and tools to ensure rigorous study design, measurement, and analysis. The following table details essential resources for conducting high-quality implementation research in cancer control:

Table: Research Reagent Solutions for Implementation Science Studies

Research Reagent	Function/Application	Examples/Specifications
Implementation Science Frameworks	Guide identification of determinants, selection of strategies, and measurement of outcomes	Consolidated Framework for Implementation Research (CFIR) [20], Expert Recommendations for Implementing Change (ERIC) [3]
Strategy Specification Tools	Precisely define implementation strategies to enhance reproducibility and measurement	Strategy specification according to ERIC guidelines: actor, action, action target, temporality, dose, implementation outcome addressed [20]
Implementation Outcome Measures	Assess success of implementation efforts	Adoption, fidelity, penetration, sustainability, cost [20]
Optimization Trial Designs	Efficiently test multiple implementation strategy components	Factorial designs, Sequential Multiple Assignment Randomized Trials (SMART), Microrandomized Trials (MRT) [20]
Stakeholder Engagement Protocols	Ensure meaningful involvement of key stakeholders throughout research process	Structured engagement across all implementation phases: planning, execution, interpretation, dissemination [3]
Mixed Methods Data Collection	Capture comprehensive implementation context and outcomes	Integration of quantitative implementation outcomes with qualitative contextual data [19]

NCI's Research Capacity and Operational Priorities

Workforce and Infrastructure Development

NCI recognizes that a robust workforce and extensive research infrastructure form the backbone of the cancer research enterprise [18]. The institute is committed to training and sustaining the cancer research workforce through programs supporting individuals at every career stage, from middle and high school students to established investigators [18]. This comprehensive approach ensures a continuous pipeline of talent to drive innovation in cancer research and implementation science.

NCI maintains a robust cancer research infrastructure including:

• NCI-Designated Cancer Centers: Distributed nationwide to provide specialized research and care capabilities
• Clinical Trials Networks: Facilitate testing of novel approaches to prevent and treat cancer
• Technology Development Facilities: Manufacture novel technologies and medicines
• Data Science Resources: Collect and analyze large and complex datasets to advance discovery [18]

Operational Excellence and Scientific Stewardship

To maximize the impact of the nation's investment in cancer research, NCI adheres to core operational priorities centered on scientific stewardship [18]. The institute maintains scientific integrity through steadfast adherence to values and practices that foster objective, high-quality research. NCI employs a rigorous, accountable funding process to distribute resources, supporting the most meritorious science as determined by peer review [18]. Evidence-based decision-making guides resource management and portfolio oversight, while transparency and public accountability ensure that cancer research findings and evidence-based information reach all beneficiaries, including patients, caregivers, health professionals, researchers, and media [18].

Future Directions and Strategic Integration

The strategic integration of implementation science within NCI's funding priorities represents a critical evolution in the nation's approach to cancer control. The 2025 scoping review of national cancer control plans proposes a pathway to better integrate implementation science principles, aiming to support more equitable, feasible, and evidence-based cancer control policies worldwide [4]. This approach emphasizes structured stakeholder engagement, systematic situational analysis, capacity assessment, economic evaluation, and impact measurement—elements often inconsistently applied in current planning efforts [3].

As implementation science continues to mature, methodological innovations such as the integration of intervention optimization frameworks offer promising approaches for enhancing the efficiency and effectiveness of implementation strategies [20]. The OPTICC Center addresses critical barriers to optimization by improving methods for identifying and prioritizing barriers, matching implementation strategies to high-priority barriers, and optimizing strategies for large-scale evaluation in community and clinical settings [6]. These advances, coupled with NCI's strategic support for rigorous implementation research, create a powerful ecosystem for accelerating the translation of evidence into practice across diverse cancer control contexts.

NCI's commitment to supporting the highest-priority scientific discoveries continues to evolve in response to emerging opportunities and challenges in cancer research. Through strategic planning, coordination, collaboration, and fiscal stewardship of federal resources, NCI maintains its pivotal role in leading the nation's efforts to prevent more cancers and improve the lives of all those affected by the disease [18].

From Theory to Practice: Methodological Frameworks and Real-World Applications

Implementation science provides systematic approaches to bridge the gap between evidence-based interventions and routine practice, a challenge particularly prevalent in cancer control research. The Consolidated Framework for Implementation Research (CFIR), the Expert Recommendations for Implementing Change (ERIC), and Proctor's Implementation Outcomes represent three complementary frameworks that, when applied together, offer a powerful methodology for optimizing cancer control initiatives [21] [2]. These frameworks provide structured approaches to address the critical barriers that have traditionally hampered the effective implementation of evidence-based cancer interventions, including underdeveloped methods for determinant identification and prioritization, incomplete knowledge of strategy mechanisms, and poor measurement of implementation constructs [2].

Cancer control optimization research specifically benefits from this integrated framework approach. Evidence-based interventions could reduce cervical cancer deaths by 90%, colorectal cancer deaths by 70%, and lung cancer deaths by 95% if widely and effectively implemented in the USA [2]. Yet, implementation remains suboptimal, creating an imperative for methodical approaches that address contextual determinants, match implementation strategies to these determinants, and rigorously evaluate implementation success [21] [3]. This protocol details the application of these frameworks within cancer control research, providing structured methodologies for researchers and drug development professionals working to optimize cancer outcomes.

Theoretical Foundation and Framework Integration

The three frameworks function synergistically when applied to cancer control optimization: CFIR provides a systematic approach to understanding context, ERIC offers a menu of implementation strategies, and Proctor's Outcomes enable measurement of implementation success [21] [22]. This integrated approach ensures that implementation strategies are selected based on a thorough understanding of contextual determinants rather than personal preference or organizational routine [2].

CFIR serves as a meta-theoretical determinant framework that categorizes contextual factors across five domains: intervention characteristics, outer setting, inner setting, individual characteristics, and implementation process [21] [2]. In cancer control research, these domains help identify potential barriers and facilitators to implementing evidence-based interventions across diverse clinical and community settings [2].

ERIC provides a compilation of 73 discrete implementation strategies, offering a standardized taxonomy for selecting and specifying strategies [21] [2]. These strategies can be matched to CFIR-identified determinants to address specific implementation barriers [22] [2].

Proctor's Implementation Outcomes include acceptability, adoption, appropriateness, feasibility, fidelity, implementation cost, penetration, and sustainability [21] [22]. These outcomes serve as necessary preconditions for attaining desired service and client outcomes, providing crucial indicators of implementation success in cancer control initiatives [21].

Table: Proctor's Implementation Outcomes Framework in the Cancer Control Context

Implementation Outcome	Definition in Cancer Control Context	Example Measurement Approaches
Acceptability	Perception among stakeholders that a cancer control intervention is agreeable	Provider satisfaction surveys, patient-reported experience measures
Adoption	Initial decision to employ an evidence-based cancer intervention	Proportion of providers offering the intervention to eligible patients
Appropriateness	Perceived fit of the intervention for addressing specific cancer control needs	Stakeholder focus groups, perceived relevance surveys
Feasibility	Extent to which the intervention can be successfully implemented in a specific setting	Resource assessment, timeline feasibility analysis
Fidelity	Degree to which the intervention is implemented as originally intended	Adherence monitoring, protocol deviation tracking
Implementation Cost	Cost impact of implementing the intervention versus alternatives	Cost-effectiveness analysis, budget impact studies
Penetration	Integration of the intervention within the service delivery system	Reach metrics, coverage rates among eligible population
Sustainability	Extent to which the intervention is maintained over time	Long-term follow-up, maintenance of intervention delivery

Conceptual Integration of Frameworks

The logical relationship between these frameworks can be visualized as an iterative, cyclical process where context informs strategy selection, which in turn drives implementation outcomes, with evaluation feedback refining subsequent implementation approaches.

Application Notes and Protocols

Protocol 1: Determinant Identification Using CFIR in Cancer Control Planning

Objective: Systematically identify contextual barriers and facilitators to implementing evidence-based cancer control interventions in specific settings.

Background: Effective implementation requires understanding the multi-level context in which cancer control interventions will operate [3] [2]. This protocol adapts CFIR for cancer control planning, particularly relevant for national cancer control plans (NCCPs) in resource-constrained settings where contextual factors significantly impact implementation success [3].

Materials:

• CFIR interview guide or survey instrument tailored to cancer control context
• Audio recording equipment or digital survey platform
• Qualitative data analysis software (e.g., NVivo, Dedoose)
• CFIR codebook with definitions adapted for cancer control

Methodology:

• Stakeholder Mapping and Recruitment: Identify and recruit key stakeholders across the cancer care continuum, including policymakers, healthcare providers, administrators, patients, and community representatives [3]. For NCCPs, ensure representation from relevant government ministries, clinical services, and affected communities.

• Data Collection: Conduct semi-structured interviews or focus groups using CFIR-guided questions across all five domains:

  • Intervention Characteristics: Assess evidence strength, relative advantage, and adaptability of the cancer control intervention
  • Outer Setting: Examine patient needs, cosmopolitanism, and external policies influencing cancer control
  • Inner Setting: Evaluate structural characteristics, networks, implementation climate, and readiness for implementation
  • Individual Characteristics: Assess knowledge, self-efficacy, and individual stage of change regarding the cancer intervention
  • Process: Examine planning, engaging, executing, and reflecting on implementation

• Data Analysis:

  • Transcribe and import qualitative data into analysis software
  • Apply deductive coding using the CFIR framework
  • Identify prominent themes within each domain
  • Prioritize determinants based on frequency and perceived impact on implementation success [2]

• Output Generation: Create a determinant summary table categorizing barriers and facilitators across CFIR domains, highlighting prioritized determinants requiring addressing.

Workflow Integration: This protocol aligns with Phase I of the OPTICC (Optimizing Implementation in Cancer Control) approach, which focuses on "identify and prioritize determinants" [2].

Protocol 2: Strategy Selection and Matching Using ERIC

Objective: Match implementation strategies from the ERIC compilation to address prioritized determinants identified through CFIR analysis.

Background: Matching strategies to determinants is often done based on preference or routine rather than systematic assessment [2]. This protocol provides a rigorous method for selecting strategies most likely to address specific implementation barriers in cancer control.

Materials:

• Prioritized determinant list from Protocol 1
• ERIC implementation strategy compilation
• CFIR-ERIC matching tool [22] [2]
• Stakeholder voting or prioritization tools

Methodology:

• Determinant Prioritization: Present prioritized determinants from Protocol 1 to implementation team and stakeholders. Use consensus-building techniques (e.g., nominal group technique) to finalize the list of determinants to target.

• Strategy Matching: Use the CFIR-ERIC matching tool to identify potential strategies for each prioritized determinant [22] [2]. This tool provides empirically-based and expert-informed matches between CFIR constructs and ERIC strategies.

• Stakeholder Review: Convene stakeholders to review matched strategies, assessing:

  • Feasibility given resource constraints
  • Acceptability to implementers and patients
  • Anticipated effectiveness in the specific cancer control context
  • Alignment with organizational priorities and capacities

• Strategy Specification: Use template for intervention description and replication (TIDieR) principles to specify:

  • Actor(s) responsible for implementing each strategy
  • Action(s) required
  • Target of the action
  • Temporality (when implemented)
  • Dose (frequency and duration)
  • Implementation outcome affected

• Protocol Refinement: Create a finalized implementation protocol detailing the selected strategies, their justification based on determinant mapping, and specification elements.

Workflow Integration: This protocol corresponds to Phase II of the OPTICC approach: "match strategies" to determinants [2].

Protocol 3: Implementation Evaluation Using Proctor's Outcomes

Objective: Assess implementation success using Proctor's implementation outcomes to determine whether preconditions for effective cancer control intervention are established.

Background: Implementation outcomes serve as necessary preconditions for attaining desired service and client outcomes in cancer control [21]. This evaluation protocol ensures systematic assessment of these critical implementation indicators.

Materials:

• Implementation outcome measures tailored to specific cancer control context
• Data collection tools (surveys, interview guides, extraction forms)
• Analysis plan with predefined implementation success criteria

Methodology:

• Measure Selection: Select or develop validated measures for each of Proctor's implementation outcomes relevant to the cancer control intervention:
  • Acceptability: Perceived satisfaction, comfort with intervention
  • Adoption: Uptake, intention to try, usage
  • Appropriateness: Perceived fit, relevance, compatibility
  • Feasibility: Actual practicality, utility within context
  • Fidelity: Delivery as intended, adherence, exposure
  • Implementation Cost: Incremental cost of implementation
  • Penetration: Reach, spread, integration
  • Sustainability: Maintenance, continuation, durability

• Data Collection Plan: Develop a mixed-methods assessment approach:

  • Quantitative measures where available (e.g., adoption rates, fidelity checklists)
  • Qualitative assessment for appropriateness, acceptability
  • Cost tracking for implementation cost
  • Longitudinal tracking for sustainability

• Assessment Timeline: Implement measurement across implementation phases:

  • Pre-implementation: Baseline assessment of appropriateness, acceptability
  • Early implementation: Initial feasibility, early adoption
  • Maintenance phase: Fidelity, penetration
  • Sustainability phase: Long-term sustainability, continued appropriateness

• Data Analysis and Interpretation: Analyze implementation outcome data, comparing to predefined success criteria. Identify implementation strengths and challenges requiring attention.

Workflow Integration: This protocol supports Phase III of the OPTICC approach, facilitating assessment of strategy effectiveness and optimization [2].

Table: Data Collection Methods for Proctor's Implementation Outcomes in Cancer Control Research

Outcome	Primary Data Collection Methods	Metrics/Measures	Assessment Timeline
Acceptability	Stakeholder surveys, focus groups	Satisfaction ratings, perceived comfort	Pre-, during, and post-implementation
Adoption	Usage logs, provider reports	Uptake rate, intention-to-use measures	Early implementation (first 3-6 months)
Appropriateness	Key informant interviews, surveys	Perceived fit, relevance scores	Pre-implementation and periodic reassessment
Feasibility	Implementation logs, resource tracking	Completion rates, resource utilization	During active implementation
Fidelity	Direct observation, adherence checks	Protocol adherence percentage, quality ratings	Ongoing throughout implementation
Implementation Cost	Economic evaluation, budget tracking	Cost compared to alternatives, budget impact	Implementation and sustainability phases
Penetration	Organizational records, coverage surveys	Reach percentage, spread across departments	Mid- and post-implementation
Sustainability	Longitudinal tracking, follow-up surveys	Maintenance of outcomes over time	6+ months post-implementation

Applied Case Example: Improving Clinical Trial Enrollment

Worked Example Applying the Integrated Framework

Background: Clinical trials often fail to reach enrollment goals, significantly impacting their ability to answer scientific questions [21]. This case example demonstrates application of the integrated framework to address poor enrollment in cancer clinical trials.

CFIR Determinant Assessment:

• Intervention Characteristics: Complex trial protocols, demanding participant schedules
• Outer Setting: Lack of community awareness about trial opportunities
• Inner Setting: Limited institutional infrastructure for trial recruitment
• Individual Characteristics: Provider discomfort discussing trials with patients
• Process: Inconsistent approach to identifying eligible patients

ERIC Strategy Selection:

• For intervention characteristics: Revise professional roles to create dedicated recruitment coordinators
• For outer setting: Develop community partnerships to increase awareness
• For inner setting: Create implementation teams specifically for trial recruitment
• For individual characteristics: Conduct ongoing training for providers on trial communication
• For process: Develop and implement tools for quality monitoring of recruitment processes

Proctor's Outcome Evaluation:

• Acceptability: Measure provider satisfaction with revised recruitment process
• Adoption: Track proportion of eligible patients offered trial participation
• Penetration: Assess reach across different patient demographic groups
• Sustainability: Monitor maintenance of improved enrollment rates over time

This integrated approach moves beyond simply identifying poor enrollment as a problem to systematically addressing the underlying determinants through matched strategies and evaluating implementation success through appropriate outcomes [21].

Table: Essential Implementation Science Resources for Cancer Control Research

Resource Category	Specific Tool/Resource	Function/Purpose	Access Source
Determinant Assessment	CFIR Interview Guide	Structured data collection on contextual factors	CFIR Technical Assistance website
Strategy Selection	CFIR-ERIC Matching Tool	Matches implementation strategies to identified barriers	Implementation Science websites
Outcome Measurement	Proctor's Implementation Outcomes Measures	Assesses implementation success	Implementation science literature
Protocol Specification	TIDIeR Template (adapted for implementation)	Specifies implementation strategies	BMJ publications
Process Modeling	Implementation Research Logic Model (IRLM)	Diagrams implementation pathways	Implementation science journals
Stakeholder Engagement	Stakeholder Analysis Template	Identifies and characterizes key stakeholders	Quality improvement resources
Economic Evaluation	Implementation Costing Tool	Captures resources required for implementation	Health economics sources

Integration with Broader Cancer Control Optimization Research

The application of CFIR, ERIC, and Proctor's Outcomes aligns with broader implementation science methodologies in cancer control optimization research. The OPTICC (Optimizing Implementation in Cancer Control) center exemplifies this integrated approach through its three-phase model: (1) identify and prioritize determinants, (2) match strategies, and (3) optimize strategies [2]. This structured methodology addresses critical gaps in current implementation practice, including underdeveloped methods for determinant identification and prioritization, incomplete knowledge of strategy mechanisms, and poor measurement of implementation constructs [2].

Similarly, applications in national cancer control planning demonstrate how these frameworks can strengthen cancer control policies, particularly in resource-constrained settings [3] [4]. Analysis of NCCPs from low and medium Human Development Index countries revealed that while many plans incorporated elements such as stakeholder engagement and impact measurement, these were often inconsistently applied without explicit theoretical frameworks to guide implementation [3]. Explicit integration of CFIR, ERIC, and Proctor's Outcomes addresses these limitations by providing structured approaches to contextual assessment, strategy selection, and outcome measurement.

The updated UK Medical Research Council Framework for developing and evaluating complex interventions further validates this integrated approach, emphasizing the importance of engaging stakeholders, considering context, developing programme theory, and refining interventions—core elements that align with the application of these implementation frameworks [22]. This synergy between implementation frameworks and established research methodologies strengthens both the science and practice of implementation in cancer control.

Implementation science (IS) is defined as "the study of methods to promote the adoption and integration of evidence-based practices and interventions into routine health care and public health settings to improve the impact on population health" [23]. While traditionally applied to clinical practice adoption, its principles are increasingly critical for optimizing clinical trial design, conduct, and subsequent translation of findings into real-world care. This case study examines the adaptation of implementation frameworks to enhance cancer clinical trials, with a focus on bridging the gap between efficacy demonstrated in controlled settings and effectiveness in diverse patient populations. The integration of IS principles addresses fundamental challenges in trial generalizability, evidence generation speed, and the systematic uptake of proven interventions into routine cancer control planning [24] [4] [25].

Quantitative Assessment of Implementation Gaps in Cancer Control

A scoping review of national cancer control plans (NCCPs) from low- and middle-income countries reveals significant gaps in applying structured implementation methods, highlighting opportunities for better integration within clinical research frameworks [24] [4].

