Prediction-Augmented Shared Decision-Making and Lung Cancer Screening Uptake.

Importance: Addressing poor uptake of low-dose computed tomography lung cancer screening (LCS) is critical, especially for those having the most to gain-high-benefit persons with high lung cancer risk and life expectancy more than 10 years. Objective: To assess the association between LCS uptake and implementing a prediction-augmented shared decision-making (SDM) tool, which enables clinicians to identify persons predicted to be at high benefit and encourage LCS more strongly for these persons. Design, Setting, and Participants: Quality improvement interrupted time series study at 6 Veterans Affairs sites that used a standard set of clinical reminders to prompt primary care clinicians and screening coordinators to engage in SDM for LCS-eligible persons. Participants were persons without a history of LCS who met LCS eligibility criteria at the time (aged 55-80 years, smoked ≥30 pack-years, and current smoking or quit <15 years ago) and were not documented to be an inappropriate candidate for LCS by a clinician during October 2017 through September 2019. Data were analyzed from September to November 2023. Exposure: Decision support tool augmented by a prediction model that helps clinicians personalize SDM for LCS, tailoring the strength of screening encouragement according to predicted benefit. Main outcome and measure: LCS uptake. Results: In a cohort of 9904 individuals, the median (IQR) age was 64 (57-69) years; 9277 (94%) were male, 1537 (16%) were Black, 8159 (82%) were White, 5153 (52%) were predicted to be at intermediate (preference-sensitive) benefit and 4751 (48%) at high benefit, and 1084 (11%) received screening during the study period. Following implementation of the tool, higher rates of LCS uptake were observed overall along with an increase in benefit-based LCS uptake (higher screening uptake among persons anticipated to be at high benefit compared with those at intermediate benefit; primary analysis). Mean (SD) predicted probability of getting screened for a high-benefit person was 24.8% (15.5%) vs 15.8% (11.8%) for a person at intermediate benefit (mean absolute difference 9.0 percentage points; 95% CI, 1.6%-16.5%). Conclusions and Relevance: Implementing a robust approach to personalized LCS, which integrates SDM, and a decision support tool augmented by a prediction model, are associated with improved uptake of LCS and may be particularly important for those most likely to benefit. These findings are timely given the ongoing poor rates of LCS uptake.

Effectiveness of a virtual quality improvement training program to improve reach of weight management programs within a large health system.

OBJECTIVE: To test effectiveness of the LEAP (Learn Engage Act Process) Program on engaging frontline Veteran Health Administration (VHA) medical center teams in continuous quality improvement (QI), a core capability for learning health systems. DATA SOURCES AND STUDY SETTING: Data sources included VHA electronic health record (EHR) data, surveys, and LEAP coaching field notes. STUDY DESIGN: A staggered difference-in-differences study was conducted. Fifty-five facilities participated in LEAP across eight randomly assigned clusters of 6-8 facilities per cluster over 2 years. Non-participating facilities were used as controls. A MOVE! weight management program team completed a Plan-Do-Study-Act cycle of change supported by learning curriculum, coaching, and virtual collaboratives in LEAP facilities. Primary outcome was program reach to Veterans. A mixed-effects model compared pre- versus post-LEAP periods for LEAP versus control facilities. LEAP adherence, satisfaction, and cost to deliver LEAP were evaluated. DATA COLLECTION/EXTRACTION METHODS: Thirty months of facility-level EHR MOVE! enrollment data were included in analyses. LEAP Satisfaction and QI skills were elicited via surveys at baseline and 6-month post-LEAP. PRINCIPAL FINDINGS: Fifty-five facilities were randomly assigned to eight time-period-based clusters to receive LEAP (71% completed LEAP) and 82 non-participating facilities were randomly assigned as controls. Reach in LEAP and control facilities was comparable in the 12-month pre-LEAP period (p = 0.07). Though LEAP facilities experienced slower decline in reach in the 12-month post-LEAP period compared with controls (p < 0.001), this is likely due to unexplained fluctuations in controls. For LEAP facilities, satisfaction was high (all mean ratings >4 on a 5-point scale), self-reported use of QI methods increased significantly (p-values <0.05) 6 months post-LEAP, and delivery cost was $4024 per facility-based team. CONCLUSION: Control facilities experienced declining reach in the 12-month post-LEAP period, but LEAP facilities did not, plus they reported higher engagement in QI, an essential capability for learning health systems.

Clinician Perceptions on Using Decision Tools to Support Prediction-Based Shared Decision Making for Lung Cancer Screening.

