Amy G. Huebschmann

Background: Theories, models, and frameworks (TMFs) are frequently used to facilitate rigorous qualitative data collection and analysis of context in dissemination and implementation (D&I) science. The Practical, Robust Implementation and Sustainability Model (PRISM), which includes contextual determinants of Reach, Effectiveness, Adoption, Implementation and Maintenance (RE-AIM) dimensions, is one of the most widely used TMFs. Yet, missing from the literature is an assessment of how PRISM can inform the collection of qualitative data across implementation phases (i.e., pre-implementation, implementation, and post-implementation). The purpose of this study was to curate a collection of PRISM-informed qualitative data collection guides and assess how questions relate to PRISM domains and RE-AIM dimensions. Methods: In this retrospective observational study, exemplar interview and focus group guides were collected from a group of D&I science experts to assess how PRISM constructs were operationalized. All guides were compiled into a database, and each question and sub-question were labelled with one or more relevant PRISM domains and RE-AIM dimensions. We calculated descriptive characteristics of all interview guides included in the database. The frequency of each PRISM domain was calculated across all interview guides, by implementation phase and by participant role. Results: Guides (n=31) were collected from 13 research studies and were primarily used in individual interviews (n=30) and across pre-implementation (n=8), implementation (n=8), and post-implementation (n=15) phases. Research settings included the Veterans Health Administration (n=16), community health settings (n=10), schools (n=4), and an academic health center (n=1). Questions and sub-questions were more commonly labelled with PRISM domains (n=667) than RE-AIM dimensions (n=303). The Perspectives of Implementers (n=201), Perspectives of Recipients (n=152) and Implementation and Sustainability Infrastructure (n=103) were the most frequently used PRISM domains and Implementation (n=113) was the most frequent RE-AIM dimension. Conclusions: Our findings demonstrate that PRISM has valuable applications in qualitative data collection with recipients and implementers across all implementation phases and highlights how PRISM can be adapted for different topics and settings. The database of qualitative guides is publicly available and can be used as a resource for D&I investigators using PRISM to guide their qualitative contextual assessment.

Background: Primary care clinics need effective, low-cost tools to screen for and address multiple health risks — and technology can provide important efficiencies. My Own Health Report (MOHR) is a web-based intervention with 3 core functions: (1) risk flagging; (2) goal-setting for patient-prioritized risks; and (3) providing resources. A prior randomized trial in primary care settings showed that MOHR was feasible and effective. However, we identified two key gaps : a need for a flexible menu of delivery options to adopt and sustain MOHR and a pragmatic way to provide structured follow-up. Methods/design: We developed and pilot-tested two distinct primary implementation strategies to provide structured follow-up: the first primary strategy, termed “Reminder-Resource message ( R ² message) ,” involves automated motivational text message nudges to remind patients of their MOHR goal and point to resources. The second, “R ² navigation ,” involves personal support - community health worker phone calls to deliver reminders and resources. Each primary strategy is paired with additional secondary strategies that together address the contextual determinants of successful implementation and sustainment identified using our Pragmatic Robust Implementation and Sustainability Model (PRISM). We engaged clinic staff, patients, and community partners to finalize the R ² message and R ² navigation strategies and secondary strategies. This individually randomized, type 2 hybrid effectiveness-implementation trial will enroll 1,000 adult patients with at least two cancer risks (including both insufficient physical activity and fruit/vegetable intake). We will evaluate the outcomes and cost-effectiveness of R ² message and R ² navigation alone or in combination in 12 primary care clinics including both (a) large metropolitan health system and (b) rural-serving clinics. Expected outcomes include: (1) improvement in behavioral risk factors; (2) implementation outcomes – including representative engagement with R ² message and R ² navigation, and cost; and (3) improvement in practice value outcomes, including patient experience ratings. Based on the trial findings, we will co-develop an implementation, adaptation and sustainment guide for the most cost-effective implementation strategy. Conclusions: We expect the implementation strategies compared will produce different levels of representative engagement, behavior change, cost, and practice value. These findings will inform pragmatic ways to improve outcomes important to patients, primary care, and society. Trial Registration# at ClinicalTrials.gov: NCT07569224 (Registration date: 04/29/2026).