Table 1: Implementation Science Element Integration in National Cancer Control Plans

Implementation Science Domain	Plans Incorporating Domain	Quality of Integration
Stakeholder Engagement	Most plans	Unstructured and incomplete
Situational Analysis	Many plans	Explicit but inconsistent
Impact Measurement	All plans	Included but variably applied
Health System Capacity Assessment	None of the plans	Not performed
Economic Evaluation	13 plans (4 low HDI, 9 medium HDI)	Activity-based costing approaches

Table 2: NCI-Funded Implementation Science Grants Focused on Scale-Up (n=17) [25]

Study Characteristic	Number of Grants	Percentage
Primary Cancer Focus
Cervical Cancer	6	35.3%
Prevention Focus	11	64.7%
Screening Focus	7	41.2%
Research Emphasis
Scale-up Barriers/Facilitators	11	64.7%
Cost/Benefit Assessment	9	52.9%
Implementation Strategy Evaluation	7	41.2%
Research Setting
Healthcare Settings	11	64.7%
International Focus	6	35.3%

Protocol 1: AI-Enabled Evidence Engineering Framework for Clinical Trials

Background and Rationale

The COVID-19 pandemic demonstrated that drug development timelines can be substantially compressed from 10 years to 12 months without sacrificing safety or efficacy [26]. However, the industry has reverted to outdated practices as urgency faded. The AI-enabled Evidence Engineering Framework addresses this regression by creating a continuous evidence generation system that combines adaptive clinical trials, synthetic controls, and traditional randomized controlled trials (RCTs) under unified governance [26]. This approach enables AI systems to evolve at software development speeds while maintaining regulatory-grade causal proof for cancer interventions.

Experimental Methodology

The framework operates through a structured, multi-stage compliance pathway:

Stage 1: TRIPOD-AI (Development Reporting Standard)

• Purpose: Transparent reporting of prediction models using AI
• Key Activities:
  • Model description and architectural specification
  • Data source documentation and preprocessing methodology
  • Algorithm selection rationale and hyperparameter tuning
• Deliverables: Complete model documentation protocol

Stage 2: PROBAST-AI (Risk Assessment Tool)

• Purpose: Quality, bias, and applicability assessment for AI prediction models
• Key Activities:
  • Bias evaluation across patient subgroups
  • Feature importance analysis and validation
  • Domain adaptation assessment
• Deliverables: Risk stratification report and mitigation strategies

Stage 3: DECIDE-AI (Early Clinical Evaluation)

• Purpose: Bridge between laboratory performance and real-world impact
• Key Activities:
  • Limited-scale clinical deployment
  • Human-AI interaction assessment
  • Workflow integration analysis
• Deliverables: Clinical usability assessment report

Stage 4: CONSORT-AI (Full-Scale Trial Reporting)

• Purpose: Gold standard for proving AI systems work in large-scale clinical trials
• Key Activities:
  • Randomized evaluation against standard of care
  • Primary and secondary endpoint assessment
  • Subgroup analysis and safety monitoring
• Deliverables: Pivotal trial results for regulatory submission

Digital Twin Integration

The TweenMe digital twin engine serves as the universal generator at the heart of the evidence framework, addressing three critical pressure points [26]:

• Synthetic Control Arm Generation: Creates matched control patients from historical data
• Trial Simulation and Power Calculations: Models trial outcomes under various scenarios
• Continuous Learning Infrastructure: Updates models with incoming trial data

Protocol 2: TrialTranslator AI Platform for Real-World Generalizability Assessment

Background and Rationale

Clinical trials often lack generalizability because less than 10% of cancer patients participate, creating representation gaps [27]. TrialTranslator addresses this by using artificial intelligence to "translate" clinical trial results to real-world populations, identifying which patients are likely to benefit most from experimental therapies.

Experimental Methodology

Data Source and Eligibility:

• Primary Data: Nationwide electronic health records (EHR) from Flatiron Health
• Trial Selection: 11 landmark randomized controlled trials for advanced solid malignancies
• Cancer Types: Advanced non-small cell lung cancer, metastatic breast cancer, metastatic prostate cancer, metastatic colorectal cancer
• Inclusion: Real-world patients meeting key trial eligibility criteria

Machine Learning Framework:

• Phenotype Development:
  • Input Variables: Demographics, comorbidities, laboratory values, prior treatments, performance status
  • Method: Gradient boosting machines for survival prediction
  • Output: Low-, medium-, and high-risk phenotype classifications

• Trial Emulation:

  • Method: Target trial emulation framework using longitudinal EHR data
  • Comparators: Real-world patients receiving standard-of-care vs. experimental regimens
  • Endpoints: Overall survival, progression-free survival, treatment benefit heterogeneity

• Validation:

  • Internal Validation: Bootstrap resampling and cross-validation
  • External Validation: Comparison with original trial results
  • Calibration: Assessment of predicted vs. observed treatment effects

Analysis Workflow:

Key Findings and Outputs

The analysis revealed that patients with low- and medium-risk phenotypes had survival times and treatment-associated survival benefits similar to those observed in randomized controlled trials [27]. In contrast, those with high-risk phenotypes showed significantly lower survival times and treatment-associated survival benefits compared to the randomized controlled trials. This demonstrates that machine learning can identify groups of real-world patients in whom randomized controlled trial results are less generalizable.

Protocol 3: Enhanced Gen3 Data Commons for Clinical Trial Analytics

Background and Rationale

Clinical trial networks require sophisticated data commons platforms that support longitudinal analytics while following FAIR (Findable, Accessible, Interoperable, and Reusable) data principles [28]. The enhanced Gen3 platform addresses limitations in existing data commons solutions through temporal data modeling and visualization capabilities essential for understanding treatment effectiveness in interventional studies.

System Architecture and Implementation

Core Technical Enhancements:

• Cloud-Native Infrastructure:

  • Kubernetes-based container orchestration
  • Automated scaling based on resource utilization metrics
  • NeuVector zero-trust security model with Layer 7 firewall capabilities

• Temporal Visualization Framework:

  • Kibana-based analytics integrated with ElasticSearch indices
  • Interactive filtering and temporal comparisons
  • Automated ETL pipelines maintaining data synchronization

• Security Architecture:

  • OAuth2 authentication patterns
  • Continuous vulnerability scanning
  • Granular access controls for multi-site studies

• ETL Pipeline Development:

  • Automated data extraction from PostgreSQL to ElasticSearch
  • Clinical trial-specific data validation
  • Temporal relationship preservation across measurements

Implementation Workflow

Step 1: Data Asset Audit

• Actions: Catalog existing clinical trial data assets, assess data quality, map data elements to standardized terminologies
• Deliverable: Comprehensive data inventory with coverage analysis

Step 2: Temporal Data Modeling

• Actions: Implement time-aware data structures, define temporal relationships, establish versioning protocols
• Deliverable: Temporal data model specification

Step 3: Visualization Dashboard Development

• Actions: Create standardized analytics dashboards, implement real-time monitoring capabilities, develop interactive filtering
• Deliverable: Operational temporal analytics platform

Step 4: Researcher Training and Access Provision

• Actions: Develop training materials, establish access controls, create documentation
• Deliverable: Fully operational research platform with supported users

The Scientist's Toolkit: Essential Research Reagents and Solutions

Table 3: Key Research Reagent Solutions for Implementation Science in Clinical Trials

Tool/Platform	Primary Function	Implementation Context
TrialTranslator AI	Generalizability assessment of clinical trial results to real-world populations	Machine learning framework for translating trial efficacy to real-world effectiveness [27]
Gen3 Data Commons	FAIR-compliant data management and temporal analytics	Cloud-native platform for longitudinal clinical trial data visualization and analysis [28]
TweenMe Digital Twin	Synthetic control arm generation and trial simulation	Creates virtual patient cohorts for augmented clinical trial designs [26]
QCIC Model	Quantitative simulation of tumor-immune interactions	Mathematical modeling of cancer-immunity cycle for treatment response prediction [29]
PROBAST-AI Framework	Risk assessment and bias evaluation for AI models	Quality control and validation of artificial intelligence algorithms in clinical trials [26]
Kibana Analytics	Temporal visualization and real-time trial monitoring	Interactive dashboards for tracking longitudinal outcomes across multiple trials [28]
ExpandNet Scaling Framework	Systematic approach for scaling evidence-based interventions	Implementation science framework for scaling successful pilots to broad adoption [25]

This case study demonstrates three protocol-driven approaches for adapting implementation frameworks to the clinical trial context. The AI-Enabled Evidence Engineering Framework addresses the critical need for accelerated evidence generation while maintaining scientific rigor [26]. The TrialTranslator platform directly tackles the challenge of clinical trial generalizability by identifying which real-world patients will benefit from experimental therapies [27]. The enhanced Gen3 data commons provides the technical infrastructure necessary for longitudinal trial analytics and cross-study comparison [28]. Together, these approaches form a comprehensive implementation science toolkit for optimizing cancer clinical trials from design through real-world application, ultimately supporting more effective and equitable cancer control planning [24] [4] [25].

Implementation Science (IS) provides a systematic framework for integrating evidence-based practices into routine healthcare, moving beyond simple technical installation to address the complex socio-technical landscape of clinical environments. Within oncology, Electronic Health Record (EHR) systems must support exceptionally complex care processes involving multiple specialists, toxic treatments requiring precise dosing, and extended patient journeys across care settings. The development of oncology-specific EHR functionality presents unique challenges that transcend mere software deployment, requiring careful consideration of workflow integration, stakeholder engagement, and adaptive governance structures [30] [31].

When applied to oncology EHR deployment, implementation science shifts the focus from "what" technology does to "how" it becomes sustainably embedded in clinical practice. This approach recognizes that successful integration requires addressing differing perspectives among stakeholders including oncologists, nurses, pharmacists, administrators, and patients [30]. Research examining the development of an EHR for Chile's national cancer plan demonstrated that employing implementation science strategies provides an alternative approach to understanding the underlying problems stakeholders face when requiring healthcare technologies [30] [31]. This methodological framework helps translate implementation strategies into requirement identification methods that are subsequently disseminated throughout implementation and deployment phases of health information systems [30].

Key Implementation Strategies and Quantitative Assessments

Core Implementation Strategies for Oncology EHR Systems

Oncology EHR implementation requires specific strategies tailored to the unique demands of cancer care. Evidence from successful deployments reveals six essential implementation strategies that address critical stakeholder needs throughout the development and deployment process [30] [31]:

• Stakeholder-Driven Requirement Identification: Actively engaging multidisciplinary stakeholders throughout the requirement gathering process to ensure the EHR addresses real-world clinical needs and workflow constraints.
• Iterative Prototyping and Feedback Loops: Developing and refining system components through cyclical testing with end-users to align technical capabilities with clinical processes.
• Clinical Workflow Integration Mapping: Analyzing and designing system functionality to seamlessly integrate with existing oncology workflows, including chemotherapy ordering, toxicity management, and multidisciplinary team coordination.
• Adaptive Governance Structures: Establishing multidisciplinary committees with authority to standardize regimens, analyze errors, and alter systems or workflows to enhance safety.
• Implementation Phase Translation: Creating systematic methods for translating identified implementation strategies into specific technical requirements throughout deployment phases.
• Stakeholder Acceptance Validation: Conducting structured assessments with stakeholders to evaluate acceptance of critical EHR functionalities post-implementation.

Quantitative Assessment of EHR Interoperability and Data Continuity

A critical component of implementation science involves establishing metrics to evaluate the effectiveness of health technology deployments. For oncology EHR systems, key quantitative assessments include interoperability measurements and data continuity evaluations, which directly impact clinical utility and research capabilities.

Table 1: Quantitative Assessment of EHR Interoperability in Oncology Practice

Assessment Domain	Metric	Value	Implementation Significance
Intra-Vendor Interoperability	Mean interoperability score for same vendor implementations	0.68 [32]	Indicates substantial but incomplete standardization even with same EHR products
Inter-Vendor Interoperability	Mean interoperability score across different vendor systems	0.22 [32]	Highlights profound challenges in cross-platform data exchange
Data Element Standardization	Proportion of clinically relevant data elements using well-accepted standards	Minimal, despite existing standards [32]	Demonstrates prioritization of billing over clinical data standardization
EHR Continuity Impact	Reduction in variable misclassification with high EHR-continuity	7-fold reduction in mean standardized difference [33]	Supports restricting research cohorts to high-continuity patients to reduce bias
EHR Continuity Sensitivity	Improvement in sensitivity with high EHR-continuity	35-fold improvement [33]	Confirms data quality improvements through continuity optimization

Table 2: Workflow Efficiency Assessments in Oncology EHR Implementation

Workflow Aspect	Measurement Approach	Findings	Data Source
Information Retrieval	Proportion of clinical time spent searching for patient information	17% of specialists spend >50% of clinical time searching [34]	Cross-sectional survey of 92 UK professionals
System Fragmentation	Number of EHR systems routinely accessed	92% access multiple systems; 29% use ≥5 systems [34]	Cross-sectional survey of 92 UK professionals
Data Organization	Perceptions of well-organized data for clinical use	Only 11% strongly agree systems provide well-organized data [34]	Cross-sectional survey
Interoperability Challenges	Reported challenges with system interoperability	24.8% report lack of interoperability as key challenge [34]	Content analysis of free-text responses

Experimental Protocols for Oncology EHR Implementation

Protocol 1: Stakeholder Acceptance Assessment for Oncology EHR Functionality

Objective: To quantitatively assess stakeholder acceptance of critical oncology EHR functionalities following system implementation.

Background: A study conducted with 27 stakeholders revealed that perception of the oncology electronic clinical record has considerable acceptance in three critical functionalities related to the clinical process of oncology patient management [30] [31]. This protocol provides a methodological framework for replicating this assessment in diverse implementation contexts.

Materials:

• Stakeholder Identification Matrix: Map multidisciplinary stakeholders including medical oncologists, nurses, pharmacists, advanced practice providers, and administrative staff.
• Functionality Assessment Tool: Structured instrument evaluating perceived usefulness, ease of use, and workflow integration of key EHR functionalities.
• Data Analysis Platform: Statistical software capable of quantitative and qualitative analysis (e.g., R, SPSS, NVivo).

Methodology:

• Stakeholder Recruitment: Recruit a representative sample of stakeholders (target n=25-30) across disciplines involved in oncology care delivery.
• Functionality Prioritization: Identify three critical oncology-specific EHR functionalities for evaluation, focusing on core clinical processes.
• Assessment Administration: Deploy structured assessment tools following sufficient system exposure (typically 4-8 weeks post-implementation).
• Data Collection: Capture both quantitative ratings (Likert scales) and qualitative feedback on functionality performance.
• Analysis: Calculate acceptance scores for each functionality and identify thematic patterns in qualitative responses.

Implementation Considerations: This assessment should be timed to allow for initial adjustment to the new system while remaining early enough to inform iterative improvements. The identified critical functionalities should reflect core oncology workflows such as chemotherapy ordering, toxicity documentation, or treatment planning [30] [31].

Protocol 2: EHR-EDC Integration for Oncology Clinical Trials

Objective: To implement automated data transfer from EHR systems to Electronic Data Capture (EDC) platforms for clinical research, reducing manual transcription error and site burden.

Background: Modern oncology trials generate highly complex datasets with tens of thousands of data points per participant in Phase I studies [35]. Traditional manual transcription processes are slow, resource-intensive, and prone to inconsistency. This protocol outlines a structured framework for implementing eSource-enabled EHR-to-EDC integration.

Materials:

• Interoperability Infrastructure: Standards-based data exchange capabilities, typically FHIR-based interoperability frameworks.
• Mapping Documentation: Structured documentation of data element mappings between source EHR fields and target EDC fields.
• Validation Tools: Automated and manual processes to verify data accuracy and completeness throughout the transfer pipeline.

Methodology:

• Preparation Phase:
  • Define specific integration objectives and success metrics.
  • Establish multidisciplinary governance structure with representatives from clinical research, IT, oncology practice, and compliance.
  • Evaluate institutional readiness across technical infrastructure, workflow standardization, and regulatory alignment.

• Planning Phase:

  • Map clinical and research workflows to identify integration points and potential conflicts.
  • Specify data requirements and mapping specifications for key oncology data elements.
  • Outline validation processes and quality control checkpoints.

• Setup Phase:

  • Configure technical integrations between EHR and EDC systems.
  • Test data flows with synthetic and de-identified real patient data.
  • Train end-users on new workflows and documentation requirements.

• Execution Phase:

  • Launch automated data extraction in a limited pilot study.
  • Monitor system behavior and data quality metrics.
  • Refine mappings based on real-world use and feedback.

• Post-Implementation Review:

  • Evaluate performance using predefined KPIs including data accuracy, completeness, reduction in source data verification volume, and time from patient visit to data availability.
  • Document lessons learned and create scaling plan for additional studies or therapeutic areas.

Implementation Considerations: Successful adoption requires addressing change management and securing site buy-in [35] [36]. A phased approach ensures systematic, compliant, and repeatable deployment across diverse research environments.

Protocol 3: Chemotherapy Ordering Safety and Workflow Integration

Objective: To implement and validate safe chemotherapy ordering processes within oncology EHR systems through standardized regimens and workflow controls.

Background: Chemotherapy ordering presents high-risk scenarios requiring multiple safeguards. EHRs bring incredible tools to the clinic, but adopting such systems requires attention to the way providers interact with them [37]. This protocol addresses the intersection of computerized order entry and workflow policy to enhance patient safety.

Materials:

• Standardized Regimen Library: Evidence-based chemotherapy regimens with embedded dose modifications, antiemetic protocols, and hydration orders.
• Governance Structure: Multidisciplinary committee (medical oncologists, nurses, pharmacists) with authority to standardize regimens and analyze errors.
• Security Framework: Role-based access controls defining chemotherapy ordering privileges.

Methodology:

• Governance Establishment:
  • Convene multidisciplinary chemotherapy governance committee with representatives from oncology, pharmacy, nursing, and administration.
  • Define evidence-based standardization protocols for antineoplastic agents including ancillary medications.
  • Establish error reporting and analysis system for continuous improvement.

• System Configuration:

  • Implement regimen-based ordering rather than individual drug orders, linking regimens to specific diagnoses.
  • Configure dose ceilings and automatic dose modification recommendations based on clinical parameters.
  • Embed standard antiemetic and hydration protocols tailored to specific chemotherapy agents.
  • Create audit trails documenting all orders entered and changes made.

• Workflow Integration:

  • Develop certification process for clinicians authorized to order chemotherapy.
  • Implement rules prohibiting nurses or pharmacists from initiating orders or dose modifications.
  • Require confirmation of pertinent data (diagnosis, regimen, patient height and weight) before order completion.
  • Mandate review and confirmation of all calculated doses before system release.

• Validation and Monitoring:

  • Conduct simulated ordering scenarios to validate safety protocols.
  • Monitor ordering patterns and exceptions for quality improvement.
  • Periodically review error reports and near-misses to refine systems.

Implementation Considerations: The governance committee must balance safety concerns with workflow efficiency, recognizing that workflow is always taken into consideration [37]. Safety concerns should take precedence over convenience in implementation decisions.