Background: Considering a patient's full risk factor profile can promote personalized shared decision making (SDM). One way to accomplish this is through encounter tools that incorporate prediction models, but little is known about clinicians' perceptions of the feasibility of using these tools in practice. We examined how clinicians react to using one such encounter tool for personalizing SDM about lung cancer screening (LCS). Design: We conducted a qualitative study based on field notes from academic detailing visits during a multisite quality improvement program. The detailer engaged one-on-one with 96 primary care clinicians across multiple Veterans Affairs sites (7 medical centers and 6 outlying clinics) to get feedback on 1) the rationale for prediction-based LCS and 2) how to use the DecisionPrecision (DP) encounter tool with eligible patients to personalize LCS discussions. Results: Thematic content analysis from detailing visit data identified 6 categories of clinician willingness to use the DP tool to personalize SDM for LCS (adoption potential), varying from "Enthusiastic Potential Adopter" (n = 18) to "Definite Non-Adopter" (n = 16). Many clinicians (n = 52) articulated how they found the concept of prediction-based SDM highly appealing. However, to varying degrees, nearly all clinicians identified challenges to incorporating such an approach in routine practice. Limitations: The results are based on the clinician's initial reactions rather than longitudinal experience. Conclusions: While many primary care clinicians saw real value in using prediction to personalize LCS decisions, more support is needed to overcome barriers to using encounter tools in practice. Based on these findings, we propose several strategies that may facilitate the adoption of prediction-based SDM in contexts such as LCS. Highlights: Encounter tools that incorporate prediction models promote personalized shared decision making (SDM), but little is known about clinicians' perceptions of the feasibility of using these tools in practice.We examined how clinicians react to using one such encounter tool for personalizing SDM about lung cancer screening (LCS).While many clinicians found the concept of prediction-based SDM highly appealing, nearly all clinicians identified challenges to incorporating such an approach in routine practice.We propose several strategies to overcome adoption barriers and facilitate the use of prediction-based SDM in contexts such as LCS.

Implementation strategies in suicide prevention: a scoping review.

BACKGROUND: Implementation strategies can be a vital leveraging point for enhancing the implementation and dissemination of evidence-based suicide prevention interventions and programming. However, much remains unknown about which implementation strategies are commonly used and effective for supporting suicide prevention efforts. METHODS: In light of the limited available literature, a scoping review was conducted to evaluate implementation strategies present in current suicide prevention studies. We identified studies that were published between 2013 and 2022 that focused on suicide prevention and incorporated at least one implementation strategy. Studies were coded by two independent coders who showed strong inter-rater reliability. Data were synthesized using descriptive statistics and a narrative synthesis of findings. RESULTS: Overall, we found that studies most commonly utilized strategies related to iterative evaluation, training, and education. The majority of studies did not include direct measurement of suicide behavior outcomes, and there were few studies that directly tested implementation strategy effectiveness. CONCLUSION: Implementation science strategies remain an important component for improving suicide prevention and intervention implementation. Future research should consider the incorporation of more type 3 hybrid designs as well as increased systematic documentation of implementation strategies. TRIAL REGISTRATION: < de-identified > .

The updated Consolidated Framework for Implementation Research based on user feedback.

BACKGROUND: Many implementation efforts fail, even with highly developed plans for execution, because contextual factors can be powerful forces working against implementation in the real world. The Consolidated Framework for Implementation Research (CFIR) is one of the most commonly used determinant frameworks to assess these contextual factors; however, it has been over 10 years since publication and there is a need for updates. The purpose of this project was to elicit feedback from experienced CFIR users to inform updates to the framework. METHODS: User feedback was obtained from two sources: (1) a literature review with a systematic search; and (2) a survey of authors who used the CFIR in a published study. Data were combined across both sources and reviewed to identify themes; a consensus approach was used to finalize all CFIR updates. The VA Ann Arbor Healthcare System IRB declared this study exempt from the requirements of 38 CFR 16 based on category 2. RESULTS: The systematic search yielded 376 articles that contained the CFIR in the title and/or abstract and 334 unique authors with contact information; 59 articles included feedback on the CFIR. Forty percent (n = 134/334) of authors completed the survey. The CFIR received positive ratings on most framework sensibility items (e.g., applicability, usability), but respondents also provided recommendations for changes. Overall, updates to the CFIR include revisions to existing domains and constructs as well as the addition, removal, or relocation of constructs. These changes address important critiques of the CFIR, including better centering innovation recipients and adding determinants to equity in implementation. CONCLUSION: The updates in the CFIR reflect feedback from a growing community of CFIR users. Although there are many updates, constructs can be mapped back to the original CFIR to ensure longitudinal consistency. We encourage users to continue critiquing the CFIR, facilitating the evolution of the framework as implementation science advances.