BACKGROUND Clinical decision support (CDS) tools within electronic health records have the potential to improve evidence-based care but often fall short. One potential approach to improve CDS effectiveness is to extend CDS tools to include clinician-specific personalization by leveraging data on clinician behavior in the EHR. OBJECTIVE The objective of this study is to determine clinician attitudes towards CDS tools in general, review common concerns, and anticipate in what ways a clinician-personalized CDS approach can solve issues clinicians have with CDS tools. METHODS Using the case example of guideline directed medical treatment for heart failure, we conducted a qualitative study using semi-structured interviews with outpatient clinicians in primary care and cardiology settings. Participants were purposively sampled to capture diverse perspectives across specialties, practice settings, and clinician types. Interviews were analyzed using a pragmatic thematic approach. RESULTS Nineteen clinicians participated. Across specialties, clinicians viewed CDS as a valuable cognitive support tool, particularly for managing complex guidelines and reducing mental workload. However, traditional CDS were consistently described as burdensome due to high alert volume, generic content, poor timing, and workflow disruption, contributing to alert fatigue and reduced trust. Clinician-personalized CDS was endorsed as a potential solution, with participants highlighting its ability to tailor information based on clinician knowledge and practice patterns, reduce redundant content, and enhance relevance. However, clinicians also raised concerns regarding the ability of algorithms to capture clinical nuance, the validity and transparency of clinician-level data, and the risk of perceived loss of autonomy or punitive feedback. CONCLUSIONS Clinician-personalized CDS represents a promising extension of current CDS approaches by incorporating clinician context to improve relevance and usability. While early perceptions are favorable, important challenges related to trust, feasibility, and implementation complexity remain. Further evaluation is needed to determine whether clinician-personalization improves clinical outcomes and clinician experience, and to identify when and how this approach provides meaningful value within health systems.

Background: Breast cancer survivors are increasing in number and living longer but often experience reduced quality of life (QOL) from treatment sequelae. Physical activity (PA) interventions can improve QOL, but many rely on costly specialists or are difficult to scale. We adapted an evidence-based peer-mentoring program (moving forward together) for web delivery (webMFT). The platform centralizes peer-coach matching, delivery, and coordination to reduce administrative burden and improve scalability. Purpose: We evaluated webMFT’s cost-effectiveness. Methods: In a 12-week randomized trial (N = 61), survivors were assigned to webMFT (n = 30; weekly phone calls plus activity self-monitoring) or MVPA Tracking (n = 31; activity self-monitoring only). From a payer’s perspective, we estimated incremental cost-effectiveness ratios (ICERs) for quality-adjusted life years (QALYs) and device-measured moderate-to-vigorous physical activity (MVPA). Intervention costs were collected from time logs for research staff and peer coaches. We conducted sensitivity analyses by bootstrapping and constructing cost-effectiveness planes and cost-effectiveness acceptability curves (CEACs). Results: In the base-case analysis the ICER for webMFT was $38 430 per QALY gained versus MVPA Tracking. At the high end of probabilistic implementation-cost sensitivity analyses that considered wider ranges of wages, platform costs, and implementation times, the mean ICER was $43 418 per QALY and the probability of cost-effectiveness was 87% at the commonly used $150 000 per QALY threshold. WebMFT increased QALYs but did not produce greater increases in device-measured PA compared with MVPA Tracking. Conclusion: At a $150 000 per QALY threshold, webMFT appears cost-effective for improving QALYs among breast cancer survivors. Larger studies are needed to confirm these findings. Clinical Trial Information: This underlying clinical trial was registered at clinicaltrials.gov (NCT05409664).