Visualization of Implementation Frameworks

Implementation Science Framework for Oncology EHR Development

Figure 1: Implementation Science Framework for Oncology EHR Development

EHR-to-EDC Integration Workflow for Clinical Research

Figure 2: EHR-to-EDC Integration Workflow for Clinical Research

Table 3: Essential Research Reagents for Oncology EHR Implementation Studies

Tool Category	Specific Tool/Resource	Function in Implementation Research	Application Context
Assessment Instruments	Stakeholder Acceptance Survey	Quantifies perceived usefulness and ease of use of EHR functionalities	Post-implementation evaluation of critical clinical features [30]
Interoperability Metrics	Intra/Inter-Vendor Interoperability Scores	Measures data exchange capability between systems using standardized data elements	Benchmarking EHR system performance and standardization [32]
Data Quality Algorithms	EHR-Continuity Prediction Algorithm	Identifies patients with high within-network care to reduce information bias	Comparative effectiveness research using EHR data [33]
Workflow Analysis Tools	Time-Motion Assessment Framework	Quantifies time spent on information retrieval and documentation tasks	Evaluating EHR impact on clinical efficiency [34]
Integration Platforms	FHIR-based Interoperability Infrastructure	Enables standards-based data exchange between EHR and research systems	EHR-to-EDC implementation for clinical trials [35]
Governance Structures	Multidisciplinary Chemotherapy Committee	Standardizes regimens, analyzes errors, and oversees safety protocols	Chemotherapy ordering system implementation [37]
Natural Language Processing	NLP Tools for Clinical Text Extraction	Extracts structured information from unstructured clinical narratives	Genomic result identification and data integration [34]

The deployment of oncology-specific EHR systems represents a critical opportunity to enhance cancer care quality, patient safety, and research capabilities. Implementation science provides essential methodologies for navigating the complex socio-technical landscape of oncology practice, ensuring that technological advancements translate into meaningful clinical improvements. The frameworks, protocols, and assessments outlined in these application notes provide researchers and implementers with structured approaches for developing, evaluating, and refining oncology EHR systems within the broader context of cancer control optimization research.

As oncology continues to evolve toward more precise, complex, and data-intensive paradigms, the thoughtful integration of implementation science into health technology deployment will become increasingly vital. By adopting these structured approaches, the oncology community can ensure that EHR systems mature from mere documentation repositories to sophisticated tools that actively enhance clinical decision-making, patient safety, and research efficiency across the cancer care continuum.

Policy implementation science is an emerging field dedicated to systematically studying the methods and strategies to adopt, integrate, and sustain evidence-based policies into routine practice to improve population health outcomes [38]. Within cancer control, policy implementation science addresses the critical gap between the development of evidence-based policies and their effective application across diverse settings and populations. This approach recognizes policy not merely as a legislative product but as a dynamic lever that can shape the entire cancer care continuum—from prevention and early detection to treatment, survivorship, and end-of-life care [39]. The fundamental goal is to understand and address the complex factors that influence how policies are enacted, executed, and sustained, with particular attention to achieving health equity by ensuring that evidence-based interventions reach underserved and disadvantaged communities [38] [1].

The recent portfolio analysis of NCI-funded implementation science grants reveals significant growth in this domain, with more than half (57.1%) of policy-focused grants awarded in fiscal years 2020-2023 [39]. This trend reflects increasing recognition that policy serves multiple conceptual roles in implementation science: as an evidence-based intervention to be implemented, as a strategy to support implementation of clinical interventions, and as context that influences implementation success [39]. This multifaceted understanding enables researchers to leverage policy at different levels—federal, state, organizational—to accelerate the translation of cancer control evidence into widespread practice.

Conceptual Frameworks for Policy Implementation

The Five-Stream Policy Framework

The Five-Stream Framework, adapted from political science, offers a comprehensive model for understanding the policy implementation process through five critical confluence points that enable progression through each stage of the policy lifecycle [38]. This framework helps explain why particular policy solutions are selected and what impact different political versus public interests have on implementation outcomes:

• Problem Stream: Involves conceptualizing the cancer control problem, identifying disparities, and building the evidence base for policy action
• Policy Solution Stream: Focuses on developing and refining evidence-based policy alternatives to address identified problems
• Political Stream: Encompasses the political context, leadership engagement, and policy windows that enable action
• Implementation Process Stream: Addresses the planning, resources, and stakeholder engagement needed for effective execution
• Outcome Evaluation Stream: Tracks policy impacts on cancer-related outcomes and health equity

Table: Conceptualizations of Policy in Implementation Science Grants

Policy Conceptualization	Number of Grants	Percentage	Primary Focus
Policy as something to implement	10	71.4%	Executing policy directives
Policy as context to understand	5	35.7%	Policy environment as influencing factor
Policy as something to adopt	4	28.6%	Policy uptake decision-making
Policy as a strategy to use	4	28.6%	Policy lever for clinical implementation

Note: Percentages total more than 100% as grants could conceptualize policy in multiple ways [39].

Visualizing the Policy Implementation Framework

Quantitative Evidence for Policy Implementation

Research Portfolio Analysis

Analysis of the National Cancer Institute's research portfolio from fiscal years 2014-2023 provides quantitative insights into current trends and gaps in policy implementation science [39]. Of 41 implementation science grants identified, 14 (34.1%) specifically focused on policy implementation, indicating growing but still limited attention to policy as an implementation lever. The distribution of these grants across the cancer continuum reveals significant concentration in prevention, with less attention to diagnosis, treatment, and survivorship phases.

Table: Policy Implementation Science Grants Across Cancer Continuum (FY2014-2023)

Cancer Continuum Focus	Number of Grants	Percentage	Example Policy Content Areas
Prevention	11	78.6%	HPV vaccination, tobacco control, UV protection
Screening	3	21.4%	Colorectal cancer screening, prostate cancer screening
Treatment	2	14.3%	Rural care delivery, fertility preservation
Survivorship	2	14.3%	Caregiver support, survivorship care planning
Diagnostic	0	0%	None identified
End-of-Life Care	0	0%	None identified

Source: Portfolio analysis of NCI-funded policy implementation science grants [39]

The analysis also documented the policy levels targeted by implementation studies, with organizational and state-level policies being most frequently addressed (8 grants each), while federal and local policies received less attention (3 and 2 grants respectively) [39]. This distribution suggests implementation science may be underutilizing multi-level policy approaches that coordinate action across different jurisdictional levels.

Quantitative Evaluation of Implementation Programs

Recent quantitative evaluations of implementation programs provide evidence for effective strategies. The American Cancer Society's ECHO programs demonstrated significant improvements in participant knowledge and confidence across four cancer care programs engaging 431 unique participants [40]. Quantitative assessment using 5-point Likert scales showed mean increases of +0.84 for knowledge and +0.77 for confidence among participants, reflecting enhanced readiness to apply evidence-based approaches in practice [40]. Importantly, 59% of participants reported planning to use the information presented within a month, suggesting strong potential for practice change.

Experimental Protocols for Policy Implementation Research

Protocol 1: Mixed-Methods Policy Implementation Evaluation

Objective: To evaluate the adoption, implementation, and sustainment of evidence-based cancer control policies across diverse settings.

Methodology:

• Policy Document Analysis: Systematic coding of policy provisions, implementation specifications, and accountability mechanisms
• Stakeholder Surveys: Quantitative assessment of policy awareness, adoption barriers, and implementation fidelity using validated instruments
• Key Informant Interviews: Qualitative exploration of contextual factors, implementation processes, and adaptation strategies
• Outcome Measurement: Pre-post assessment of policy-targeted outcomes using cancer registry data, clinical metrics, or public health surveillance data

Data Collection Tools:

• Policy Implementation Fidelity Index (assesses adherence to core policy elements)
• Organizational Readiness for Implementation Change (ORIC) scale
• Implementation Climate Scale (evaluates organizational support for policy implementation)
• Patient-reported outcome measures relevant to policy goals

Analysis Plan:

• Quantitative: Descriptive statistics, regression models assessing factors associated with implementation success
• Qualitative: Thematic analysis using framework method
• Integration: Mixed-methods matrix analysis connecting implementation processes with outcomes

Protocol 2: Stakeholder-Engaged Policy Adaptation

Objective: To adapt evidence-based cancer control policies for specific contexts while maintaining core effective elements.

Methodology:

• Stakeholder Mapping: Identify and categorize policy implementers, influencers, and beneficiaries
• Policy Component Analysis: Deconstruct existing evidence-based policies into core components and adaptable features
• Delphi Consensus Process: Engage diverse stakeholders in rating essential elements and identifying necessary adaptations
• Rapid-Cycle Testing: Pilot test adapted policies with iterative refinement based on implementation feedback

Implementation Framework: This protocol utilizes the Expert Recommendations for Implementing Change (ERIC) compilation of implementation strategies, with particular focus on:

• Tailoring strategies to address contextual barriers
• Developing stakeholder interrelationships
• Training and educating stakeholders
• Supporting policy implementers

Evaluation Metrics:

• Stakeholder engagement metrics (participation rates, diversity)
• Policy feasibility and acceptability ratings
• Implementation cost and resource requirements
• Early indicators of effectiveness

Visualizing the Policy Implementation Research Workflow

The Scientist's Toolkit: Research Reagent Solutions

Table: Essential Resources for Policy Implementation Science Research

Resource Category	Specific Tool/Resource	Function/Purpose	Access Point
Implementation Frameworks	Five-Stream Policy Framework [38]	Guides analysis of policy development and implementation processes	Academic literature
	ERIC (Expert Recommendations for Implementing Change) [3]	Provides standardized compilation of implementation strategies	Implementation Science Journal
Data Resources	ICCP Portal [3] [4]	Repository of National Cancer Control Plans for comparative analysis	International Cancer Control Partnership
	NCI Research-Tested Intervention Programs (RTIPs) [1]	Database of evidence-based cancer control programs and implementation materials	National Cancer Institute
	EBCCP (Evidence-Based Cancer Control Programs) [23]	Searchable database of cancer control programs and implementation materials	National Cancer Institute
Evaluation Tools	Policy Implementation Fidelity Index	Quantitative assessment of adherence to policy elements	Research instrumentation
	ORIC (Organizational Readiness for Implementing Change)	Measures organizational preparedness for implementation	Implementation Science
	Implementation Climate Scale	Assesses organizational support for evidence-based practice	Implementation Science
Training Resources	TIDIRC (Training Institute for Dissemination and Implementation Research in Cancer) [1]	Builds capacity in implementation science methods	National Cancer Institute

Application Notes: Implementing Policy in Resource-Constrained Settings

Recent scoping reviews of National Cancer Control Plans in low- and middle-income countries reveal both opportunities and challenges for policy implementation [3] [4]. Analysis of 33 plans from low and medium Human Development Index countries found that while many incorporated key implementation science elements like stakeholder engagement and impact measurement, these approaches were often inconsistently applied [3]. Critically, none of the plans assessed health system capacity to determine readiness for implementing new interventions, and stakeholder engagement processes were typically unstructured [3].

Based on these findings, researchers have proposed a structured pathway for integrating implementation science into cancer control planning:

• Structured Stakeholder Analysis: Map all relevant stakeholders across government, healthcare organizations, community groups, and patient advocates with explicit attention to power dynamics and interests

• Systematic Situational Analysis: Assess epidemiology, health system capacity, resource availability, and political context using standardized assessment tools

• Explicit Capacity Assessment: Evaluate existing infrastructure, workforce capabilities, and financial resources for policy implementation

• Costed Implementation Plans: Develop detailed budgets that account for all implementation activities, not just intervention costs

• Integrated Monitoring & Evaluation: Establish implementation metrics alongside outcome indicators to track policy execution and impact

This pathway emphasizes realistic goal-setting informed by implementation constraints and structured adaptation of evidence-based policies to local contexts while preserving core effective elements [3]. The approach enables policymakers in resource-constrained settings to make strategic decisions about which cancer control policies to prioritize based on both evidence of effectiveness and feasibility of implementation.

Policy implementation science represents a transformative approach to accelerating the impact of cancer control research on population health. By systematically studying how evidence-based policies are adopted, executed, and sustained across diverse contexts, this field addresses the critical implementation gap that often separates policy intent from real-world impact. The conceptual frameworks, experimental protocols, and implementation resources outlined in these application notes provide researchers with practical tools to advance this emerging field.

Future directions for policy implementation science in cancer control include developing precision implementation approaches that match specific policy strategies to particular contexts and populations [1], expanding research on policies addressing later stages of the cancer continuum (particularly diagnosis, treatment, and survivorship) [39], and strengthening methods for economic evaluation of policy implementation to guide resource allocation in constrained environments [3]. Additionally, there is critical need for more research on de-implementing ineffective or harmful policies and practices through policy mechanisms [39].

As the field evolves, policy implementation science holds promise for creating a rapid-learning cancer control system where policies are continuously refined based on implementation data and stakeholder feedback, ultimately reducing the burden of cancer for all populations.

Application Notes: Implementation Strategies and Outcomes

Implementation Science (IS) provides a structured framework to address the multi-faceted barriers that prevent underserved communities from receiving equitable cancer care. These barriers include geographic distance, economic challenges, workforce shortages, and cultural factors that collectively contribute to delayed diagnoses, increased morbidity, and higher cancer mortality rates [41] [42]. The following notes summarize contemporary IS applications designed to close this gap.

Key Implementation Strategies and Evidence

Table 1: Evidence-Based Interventions for Underserved Communities

Strategy	Target Population/Setting	Key Outcomes & Evidence
Patient Navigation	Diverse oncology patients at safety-net and tertiary healthcare systems [43].	Reach: Successfully engaged 429 patients; participants were more likely to be Hispanic (31% vs 21% underlying population) and from a minority race (30% vs 24%) [43].
Telehealth & Remote Service Delivery	Rural populations; patients in Frontier and Remote (FAR) areas [41].	Genetic Counseling: A 12-minute pre-test video successfully increased knowledge and empowered decision-making [41].Exercise Oncology: Remote, supervised group exercise was piloted for 30 couples coping with cancer [41].
Point-of-Care (POC) Testing	Rural populations and those living in persistent poverty [41].	Workflow: POC genetic testing integrated into oncology care, using an electronic platform (REDCap) to automate collection of family history and deliver pre-test education [41].
e-Community Health Interventions	Isolated counties with high rates of obesity and diabetes [41].	Program: "Eat Move Live" (EML), a grassroots, community-centric program adapted for electronic delivery to improve healthy behaviors and reduce cancer risk [41].
Implementation Facilitation	Veterans Health Administration (VA) hospitals [5].	Protocol: A hybrid type 3 trial is comparing external facilitation to patient navigation for improving liver and colon cancer screening completion [5].

Experimental Protocols

Protocol: Patient Navigation for Diverse Trial Enrollment

This protocol is adapted from a 2025 study implementing a navigation program to support the enrollment and retention of a diverse patient population in cancer clinical trials [43].

1. Objective: To assess the feasibility, acceptability, and preliminary effectiveness of a patient navigation program designed to improve the reach of cancer clinical trials among underrepresented groups.

2. Study Design:

• Setting: Conducted at two demographically diverse healthcare settings: a university-based tertiary healthcare system and an integrated safety-net healthcare system [43].
• Evaluation Framework: The Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework guided the evaluation [43].
• Data Sources: Programmatic data, electronic medical records, structured surveys of patients and staff, and qualitative patient interviews [43].

3. Participants:

• Patients: Adult oncology patients, with a focus on reaching populations historically underrepresented in clinical trials.
• Staff: Clinicians, clinic staff, and clinical research personnel.

4. Intervention Components:

• Navigation Support: Four dedicated oncology patient navigators provided individualized assistance to patients, including education and coordination [43].
• Financial Navigation: Addressed "financial toxicity" by providing resources and reimbursement for nonmedical trial-related costs for eligible patients [43].
• Workflow Integration: The program was integrated into existing clinical workflows, with site-specific referral pathways (e.g., via nurse managers or self-referral) [43].
• Tailored Implementation Strategies: Based on stakeholder feedback, strategies included creating bilingual educational materials, colocating navigators with research staff, and developing new clinical workflows and quality monitoring systems [43].

5. Outcome Measures:

• Reach: Proportion and demographic characteristics of oncology patients participating in the program compared to the underlying patient population [43].
• Effectiveness: Proximal outcomes included the percentage of navigated patients who received financial navigation and the percentage of non-enrolled patients who expressed interest in future trial participation [43].
• Implementation: Assessed via patient-reported experience and program fidelity (documentation of encounter summaries) [43].

Protocol: Comparing Implementation Strategies for GI Cancer Screening

This protocol outlines a large, cluster-randomized implementation study within the Veterans Health Administration (VA) to compare two evidence-based strategies for improving cancer screening [5].

1. Objective: To compare the effectiveness of External Implementation Facilitation versus Patient Navigation for increasing the reach of hepatocellular carcinoma (HCC) and colorectal cancer (CRC) screening.

2. Study Design:

• Design: Two hybrid type 3, cluster-randomized trials [5].
• Sites: 24 VA sites for the HCC trial and 32 sites for the CRC trial. Sites are eligible if they perform below the VA national median for the respective GI cancer screening metric [5].
• Randomization: Sites are cluster-randomized to either the Facilitation or Patient Navigation arm, stratified by site size and structural characteristics (e.g., on-site GI care) [5].

3. Participants:

• Veterans: Passively enrolled based on their site of primary care. Inclusion criteria include being ≥18 years old and eligible for CRC (≥45 with an abnormal stool test) or HCC (diagnosis of cirrhosis) screening [5].
• Providers: Healthcare providers or staff working at participating VA sites and engaged in the cancer screening pathways [5].

4. Intervention Arms:

• Arm 1: Implementation Facilitation (IF)
  • Approach: Sites use "Getting To Implementation (GTI)," a manualized intervention with a seven-step playbook, training, and external facilitation [5].
  • Dosage: Sites receive ~20 hours of support over 12 months, including bi-weekly virtual meetings with facilitators (a clinical expert and an evaluation expert) who guide teams through goal setting, barrier identification, and iterative tests of change [5].
• Arm 2: Patient Navigation (PN)
  • Approach: Sites receive a "Patient Navigation Toolkit" and an introductory call with an expert [5].
  • Dosage: The toolkit promotes core activities (identifying Veterans, conducting outreach, documenting results). Sites then submit monthly tracking reports and have optional monthly progress calls [5].

5. Data Collection and Outcomes:

• Primary Outcome (Reach): Proportion of eligible patients who complete guideline-concordant screening (colonoscopy after abnormal stool test for CRC; ultrasound/contrasted imaging within 6 months for HCC) [5].
• Data Sources: Electronic medical records, national VA datasets, and pre-/post-intervention surveys and interviews with clinicians and a subset of Veterans [5].
• Analysis: Generalized linear mixed models (GLMMs) will assess differences in reach between the two arms at 12 months [5].

Implementation Science Framework and Logic

The OPTICC Center (Optimizing Implementation in Cancer Control) identifies critical barriers that must be overcome to optimize evidence-based intervention (EBI) implementation, including underdeveloped methods for identifying determinants and incomplete knowledge of strategy mechanisms [44] [6]. The following diagram illustrates the core implementation logic for addressing cancer care disparities.

Table 2: Essential Resources for Implementation Research in Cancer Control

Resource Category	Specific Tool / Resource	Function & Application
Evaluation Frameworks	RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) [43] [5]	Provides a structured framework for planning and evaluating the impact of implementation strategies across multiple domains.
Determinants Frameworks	Consolidated Framework for Implementation Research (CFIR) [5]	Used to systematically identify and assess contextual barriers and facilitators pre- and post-intervention.
Data Repositories	NCI Data Catalog (e.g., SEER, GDC, TCIA) [45]	Provides access to a wide array of cancer-related data, including incidence, survival, genomics, and medical images for secondary analysis.
Implementation Strategy Compilations	Expert Recommendations for Implementing Change (ERIC) [43]	A compilation of 73 defined implementation strategies used to select and specify strategies for addressing local barriers.
Program Implementation Tools	Getting To Implementation (GTI) Playbook [5]	A manualized, step-by-step intervention that guides site teams through implementation with the aid of external facilitation.
Data Collection & Management	REDCap (Research Electronic Data Capture) [41] [43]	A secure web platform for building and managing online surveys and databases, used for automating patient intake and tracking program data.