Maintaining Implementation through Dynamic Adaptations (MIDAS): protocol for a cluster-randomized trial of implementation strategies to optimize and sustain use of evidence-based practices in Veteran Health Administration (VHA) patients.

BACKGROUND: The adoption and sustainment of evidence-based practices (EBPs) is a challenge within many healthcare systems, especially in settings that have already strived but failed to achieve longer-term goals. The Veterans Affairs (VA) Maintaining Implementation through Dynamic Adaptations (MIDAS) Quality Enhancement Research Initiative (QUERI) program was funded as a series of trials to test multi-component implementation strategies to sustain optimal use of three EBPs: (1) a deprescribing approach intended to reduce potentially inappropriate polypharmacy; (2) appropriate dosing and drug selection of direct oral anticoagulants (DOACs); and (3) use of cognitive behavioral therapy as first-line treatment for insomnia before pharmacologic treatment. We describe the design and methods for a harmonized series of cluster-randomized control trials comparing two implementation strategies. METHODS: For each trial, we will recruit 8-12 clinics (24-36 total). All will have access to relevant clinical data to identify patients who may benefit from the target EBP at that clinic and provider. For each trial, clinics will be randomized to one of two implementation strategies to improve the use of the EBPs: (1) individual-level academic detailing (AD) or (2) AD plus the team-based Learn. Engage. Act. PROCESS: (LEAP) quality improvement (QI) learning program. The primary outcomes will be operationalized across the three trials as a patient-level dichotomous response (yes/no) indicating patients with potentially inappropriate medications (PIMs) among those who may benefit from the EBP. This outcome will be computed using month-by-month administrative data. Primary comparison between the two implementation strategies will be analyzed using generalized estimating equations (GEE) with clinic-level monthly (13 to 36 months) percent of PIMs as the dependent variable. Primary comparative endpoint will be at 18 months post-baseline. Each trial will also be analyzed independently. DISCUSSION: MIDAS QUERI trials will focus on fostering sustained use of EBPs that previously had targeted but incomplete implementation. Our implementation approaches are designed to engage frontline clinicians in a dynamic optimization process that integrates the use of actional clinical data and making incremental changes, designed to be feasible within busy clinical settings. TRIAL REGISTRATION: ClinicalTrials.gov: NCT05065502 . Registered October 4, 2021-retrospectively registered.

Implementation of a Web-Based Tool for Shared Decision-making in Lung Cancer Screening: Mixed Methods Quality Improvement Evaluation.

BACKGROUND: Lung cancer risk and life expectancy vary substantially across patients eligible for low-dose computed tomography lung cancer screening (LCS), which has important consequences for optimizing LCS decisions for different patients. To account for this heterogeneity during decision-making, web-based decision support tools are needed to enable quick calculations and streamline the process of obtaining individualized information that more accurately informs patient-clinician LCS discussions. We created DecisionPrecision, a clinician-facing web-based decision support tool, to help tailor the LCS discussion to a patient's individualized lung cancer risk and estimated net benefit. OBJECTIVE: The objective of our study is to test two strategies for implementing DecisionPrecision in primary care at eight Veterans Affairs medical centers: a quality improvement (QI) training approach and academic detailing (AD). METHODS: Phase 1 comprised a multisite, cluster randomized trial comparing the effectiveness of standard implementation (adding a link to DecisionPrecision in the electronic health record vs standard implementation plus the Learn, Engage, Act, and Process [LEAP] QI training program). The primary outcome measure was the use of DecisionPrecision at each site before versus after LEAP QI training. The second phase of the study examined the potential effectiveness of AD as an implementation strategy for DecisionPrecision at all 8 medical centers. Outcomes were assessed by comparing absolute tool use before and after AD visits and conducting semistructured interviews with a subset of primary care physicians (PCPs) following the AD visits. RESULTS: Phase 1 findings showed that sites that participated in the LEAP QI training program used DecisionPrecision significantly more often than the standard implementation sites (tool used 190.3, SD 174.8 times on average over 6 months at LEAP sites vs 3.5 SD 3.7 at standard sites; P<.001). However, this finding was confounded by the lack of screening coordinators at standard implementation sites. In phase 2, there was no difference in the 6-month tool use between before and after AD (95% CI -5.06 to 6.40; P=.82). Follow-up interviews with PCPs indicated that the AD strategy increased provider awareness and appreciation for the benefits of the tool. However, other priorities and limited time prevented PCPs from using them during routine clinical visits. CONCLUSIONS: The phase 1 findings did not provide conclusive evidence of the benefit of a QI training approach for implementing a decision support tool for LCS among PCPs. In addition, phase 2 findings showed that our light-touch, single-visit AD strategy did not increase tool use. To enable tool use by PCPs, prediction-based tools must be fully automated and integrated into electronic health records, thereby helping providers personalize LCS discussions among their many competing demands. PCPs also need more time to engage in shared decision-making discussions with their patients. TRIAL REGISTRATION: ClinicalTrials.gov NCT02765412; https://clinicaltrials.gov/ct2/show/NCT02765412.