Background: Clinical decision support (CDS) is one strategy to increase evidence-based practices by clinicians. Despite its potential, CDS tools produce mixed results and are often disliked by clinicians. Principles from behavioral economics such as “nudges” may improve the effectiveness and clinician satisfaction of CDS tools. This paper outlines a pragmatic approach grounded in implementation science to identify and prioritize how to incorporate different types of nudges into CDS tools. Objective: The purpose of this paper is to describe a systematic and pragmatic approach grounded in implementation science to identify and prioritize how best to incorporate different types of nudges into CDS tools. We provide a case example of how this systematic approach was applied to design a CDS tool to improve guideline-concordant prescribing of mineralocorticoid receptor antagonists for patients with heart failure and reduced ejection fraction. Methods: We applied the Messenger, Incentives, Norms, Defaults, Salience, Priming, Affect, Commitments, and Ego nudge framework and the Practical, Robust Implementation and Sustainability Model implementation science framework to systematically and pragmatically identify and prioritize different types of nudges for CDS tools. To illustrate how these frameworks can be applied in a real-life scenario, we use a case example of a CDS tool to improve guideline-concordant prescribing for patients with heart failure. We describe a process of how these frameworks can be used pragmatically by clinicians and informaticists or more technical CDS builders to apply nudge theory to CDS tools. Results: We defined four iterative steps guided by the Practical, Robust Implementation and Sustainability Model: (1) engage partners for user-centered design, (2) develop a shared understanding of the nudge types, (3) determine the overarching CDS format, and (4) brainstorm and prioritize nudge types to address each modifiable contextual issue. These steps are iterative and intended to be adapted to align with the local resources and needs of various clinical scenarios and settings. We provide illustrative examples of how this approach was applied to the case example, including who we engaged, details of nudge design decisions, and lessons learned. Conclusions: We present a pragmatic approach to guide the selection and prioritization of nudges, informed by implementation science. This approach can be used to comprehensively and systematically consider key issues when designing CDS to optimize clinician satisfaction, effectiveness, equity, and sustainability while minimizing the potential for unintended consequences. This approach can be adapted and generalized to other health settings and clinical situations, advancing the goals of learning health systems to expedite the translation of evidence into practice.

Background: Virtual care interventions have the potential to improve access to care and serial medication intensification for patients with chronic heart failure with reduced ejection fraction (HFrEF). However, concerns remain that these interventions might unintentionally create or widen existing disparities in care delivery and patient outcomes. Objective: This study aimed to characterize the health care use patterns of patients who have HFrEF, including specialty type and frequency of in-person and virtual visits. Methods: We conducted a retrospective cohort study of patients with HFrEF within a large health system. Inclusion criteria were patients alive with an ejection fraction ≤40% as of September 1, 2021, and at least one virtual or in-person outpatient visit to a primary care or cardiology clinician in the prior year. Descriptive statistics were used to evaluate baseline patient demographics and clinical use data and outcomes. Univariate analyses were performed both with virtual visits as a variable (received or did not receive) using the chi-square test for association and as a discrete outcome using the Wilcoxon rank-sum test to capture potentially important predictor variables that could influence use or frequency of using virtual visits. The primary outcome of interest was the odds of at least one virtual visit during the 1-year evaluation period from 2021 to 2022. Descriptive statistics were used to evaluate baseline patient demographics and care use. A logistic regression model was used to model at least one primary care or cardiology virtual visit. Results: A total of 8481 patients were included in the analysis. The mean age was 65.9 years (SD 15.1), 5672 (66.9%) patients were male and 6608 (77.9%) patients were non-Hispanic White. The majority of patients had no cardiology (7938/8481, 93.6%) or primary care (7955/8481, 93.8%) virtual visits during the evaluation period. Multivariable logistic regression showed significantly higher odds of having at least one virtual visit for patients with certain digital access—for example, email on file (odds ratio [OR] 9.3, P≤.001), cell phone on file (OR 2.9, P≤.001), and active electronic health record patient portal (OR 2.8, P≤.001)—than those without. Age, race, ethnicity, rurality, and Social Vulnerability Index were not associated with virtual visits. Conclusions: Only a minority of patients with HFrEF were seen via virtual visits. Patients who regularly used digital technology were more likely to have virtual visits. Patients were more likely to be seen in a cardiology clinic than by a primary care provider. Although there was no evidence of an association between social determinants of health factors like race, ethnicity, or rurality with digital divide indicators, these findings should be interpreted with caution given the limitations of these data. Future studies should aim to replicate the findings of this study and explore ways to enhance the effective and equitable use of virtual visits.

Introduction: Our overall goal was to enhance the usability and interactivity of the RE-AIM website (re-aim.org) and improve resources to support the application of the RE-AIM framework within the context of dissemination & implementation (D&I) research and practice. Methods: We applied a mixed-methods approach to obtain user feedback from 24 D&I researchers and practitioners. Usability (System Usability Scale) and interactivity (Interactivity Scale) were assessed through validated surveys, at baseline and after two iterative rounds of website modifications (Phase 1 and Phase 2). We also conducted qualitative assessments at each phase. Results: Qualitative baseline and Phase 1 findings indicated a need to simplify organization, enhance information accessibility, provide concrete guidance on applying RE-AIM, and clarify contextual factors related to RE-AIM constructs. After streamlining website and homepage organization, Phase 2 qualitative results suggested improved user navigation experience; users also requested greater interactivity. Modifications included: new interactive planning tool and a video introduction of contextual factors influencing RE-AIM outcomes. Significant improvements were found in the SUS score from baseline to Phase 1(64.2[SD18.7] to 80.8 [SD 12.1] (p < .05) and remained higher in Phase 2(77.1[SD 15] (p = 0.08). Interactivity also improved from baseline to Phase 2(3.5[SD1.2] to 41[0.9], though not statistically significant. Conclusion: User-centered feedback on online resources, as exemplified by this use case example of enhancements to the RE-AIM website, are important in bridging the gap between research and practice, and the revised website should be more accessible and useful to users.