Navigating Implementation Barriers: Strategies for Optimization and Scale-Up

Implementation science (IS) provides a structured framework for integrating evidence-based interventions into cancer control planning, yet critical barriers persist. A scoping review of 33 National Cancer Control Plans (NCCPs) from low and medium Human Development Index (HDI) countries reveals that unstructured stakeholder engagement and failure to assess health system capacity represent the most significant impediments to effective implementation [3]. While many plans incorporated elements like impact measurement, none conducted systematic health system capacity assessments to determine readiness for implementing new interventions [3] [4]. This application note analyzes these critical barriers and provides structured protocols to address these gaps through validated IS methodologies, enabling researchers to develop more equitable and feasible cancer control policies, particularly in resource-constrained settings.

Key Quantitative Findings: Analysis of NCCP Implementation Gaps

Table 1: Documented Implementation Science Gaps in National Cancer Control Plans (n=33)

Implementation Domain	Plans Incorporating Domain	Key Deficiencies Identified
Stakeholder Engagement	Most plans	Unstructured and incomplete engagement processes; 5 plans lacked mechanisms for engaging responsible entities
Health System Capacity Assessment	0 plans	No plans assessed health system capacity to determine readiness for new interventions
Economic Evaluation	4 low HDI & 9 medium HDI countries	Generally used activity-based costing approaches
Impact Measurement	All plans	Included key performance indicators but often without clear implementation mechanisms
Situational Analysis	Many plans	Elements present but not explicitly or consistently applied

The analysis revealed that although stakeholder engagement was commonly attempted, the processes were typically "unstructured and incomplete" [3]. This lack of systematic approach undermines the effectiveness of engagement and compromises implementation success. Furthermore, the universal absence of health system capacity assessment creates a fundamental disconnect between intervention goals and real-world implementation capabilities [3] [4].

Table 2: Sustainability of Evidence-Based Interventions in CDC's Colorectal Cancer Control Program

Evidence-Based Intervention	Percentage of Clinics Reporting Sustainability	Key Factors Associated with Sustainability
Provider Reminders	82.0%	Pre-existing implementation; dedicated screening champions
Client Reminders	69.8%	Clinic integration; multi-EBI implementation
Provider Assessment & Feedback	63.8%	Program champion support; organizational commitment
Reducing Structural Barriers	55.6%	Resource allocation; system-level adaptations

Data from the CDC's Colorectal Cancer Control Program demonstrates significant variation in sustainability rates across different evidence-based interventions, highlighting how intervention characteristics and contextual factors influence long-term implementation success [46]. Provider reminders showed the highest sustainability rates (82.0%), while structural interventions faced greater challenges (55.6%) [46].

Experimental Protocols for Addressing Critical Barriers

Protocol for Structured Stakeholder Engagement

Purpose: To transform unstructured engagement into a systematic process that identifies implementation barriers and facilitators across multiple health system levels.

Materials:

• Consolidated Framework for Implementation Research (CFIR) interview guides [47]
• Stakeholder mapping templates
• Engagement tracking system

Procedure:

• Stakeholder Mapping: Identify and categorize stakeholders across micro (providers, patients), meso (clinic administrators), and macro (policy makers) levels [48]
• Structured Assessment: Conduct CFIR-informed interviews and surveys to assess barriers and facilitators
• Iterative Feedback: Establish ongoing consultation mechanisms through:
  • Biannual stakeholder working groups
  • Implementation feedback cycles
  • Adaptive planning sessions
• Integration: Incorporate stakeholder input into intervention design and implementation strategy selection

Validation: This protocol was validated through expert consultation with six IS specialists selected based on peer-reviewed publications and experience advising resource-constrained settings [3].

Protocol for Health System Capacity Assessment

Purpose: To systematically evaluate health system readiness for implementing evidence-based cancer control interventions.

Materials:

• WHO Health System Building Blocks framework [3]
• Capacity assessment checklist
• Readiness scoring tool

Procedure:

• Workforce Capacity: Map available human resources, skill gaps, and training needs
• Infrastructure Assessment: Evaluate physical infrastructure, equipment, and technological capabilities
• Supply Chain Analysis: Assess medication and commodity availability, procurement systems, and distribution networks
• Financial Resource Mapping: Document available funding, financing flows, and resource allocation mechanisms
• Information Systems Review: Evaluate data collection, monitoring, and health information systems

Implementation: Integrate capacity findings into intervention adaptation and implementation planning to ensure alignment with system capabilities.

Protocol for Comparative Effectiveness Trials

Purpose: To compare implementation strategies for cancer screening in real-world settings.

Materials:

• Getting To Implementation (GTI) manualized intervention [47]
• Patient Navigation Toolkit [47]
• CFIR-mapped surveys [47]

Procedure:

• Site Selection: Identify sites below national median on cancer screening completion rates [47]
• Cluster Randomization: Randomize sites to implementation facilitation or patient navigation arms
• Intervention Delivery:
  • Implementation Facilitation: External facilitators guide sites through GTI's seven-step process during bi-weekly virtual meetings over six months [47]
  • Patient Navigation: Sites receive Patient Navigation Toolkit with monthly progress discussion opportunities [47]
• Data Collection: Extract primary outcome data from electronic medical records and administer CFIR-mapped surveys to assess implementation determinants [47]
• Analysis: Compare screening completion rates and identify contextual factors influencing implementation success

Visualization of Implementation Pathways

Stakeholder Engagement and Capacity Assessment Workflow

Multi-level Implementation Strategy Framework

The Scientist's Toolkit: Research Reagent Solutions

Research Tool	Application in Cancer Control	Key Function
Consolidated Framework for Implementation Research (CFIR)	Assessing multilevel barriers and facilitators	Provides structured approach to identify implementation determinants
Expert Recommendations for Implementing Change (ERIC)	Selecting implementation strategies	Compilation of 73 implementation strategies for cancer control planning
Getting To Implementation (GTI)	Supporting evidence-based intervention implementation	Seven-step playbook for context-specific strategy selection
Implementation Facilitation Manual	Building organizational capacity	Guidelines for external facilitators supporting implementation
Sustainability Framework	Planning for long-term intervention maintenance	Identifies determinants for sustaining evidence-based interventions

The critical barriers of unstructured stakeholder engagement and unassessed health system capacity represent fundamental challenges in cancer control implementation. The protocols and frameworks presented herein provide actionable methodologies for addressing these gaps through structured approaches. By integrating systematic stakeholder mapping, comprehensive capacity assessment, and comparative effectiveness research, implementation scientists can develop more realistic and sustainable cancer control policies. Future research should focus on adapting these protocols for specific resource-constrained contexts and evaluating their impact on implementation outcomes across the cancer care continuum.

The Optimizing Implementation in Cancer Control (OPTICC) Center represents a strategic initiative designed to overcome critical barriers in the implementation of evidence-based interventions (EBIs) in cancer care. Funded by the National Cancer Institute as part of the Implementation Science Centers in Cancer Control (ISC3), OPTICC operates as a collaborative partnership between the University of Washington, Kaiser Permanente Washington Health Research Institute, and the Fred Hutchinson Cancer Research Center [2] [49]. The center's mission addresses the grand challenge that EBIs, while demonstrating dramatic potential in research settings (e.g., reducing cervical cancer deaths by 90% and colorectal cancer deaths by 70%), often fail to achieve comparable impact when implemented in real-world clinical and community settings due to suboptimal implementation approaches [2] [50]. The OPTICC model systematically addresses the entire implementation pathway through a structured, three-stage process that enables precision matching of implementation strategies to contextual barriers, moving beyond the traditional approach of selecting strategies based on organizational preference or routine [51] [52].

The Systematic Optimization Framework

The OPTICC Center's approach conceptualizes optimization as a three-stage process wherein strategies employed to implement EBIs truly address key barriers in specific settings and reflect the best available methods to address those barriers [6] [51]. This systematic framework ensures that implementation efforts are both precise and efficient, avoiding the common pitfall of strategy-barrier mismatch that plagues traditional implementation approaches.

Table 1: The Three-Stage OPTICC Optimization Framework

Stage	Core Objective	Key Outputs	Traditional Limitations Addressed
Stage 1: Identify & Prioritize Determinants	Provide robust methods for determinant identification and prioritization	List of determinants ordered by priority scores using criticality, chronicity, and ubiquity criteria	Methods subject to recall bias; insufficient user engagement; prioritization based on feasibility rather than impact [51] [2]
Stage 2: Match Strategies	Use causal pathway diagrams to match strategies to determinants	Explicit representations of hypothesized causal pathways connecting strategies to mechanisms and outcomes	Incomplete knowledge of strategy mechanisms; guesswork in strategy selection [51] [2]
Stage 3: Optimize Strategies	Support rapid testing of strategies in analog or real-world conditions	Optimized strategy components ready for large-scale evaluation	Reliance on RCTs focusing only on distal outcomes; inability to identify active strategy components [51] [2]

The framework specifically addresses four critical barriers that have hampered implementation science: (1) underdeveloped methods for barrier identification and prioritization, (2) incomplete knowledge of implementation strategy mechanisms, (3) underutilization of existing methods for optimizing strategies, and (4) poor measurement of implementation constructs [6] [50]. By systematically addressing these barriers through its staged approach, OPTICC moves the field from "implementation as usual" toward a precision approach that ensures the right strategies are applied to the highest-priority barriers in a manner that maximizes effectiveness and efficiency [52].

Stage 1: Methods to Identify & Prioritize Determinants

Protocol for Determinant Identification

The initial stage employs four complementary methods to overcome limitations of traditional determinant assessment, which often relies solely on interviews, focus groups, or surveys that are subject to recall bias, social desirability bias, and low participant insight [51] [2]. The integrated protocol generates a comprehensive determinant profile that reflects both established evidence and novel contextual factors.

• Rapid Evidence Reviews: Conducted within three months or fewer, these reviews summarize and synthesize research literature on known determinants for implementing EBIs in specific settings of interest. Unlike traditional systematic reviews, rapid reviews maintain a tight scope focused specifically on implementation determinants. The process involves collaboration between research experts and practice partners to clarify questions and scope, with data abstraction focusing on determinants and information about timing, modifiability, frequency, duration, and prevalence. The output is a list of determinants organized by consumer, provider, team, organization, system, or policy level that informs subsequent assessment tools [51].

• Rapid Ethnographic Assessment: This method efficiently gathers rich ethnographic data about determinants by seeking to understand the people, tasks, and environments involved from stakeholder perspectives. The assessment includes semi-structured observations (including shadowing intended EBI users) to offset self-report biases. Researchers document activities, interactions, events, physical layout, and flows of communication through combined written and audio-recorded field notes. Ethnographic interviews range from informal during observation to formal scheduled interactions with key informants, using unstructured, descriptive, task-related questions to document barriers and their characteristics [51].

• Design Probes: As a user-centered research approach, design probes utilize toolkits including items such as disposable cameras, albums, and illustrated cards to capture new perspectives not revealed through observation and interviews alone. End users are prompted to complete tasks such as "Describe a typical day" or "Describe using [the EBI]" through photographs, diary entries, maps, or collages over one week. This method generates insights into lived experiences, feelings, and attitudes. In follow-up interviews, participants reflect on their engagement with the tasks, enabling researchers to identify new determinants, corroborate findings from other methods, and describe determinant meaning and importance to end users [51].

Protocol for Determinant Prioritization

Following identification, determinants are systematically prioritized using three criteria that move beyond traditional stakeholder ratings of feasibility to address factors with greatest potential impact on implementation success [51]:

• Criticality: Assesses how a determinant affects or likely affects an implementation outcome. Some determinants are prerequisites for outcomes (e.g., awareness of the EBI), while others influence outcomes through their potency (e.g., strength of negative attitudes).

• Chronicity: Measures how frequently a determinant event occurs (e.g., shortages of critical supplies) or how long a determinant state persists (e.g., unsupportive leadership).

• Ubiquity: Evaluates how pervasive a determinant is across the implementation setting.

For each identified determinant, granular data generated through the identification methods are organized in a table by these three criteria. Three researchers and three stakeholders independently rate each determinant using a 4-point Likert scale (0-3). Priority scores and inter-rater agreement are then calculated, producing a final list of determinants ordered by priority scores [51].

Stage 2: Methods to Match Strategies

Protocol for Developing Causal Pathway Diagrams

Stage 2 focuses on matching implementation strategies to the high-priority determinants identified in Stage 1 through the development of causal pathway diagrams (CPDs). Drawing on Agile Science principles, CPDs explicitly represent the best available evidence and hypotheses about mechanisms by which implementation strategies impact target determinants and subsequent implementation outcomes [51]. The CPD development protocol involves specifying seven key factors:

• Implementation Strategy: The specific strategy intended to influence the target determinant, clearly defined and operationalized.

• Mechanism: The hypothesized process or event through which the strategy affects the determinant—the "how" or "why" the strategy works.

• Prioritized Target Determinant: The specific high-priority barrier or facilitator identified in Stage 1 that the strategy is designed to address.

• Proximal Outcomes: Observable, measurable short-term changes that rapidly indicate strategy impact on the mechanism, determinant, and concrete behaviors leading to distal implementation outcomes. These should be detectable immediately after strategy exposure or following a single strategy dose.

• Preconditions: Factors necessary for implementation mechanism activation, including intrapersonal, interpersonal, organizational, or other factors that must be present for the strategy to activate the mechanism or for the mechanism to affect the determinant.

• Moderators: Factors that increase or decrease the level of influence that an implementation strategy has on prioritized outcomes, operating at multiple levels (intrapersonal, interpersonal, organizational) and potentially affecting strategy impact at multiple points on the causal path.

• Implementation Outcomes: The ultimate distal outcomes that should be altered by determinant changes, such as adoption, fidelity, or sustainment [51].

CPD Application in Implementation Studies

In practice, CPDs serve as an organizing structure for accumulating knowledge across studies and drive precision in terminology for easier comparison of results. They articulate testable hypotheses about factors influencing implementation strategy functions, formulate proximal outcomes for rapid assessment, inform choice of study designs by clarifying temporal dynamics, and make evidence more usable for implementers [51]. The explicit specification of mechanisms addresses the critical barrier of incomplete knowledge about how strategies produce their effects, moving beyond the traditional approach of selecting strategies based on general effectiveness rather than precise barrier-mechanism alignment [2] [52].

Table 2: Causal Pathway Diagram Components and Specifications

Component	Definition	Specification Requirements	Measurement Approach
Implementation Strategy	The specific approach used to enhance adoption of EBIs	Clear definition, operationalization, and specification of dose, format, and source	Documented through strategy specification tools and tracking logs
Mechanism	The process or event through which strategies affect determinants	Explicit hypothesis about how strategy works; stated as a testable pathway	Measured through proximal outcomes that capture mechanism activation
Target Determinant	The prioritized barrier or facilitator from Stage 1	Clear link to prioritization criteria (criticality, chronicity, ubiquity)	Previously identified and rated through Stage 1 methods
Proximal Outcomes	Short-term, observable changes indicating mechanism activation	Must be measurable quickly after strategy exposure; concrete and actionable	Brief surveys, direct observation, rapid assessment tools
Preconditions	Factors necessary for mechanism activation	Explicit statement of necessary conditions before strategy deployment	Assessment of contextual factors prior to implementation
Moderators	Factors that amplify or weaken strategy effects	Identification of potential effect modifiers at multiple levels	Measured at baseline and throughout implementation
Distal Implementation Outcomes	Ultimate outcomes of implementation efforts	Clear linkage through causal pathway from strategy to outcome	Standard implementation outcome measures (adoption, fidelity, etc.)

Stage 3: Methods to Optimize Strategies

Protocol for Strategy Optimization

Stage 3 addresses limitations of traditional implementation research, which often jumps from pilot studies to randomized controlled trials (RCTs) without optimizing strategy components [51]. This approach leaves researchers unable to determine which components drive effects, if all components are needed, or if the delivery format, source, or dose is optimal. The OPTICC optimization protocol employs multiple methods for efficient and economical strategy refinement:

• Factorial Designs: Using principles from the Multiphase Optimization Strategy (MOST), factorial designs efficiently test multiple strategy components simultaneously to identify active ingredients and their interactions. These designs enable researchers to determine which components contribute meaningfully to outcomes and which can be eliminated to improve efficiency. The approach involves screening multiple potential strategy variations to identify the most promising combinations for further testing, moving beyond the "package" approach of multicomponent strategies that obscures active ingredients [51] [53].

• Efficient Prototyping: This method supports rapid iteration and refinement of strategy components before large-scale evaluation. Drawing from user-centered design principles, efficient prototyping involves creating preliminary versions of implementation strategies and obtaining feedback from stakeholders through structured processes. The iterative cycle of design-feedback-refinement continues until strategies demonstrate sufficient promise for more rigorous testing, ensuring that strategies are optimized for acceptability, appropriateness, and feasibility before significant resources are invested in evaluation [53].

• Rapid Analog Methods (RAM) Tests: RAM tests assess signals of strategy effectiveness using proximal outcomes in controlled or analog conditions before proceeding to real-world trials. These tests evaluate whether strategies demonstrate anticipated effects on immediate targets using outcomes that can be measured quickly following strategy exposure. The approach allows for efficient screening of multiple strategy variations and focuses resources on the most promising candidates for further evaluation [53].

Optimization in Practice

Applied examples from OPTICC-funded research demonstrate the optimization protocol in practice. The ProCRCScreen study, focused on increasing colorectal cancer screening in federally qualified health centers, employs factorial designs to optimize practice facilitation by testing different strategy components and their combinations [53]. Similarly, the PATH study (Patient-centered Approach to Tailoring HPV self-sampling) uses efficient prototyping to maximize patient preference for outreach materials and maximize reach of home HPV testing through tailored approaches [53]. These practical applications highlight how Stage 3 methods enable researchers to construct strategies that precisely impact their target determinants while maximizing efficiency and effectiveness.

Research Reagents and Tools

Table 3: Essential Research Reagents for OPTICC Methodology Implementation

Research Reagent	Function/Purpose	Application Context	Implementation Considerations
Determinant Assessment Toolkit	Standardized protocols for identifying implementation determinants	Stage 1: Combining rapid evidence reviews, ethnographic assessment, and design probes	Requires training in mixed methods; adaptable to specific settings and EBIs
Determinant Prioritization Matrix	Structured tool for rating determinants by criticality, chronicity, and ubiquity	Stage 1: Systematic prioritization of determinants following identification	Involves multiple raters (researchers and stakeholders); calculates priority scores and inter-rater agreement
Causal Pathway Diagram Templates	Standardized frameworks for specifying strategy-mechanism-determinant relationships	Stage 2: Matching strategies to determinants using hypothesized causal pathways	Supports explicit hypothesis testing; requires understanding of implementation mechanisms
Proximal Outcome Measures	Brief assessment tools capturing short-term changes indicating mechanism activation	Stage 2 & 3: Testing causal pathways and strategy optimization	Must be sensitive to immediate changes; actionable and pragmatic for rapid cycling
Strategy Prototyping Protocols	Structured approaches for iterative strategy development and refinement	Stage 3: Efficient prototyping of implementation strategies	Incorporates user-centered design principles; requires stakeholder engagement throughout
Factorial Experimental Designs	Efficient experimental designs for testing multiple strategy components simultaneously	Stage 3: Optimization of multicomponent implementation strategies	Based on MOST framework; enables identification of active ingredients and interactions
Implementation Laboratory Network	Diverse clinical and community partners for rapid, "in-clinic" implementation studies	All Stages: Real-world testing and refinement of OPTICC methods	Includes primary care clinics, health systems, cancer centers, and health departments across multiple states [2]

Application in Cancer Control Research

The OPTICC model has been applied across multiple cancer control contexts, demonstrating its versatility and effectiveness. Implementation studies within the OPTICC network focus on optimizing evidence-based interventions across the cancer care continuum, including prevention, screening, diagnosis, treatment, and survivorship [2] [53]. The Implementation Laboratory (I-Lab) core coordinates a network of diverse clinical and community sites where studies are conducted to optimize EBI implementation, implement cancer control EBIs, and shape the Center's agenda [2]. This laboratory includes eight networks and organizations across six states, representing primary care clinics, larger health systems, cancer centers, and health departments [2].