Conceptualizing outcomes for use with the Consolidated Framework for Implementation Research (CFIR): the CFIR Outcomes Addendum.

BACKGROUND: The challenges of implementing evidence-based innovations (EBIs) are widely recognized among practitioners and researchers. Context, broadly defined as everything outside the EBI, includes the dynamic and diverse array of forces working for or against implementation efforts. The Consolidated Framework for Implementation Research (CFIR) is one of the most widely used frameworks to guide assessment of contextual determinants of implementation. The original 2009 article invited critique in recognition for the need for the framework to evolve. As implementation science has matured, gaps in the CFIR have been identified and updates are needed. Our team is developing the CFIR 2.0 based on a literature review and follow-up survey with authors. We propose an Outcomes Addendum to the CFIR to address recommendations from these sources to include outcomes in the framework. MAIN TEXT: We conducted a literature review and surveyed corresponding authors of included articles to identify recommendations for the CFIR. There were recommendations to add both implementation and innovation outcomes from these sources. Based on these recommendations, we make conceptual distinctions between (1) anticipated implementation outcomes and actual implementation outcomes, (2) implementation outcomes and innovation outcomes, and (3) CFIR-based implementation determinants and innovation determinants. CONCLUSION: An Outcomes Addendum to the CFIR is proposed. Our goal is to offer clear conceptual distinctions between types of outcomes for use with the CFIR, and perhaps other determinant implementation frameworks as well. These distinctions can help bring clarity as researchers consider which outcomes are most appropriate to evaluate in their research. We hope that sharing this in advance will generate feedback and debate about the merits of our proposed addendum.

Variation in Eligible Patients' Agreeing to and Receiving Lung Cancer Screening: A Cohort Study.

INTRODUCTION: Little is known about how clinicians make low-dose computed tomography lung cancer screening decisions in practice. Investigators assessed the factors associated with real-world decision making, hypothesizing that lung cancer risk and comorbidity would not be associated with agreeing to or receiving screening. Though these factors are key determinants of the benefit of lung cancer screening, they are often difficult to incorporate into decisions without the aid of decision tools. METHODS: This was a retrospective cohort study of patients meeting current national eligibility criteria and deemed appropriate candidates for lung cancer screening on the basis of clinical reminders completed over a 2-year period (2013-2015) at 8 Department of Veterans Affairs medical facilities. Multilevel mixed-effects logistic regression models (conducted in 2019-2020) assessed predictors (age, sex, lung cancer risk, Charlson Comorbidity Index, travel distance to facility, and central versus outlying decision-making location) of primary outcomes of agreeing to and receiving lung cancer screening. RESULTS: Of 5,551 patients (mean age=67 years, 97% male, mean lung cancer risk=0.7%, mean Charlson Comorbidity Index=1.14, median travel distance=24.2 miles), 3,720 (67%) agreed to lung cancer screening and 2,398 (43%) received screening. Lung cancer risk and comorbidity score were not strong predictors of agreeing to or receiving screening. Empirical Bayes adjusted rates of agreeing to and receiving screening ranged from 22% to 84% across facilities and from 19% to 85% across clinicians. A total of 33.7% of the variance in agreeing to and 34.2% of the variance in receiving screening was associated with the facility or the clinician offering screening. CONCLUSIONS: Substantial variation was found in Veterans agreeing to and receiving lung cancer screening during the Veterans Affairs Lung Cancer Screening Demonstration Project. This variation was not explained by differences in key determinants of patient benefit, whereas the facility and clinician advising the patient had a large impact on lung cancer screening decisions.

Research translation for military and veteran health: research, practice, policy.