BACKGROUND Clinical decision support (CDS) is one strategy to increase evidence-based practices by clinicians. Despite its potential, CDS tools have had mixed results and are often disliked by clinicians. Principles from behavioral economics, including “nudges,” may improve the effectiveness and clinician satisfaction of CDS tools. OBJECTIVE This paper outlines a pragmatic approach grounded in implementation science to identify and prioritize how to incorporate different types of nudges into CDS tools. METHODS We applied the Messenger, Incentives, Norms, Defaults, Salience, Priming, Affect, Commitments and Ego (MINDSPACE) nudge framework and the Practical, Robust Implementation and Sustainability Model (PRISM) implementation science framework to systematically and pragmatically identify and prioritize different types of nudges for CDS tools. A case example of a CDS tool to improve guideline-concordant prescribing for patients with heart failure was used to illustrate how these frameworks can be applied in real-life scenarios. We describe a process of how these frameworks can be used pragmatically by clinicians and informaticists or more technical CDS builders to apply nudge theory to CDS tools. RESULTS Four iterative steps guided by PRISM were defined: 1) engage partners for user-centered design, 2) develop a shared understanding of the nudge types, 3) determine the nudge type for the overarching CDS format, and 4) brainstorm and prioritize nudge types and forms to address each modifiable contextual issue. These steps are iterative and intended to be adapted to align with the local resources and needs of various clinical scenarios and settings. We provide illustrative examples of how this approach was applied to the case example, including who we engaged, details of nudge design decisions, and lessons learned. CONCLUSIONS We present a pragmatic approach to guide the selection and prioritization of nudges, informed by implementation science. This approach can be used to comprehensively and systematically consider key issues in designing CDS to optimize clinician satisfaction, effectiveness, equity, and sustainability while minimizing the potential for unintended consequences. The findings can be adapted and generalized to other health settings and clinical situations, advancing the goals of learning health systems to expedite the translation of evidence into practice.

Translational research needs to show value through impact on measures that matter to the public, including health and societal benefits. To this end, the Translational Science Benefits Model (TSBM) identified four categories of impact: Clinical, Community, Economic, and Policy. However, TSBM offers limited guidance on how these areas of impact relate to equity. Central to the structure of our Center for American Indian and Alaska Native Diabetes Translation Research are seven regional, independent Satellite Centers dedicated to community-engaged research. Drawing on our collective experience, we provide empirical evidence about how TSBM applies to equity-focused research that centers community partnerships and recognizes Indigenous knowledge. For this special issue – “Advancing Understanding and Use of Impact Measures in Implementation Science” – our objective is to describe and critically evaluate gaps in the fit of TSBM as an evaluation approach with sensitivity to health equity issues. Accordingly, we suggest refinements to the original TSBM Logic model to add: 1) community representation as an indicator of providing community partners “a seat at the table” across the research life cycle to generate solutions (innovations) that influence equity and to prioritize what to evaluate, and 2) assessments of the representativeness of the measured outcomes and benefits.

With the widespread implementation of electronic health records (EHRs), there has been significant progress in developing learning health systems (LHSs) aimed at improving health and health care delivery through rapid and continuous knowledge generation and translation. To support LHSs in achieving these goals, implementation science (IS) and its frameworks are increasingly being leveraged to ensure that LHSs are feasible, rapid, iterative, reliable, reproducible, equitable, and sustainable. However, 6 key challenges limit the application of IS to EHR-driven LHSs: barriers to team science, limited IS experience, data and technology limitations, time and resource constraints, the appropriateness of certain IS approaches, and equity considerations. Using 3 case studies from diverse health settings and 1 IS framework, we illustrate these challenges faced by LHSs and offer solutions to overcome the bottlenecks in applying IS and utilizing EHRs, which often stymie LHS progress. We discuss the lessons learned and provide recommendations for future research and practice, including the need for more guidance on the practical application of IS methods and a renewed emphasis on generating and accessing inclusive data.