Specific research applications include the ProCRCScreen study, which aims to increase colorectal cancer screening in federally qualified health centers through optimized implementation, utilizing Stage I methods (rapid evidence reviews, rapid ethnography, design probes, determinant prioritization), Stage II methods (causal pathway diagrams), and Stage III methods (factorial design) [53]. Similarly, the PATH study focuses on developing patient-centered outreach materials to increase home HPV testing for cervical cancer screening, employing causal pathway diagrams (Stage II) and efficient prototyping with factorial designs (Stage III) to maximize patient preference and reach [53]. These practical applications demonstrate how the systematic OPTICC approach enables precision implementation tailored to specific cancer control challenges and contexts.

The OPTICC Center model represents a significant advancement in implementation science methodology for cancer control. Through its systematic three-stage approach to identifying and prioritizing determinants, matching strategies using causal pathway diagrams, and optimizing strategies through efficient experimental methods, OPTICC addresses critical barriers that have limited the impact of implementation science. The structured protocols, tools, and reagents developed by OPTICC provide implementation researchers and practitioners with a comprehensive framework for moving beyond "implementation as usual" toward precision implementation that ensures strategies are optimally matched to context and barriers. As the field continues to evolve, the OPTICC approach offers a promising pathway for realizing the full potential of evidence-based interventions to reduce the cancer burden and address disparities in cancer outcomes.

Economic Evaluation and Costing in Resource-Constrained Settings

Within the framework of implementation science for cancer control optimization research, economic evaluations are paramount for informing decisions about the allocation of scarce resources. These analyses provide critical data on the costs and outcomes of implementing evidence-based interventions (EBIs), ensuring that scaling efforts are not only effective but also efficient and financially viable, particularly in resource-constrained settings [3] [54]. The core challenge is to extend the benefits of EBIs to larger populations, often with the hope of achieving economies of scale, without which the sustainability of cancer control programs can be compromised [54]. This document outlines standardized protocols and application notes for conducting robust economic evaluations, with a specific focus on contexts where financial and health system capacities are limited.

Core Economic Evaluation Frameworks and Methods

Economic evaluations in healthcare estimate the value for money of health technologies through the assessment of comparative costs and clinical impacts [55]. Selecting the appropriate type of analysis is the first critical step in the evaluation process. The table below summarizes the primary evaluation methods applicable to cancer control implementation.

Table 1: Types of Full Economic Evaluations

Analysis Type	Primary Question	Cost Measurement	Outcome Measurement	Key Metric
Cost-Effectiveness Analysis (CEA)	What is the cost per unit of natural effect?	Monetary units	Natural units (e.g., lives saved, cases detected)	Incremental Cost-Effectiveness Ratio (ICER)
Cost-Utility Analysis (CUA)	What is the cost per unit of health-related quality of life?	Monetary units	Quality-Adjusted Life Years (QALYs) or Disability-Adjusted Life Years (DALYs)	Incremental Cost-Utility Ratio (ICUR)
Cost-Benefit Analysis (CBA)	Do the benefits outweigh the costs?	Monetary units	Monetary units	Net Benefit, Cost-Benefit Ratio
Cost-Minimization Analysis (CMA)	What is the least costly option?	Monetary units	Assumed to be equivalent	Difference in cost

Beyond full economic evaluations, partial evaluations such as cost-analysis and budget impact analysis are also valuable. Cost-analysis describes and quantifies the costs associated with an intervention without linking them to outcomes, which is often a necessary first step. Budget impact analysis forecasts the financial consequence of adopting a new intervention within a specific healthcare system, which is crucial for planning in resource-constrained environments [54].

A systematic survey of economic evaluations in oncology found that the majority are based on data from randomized controlled trials (82%) and utilize a cost-utility approach (82%) [55]. Common model structures include Markov models (49%) and partitioned survival models (17%) to extrapolate clinical outcomes, such as overall survival and progression-free survival, beyond the duration of clinical studies [55].

Application Notes: Costing in Resource-Constrained Settings

Key Cost Components for Scaling EBIs

When scaling cancer control EBIs, a comprehensive identification of costs is essential. These costs can be categorized as follows [54]:

• Direct Costs: These include both direct medical costs (e.g., medications, laboratory tests, healthcare professional time) and direct non-medical costs (e.g., patient transportation, caregiver expenses).
• Indirect Costs: These encompass a broader range of expenditures, including:
  • Capital costs: For building or renovating facilities.
  • Utility costs: For electricity, water, and internet.
  • Opportunity costs: The value of the next best alternative foregone by investing resources in the chosen intervention.
  • Productivity costs: Losses related to patient or caregiver absence from work.
  • Maintenance costs: For equipment and infrastructure.
  • Support personnel costs: For administrative and management staff.

Protocol: Micro-Costing of an EBI Implementation Strategy

Aim: To conduct a detailed, bottom-up costing of a specific implementation strategy (e.g., clinical reminders for cancer screening) from a designated perspective (e.g., healthcare system).

Materials & Methods:

• Perspective: Define the viewpoint of the analysis (e.g., healthcare provider, payer, societal) as it determines which costs are relevant.
• Time Horizon: Specify the period over which costs are collected (e.g., one year for a pilot program).
• Data Collection Tools: Standardized data extraction forms, time-and-motion study templates, and activity logs.

Procedure:

• Identify Resource Inputs: Create a comprehensive list of all resources required for the implementation strategy. This includes personnel (e.g., coordinators, trainers), materials (e.g., printed guidelines, software licenses), equipment, and space.
• Measure Quantities: For each resource, quantify the volume used. For personnel, record time spent on implementation activities. For materials, record the number of units consumed.
• Assign Unit Costs: Attach a monetary value to each unit of resource. Use source data such as:
  • Personnel: Salary and benefit data from human resources departments.
  • Materials and Equipment: Purchase invoices or price catalogs.
  • Space: Local rental market rates or facility accounting data.
• Calculate Total Costs: For each resource item, multiply the quantity by the unit cost. Sum all costs to generate the total cost of the implementation strategy.
• Conduct Sensitivity Analysis: Vary key cost parameters (e.g., personnel time, price of materials) in a one-way or probabilistic sensitivity analysis to test the robustness of the results and identify key cost drivers.

Protocol: Costing the Scale-Up of a Cervical Cancer Screening Program

Aim: To estimate the incremental costs of scaling up a cervical cancer screening program based on visual inspection with acetic acid (VIA) from a pilot district to a national level.

Materials & Methods:

• Framework: Use the "ingredients approach" to identify all resources needed for the scaled-up program.
• Costing Perspective: Ministry of Health and patient (societal).
• Modeling Tool: Microsoft Excel-based costing model.

Procedure:

• Define Scale-Up Parameters: Determine the target population size, geographic coverage, and implementation timeframe (e.g., 5 years).
• Map the Scaling Process: Identify all activities required for horizontal (phased) or vertical (policy-driven) scale-up. Key activities may include:
  • Training of Trainers: Master trainers educate regional trainers.
  • Health Worker Training: Regional trainers conduct VIA training for nurses at primary health centers.
  • Community Mobilization: Awareness campaigns to encourage screening uptake.
  • Service Delivery: Conducting VIA tests, managing results, and providing treatment (e.g., cryotherapy).
  • Monitoring & Supervision: Ongoing program oversight and quality assurance.
• Identify and Value Scale-Up Costs: Categorize and collect data for costs, differentiating between initial setup (investment) and recurrent costs. Table 2: Illustrative Cost Categories for Scaling a Screening Program

  Cost Category	Initial Setup Costs (Year 1)	Recurrent Costs (Annual)
  Personnel	Salaries for temporary master trainers	Salaries for screening nurses, supervisors, data clerks
  Training	Travel, per diems, training materials for workshops	Refresher training materials
  Equipment & Supplies	Purchase of cryotherapy machines, examination tables	VIA supplies (acetic acid, swabs, gloves), cryogas
  Infrastructure & Utilities	-	Rent, electricity, water, and cleaning for clinics
  Communication & Transport	Development of awareness materials (posters, radio spots)	Transport for supervision and sample referral
  Monitoring & Data Management	Development and printing of registers, data tools	Data entry, reporting, and program management

• Analyze for Economies of Scale: Calculate the average cost per person screened at different levels of scale (e.g., district vs. national). Observe if the average cost decreases as the number of people screened increases, indicating economies of scale.
• Report Findings: Present total costs, average cost per person screened, and a breakdown of costs by category and year. Highlight the major cost drivers to inform budget planning.

Diagram 1: Economic Evaluation Workflow

The Scientist's Toolkit: Research Reagent Solutions

In the context of economic evaluation, "research reagents" refer to the key methodological tools and data sources required to conduct a rigorous analysis. The following table details these essential components.

Table 3: Key Methodological Tools for Economic Evaluation

Tool / Resource	Function / Definition	Application Notes
Costing Inventory Template	A standardized checklist and data extraction form to identify and record all relevant costs (direct and indirect).	Ensures no major cost categories are missed during the micro-costing protocol. Crucial for consistency across sites in multi-center studies [54].
Modeling Software (e.g., R, TreeAge, Excel)	Software platforms used to build mathematical models (e.g., Markov, Partitioned Survival) to simulate disease progression and extrapolate outcomes.	Markov models are used in nearly half of oncology economic evaluations. Excel is commonly used for statistical testing and simpler models [55].
Clinical Trial Data (e.g., from RCTs)	The primary source of efficacy and safety data for the evidence-based intervention.	82% of economic evaluations in oncology are based on RCT data. This forms the foundation of the clinical effectiveness estimate [55].
Survival Analysis Software & Distributions	Tools and statistical distributions (e.g., Weibull, Exponential, Gompertz) used to extrapolate survival data beyond the trial period.	The Weibull distribution is the most commonly used (64%) for overall survival extrapolation. Justification for the chosen distribution is often lacking [55].
National Utility Weights	Country-specific values that reflect the preference for a health state, typically on a scale from 0 (death) to 1 (full health), used to calculate QALYs.	Necessary for cost-utility analysis. Values should be sourced from the local population or a relevant, validated dataset for the specific condition.
Sensitivity Analysis Scripts	Pre-written code (e.g., in R) to perform one-way, multi-way, and probabilistic sensitivity analyses automatically.	Tests the robustness of the model results and identifies key drivers of cost-effectiveness, which is critical for decision-makers under uncertainty.

Integrating rigorous economic evaluations into implementation science for cancer control provides a structured framework for achieving equitable and feasible policies, especially in resource-constrained settings [3]. By applying the detailed protocols for micro-costing and scale-up costing, and by utilizing the essential tools outlined in the Scientist's Toolkit, researchers can generate high-quality evidence on resource requirements and efficiency. This enables realistic goal setting, informed strategic planning, and ultimately, the optimization of cancer control implementation to maximize population health impact with the available resources.

Strategies for Effective Scale-Up and Spread of Evidence-Based Interventions

Scale-up is a systematic process aimed at expanding the coverage of evidence-based interventions (EBIs) to benefit larger populations across broader geographic areas or healthcare settings [56]. Within the context of cancer control optimization, effective scale-up is critical for translating research findings into widespread clinical practice, thereby improving patient outcomes and health system efficiency [24]. This document outlines evidence-based strategies and provides practical protocols for the successful scale-up of cancer control interventions, specifically designed for researchers, scientists, and drug development professionals.

The distinction between scale-up and spread is important for implementation planning. Scale-up typically refers to a deliberate, often phased, effort to expand successful local programs to regional, national, or international levels, frequently involving policy or system-level changes. In contrast, spread often suggests a more organic, diffusion-based process within an existing health system [56]. For cancer control, a systematic scale-up approach is often necessary to achieve population-level impact.

Core Scaling-Up Strategies and Components

A systematic review of scaling up EBPs in primary care identified five key strategic components that are equally critical in the context of cancer control [56]. These components provide a framework for developing a comprehensive scale-up plan.

Table 1: Key Components of Scaling-Up Strategies for Cancer Interventions

Component Category	Description	Application in Cancer Control
Healthcare Infrastructure	Providing medical equipment, changing health system linkages, and enhancing service delivery platforms [56].	Integrating cancer screening tools into primary care, establishing referral networks for specialized oncology care.
Policy and Regulation	Revising policies to support widespread implementation of EBIs [56].	Updating national cancer control plans (NCCPs) to include new evidence-based therapies or preventive measures.
Financing	Modifying payment mechanisms and securing sustainable funding [56].	Developing reimbursement models for molecular profiling in personalized cancer therapy.
Human Resources	Training, deploying, and supporting healthcare providers; changing administrative roles [56].	Training primary care physicians in early cancer detection and community health workers in patient navigation.
Patient and Public Involvement	Engaging patients and the public in recruitment, promotion, or intervention design [56].	Involving cancer survivors in awareness campaigns and co-designing patient education materials.

These components can be operationalized through two primary scaling approaches [57]:

• Vertical Scaling: Involves the simultaneous introduction of interventions throughout a health system, often institutionalized through policy, regulatory, or financial changes. Example: A national policy mandating the inclusion of a new, cost-effective cancer drug in the essential medicines list.
• Horizontal Scaling: Involves a phased introduction of an intervention to different sites and populations. Example: Rolling out a successful hospital-based survivorship care program to other oncology centers in a stepwise manner.

Economic Evaluation of Scale-Up

Economic considerations are paramount for sustainable scale-up. A systematic review highlights that rigorous economic evaluations are essential yet often lacking in scale-up studies [57]. Understanding the full cost implications is vital for decision-makers operating under resource constraints, particularly in low- and middle-income countries (LMICs).

Table 2: Cost Components in Economic Evaluations of Scaling-Up Cancer Interventions

Cost Category	Description	Examples in Cancer Control Scale-Up
Direct Medical Costs	Costs directly associated with the delivery of the medical intervention [57].	Costs of chemotherapeutic drugs, laboratory tests for monitoring, and administration supplies.
Direct Non-Medical Costs	Direct costs not related to medical treatment [57].	Transportation costs for patients to access treatment centers, caregiver expenses.
Indirect Costs	Costs related to lost productivity [57].	Patient and caregiver time away from work due to illness or treatment appointments.
Capital Costs	One-time investments in physical assets [57].	Purchasing radiotherapy machines, building new cancer centers, or acquiring IT systems.
Health System Costs (Scaling-Up)	Costs specific to the scaling-up strategy itself [57].	Costs for health professional training, program management, monitoring and evaluation systems, and community mobilization for a new screening program.

The most common methods for economic evaluation are cost-effectiveness analysis (CEA), which measures health gains (e.g., cost per Quality-Adjusted Life Year (QALY) gained), and cost-benefit analysis (CBA), which values both costs and a broader range of benefits in monetary terms [57]. When scaling up cancer interventions, it is critical to evaluate the cost-effectiveness of the scaling-up strategy itself, not just the underlying intervention, as scale-up can incur significant additional costs and potentially lead to economies or diseconomies of scale [57].

Implementation Science Framework for Cancer Control Planning

Implementation science (IS) provides theories and methods to enhance the adoption and integration of EBIs. A recent scoping review of cancer control plans (NCCPs) in LMICs revealed that while many plans incorporate IS elements like stakeholder engagement and impact measurement, these are often applied inconsistently and without structured methodologies [24]. Furthermore, a critical gap was identified: none of the assessed plans included a formal assessment of health system capacity to determine readiness for implementing new interventions [24].

To address these gaps, the following pathway is proposed for integrating IS into national cancer control planning [24] [4]:

• Structured Situational Analysis: Systematically analyze the cancer burden, existing services, and policy landscape.
• Explicit Health System Capacity Assessment: Evaluate workforce, infrastructure, and financing before selecting interventions for scale-up.
• Planned, Inclusive Stakeholder Engagement: Involve patients, providers, policymakers, and payers throughout the planning process.
• Economic Evaluation and Costing: Integrate cost-effectiveness and budget impact analyses from the outset.
• Integrated Impact Measurement: Define key performance indicators and assign responsibility for monitoring and evaluation.

Experimental Protocols for Scaling-Up Studies

Protocol 1: Stepped-Wedge Cluster Randomized Trial for Horizontal Scaling

This design is ideal for evaluating the phased rollout of a cancer control intervention across multiple sites when a simultaneous rollout is not feasible.

Objective: To assess the effectiveness and implementation outcomes of a patient navigation program to reduce time to diagnosis across 12 primary care clinics. Design: Stepped-wedge cluster randomized trial (SW-CRT). Clinics (clusters) are randomly assigned to sequences determining when they will cross over from control to intervention phase. Primary Outcome: Time from suspicious finding to definitive diagnosis. Secondary Outcomes: Scale-up coverage, cost per patient navigated, provider fidelity.

Procedure:

• Baseline Period (Months 1-3): Collect baseline data on primary and secondary outcomes from all 12 clinics.
• Randomization (Month 4): Randomly assign clinics to one of four sequences (3 clinics each) to initiate the intervention at 3-month intervals.
• Intervention Rollout (Months 5-16):
  • Sequence 1: Starts intervention at Month 5.
  • Sequence 2: Starts intervention at Month 8.
  • Sequence 3: Starts intervention at Month 11.
  • Sequence 4: Starts intervention at Month 14.
• Intervention Components:
  • Training: 2-day workshop for assigned patient navigators.
  • Clinical Toolkit: Standardized protocols for tracking patients and overcoming barriers.
  • Clinical Support: Weekly supervision calls for navigators.
• Data Collection: Collect outcome data continuously from all clinics throughout the trial period, regardless of intervention status.

Protocol 2: Assessing Scaling-Up Coverage and Fidelity

This protocol provides a methodology for monitoring key scale-up process metrics.

Objective: To measure the coverage and fidelity of a scaled-up colorectal cancer screening program in a network of community health centers. Indicators:

• Coverage: (Number of eligible individuals offered screening / Total number of eligible individuals in target population) x 100 [56].
• Fidelity Score: Adherence to the core screening protocol components (e.g., proper specimen collection, patient education, result notification).

Procedure:

• Define Denominator: Use electronic health records to define the target population (e.g., adults aged 50-75 without prior history of CRC).
• Extract Numerator Data: Monthly extraction of data on the number of individuals offered a screening test (FIT kit or colonoscopy referral).
• Fidelity Audit: Randomly select 5% of patient records per month per site to audit against a 10-item fidelity checklist.
• Data Analysis:
  • Calculate monthly coverage rates for each site and overall.
  • Calculate average fidelity scores.
  • Plot coverage and fidelity over time to identify trends and sites needing support.