Military service presents unique challenges and opportunities for health care and public health. In the USA, there are over 2 million military servicemembers, 20 million veterans, and millions more military and veteran family members. Military servicemembers and eligible family members, many veterans, and retirees receive health care through the two largest learning health care systems in the USA, managed and delivered through the Departments of Defense (DoD), Veterans Affairs (VA), and contracted health care organizations. Through a network of collaborative relationships, DoD, VA, and partnering health care and research organizations (university, corporate, community, and government) accelerate research translation into best practices and policy across the USA and beyond. This article outlines military and veteran health research translation as summarized from a collaborative workshop led by experts across health care research, practice, and administration in DoD, VA, the National Institutes of Health, and affiliated universities. Key themes and recommendations for research translation are outlined in areas of: (a) stakeholder engagement and collaboration; (b) implementation science methods; and (c) funding along the translation continuum. Overall, the ability to rapidly translate research into clinical practice and policy for positive health outcomes requires collaborative relationships among many stakeholders. This includes servicemembers, veterans, and their families along with researchers, health care clinicians, and administrators, as well as policymakers and the broader population.

The LEAP Program: Quality Improvement Training to Address Team Readiness Gaps Identified by Implementation Science Findings.

BACKGROUND: Integrating evidence-based innovations (EBIs) into sustained use is challenging; most implementations in health systems fail. Increasing frontline teams' quality improvement (QI) capability may increase the implementation readiness and success of EBI implementation. OBJECTIVES: Develop a QI training program ("Learn. Engage. Act. Process." (LEAP)) and evaluate its impact on frontline obesity treatment teams to improve treatment delivered within the Veterans Health Administration (VHA). DESIGN: This was a pre-post evaluation of the LEAP program. MOVE! coordinators (N = 68) were invited to participate in LEAP; 24 were randomly assigned to four starting times. MOVE! coordinators formed teams to work on improvement aims. Pre-post surveys assessed team organizational readiness for implementing change and self-rated QI skills. Program satisfaction, assignment completion, and aim achievement were also evaluated. PARTICIPANTS: VHA facility-based MOVE! teams. INTERVENTIONS: LEAP is a 21-week QI training program. Core components include audit and feedback reports, structured curriculum, coaching and learning community, and online platform. MAIN MEASURES: Organizational readiness for implementing change (ORIC); self-rated QI skills before and after LEAP; assignment completion and aim achievement; program satisfaction. KEY RESULTS: Seventeen of 24 randomized teams participated in LEAP. Participants' self-ratings across six categories of QI skills increased after completing LEAP (p< 0.0001). The ORIC measure showed no statistically significant change overall; the change efficacy subscale marginally improved (p < 0.08), and the change commitment subscale remained the same (p = 0.66). Depending on the assignment, 35 to 100% of teams completed the assignment. Nine teams achieved their aim. Most team members were satisfied or very satisfied (81-89%) with the LEAP components, 74% intended to continue using QI methods, and 81% planned to continue improvement work. CONCLUSIONS: LEAP is scalable and does not require travel or time away from clinical responsibilities. While QI skills improved among participating teams and most completed the work, they struggled to do so amid competing clinical priorities.

The "State of Implementation" Progress Report (SIPREP): a pilot demonstration of a navigation system for implementation.

BACKGROUND: Implementation of new clinical programs across diverse facilities in national healthcare systems like the Veterans Health Administration (VHA) can be extraordinarily complex. Implementation is a dynamic process, influenced heavily by local organizational context and the individual staff at each medical center. It is not always clear in the midst of implementation what issues are most important to whom or how to address them. In recognition of these challenges, implementation researchers within VHA developed a new systemic approach to map the implementation work required at different stages and provide ongoing, detailed, and nuanced feedback about implementation progress. METHODS: This observational pilot demonstration project details how a novel approach to monitoring implementation progress was applied across two different national VHA initiatives. Stage-specific grids organized the implementation work into columns, rows, and cells, identifying specific implementation activities at the site level to be completed along with who was responsible for completing each implementation activity. As implementation advanced, item-level checkboxes were crossed off and cells changed colors, offering a visual representation of implementation progress within and across sites across the various stages of implementation. RESULTS: Applied across two different national initiatives, the SIPREP provided a novel navigation system to guide and inform ongoing implementation within and across facilities. The SIPREP addressed different needs of different audiences, both described and explained how to implement the program, made ample use of visualizations, and revealed both what was happening and not happening within and across sites. The final SIPREP product spanned distinct stages of implementation. CONCLUSIONS: The SIPREP made the work of implementation explicit at the facility level (i.e., who does what, and when) and provided a new common way for all stakeholders to monitor implementation progress and to help keep implementation moving forward. This approach could be adapted to a wide range of settings and interventions and is planned to be integrated into the national deployment of two additional VHA initiatives within the next 12 months.

The Phased Implementation of a National Telehealth Weight Management Program for Veterans: Mixed-Methods Program Evaluation.

BACKGROUND: The burden of obesity is high among US veterans, yet many face barriers to engaging in in-person, facility-based treatment programs. To improve access to weight-management services, the Veterans Health Administration (VHA) developed TeleMOVE, a home-based, 82-day curriculum that utilizes in-home messaging devices to promote weight loss in VHA patients facing barriers to accessing facility-based services. OBJECTIVE: The primary aim was to establish preliminary evidence for the program by comparing outcomes for TeleMOVE with standard, facility-based MOVE weight-management services (group, individual modalities) over the evaluation period based on the number of patients enrolled per site and the program's clinical effectiveness, as demonstrated by average weight lost per patient. The secondary aim was to understand factors influencing TeleMOVE implementation variability across demonstration sites to develop recommendations to improve national program dissemination. METHODS: We employed a formative mixed-methods design to evaluate the phased implementation of TeleMOVE at 9 demonstration sites and compare patient- and site-level measures of program uptake. Data were collected between October 1, 2009 and September 30, 2011. Patient-level program outcomes were extracted from VHA patient care databases to evaluate program enrollment rates and clinical outcomes. To assess preliminary clinical effectiveness, weight loss outcomes for veterans who enrolled in TeleMOVE were compared with outcomes for veterans enrolled in standard MOVE! at each demonstration site, as well as with national averages during the first 2 years of program implementation. For the secondary aim, we invited program stakeholders to participate in 2 rounds of semistructured interviews about aspects of TeleMOVE implementation processes, site-level contextual factors, and program delivery. Twenty-eight stakeholders participated in audio-recorded interviews. RESULTS: Although stakeholders at 3 sites declined to be interviewed, objective program uptake was high at 2 sites, delayed-high at 2 sites, and low at 5 sites. At 6 months post enrollment, the mean weight loss was comparable for TeleMOVE (n=417) and MOVE! (n=1543) participants at -5.2 lb (SD 14.4) and -5.1 lb (SD 12.2), respectively (P=.91). All sites reported high program complexity because TeleMOVE required more staff time per participant than MOVE! due to logistical and technical assistance issues related to the devices. High-uptake sites overcame implementation challenges by leveraging communication networks with stakeholders, adapting the program to patient needs whenever possible, setting programmatic goals and monitoring feedback of results, and taking time to reflect and evaluate on delivery to foster incremental delivery improvements, whereas low-uptake sites reported less leadership support and effective communication among stakeholders. CONCLUSIONS: This implementation evaluation of a clinical telehealth program demonstrated the value of partnership-based research in which researchers not only provided operational leaders with feedback regarding the effectiveness of a new program but also relevant feedback into contextual factors related to program implementation to enable adaptations for national deployment efforts.

Quality Improvement and Personalization for Statins: the QUIPS Quality Improvement Randomized Trial of Veterans' Primary Care Statin Use.

BACKGROUND: Implementation of new practice guidelines for statin use was very poor. OBJECTIVE: To test a multi-component quality improvement intervention to encourage use of new guidelines for statin use. DESIGN: Cluster-randomized, usual-care controlled trial. PARTICIPANTS: The study population was primary care visits for patients who were recommended statins by the 2013 guidelines, but were not receiving them. We excluded patients who were over 75 years old, or had an ICD9 or ICD10 code for end-stage renal disease, muscle pain, pregnancy, or in vitro fertilization in the 2 years prior to the study visit. INTERVENTIONS: A novel quality improvement intervention consisting of a personalized decision support tool, an educational program, a performance measure, and an audit and feedback system. Randomization was at the level of the primary care team. MAIN MEASURES: Our primary outcome was prescription of a medium- or high-strength statin. We studied how receiving the intervention changed care during the quality improvement intervention compared to before it and if that change continued after the intervention. KEY RESULTS: Among 3787 visits to 43 primary care providers, being in the intervention arm tripled the odds of patients being prescribed an appropriate statin (OR 3.0, 95% CI 1.8-4.9), though the effect resolved after the personalized decision support ended (OR 1.7, 95% CI 0.99-2.77). CONCLUSIONS: A simple, personalized quality improvement intervention is promising for enabling the adoption of new guidelines. CLINICALTRIALS. GOV IDENTIFIER: NCT02820870.