The Scientist's Toolkit: Research Reagents and Essential Materials

Table 3: Key Reagents and Materials for Cancer Control Implementation Research

Item/Tool	Function/Application	Example in Scale-Up Research
Implementation Science Frameworks	Provide a structured guide for planning, executing, and evaluating the implementation process [24].	Using the Consolidated Framework for Implementation Research (CFIR) to identify barriers and facilitators before scaling a new genetic testing service.
Economic Evaluation Models	Simulate the costs and health outcomes of an intervention under different scale-up scenarios [57].	A Markov model to estimate the long-term cost-effectiveness of scaling up HPV self-sampling for cervical cancer screening.
Stakeholder Engagement Platforms	Formal structures to facilitate collaboration and input from key groups [24].	Establishing a multi-stakeholder steering committee (patients, oncologists, payers) to guide the scale-up of a palliative care program.
Fidelity Assessment Tools	Measure the degree to which an intervention is implemented as originally intended [56].	A checklist and direct observation tool to ensure community health workers are delivering a smoking cessation intervention with high fidelity.
Data Visualization Software	Creates clear, accessible charts and graphs to communicate scale-up progress and outcomes to diverse audiences [58].	Using software to generate line charts showing trends in screening coverage or bar charts comparing fidelity scores across sites.

Visualization of a Molecular Pathway and Intervention Target

Understanding the molecular basis of a cancer is often a prerequisite for developing and scaling targeted therapies. The diagram below illustrates a simplified signaling pathway, a common target for new drugs.

In the field of implementation science for cancer control, a critical tension exists between maintaining fidelity to evidence-based interventions (EBIs) and adapting them to fit diverse local contexts. Fidelity refers to "the degree to which an intervention is delivered, enacted, and received as intended by its developers," while adaptation represents "the deliberate alteration of an intervention's design or delivery to improve its fit in a given context" [59]. Achieving the optimal balance between these competing demands is essential for successful implementation and sustainability of cancer control programs, particularly when addressing disparities in healthcare access and quality across different populations and settings.

The National Cancer Institute's Optimizing Implementation in Cancer Control (OPTICC) center highlights that EBI implementation is often suboptimal in routine practice, noting that "evidence-based interventions (EBIs) could reduce cervical cancer deaths by 90%, colorectal cancer deaths by 70%, and lung cancer deaths by 95% if widely and effectively implemented in the USA" [44]. This striking gap between potential and actual impact underscores the urgent need for sophisticated approaches to balancing adaptation and fidelity in cancer control implementation research. The Extension for Community Healthcare Outcomes (ECHO) model exemplifies this challenge, as it must maintain core elements while adapting to different cancer types, healthcare settings, and professional needs [40].

Quantitative Evidence: Measuring Adaptation and Fidelity Outcomes

Robust quantitative evaluation provides critical insights into how adaptation-fidelity balance influences implementation success in cancer control. The American Cancer Society's ECHO programs demonstrate how structured implementation strategies can maintain core elements while allowing contextual adaptation.

Table 1: Quantitative Outcomes from ACS ECHO Cancer Programs

Program Characteristic	Program A (Tobacco Cessation)	Program B (Colorectal Screening)	Program C (Prostate Screening)	Program D (Caregiving)
Participants	195	45	59	132
Program Length	4 months	7 months	9 months	7 months
Program Type	Public	Private	Private	Private
Knowledge Increase (5-point scale)	+0.84 (average)	+0.84 (average)	+0.84 (average)	+0.84 (average)
Confidence Increase (5-point scale)	+0.77 (average)	+0.77 (average)	+0.77 (average)	+0.77 (average)
Participants Planning to Use Information Within 1 Month	59%	59%	59%	59%

Source: Adapted from American Cancer Society ECHO Program evaluation [40]

The ACS ECHO implementation maintained fidelity to the core ECHO model elements—including virtual telementoring, didactic presentations, and case-based learning—while allowing adaptations to cancer focus, program length, and recruitment strategies based on specific context needs [40]. This balanced approach resulted in consistent knowledge and confidence improvements across all four programs, demonstrating that "both fidelity and adaptation are necessary for high-quality implementation" [59].

Conceptual Framework and Theoretical Underpinnings

The relationship between adaptation, fidelity, and implementation success can be visualized through a conceptual framework that highlights their dynamic interaction:

Figure 1: Dynamic Framework for Balancing Fidelity and Adaptation in Implementation Science. This diagram illustrates the continuous interaction between maintaining core intervention elements (fidelity) and making contextually appropriate modifications (adaptation) to achieve optimal implementation and health outcomes.

The framework illustrates how evidence-based interventions and contextual assessment inform both fidelity monitoring and adaptation processes, which together determine implementation outcomes and ultimately impact health outcomes. The bidirectional relationship between fidelity and adaptation reflects their interconnected nature in successful implementation.

Experimental Protocols and Methodologies

Protocol 1: Hybrid Type 3 Trial for GI Cancer Screening Implementation

A large cluster-randomized implementation study protocol provides a rigorous methodology for comparing implementation strategies while balancing adaptation and fidelity [5].

Aim: To compare the effectiveness of patient navigation versus external facilitation for supporting hepatocellular carcinoma (HCC) and colorectal cancer (CRC) screening completion.

Design: Two hybrid type 3, cluster-randomized trials comparing implementation strategies while monitoring intervention fidelity and allowing prescribed adaptations.

Settings and Participants:

• 24 sites for HCC trial, 32 sites for CRC trial in Veterans Health Administration
• Inclusion criteria: Sites below VA national median on GI cancer screening completion
• Patient participants: Veterans with cirrhosis (HCC trial) or abnormal stool tests (CRC trial)

Implementation Strategies:

• Facilitation Arm: Sites participate in "Getting To Implementation" (GTI), a manualized intervention with external facilitation
• Patient Navigation Arm: Sites receive Patient Navigation Toolkit with core components and flexible implementation

Fidelity and Adaptation Management:

• Fidelity tracked monthly using standard data collection forms
• Adaptations documented using FRAME-IS (Framework for Reporting Adaptations and Modifications to Evidence-based Implementation Strategies)
• Core functions preserved while allowing contextual adaptations

Outcomes Measurement:

• Primary: Reach of cancer screening completion
• Secondary: Implementation outcomes (adoption, appropriateness, feasibility, sustainability)
• Mechanisms: Multi-level determinants, preconditions, and moderators

This protocol explicitly addresses the adaptation-fidelity balance by "defining the mechanisms of actions of complex strategies" to "aid in replication and scaling, a major implementation challenge" [5].

Protocol 2: Optimization Approach for Program Scale-Up

The Choose to Move (CTM) Phase 4 study exemplifies a systematic approach to optimizing programs for scale-up while maintaining core functions [60].

Optimization Process:

• Analysis of previous implementation data to identify resource-intensive components
• Identification of core functions (essential behavior change techniques that drive effectiveness)
• Systematic adaptation to reduce resource use while preserving core functions
• Evaluation using hybrid effectiveness-implementation design

Key Adaptation:

• Reduced activity coach hours by 40% (from 67 to 40 hours per program)
• Maintained all core functions including goal setting, action planning, and peer support
• Modified program structure (shorter consultation, more group meetings, eliminated check-ins)

This approach demonstrates how "optimizing effective health-promoting programs to enhance their scalability and sustainability provides an important pathway to improved population health" while maintaining fidelity to core intervention mechanisms [60].

The Scientist's Toolkit: Essential Methods and Frameworks

Table 2: Research Reagent Solutions for Balancing Adaptation and Fidelity

Tool/Framework	Primary Function	Application Context
FRAME-IS	Document adaptations to implementation strategies	Systematic tracking of modifications during implementation
Core Functions Framework	Identify essential elements that must be preserved	Distinguishing between adaptable forms and immutable functions
Hybrid Type 3 Trial Design	Compare implementation strategies while monitoring outcomes	Simultaneously testing implementation strategies and evaluating health outcomes
Getting To Implementation (GTI)	Structured facilitation process for implementation	Guided implementation with balanced adaptation and fidelity
RE-AIM Framework	Evaluate multiple implementation dimensions	Assessing Reach, Effectiveness, Adoption, Implementation, Maintenance

The FRAME-IS framework is particularly valuable for "documenting the adaptations made during the study" while maintaining fidelity assessment [59]. The core functions approach helps distinguish "the essential elements (i.e., specific behavior change techniques such as goal setting and action planning) of the intervention that drive change and make the EBI 'work'" from adaptable elements [60].

Implementation Pathways: Strategic Approaches for Cancer Control

The OPTICC center outlines a three-stage approach to optimizing EBI implementation in cancer control: "(I) identify and prioritize determinants, (II) match strategies, and (III) optimize strategies" [44]. This approach acknowledges four critical barriers that must be overcome:

• Underdeveloped methods for determinant identification and prioritization
• Incomplete knowledge of strategy mechanisms
• Underuse of methods for optimizing strategies
• Poor measurement of implementation constructs

The pathway for addressing these barriers can be visualized as follows:

Figure 2: Implementation Pathway for Addressing Critical Barriers in Cancer Control. This diagram outlines the strategic approach to overcoming key challenges in implementation science through targeted solutions that collectively contribute to optimized evidence-based intervention implementation.

The pathway illustrates how addressing each barrier with specific solutions contributes to optimized implementation, emphasizing that "for implementation science to support optimized EBI implementation, four critical barriers must be overcome" [44].

Case Applications in Cancer Control

Case 1: Audit and Feedback for Transition Care

The Bridging the Gap study implemented an audit and feedback (AF)-based intervention across five pediatric diabetes centers, explicitly addressing the "balance between fidelity and adaptation in complex multi-faceted and multi-site interventions" [59].

Fidelity Assessment Framework:

• Delivery: Whether research team delivered intervention as intended
• Receipt: Whether diabetes team members engaged with intervention
• Enactment: Whether participants used feedback to adjust care delivery

Adaptation Management:

• Sites permitted to change quality improvement initiatives within study parameters
• Structured flexibility using Got Transition framework core elements
• Preservation of fidelity while enabling contextual adaptation

This approach demonstrates how "fidelity was preserved while still enabling participants to adapt accordingly" through structured flexibility [59].

Case 2: Quantitative Evaluation of ECHO Model

The American Cancer Society's implementation of the ECHO model across four distinct cancer control programs maintained fidelity to core elements while adapting to different cancer types and professional audiences [40].

Quantitative Evaluation Strategy:

• Pre- and post-program assessments of knowledge and confidence
• Standardized Likert scales (1-5) for consistent measurement
• Aggregated analysis across programs with individual program tracking

Balanced Implementation:

• Core ECHO model elements preserved across all programs
• Adaptations in cancer focus, program length, and recruitment strategies
• Consistent positive outcomes despite contextual variations

This case demonstrates how "quantitative data can also highlight areas of improvement and inform strategies for ECHO program sustainability and expansion into new domains or regions" while maintaining fidelity to core principles [40].

The balance between adaptation and fidelity represents a central challenge in implementation science for cancer control. The evidence and protocols presented demonstrate that successful implementation requires neither rigid adherence to intervention protocols nor uncontrolled adaptation, but rather a strategic balance that preserves core functions while allowing contextual fit. The quantitative outcomes from cancer control implementations show that structured approaches to this balance can yield consistent improvements in knowledge, confidence, and implementation outcomes across diverse settings and cancer types.

As the field advances, greater attention to mechanism studies, improved measurement of implementation constructs, and systematic approaches to optimization will enhance our ability to achieve the optimal adaptation-fidelity balance. This balance is essential for realizing the full potential of evidence-based interventions to reduce cancer burden and address disparities in cancer care and outcomes.

Measuring Impact: Validation, Portfolio Analysis, and Comparative Outcomes

Portfolio Analysis of NCI-Funded Policy Implementation Science Grants

The dynamic landscape of cancer implementation science (IS) has seen a significant rise in the use of policy as a tool to improve cancer control outcomes. Policy functions not only as a directive to be adopted or implemented but also as contextual influence and as an active strategy to shape effective implementation of cancer control interventions [39] [61]. This portfolio analysis examines National Cancer Institute-funded policy implementation science grants awarded between fiscal years 2014–2023, exploring characteristics, methodological approaches, and gaps in the research portfolio. The analysis identifies critical opportunities to expand policy IS, particularly regarding policies impacting cancer diagnosis, treatment, survivorship, and environmental exposures [61]. Understanding the current scope and limitations of this research portfolio provides valuable insights for researchers aiming to optimize cancer control through evidence-based policy implementation.

Quantitative Portfolio Analysis

Grant Characteristics and Distribution

Between fiscal years 2014 and 2023, the National Cancer Institute funded 14 policy implementation science grants, representing 34.1% of the 41 IS grants initially identified [39] [61]. More than half (n=8, 57.1%) were awarded in FY2020-2023, indicating recent growth in this research area [39].

Table 1: Administrative Characteristics of NCI-Funded Policy Implementation Science Grants (FY 2014-2023)

Characteristic	Category	Frequency	Percentage
Funding Mechanism	R01	7	50%
	R21	4	28.6%
	R37	2	14.3%
	U01	1	7.1%
Notice of Funding Opportunity	Dissemination and Implementation Research in Health	7	50%
	NIH Research Project Grant (Parent R01)	3	21.4%
	Linking Provider Recommendation to Adolescent HPV Vaccine Uptake	1	7.1%
	Tobacco Control Policies to Promote Health Equity	1	7.1%
Project Location	United States	14	100%

Cancer-Specific Focus Areas

The analysis revealed distinct patterns in cancer focus across the continuum. Most grants (71.4%) addressed cancer prevention, with significantly less attention to other phases of the cancer control continuum [39].

Table 2: Cancer Characteristics of NCI-Funded Policy Implementation Science Grants

Characteristic	Category	Frequency	Percentage
Cancer Continuum Focus	Prevention	10	71.4%
	Screening	3	21.4%
	Treatment	2	14.3%
	Survivorship	2	14.3%
	Diagnosis	0	0%
Cancer Content Area	HPV Vaccination	2	14.3%
	Tobacco Control	2	14.3%
	UV Protection	2	14.3%
	Nutrition	1	7.1%
	Genomics	1	7.1%
	Rural Care Delivery	1	7.1%
	Fertility Preservation	1	7.1%
	De-implementing Low-Value Care	1	7.1%
Environmental Exposures	Policies Addressing Toxic Exposures	0	0%

Policy Conceptualization and Implementation Focus

Grants demonstrated varied conceptualizations of policy within implementation science frameworks, with most viewing policy as an implementation target rather than a strategy or contextual factor [39].

Table 3: Policy Conceptualization in NCI-Funded Implementation Science Grants

Policy Conceptualization	Frequency	Percentage
Policy as something to implement	10	71.4%
Policy as context to understand	5	35.7%
Policy as something to adopt	4	28.6%
Policy as a strategy to use	4	28.6%

Note: Percentages exceed 100% as grants could be coded for multiple conceptualizations.

The portfolio analysis revealed that grants often focused on multiple policy levels simultaneously. Organizational and state-level policies were most frequently addressed (8 grants each), while federal (3 grants) and local policies (2 grants) received less attention [39].

Methodological Protocols for Policy Implementation Science Research

Portfolio Analysis Methodology

The NCI portfolio analysis followed rigorous methodological protocols that can be adapted for future research in this domain [39] [61]:

Sample Identification Protocol:

• Data Source: Utilize the NIH Query View Report (QVR) tool to identify relevant grants
• Search Strategy: Apply keyword searches to titles, abstracts, and specific aims using two keyword groups:
  • Implementation Science terms: "implementation science," "implementation research," "healthcare delivery," "cancer care delivery," "improvement science," "quality improvement"
  • Policy terms: "policy," "policies," "law," "legal," "legislation," "ordinance," "statute," "regulation," "regulatory," "code," "rule"
• Filter Criteria: Limit to new and competing continuance grants within specified fiscal years (2014-2023)
• Grant Mechanisms: Include research grant mechanisms (U, P, and R series) while excluding training and capacity-building mechanisms

Eligibility Screening Protocol:

• Primary Review: Three independent coders review abstracts and specific aims to verify implementation science focus
• Secondary Review: Verify policy focus using established definitions:
  • Bogenschneider's definition: "the development, enactment, and implementation of a plan or course of action carried out through law, rule, code, or other mechanism in the public or private sector"
  • CDC definition: "a law, regulation, procedure, administrative action, incentive, or voluntary practice of governments and other institutions"
• Expert Consultation: Share preliminary findings with NCI IS Team to identify additional grants not captured by initial search

Coding and Analysis Protocol:

• Codebook Development: Adapt from established NIH and NCI portfolio analysis codebooks
• Variables Coded: Policy conceptualization methods, policy level targeted, alignment with NCI activities, cancer continuum focus, cancer type(s), cancer content area, study design, and theoretical frameworks used
• Dual Coding Process: Two independent coders review each grant with discrepancies resolved through consensus discussion
• Data Analysis: Calculate frequency and descriptive statistics to characterize the grant portfolio

Implementation Science Study Designs for Policy Research

The portfolio analysis revealed several effective methodological approaches for policy implementation science:

Mixed-Methods Implementation Research Protocol:

• Qualitative Component: Conduct semi-structured interviews and focus groups with policymakers, healthcare administrators, and implementers to identify barriers and facilitators
• Quantitative Component: Administer structured surveys to assess implementation outcomes across multiple sites
• Policy Analysis: Systematic document review of relevant policies, legislation, and implementation guidelines
• Integration Analysis: Triangulate qualitative and quantitative data to identify implementation determinants

Hybrid Effectiveness-Implementation Design Protocol:

• Type 2 Hybrid Design: Simultaneously test clinical interventions and implementation strategies
• Clinical Outcomes: Measure cancer-related clinical endpoints (screening rates, treatment adherence)
• Implementation Outcomes: Assess adoption, fidelity, cost, and sustainability of policy implementation
• Contextual Assessment: Evaluate organizational and policy context moderating implementation success

Figure 1: Research Workflow for Policy Implementation Science Portfolio Analysis. This diagram illustrates the sequential process for conducting portfolio analysis in policy implementation science, from initial research question development through future research prioritization.

Research Reagents and Tools for Policy Implementation Science

Table 4: Essential Research Tools for Policy Implementation Science in Cancer Control

Tool Category	Specific Resource	Application in Policy IS
Theories, Models & Frameworks	Consolidated Framework for Implementation Research (CFIR)	Identifying multilevel implementation determinants in policy contexts
	RE-AIM Framework	Evaluating Reach, Effectiveness, Adoption, Implementation, and Maintenance of policy interventions
	Exploration, Preparation, Implementation, Sustainment (EPIS)	Examining policy implementation across phases
	Implementation Research Logic Model	Planning and executing policy implementation projects
Implementation Strategies	Expert Recommendations for Implementing Change (ERIC)	Selecting and specifying strategies for policy implementation
	Implementation Mapping	Using intervention mapping to develop implementation strategies
Measures & Outcomes	Implementation Outcomes Framework	Measuring adoption, fidelity, penetration, sustainability
	Systematic Reviews of Implementation Measures	Identifying psychometrically strong measures for policy contexts
Study Designs	Effectiveness-Implementation Hybrid Designs	Simultaneously testing policy effectiveness and implementation
	Quasi-Experimental Designs	Evaluating policy implementation in real-world settings
	Mixed Methods Approaches	Integrating qualitative and quantitative policy implementation data

The NCI Division of Cancer Control and Population Sciences provides curated research tools specifically for implementation science, including theories, frameworks, measures, and study design resources that can be applied to policy research [62].