Designing for Clinical Change: Creating an Intervention to Implement New Statin Guidelines in a Primary Care Clinic.

BACKGROUND: Recent clinical practice guidelines from major national organizations, including a joint United States Department of Veterans Affairs (VA) and Department of Defense (DoD) committee, have substantially changed recommendations for the use of the cholesterol-lowering statin medications after years of relative stability. Because statin medications are among the most commonly prescribed treatments in the United States, any change in their use may have significant implications for patients and providers alike. Prior research has shown that effective implementation interventions should be both user centered and specifically chosen to address identified barriers. OBJECTIVE: The objectives of this study were to identify potential determinants of provider uptake of the new statin guidelines and to use that information to tailor a coordinated and streamlined local quality improvement intervention focused on prescribing appropriate statins. METHODS: We employed user-centered design principles to guide the development and testing of a multicomponent guideline implementation intervention to improve statin prescribing. This paper describes the intervention development process whereby semistructured qualitative interviews with providers were conducted to (1) illuminate the knowledge, attitudes, and behaviors of providers and (2) elicit feedback on intervention prototypes developed to align with and support the use of the VA/DoD guidelines. Our aim was to use this information to design a local quality improvement intervention focused on statin prescribing that was tailored to the needs of primary care providers at our facility. Cabana's Clinical Practice Guidelines Framework for Improvement and Nielsen's Usability Heuristics were used to guide the analysis of data obtained in the intervention development process. RESULTS: Semistructured qualitative interviews were conducted with 15 primary care Patient Aligned Care Team professionals (13 physicians and 2 clinical pharmacists) at a single VA medical center. Findings highlight that providers were generally comfortable with the paradigm shift to risk-based guidelines but less clear on the need for the VA/DoD guidelines in specific. Providers preferred a clinical decision support tool that helped them calculate patient risk and guide their care without limiting autonomy. They were less comfortable with risk communication and performance measurement systems that do not account for shared decision making. When possible, we incorporated their recommendations into the intervention. CONCLUSIONS: By combining qualitative methods and user-centered design principles, we could inform the design of a multicomponent guideline implementation intervention to better address the needs and preferences of providers, including clear and direct language, logical decision prompts with an option to dismiss a clinical decision support tool, and logical ordering of feedback information. Additionally, this process allowed us to identify future design considerations for quality improvement interventions.

Evaluation of a national telemedicine initiative in the Veterans Health Administration: Factors associated with successful implementation.

Background The Consolidated Framework for Implementation Research was used to evaluate implementation facilitators and barriers of Specialty Care Access Network-Extension for Community Healthcare Outcomes (SCAN-ECHO) within the Veterans Health Administration. SCAN-ECHO is a video teleconferencing-based programme where specialist teams train and mentor remotely-located primary care providers in providing routine speciality care for common chronic illnesses. The goal of SCAN-ECHO was to improve access to speciality care for Veterans. The aim of this study was to provide guidance and support for the implementation and spread of SCAN-ECHO. Methods Semi-structured telephone interviews with 55 key informants (primary care providers, specialists and support staff) were conducted post-implementation with nine sites and analysed using Consolidated Framework for Implementation Research constructs. Data were analysed to distinguish sites based on level of implementation measured by the numbers of SCAN-ECHO sessions. Surveys with all SCAN-ECHO sites further explored implementation information. Results Analysis of the interviews revealed three of 14 Consolidated Framework for Implementation Research constructs that distinguished between low and high implementation sites: design quality and packaging; compatibility; and reflecting and evaluating. The survey data generally supported these findings, while also revealing a fourth distinguishing construct - leadership engagement. All sites expressed positive attitudes toward SCAN-ECHO, despite struggling with the complexity of programme implementation. Conclusions Recommendations based on the findings include: (a) expend more effort in developing and distributing educational materials; (b) restructure the delivery process to improve programme compatibility;

Comprehensive process model of clinical information interaction in primary care: results of a "best-fit" framework synthesis.