Critical Research Gaps and Future Directions

The portfolio analysis identified significant opportunities for expanding policy implementation science in cancer control [39] [61]:

Underexplored Areas in the Cancer Continuum

• Diagnosis and Treatment: No grants focused on policy impacts during cancer diagnosis, and only two addressed treatment
• Survivorship Care: Limited research (2 grants) on policies supporting cancer survivors and caregivers
• Environmental Exposures: No grants examined policies addressing toxic and environmental exposures
• Health Equity: Limited focus on policies specifically designed to reduce cancer disparities

Methodological Advancements Needed

• Policy Implementation Mechanisms: Incomplete knowledge of how implementation strategies work in policy contexts
• Determinant Prioritization: Underdeveloped methods for identifying and prioritizing policy implementation determinants
• Strategy Optimization: Limited use of methods for optimizing implementation strategies for policy contexts
• Measurement Advances: Need for reliable, valid, pragmatic measures of policy implementation constructs

Scale-Up Research Opportunities

Analysis of NCI-funded scale-up research revealed only 17 implementation science grants focused on scale-up between 2005-2024, with most examining factors related to scale-up rather than testing specific strategies [25]. This represents a significant opportunity for policy implementation science to develop and test strategies for scaling up evidence-based policies across diverse settings and populations.

This portfolio analysis demonstrates that while the NCI funds policy implementation science across the cancer continuum, significant opportunities exist to expand this research, particularly regarding policies that impact cancer diagnosis, treatment, survivorship, and environmental exposures [61]. The field would benefit from increased attention to policy implementation mechanisms, improved methods for determinant identification and prioritization, and advanced approaches for optimizing implementation strategies. By addressing these gaps, researchers can contribute to the development of more effective, equitable, and efficiently implemented cancer control policies that ultimately reduce the cancer burden.

The effective scaling of evidence-based interventions (EBIs) is a critical frontier in cancer control, representing the bridge between proven scientific discoveries and population-wide health impact. The National Cancer Institute (NCI) defines scale-up research as examining how to bring EBIs to multiple sites or settings to reach a greater proportion of the population who could benefit [25]. Unlike implementation research which focuses on adoption in specific settings, scale-up research addresses the challenge of building infrastructure to support full-scale implementation across diverse contexts [25]. This application note examines the current landscape, gaps, and methodological approaches for scale-up research across the cancer continuum, providing structured protocols for researchers addressing these complex challenges.

Current Landscape of Cancer Scale-Up Research

Portfolio Analysis of Funded Research

Analysis of the NCI's investment portfolio reveals significant gaps in scale-up research. A systematic review of NCI-funded implementation science grants identified only 17 out of 61 grants focused specifically on scale-up, indicating this remains an emerging priority with limited funding allocation [25]. The distribution of these grants shows particular patterns in focus and geographic application.

Table 1: Analysis of NCI-Funded Scale-Up Research Grants (2016-2024)

Characteristic	Category	Number of Grants	Percentage
Geographic Focus	Domestic	11	64.7%
	International	6	35.3%
Research Focus	Scale-up Factors (barriers/facilitators)	11	64.7%
	Costs and Benefits of EBI Delivery	9	52.9%
	Implementation Strategies for Scale-up	7	41.2%
Cancer Continuum	Prevention	11	64.7%
	Screening	7	41.2%
Cancer Type	Cervical Cancer	6	35.3%
	Other Cancers	11	64.7%
Setting	Healthcare Settings	11	64.7%
	Other Settings	6	35.3%

Global Application in National Cancer Control Planning

The application of implementation science to national cancer control planning demonstrates the global dimension of scale-up challenges. A scoping review of National Cancer Control Plans (NCCPs) and strategies from low and medium Human Development Index (HDI) countries revealed inconsistent application of implementation science principles [3]. While many NCCPs incorporated elements such as stakeholder engagement and impact measurement, these were often unstructured and incompletely applied [3]. Critical gaps were identified in health system capacity assessment, with none of the plans assessing readiness for implementing new interventions [3].

Methodological Gaps and Research Opportunities

Key Barriers to Optimization

The OPTICC Center has identified critical methodological barriers impeding optimized scale-up in cancer control [6]. These barriers represent primary targets for future research and protocol development:

• Underdeveloped Methods for Barrier Identification: Inadequate approaches for systematically identifying and prioritizing implementation barriers across diverse settings where cancer control EBIs are delivered [6].
• Incomplete Knowledge of Strategy Mechanisms: Limited understanding of how implementation strategies work to overcome specific barriers, including their active components and contextual modifiers [6].
• Underutilization of Optimization Methods: Failure to employ existing implementation science methods for tailoring and optimizing strategies before large-scale deployment [6].
• Poor Measurement of Implementation Constructs: Inadequate measurement approaches for key implementation outcomes, processes, and mechanisms across scaled networks [6].

Priority Research Domains

Based on the portfolio analysis and scoping reviews, three priority domains emerge for methodological advancement:

• Economic Evaluation of Scaled Implementation: Only 9 of 17 NCI scale-up grants assessed costs and benefits of scaled EBI delivery [25]. Robust economic evaluation methods are needed across diverse healthcare systems.
• Strategy Effectiveness Across Contexts: Limited evidence exists for how implementation strategies perform when scaled across heterogeneous settings and populations [25] [3].
• Capacity Assessment and Building: Systematic approaches to assessing and building implementation capacity within healthcare systems are notably absent from existing NCCPs [3].

Application Notes and Experimental Protocols

Protocol 1: Multi-Site Barrier and Facilitator Assessment

Purpose: To systematically identify and prioritize barriers and facilitators to EBI implementation across multiple scaling sites.

Workflow:

Methodology:

• Stakeholder Mapping: Identify key informants across implementation levels (policymakers, healthcare administrators, clinicians, patients, community representatives) using snowball sampling.
• Data Collection Framework: Employ mixed-methods approach combining:
  • Structured surveys using validated instruments (e.g., Consolidated Framework for Implementation Research)
  • Semi-structured interviews until thematic saturation (target n=15-30 per stakeholder group)
  • Document review of organizational policies, clinical guidelines, and operational procedures
• Analysis: Conduct rapid qualitative analysis using framework method, dual independent coding with reconciliation. Quantitatively analyze survey data using descriptive statistics and factor analysis.
• Prioritization: Convene stakeholder groups to rate barriers by importance and changeability using modified Delphi techniques. Create prioritization matrices to guide strategy selection.

Protocol 2: Implementation Strategy Matching and Tailoring

Purpose: To systematically match implementation strategies to address prioritized barriers and tailor them to specific contexts.

Workflow:

Methodology:

• Strategy Identification: Using the Expert Recommendations for Implementing Change (ERIC) compilation, systematically map strategies to address prioritized barriers [3].
• Mechanism Specification: For each strategy, explicitly specify the hypothesized mechanisms of action through which they address specific barriers using causal pathway diagrams.
• Contextual Adaptation: Conduct iterative focus groups with implementation teams to adapt strategy specifications to local contexts while preserving core functions.
• Co-Design Sessions: Convene multi-stakeholder design teams to create implementation protocols, materials, and training resources.
• Feasibility Testing: Conduct rapid-cycle pilot tests of adapted strategies using plan-do-study-act cycles. Measure feasibility, acceptability, and appropriateness using validated scales.

Protocol 3: Scaling Readiness Assessment

Purpose: To assess organizational and system readiness for scaling EBIs across multiple sites.

Workflow:

Methodology:

• Assessment Framework: Adapt established implementation frameworks (e.g., Consolidated Framework for Implementation Research, Integrated Promoting Action on Research Implementation in Health Services) to develop context-specific assessment tools.
• Organizational Capacity Audit: Systematically document available resources (workforce, financial, technological, physical infrastructure) and gaps through document review and key informant interviews.
• Implementation Climate Survey: Administer validated instruments (e.g., Implementation Climate Scale) to assess organizational support for EBI implementation.
• Leadership Engagement: Assess leadership support through structured interviews and commitment measures.
• Synthesis and Planning: Develop composite readiness scores across domains. Convene stakeholders to interpret results and co-create capacity building plans addressing identified gaps.

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Reagents for Scale-Up Research Methodology

Research Reagent	Function/Application	Key Characteristics
Implementation Science Frameworks (e.g., CFIR, RE-AIM)	Provides systematic approach to conceptualizing, implementing, and evaluating scale-up efforts	Comprehensive, multi-level, specifies constructs and relationships
Barrier Assessment Instruments	Standardized tools for identifying implementation determinants across contexts	Validated, reliable, applicable across settings and EBIs
Implementation Strategy Compilations (e.g., ERIC)	Repository of discrete implementation strategies for systematic selection and specification	Comprehensive, clearly defined, mechanism-oriented
Implementation Outcome Measures	Validated instruments for assessing acceptability, feasibility, appropriateness, and fidelity	Psychometrically robust, pragmatic, sensitive to change
Mixed Methods Data Collection Protocols	Structured approaches for collecting qualitative and quantitative data across multiple sites	Flexible, comprehensive, efficient for multi-site coordination
Stakeholder Engagement Platforms	Structured approaches for engaging diverse stakeholders throughout research process	Inclusive, systematic, power-balancing
Costing and Economic Evaluation Tools	Methods for capturing implementation, intervention, and opportunity costs across scale-up contexts	Comprehensive, standardized, comparable across settings

Future Directions and Funding Landscape

Recent funding announcements reflect growing recognition of scale-up research needs. The Patient-Centered Outcomes Research Institute (PCORI) has announced upcoming funding opportunities opening December 2025 focused on "Partnering Research and Community Organizations for Comparative Clinical Effectiveness Research Across the Cancer Care Continuum" [63]. This initiative specifically seeks to support "intentional collaborations between researchers, community organizations, health systems and others, along with the implementation of community-based solutions" to address variations in cancer care and outcomes [63]. Additionally, PCORI's Limited Competition PFA provides opportunities for implementation awards focused on "putting findings into practice" [63].

Emerging methodologies include advanced analytics and artificial intelligence applications, as demonstrated by oncology-specific large language models like Woollie, which show promise for analyzing real-world data across institutions to track cancer progression and outcomes at scale [64]. These technological advances present new opportunities for understanding implementation pathways and optimizing scale-up approaches.

The application of Implementation Science (IS) in cancer control planning is critically important for translating evidence-based interventions into real-world practice. This importance is magnified in resource-constrained settings, where strategic allocation of limited resources can significantly impact cancer outcomes [3]. This analysis provides a detailed comparison of how IS principles are applied within the National Cancer Control Plans (NCCPs) and strategies of low and medium Human Development Index (HDI) countries. The HDI serves as a summary measure of national average achievement in key dimensions of human development: a long and healthy life, educational attainment, and a decent standard of living [3]. By examining IS applications through this lens, we aim to elucidate context-specific barriers and facilitators, offering structured protocols to enhance the design and execution of cancer control initiatives in these settings. The findings are framed within a broader thesis on optimizing cancer control research, providing actionable guidance for researchers, scientists, and drug development professionals working in global health equity.

Background: Cancer Burden and the Imperative for IS

The global cancer burden is disproportionately shifting towards low and medium HDI countries. Current projections indicate that by 2050, cancer cases are expected to rise by 76.6% and deaths by 89.7% globally compared to 2022 estimates. This increase is not uniform; low-HDI countries are projected to experience a 142% increase in cancer cases and a 146% increase in cancer deaths, nearly triple the burden compared to the modest increases (42% for cases, 57% for deaths) expected in very high–HDI countries [65]. This disparity underscores an urgent need for effective cancer control planning tailored to resource-constrained settings.

The profile of leading cancers also varies significantly across the HDI spectrum. In low and medium HDI countries, there is a persistent burden of infection-associated cancers, such as cervical and liver cancer, alongside a rising incidence of cancers linked to lifestyle changes, such as breast and colorectal cancer [66]. This complex landscape demands a strategic approach to cancer control that IS is uniquely positioned to provide, enabling the sustainable integration of evidence-based interventions into routine practice despite limited resources [3].

A scoping review of NCCPs and strategies from 16 low and 17 medium HDI countries reveals significant gaps and variations in the application of core IS domains [3]. The data below summarize the incorporation of key IS elements across these plans.

Table 1: Incorporation of IS Domains in NCCPs of Low and Medium HDI Countries [3]

IS Domain	Description	Findings in Low HDI Countries (n=16)	Findings in Medium HDI Countries (n=17)
Stakeholder Engagement	Involvement of relevant parties in plan development	Typically unstructured and incomplete	Typically unstructured and incomplete
Situational Analysis	Assessment of current cancer burden and context	Incorporated, but often not explicit	Incorporated, but often not explicit
Capacity/Health Technology Assessment	Evaluation of health system readiness for new interventions	None of the plans assessed health system capacity	None of the plans assessed health system capacity
Economic Evaluation	Costing of the plan and resource allocation	4 countries included costed plans, generally using an activity-based approach	9 countries included costed plans, generally using an activity-based approach
Impact Measurement	Use of indicators to measure progress and outcomes	All plans included impact measures (e.g., KPIs), but 5 lacked mechanisms for engagement to achieve targets	All plans included impact measures (e.g., KPIs), but 5 lacked mechanisms for engagement to achieve targets

Table 2: Projected Cancer Burden and Key Epidemiological Indicators (2022-2050) [65]

Metric	Low HDI Countries	Medium HDI Countries	Very High HDI Countries
Projected Increase in Cancer Cases by 2050	142.1%	Data not specified in results	41.7%
Projected Increase in Cancer Deaths by 2050	146.1%	Data not specified in results	56.8%
Mortality-to-Incidence Ratio (MIR) in 2022	69.9% (All cancers)	Data not specified in results	Lower than low HDI countries
Exemplar High-MIR Cancer	Pancreatic Cancer (89.4%)	Data not specified in results	Data not specified in results

Detailed Experimental Protocols for IS Application

To address the identified gaps, the following protocols provide a structured methodology for integrating IS into cancer control planning. These protocols are designed to be adaptable to the specific contexts of low and medium HDI countries.

Protocol for Situational and Contextual Analysis

Objective: To conduct a comprehensive, multi-level analysis of the cancer control landscape, identifying key barriers, facilitators, and readiness for implementation.

Workflow:

Methodology:

• Quantitative Data Collection (Desk Review):
  • Sources: Gather data from the Global Cancer Observatory (GLOBOCAN) on incidence, mortality, and prevalence for 36 cancer types [65]. Supplement with national health statistics, registry data, and demographic reports.
  • Analysis: Calculate key metrics such as Mortality-to-Incidence Ratios (MIRs) to assess health system performance and survival gaps. Project future burden based on demographic trends [65].
• Qualitative Data Collection (Stakeholder Engagement):
  • Participants: Purposively sample a wide range of stakeholders, including policymakers, ministry of health officials, clinical providers (oncologists, nurses, primary care), community health workers, patients, and caregivers [3] [67].
  • Methods: Conduct semi-structured interviews and Focus Group Discussions (FGDs). Guides should explore perceptions of current cancer services, barriers to care, and potential solutions.
  • Ethics: Obtain informed consent and ensure confidentiality.
• Health System Readiness Assessment:
  • Framework: Utilize the WHO's health system building blocks (Service Delivery, Health Workforce, Health Information Systems, Access to Essential Medicines, Financing, and Leadership/Governance) as a structured framework for assessment [3].
  • Tool: Develop a mixed-methods assessment tool combining surveys of facility managers and healthcare workers with checklists for direct observation of infrastructure and commodity availability.
• Data Synthesis and Mapping:
  • Integration: Triangulate quantitative, qualitative, and readiness assessment data.
  • Framework Analysis: Map identified barriers and facilitators to an established IS framework, such as the Consolidated Framework for Implementation Research (CFIR), to systematically categorize influences across levels (e.g., intervention characteristics, outer setting, inner setting, individuals, process) [17].
  • Output: Produce a consolidated report that identifies priority areas for intervention and provides a baseline for monitoring.

Protocol for Stakeholder Engagement and Capacity Building

Objective: To establish a structured, iterative, and equitable process for engaging stakeholders throughout the NCCP lifecycle and build core IS capacity among practitioners.

Workflow:

Methodology:

• Stakeholder Mapping and Analysis:
  • Identification: Systematically identify all relevant individuals, groups, and organizations affected by or influencing cancer control.
  • Analysis: Categorize stakeholders based on their influence, interest, and impact. Prioritize those from marginalized groups or those experiencing health inequities to ensure their voices are centered [67] [17].
• Establish a Multi-stakeholder Coordination Committee:
  • Composition: Form a committee with balanced representation from government, civil society, professional associations, academia, and patient advocacy groups.
  • Function: This committee should be involved in all stages, from situational analysis and priority-setting to monitoring and evaluation, ensuring ownership and relevance [3].
• IS Capacity Building for Practitioners:
  • Curriculum: Adapt existing training models, such as the Cancer Control Implementation Science Base Camp (CCISBC), which focuses on competencies like assessing context, adapting evidence-based interventions, and proposing implementation strategies [67].
  • Principles: Design training with a health equity lens, clear terminology, and a focus on reframing IS theories and frameworks as practical tools [67].
  • Delivery: Utilize user-centered design and interactive, facilitated sessions rather than purely self-directed learning to optimize engagement and knowledge retention [67].
• Co-development of Implementation Plans:
  • Process: Facilitate workshops where stakeholders jointly define implementation strategies, assign responsibilities, and develop timelines. This process brokers knowledge between researchers and practitioners, enhancing the practical fit of the plan [67].

Protocol for Implementation Strategy Design and Evaluation

Objective: To design, pilot, and evaluate contextually appropriate implementation strategies using rigorous, yet pragmatic, scientific methods.

Methodology:

• Selection of Evidence-Based Interventions (EBIs):
  • Process: Based on the situational analysis, select high-impact EBIs (e.g., tobacco cessation programs, HPV vaccination, cervical cancer screening) that address the priority cancers and are feasible within the resource constraints.
  • Adaptation: Use a structured process to adapt the EBI to the local context while preserving its core functional components [67].
• Implementation Strategy Design:
  • Approach: Draw on IS frameworks like the Expert Recommendations for Implementing Change (ERIC) to select and specify strategies [3].
  • Tailoring: Tailor strategies to address the specific barriers identified in the CFIR-based analysis. For example, if a barrier is limited clinician time, a strategy could be integrating PROs into existing clinical workflows [17].
• Pragmatic Trial and Evaluation:
  • Design: Employ pragmatic cluster randomized controlled trials (RCTs) or rapid-cycle approaches (e.g., A/B testing) to test the effectiveness of implementation strategies in real-world settings [17].
  • Measures: Evaluate outcomes across multiple dimensions:
    • Effectiveness: Uptake of the EBI (e.g., screening rates).
    • Implementation: Fidelity, cost, and acceptability.
    • Mechanisms: Understand how and why strategies work through mixed methods [17].
  • Equity: Actively monitor outcomes across different subpopulations (e.g., by gender, socioeconomic status, geography) to assess and promote equitable implementation [17].

This toolkit outlines essential materials and resources for conducting implementation science research in cancer control within low and medium HDI settings.