Objective: To describe a new, comprehensive process model of clinical information interaction in primary care (Clinical Information Interaction Model, or CIIM) based on a systematic synthesis of published research. Materials and Methods: We used the "best fit" framework synthesis approach. Searches were performed in PubMed, Embase, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycINFO, Library and Information Science Abstracts, Library, Information Science and Technology Abstracts, and Engineering Village. Two authors reviewed articles according to inclusion and exclusion criteria. Data abstraction and content analysis of 443 published papers were used to create a model in which every element was supported by empirical research. Results: The CIIM documents how primary care clinicians interact with information as they make point-of-care clinical decisions. The model highlights 3 major process components: (1) context, (2) activity (usual and contingent), and (3) influence. Usual activities include information processing, source-user interaction, information evaluation, selection of information, information use, clinical reasoning, and clinical decisions. Clinician characteristics, patient behaviors, and other professionals influence the process. Discussion: The CIIM depicts the complete process of information interaction, enabling a grasp of relationships previously difficult to discern. The CIIM suggests potentially helpful functionality for clinical decision support systems (CDSSs) to support primary care, including a greater focus on information processing and use. The CIIM also documents the role of influence in clinical information interaction; influencers may affect the success of CDSS implementations. Conclusion: The CIIM offers a new framework for achieving CDSS workflow integration and new directions for CDSS design that can support the work of diverse primary care clinicians.

Behavioral Treatment for Veterans with Obesity: 24-Month Weight Outcomes from the ASPIRE-VA Small Changes Randomized Trial.

BACKGROUND: Small Changes (SC) is a weight management approach that demonstrated superior 12-month outcomes compared to the existing MOVE!® Weight Management Program at two Veterans Affairs (VA) sites. However, approaches are needed to help graduates of treatment continue to lose or maintain their weight over the longer term. OBJECTIVE: The purpose of the present study was to examine the effectiveness of a second year of low-intensity SC support compared to support offered by the usual care MOVE! programs. DESIGN: Following participation in the year-long Aspiring to Lifelong Health in VA (ASPIRE-VA) randomized controlled trial, participants were invited to extend their participation in their assigned program for another year. Three programs were extended to include six SC sessions delivered via telephone (ASPIRE-Phone) or an in-person group (ASPIRE-Group), or 12 sessions offered by the MOVE! programs. PARTICIPANTS: Three hundred thirty-two overweight/obese veterans who consented to extend their participation in the ASPIRE-VA trial by an additional year. MAIN MEASURES: Twenty-four-month weight change (kg). KEY RESULTS: Twenty-four months after baseline, participants in all three groups had modest weight loss (-1.40 kg [-2.61 to -0.18] in the ASPIRE-Group, -2.13 kg [-3.43 to -0.83] in ASPIRE-Phone, and -1.78 kg [-3.07 to -0.49] in MOVE!), with no significant differences among the three groups. Exploratory post hoc analyses revealed that participants diagnosed with diabetes initially benefited from the ASPIRE-Group program (-2.6 kg [-4.37 to 0.83]), but experienced significant weight regain during the second year (+2.8 kg [0.92-4.69]) compared to those without diabetes. CONCLUSIONS: Participants in all three programs lost weight and maintained a statistically significant, though clinically modest, amount of weight loss over a 24-month period. Although participants in the ASPIRE-Group initially had greater weight loss, treatment was not sufficient to sustain weight loss through the second year, particularly in veterans with diabetes. Consistent, continuous-care treatment is needed to address obesity in the VA.

Evaluation of the Veterans Health Administration's Specialty Care Transformational Initiatives to Promote Patient-Centered Delivery of Specialty Care: A Mixed-Methods Approach.

INTRODUCTION: Veteran's Affairs Office of Specialty Care (OSC) launched four national initiatives (Electronic-Consults [e-Consults], Specialty Care Access Networks-Extension for Community Healthcare Outcomes [SCAN-ECHO], Mini-Residencies, and Specialty Care Neighborhood) to improve specialty care delivery and funded a center to evaluate the initiatives. METHODS: The evaluation, guided by two implementation frameworks, provides formative (administrator/provider interviews and surveys) and summative data (quantitative data on patterns of use) about the initiatives to OSC. RESULTS: Evaluation of initiative implementation is assessed through CFIR (Consolidated Framework for Implementation Research)-grounded qualitative interviews to identify barriers/facilitators. Depending on high or low implementation, factors such as receiving workload credit, protected time, existing workflow/systems compatibility, leadership engagement, and access to information/resources were considered implementation barriers or facilitators. Findings were shared with OSC and used to further refine implementation at additional sites. Evaluation of other initiatives is ongoing. CONCLUSIONS: The mixed-methods approach has provided timely information to OSC about initiative effect and impacted OSC policies on implementation at additional sites.