Table 3: Essential Research Reagents and Resources for IS in Cancer Control

Item/Resource	Category	Function and Application in IS Research
GLOBOCAN Database	Data Source	Provides population-based estimates of cancer incidence, mortality, and prevalence for 185 countries; essential for situational analysis and burden estimation [65].
WHO Health System Building Blocks	Analytical Framework	A structured model for assessing health system readiness and capacity, guiding the evaluation of barriers related to workforce, financing, and information systems [3].
Consolidated Framework for Implementation Research (CFIR)	Theoretical Framework	A meta-theoretical framework used to systematically assess multilevel contextual barriers and facilitators influencing implementation; guides data collection and analysis [17].
Expert Recommendations for Implementing Change (ERIC)	Methodology Compilation	A standardized compilation of 73 implementation strategies; used for selecting, specifying, and reporting strategies to address identified barriers [3].
Cancer Control Implementation Science Base Camp (CCISBC) Model	Capacity Building Tool	A structured curriculum designed to build core IS competencies among cancer control practitioners, focusing on context assessment, adaptation, and equity [67].
Stakeholder Interview & FGD Guides	Data Collection Tool	Semi-structured protocols for qualitative data collection to understand stakeholder perspectives, experiences, and local context [3] [67].
Protocols for Pragmatic Cluster RCTs	Experimental Design	A detailed methodology for conducting real-world trials that test implementation strategies across groups (e.g., clinics, communities) rather than individuals [17].
Mixed-Methods Analysis Plan	Data Analysis Guide	A protocol for integrating quantitative and qualitative data to provide a comprehensive understanding of implementation processes and outcomes [17].

Within the framework of implementation science methods for cancer control optimization, measuring implementation outcomes is crucial for establishing the success of integrating evidence-based interventions (EBIs) into routine practice. These outcomes, including adoption, penetration, and sustainability, provide a measurable framework for evaluating the real-world effectiveness of implementation strategies [68]. Their systematic validation is fundamental to translating research evidence into sustained clinical impact, particularly in complex oncology care environments where significant heterogeneity exists across patient pathways and healthcare settings [3]. This document provides detailed application notes and experimental protocols for the validation of these core outcomes, contextualized specifically for cancer control research.

Application Notes: Core Concepts and Metrics

Validating implementation outcomes requires a clear understanding of their definitions and the quantitative metrics used for their measurement. The following section outlines the theoretical and practical considerations for these three outcomes.

Defining the Outcomes

• Adoption is defined as the intention, initial decision, or action to try or employ an evidence-based intervention. It is often characterized as the uptake of an EBI by a provider or organization and is typically measured at the level of the clinician, team, or facility.
• Penetration refers to the integration of the EBI within a specific setting, often measured as the proportion of the target audience that uses the EBI or the extent to which the EBI is integrated within a service setting. It is a measure of the spread or depth of implementation within an adopting entity.
• Sustainability signifies the extent to which a newly implemented EBI is maintained or institutionalized within an organization's ongoing, stable operations. It reflects the long-term viability of the intervention after initial implementation efforts and resources have diminished [46].

The table below summarizes illustrative quantitative findings from implementation science studies, highlighting the variability in these outcomes across different contexts and interventions.

Table 1: Sample Quantitative Findings on Implementation Outcomes from Cancer Control Research

Evidence-Based Intervention (EBI)	Implementation Outcome	Quantitative Finding	Context / Source
Provider Reminders	Sustainability	82.0% of clinics reported the EBI as sustainable [46]	CDC's Colorectal Cancer Control Program
Reducing Structural Barriers	Sustainability	55.6% of clinics reported the EBI as sustainable [46]	CDC's Colorectal Cancer Control Program
Various EBIs	Sustainability Improvement	Sustainability rates increased by 13 to 34 percentage points over a 5-year program period [46]	CDC's Colorectal Cancer Control Program
Use of Implementation Taxonomies	Adoption of Best Practices	Underutilization; fewer than a third of empirical papers use a full implementation outcome framework [68]	Systematic Review of Healthcare Settings

Key Measurement Instruments and Frameworks

A critical step in validation is the selection of robust measurement tools. The field has moved towards standardized frameworks to ensure consistency and reliability.

Table 2: Key Frameworks and Tools for Validating Implementation Outcomes

Framework / Tool Name	Primary Function	Relevance to Validation
Proctor's Taxonomy [69] [68]	Defines and distinguishes a standardized set of implementation outcomes, including adoption, penetration, and sustainability.	Serves as the conceptual foundation for outcome validation, ensuring clarity and consistency in what is being measured.
Expert Recommendations for Implementing Change (ERIC) [3] [69]	Provides a standardized compilation of implementation strategies, grouped into nine domains.	Allows researchers to precisely document and link the specific strategies used to achieve the outcomes being validated.
Consolidated Framework for Implementation Research (CFIR) [68]	A meta-theoretical framework comprising constructs that influence implementation success.	Used to map and identify the influences (e.g., inner setting, characteristics of individuals) on implementation outcomes, providing explanatory context for validation data.
Validated Implementation Outcome Instruments [68]	Specific tools (e.g., surveys, questionnaires) with established psychometric properties for measuring outcomes.	Provides the direct method for quantitative data collection. Their reliability and validity are paramount for rigorous outcome validation.

Experimental Protocols

This section provides detailed, actionable protocols for measuring and validating adoption, penetration, and sustainability in cancer control research studies.

Protocol 1: Validating Adoption and Penetration

Aim: To assess the initial uptake (adoption) and subsequent integration (penetration) of an evidence-based colorectal cancer screening reminder system within a network of primary care clinics.

Materials and Reagents:

• Electronic Health Record (EHR) System: To extract data on patient eligibility and provider actions.
• Clinical Data Warehouse: For aggregating and analyzing clinic-level data.
• Web-Based Data Reporting Tool (e.g., CBAR): For standardized data entry and reporting by clinic staff [46].
• Survey Platform (e.g., Qualtrics, RedCap): To administer validated adoption surveys to clinic leadership and staff.

Methodology:

• Baseline Assessment:
  • Prior to implementation, use the EHR to establish a baseline count of the total number of screening-eligible patients and the total number of clinicians in each participating clinic.
  • Administer a pre-implementation survey to assess organizational readiness and intention to adopt the new reminder system.

• Implementation Phase:

  • Roll out the provider reminder system, providing training and technical assistance as per the implementation strategy.

• Data Extraction and Calculation:

  • Adoption Metric: At 3 and 6 months post-implementation, calculate the proportion of eligible clinicians within each clinic who have actively used the reminder system at least once. This is a binary (yes/no) measure at the individual level.
  • Penetration Metric: Concurrently, calculate the proportion of screening-eligible patients within each clinic for whom the reminder system was activated in their record during the measurement period. This provides a continuous measure of integration at the clinic level.

• Data Analysis:

  • Report adoption and penetration rates descriptively (frequencies, percentages) for each clinic and aggregate across clinics.
  • Use logistic regression to explore associations between clinic characteristics (e.g., size, location) and the likelihood of adoption.

Workflow Diagram:

Protocol 2: Validating Sustainability

Aim: To evaluate the long-term sustainability of evidence-based interventions in a cancer control program after the conclusion of initial external funding and support.

Materials and Reagents:

• Programmatic Clinic Data Repository: A centralized database (e.g., the CRCCP Clinic Data) for longitudinal tracking [46].
• Semi-Structured Interview Guides: For qualitative feedback from clinic implementers.
• Costing Data: Information on post-funding resource allocation for the EBI.

Methodology:

• Define Sustainability Operationally:
  • Define a sustained EBI as one that is "fully integrated into clinic operations" and reported as "sustainable without CRCCP resources" [46]. This creates a binary outcome (sustained/not sustained) for each EBI per clinic.

• Longitudinal Data Collection:

  • Collect annual clinic-level data for the duration of the program and for a defined follow-up period (e.g., 1-2 years) after the primary program funding ends.
  • Data points should include EBI implementation status, sustainability status (using the predefined options), and clinic characteristics.

• Measurement and Analysis:

  • Cross-sectional Analysis: For a snapshot in time (e.g., the last year of program participation), calculate the proportion of clinics that report each EBI as sustainable. Exclude clinics with less than one year of participation [46].
  • Longitudinal Analysis: Track sustainability rates for each EBI across all "clinic-years" to observe trends over time.
  • Statistical Modeling: Use logistic regression to identify factors associated with sustainability. Explanatory variables should include clinic type, pre-implementation experience with the EBI, the presence of a dedicated screening champion, and the number of EBIs integrated [46].

Logical Validation Pathway:

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials and Tools for Validating Implementation Outcomes

Item Name	Function / Application in Validation	Example / Specification
Programmatic Clinic Data Repository	Centralized database for longitudinal tracking of EBI implementation, sustainability status, and clinic-level outcomes.	CDC's CRCCP Clinic Data; includes clinic characteristics, EBI implementation, and sustainability data [46].
Validated Implementation Instruments	Standardized tools with established psychometric properties for quantitatively measuring implementation outcomes.	Instruments identified via systematic review and mapped to frameworks like CFIR [68].
ERIC Implementation Strategies Compilation	A standardized glossary of strategies used to support EBI implementation.	Used to precisely document and report the specific strategies deployed, linking them to observed outcomes [3] [69].
Web-Based Data Reporting Tool (e.g., CBAR)	Facilitates standardized, iterative, and auditable data entry directly from implementation sites.	Used by CRCCP recipients for annual clinic-level data reporting [46].
Qualitative Data Collection Tools	Guides for interviews and focus groups used to triangulate quantitative findings and explain mechanisms.	Semi-structured interview guides for clinic staff on barriers/facilitators to sustainability.
Statistical Analysis Software	For performing descriptive statistics, regression modeling, and quantitative bias analysis.	R software, SAS, or Stata; used for logistic regression modeling of sustainability factors [46].

Application Note: Community-Centric Cancer Control in a Post-Conflict Setting

Background and Rationale

A community-centric cancer control project was implemented in the Northern Province of Sri Lanka, a rural, post-conflict region characterized by limited healthcare infrastructure and low cancer screening rates [70]. This region faces a high burden of late-stage cancer diagnoses, with oral cancer being the most prevalent, diverging from national trends where breast cancer is most common [70]. The project aimed to leverage community engagement and local partnerships to increase cancer awareness, promote early detection, and improve equitable access to screening services, demonstrating the core implementation science principle of adapting evidence-based interventions to specific contextual barriers [70].

Quantitative Outcomes and Impact

The project successfully conducted six one-day, oncologist-led community health camps across all five districts of the Northern Province between June 2022 and July 2023 [70]. The table below summarizes the key implementation metrics and participant engagement data.

Table 1: Implementation Metrics for Community-Centric Cancer Control Project

Metric	Result	Context/Explanation
Health Camps Conducted	6	One-day camps in Jaffna, Kilinochchi, Mullaitivu, Mannar, and Vavuniya districts [70].
Community Cancer Educators (CCEs) Trained	25	Nursing students from Jaffna Nurse Training School [70].
Core Implementation Team	3 onco-surgeons, 2 oral-maxillofacial surgeons, 9-15 cancer nurses, 15-20 CCEs per camp	Multidisciplinary team supported by local volunteers and university staff [70].
Screening Eligibility	Individuals ≥25 years providing verbal consent	Focus on oral and breast cancer screening [70].
Educational Method	Culturally tailored Tamil dance and drama	Used to communicate cancer risk factors and early detection messages [70].

Feedback from both community participants and healthcare providers indicated that this approach improved cancer awareness, encouraged participation in population screening, and supported early cancer detection, thereby strengthening community engagement in a resource-limited setting [70].

Experimental Protocol: Community-Centric Cancer Education and Screening

This protocol details the multi-stage, community-centric approach for delivering cancer education and screening in rural, underserved populations, as implemented in Northern Sri Lanka [70]. The long-term goal is to increase awareness, promote early detection, and improve equitable access to cancer prevention services.

Detailed Methodology

Stage 1: Planning and Stakeholder Engagement (Months 1-6)

• Objective: Secure institutional support and align engagement strategies across all five districts of the Northern Province [70].
• Procedure:
  • Establish collaboration between international cancer control organizations (e.g., CancerCare Manitoba), local oncologists, and academic departments (e.g., Community Medicine, Drama and Theatre) [70].
  • Formally communicate project plans and outcomes to provincial and regional health directors to ensure alignment with official health planning [70].
  • Partner with a local non-governmental organization (NGO) to facilitate connections with Medical Officers of Health (MOHs), public health officers, and community leaders at the district level [70].
  • Collaboratively select neutral, familiar, non-clinical community spaces (e.g., schools, community halls) as health camp venues to reduce stigma and increase participant comfort [70].

Stage 2: Training of Community Cancer Educators (CCEs) (1 Day)

• Objective: Equip local nursing students with the knowledge and skills to effectively communicate cancer-related messages [70].
• Participant Selection: Recruit 25 nursing students based on interest in community health education and willingness to support their communities [70].
• Training Content and Procedure:
  • Morning Session (Clinical Knowledge): Led by oncologists and a psychiatrist. Covers:
    • Cancer development and lifestyle-related risk factors relevant to the local context (e.g., tobacco use, betel quid chewing) [70].
    • Cancer prevention strategies and the importance of early detection [70].
    • Communication strategies for sensitive conversations and managing emotional distress related to cancer screening [70].
  • Afternoon Session (Culturally-Tailored Communication): Facilitated by a Drama and Theatre Action Group. Focuses on using creative methods like Tamil dance and drama to engage community members and convey information in a familiar, accessible manner [70].

Stage 3: Health Camp Delivery

• 3a: Community Education Session
  • Objective: Provide accessible cancer education to all community members [70].
  • Procedure:
    • Begin with a cultural performance (Tamil dance) by CCEs to create a welcoming environment and ease anxiety [70].
    • Conduct an oncologist-led education session covering lifestyle risk factors, prevention, and the benefits of early detection [70].
    • Hold a question-and-answer session to address community concerns [70].
• 3b: Screening Session
  • Objective: Conduct basic screening for oral and breast cancers [70].
  • Procedure:
    • Register individuals aged 25 and older who provide verbal consent [70].
    • Administer a brief sociodemographic questionnaire to capture basic demographics, lifestyle risk factors, and family history of cancer [70].
    • Divide participants into smaller groups. A cancer nurse and CCEs explain the screening process and answer questions [70].
    • Perform clinical examinations for oral and breast cancer [70].

Workflow Visualization

Application Note: The OPTICC Center - A Protocol for Optimizing Implementation

Background and Rationale

The Optimizing Implementation in Cancer Control (OPTICC) center is a National Cancer Institute-funded Implementation Science Center designed to address critical barriers in the implementation of evidence-based interventions (EBIs) in cancer control [2]. Widespread and effective implementation of EBIs could drastically reduce cancer deaths (e.g., cervical cancer deaths by 90%, colorectal by 70%) [2]. However, "implementation as usual" often yields suboptimal results due to underdeveloped methods for identifying determinants, incomplete knowledge of strategy mechanisms, underuse of optimization methods, and poor measurement of implementation constructs [2]. OPTICC's mission is to develop, test, and refine efficient methods to overcome these barriers [2].

Core Structure and Aims

OPTICC operates through three integrated cores to achieve its mission [2]:

• Administrative Core: Plans, coordinates, and evaluates the Center's activities and leads capacity-building efforts.
• Implementation Laboratory Core (I-Lab): Coordinates a network of diverse clinical and community sites (e.g., primary care clinics, health systems, cancer centers) where implementation studies are conducted.
• Research Program Core: Conducts innovative implementation studies, including measurement and methods development, and pilot studies.

The Center's work is guided by three specific aims, which are to 1) develop a research program for optimizing EBI implementation, 2) support a diverse implementation laboratory, and 3) build implementation science capacity in cancer control [2].

Experimental Protocol: The OPTICC Three-Stage Optimization Approach

This protocol outlines OPTICC's three-stage approach to optimizing the implementation of evidence-based interventions (EBIs) in cancer control, informed by a transdisciplinary team leveraging multiphase optimization strategies, user-centered design, and agile science [2].

Detailed Methodology

Stage I: Identify and Prioritize Determinants

• Objective: Systematically identify and prioritize the barriers (e.g., resource constraints, low health literacy) and facilitators to EBI implementation [2].
• Procedure:
  • Identification: Use mixed methods, including stakeholder interviews, focus groups, and surveys, guided by general determinants frameworks like the Consolidated Framework for Implementation Research (CFIR) [2].
  • Prioritization: Apply advanced methods to move beyond identifying "feasible" determinants and instead focus on those with the greatest potential to impact implementation success, overcoming the limitation of traditional methods that often identify more determinants than can be addressed [2].

Stage II: Match Implementation Strategies to Determinants

• Objective: Select implementation strategies (e.g., clinical reminders, audit and feedback) that effectively address the prioritized determinants [2].
• Procedure:
  • Strategy Selection: Move beyond selection based on personal preference or organizational routine by focusing on strategy mechanisms (the processes through which strategies produce their effects) [2].
  • Matching: Develop and use knowledge of strategy mechanisms to effectively match strategies to specific determinants. For example, matching clinical reminders [strategy] to address provider habitual behavior [determinant] by leveraging the mechanism of providing a cue to action at the point of care [2].

Stage III: Optimize Strategy Package

• Objective: Develop the most effective, efficient, and cost-effective combination of implementation strategies [2].
• Procedure:
  • Component Testing: Use methods like multiphase optimization strategy (MOST) to determine which strategy components are essential, how they interact, and which combination is most cost-effective, rather than testing multi-component strategies as a single package [2].
  • Delivery Optimization: Optimize the mode of delivery (e.g., in-person vs. virtual), format, source, and dose of strategy components before proceeding to large-scale randomized controlled trials (RCTs) [2].

Workflow Visualization

Table 2: Essential "Research Reagent Solutions" for Implementation Science in Cancer Control

Tool/Resource Name	Type/Format	Primary Function in Research	Example/Context of Use
Community Cancer Educators (CCEs)	Human Resource / Trained Personnel	Act as culturally competent liaisons to deliver cancer education and support screening within their communities [70].	Local nursing students in Sri Lanka trained to use dance and drama for education [70].
Implementation Laboratory (I-Lab)	Research Network / Partnership Infrastructure	Provides a diverse network of clinical and community sites for conducting rapid, real-world implementation studies [2].	OPTICC's network of primary care clinics, health systems, and cancer centers across six states [2].
Evidence-Based Cancer Control Programs (EBCCP)	Database / Repository	A searchable database of over 200 evidence-based cancer control programs and implementation materials for program planners [71].	Used by public health practitioners to find and immediately access proven interventions across various health topics and populations [71].
Consolidated Framework for Implementation Research (CFIR)	Determinants Framework	A meta-theoretical framework used to identify potential barriers and facilitators to implementation across multiple domains [2].	Guides the "Identify Determinants" phase (Stage I) of the OPTICC protocol through interviews or surveys [2].
Multiphase Optimization Strategy (MOST)	Methodological Framework	An engineering-inspired framework for optimizing multi-component behavioral, biobehavioral, and biomedical interventions [2].	Used in the "Optimize Strategies" phase (Stage III) to efficiently build an optimized implementation strategy package [2].

Conclusion

The integration of implementation science into cancer control is no longer optional but essential for translating research investments into tangible population health benefits. Synthesizing insights across the four intents reveals a clear pathway: foundational principles provide the necessary groundwork, methodological applications offer practical tools, troubleshooting strategies address real-world barriers, and validation studies confirm the impact. Future directions must prioritize 'precision implementation'—tailoring strategies to specific contexts and populations—and address critical gaps in scaling up interventions, particularly in treatment and survivorship. For biomedical researchers and drug development professionals, embracing these methods is crucial for designing more implementable clinical trials, ensuring equitable access to innovations, and ultimately reducing the global cancer burden through sustainable, evidence-based policies and practices.

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