Facility-level program components leading to population impact: a coincidence analysis of obesity treatment options within the Veterans Health Administration.

Obesity is a well-established risk factor for increased morbidity and mortality. Comprehensive lifestyle interventions, pharmacotherapy, and bariatric surgery are three effective treatment approaches for obesity. The Veterans Health Administration (VHA) offers all three domains but in different configurations across medical facilities. Study aim was to explore the relationship between configurations of three types of obesity treatments, context, and population impact across VHA using coincidence analysis. This was a cross-sectional analysis of survey data describing weight management treatment components linked with administrative data to compute population impact for each facility. Coincidence analysis was used to identify combinations of treatment components that led to higher population impact. Facilities with higher impact were in the top two quintiles for (1) reach to eligible patients and (2) weight outcomes. Sixty-nine facilities were included in the analyses. The final model explained 88% (29/33) of the higher-impact facilities with 91% consistency (29/32) and was comprised of five distinct pathways. Each of the five pathways depended on facility complexity-level plus factors from one or more of the three domains of weight management: comprehensive lifestyle interventions, pharmacotherapy, and/or bariatric surgery. Three pathways include components from multiple treatment domains. Combinations of conditions formed "recipes" that lead to higher population impact. Our coincidence analyses highlighted both the importance of local context and how combinations of specific conditions consistently and uniquely distinguished higher impact facilities from lower impact facilities for weight management.

Obesity can contribute to increased rates of ill health and earlier death. Proven treatments for obesity include programs that help people improve lifestyle behaviors (e.g., being physically active), medications, and/or bariatric surgery. In the Veterans Health Administration (VHA), all three types of treatments are offered, but not at every medical center­in practice, individual medical centers offer different combinations of treatment options to their patients. VHA medical centers also have a wide range of population impact. We identified high-impact medical centers (centers with the most patients participating in obesity treatment who would benefit from treatment AND that reported the most weight loss for their patients) and examined which treatment configurations led to better population-level outcomes (i.e., higher population impact). We used a novel analysis approach that allows us to compare combinations of treatment components, instead of analyzing them one-by-one. We found that optimal combinations are context-sensitive and depend on the type of center (e.g., large centers affiliated with a university vs. smaller rural centers). We list five different "recipes" of treatment combinations leading to higher population-level impact. This information can be used by clinical leaders to design treatment programs to maximize benefits for their patients.

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.

Deriving Weight From Big Data: Comparison of Body Weight Measurement-Cleaning Algorithms.

BACKGROUND: Patient body weight is a frequently used measure in biomedical studies, yet there are no standard methods for processing and cleaning weight data. Conflicting documentation on constructing body weight measurements presents challenges for research and program evaluation. OBJECTIVE: In this study, we aim to describe and compare methods for extracting and cleaning weight data from electronic health record databases to develop guidelines for standardized approaches that promote reproducibility. METHODS: We conducted a systematic review of studies published from 2008 to 2018 that used Veterans Health Administration electronic health record weight data and documented the algorithms for constructing patient weight. We applied these algorithms to a cohort of veterans with at least one primary care visit in 2016. The resulting weight measures were compared at the patient and site levels. RESULTS: We identified 496 studies and included 62 (12.5%) that used weight as an outcome. Approximately 48% (27/62) included a replicable algorithm. Algorithms varied from cutoffs of implausible weights to complex models using measures within patients over time. We found differences in the number of weight values after applying the algorithms (71,961/1,175,995, 6.12% to 1,175,177/1,175,995, 99.93% of raw data) but little difference in average weights across methods (93.3, SD 21.0 kg to 94.8, SD 21.8 kg). The percentage of patients with at least 5% weight loss over 1 year ranged from 9.37% (4933/52,642) to 13.99% (3355/23,987). CONCLUSIONS: Contrasting algorithms provide similar results and, in some cases, the results are not different from using raw, unprocessed data despite algorithm complexity. Studies using point estimates of weight may benefit from a simple cleaning rule based on cutoffs of implausible values; however, research questions involving weight trajectories and other, more complex scenarios may benefit from a more nuanced algorithm that considers all available weight data.

Facility-level conditions leading to higher reach: a configurational analysis of national VA weight management programming.

BACKGROUND: While the Veterans Health Administration (VHA) MOVE! weight management program is effective in helping patients lose weight and is available at every VHA medical center across the United States, reaching patients to engage them in treatment remains a challenge. Facility-based MOVE! programs vary in structures, processes of programming, and levels of reach, with no single factor explaining variation in reach. Configurational analysis, based on Boolean algebra and set theory, represents a mathematical approach to data analysis well-suited for discerning how conditions interact and identifying multiple pathways leading to the same outcome. We applied configurational analysis to identify facility-level obesity treatment program arrangements that directly linked to higher reach. METHODS: A national survey was fielded in March 2017 to elicit information about more than 75 different components of obesity treatment programming in all VHA medical centers. This survey data was linked to reach scores available through administrative data. Reach scores were calculated by dividing the total number of Veterans who are candidates for obesity treatment by the number of "new" MOVE! visits in 2017 for each program and then multiplied by 1000. Programs with the top 40 % highest reach scores (n = 51) were compared to those in the lowest 40 % (n = 51). Configurational analysis was applied to identify specific combinations of conditions linked to reach rates. RESULTS: One hundred twenty-seven MOVE! program representatives responded to the survey and had complete reach data. The final solution consisted of 5 distinct pathways comprising combinations of program components related to pharmacotherapy, bariatric surgery, and comprehensive lifestyle intervention; 3 of the 5 pathways depended on the size/complexity of medical center. The 5 pathways explained 78 % (40/51) of the facilities in the higher-reach group with 85 % consistency (40/47). CONCLUSIONS: Specific combinations of facility-level conditions identified through configurational analysis uniquely distinguished facilities with higher reach from those with lower reach. Solutions demonstrated the importance of how local context plus specific program components linked together to account for a key implementation outcome. These findings will guide system recommendations about optimal program structures to maximize reach to patients who would benefit from obesity treatment such as the MOVE!

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.

Construction and Use of Body Weight Measures from Administrative Data in a Large National Health System: A Systematic Review.

OBJECTIVE: Administrative data are increasingly used in research and evaluation yet lack standardized guidelines for constructing measures using these data. Body weight measures from administrative data serve critical functions of monitoring patient health, evaluating interventions, and informing research. This study aimed to describe the algorithms used by researchers to construct and use weight measures. METHODS: A structured, systematic literature review of studies that constructed body weight measures from the Veterans Health Administration was conducted. Key information regarding time frames and time windows of data collection, measure calculations, data cleaning, treatment of missing and outlier weight values, and validation processes was collected. RESULTS: We identified 39 studies out of 492 nonduplicated records for inclusion. Studies parameterized weight outcomes as change in weight from baseline to follow-up (62%), weight trajectory over time (21%), proportion of participants meeting weight threshold (46%), or multiple methods (28%). Most (90%) reported total time in follow-up and number of time points. Fewer reported time windows (54%), outlier values (51%), missing values (34%), or validation strategies (15%). CONCLUSIONS: A high variability in the operationalization of weight measures was found. Improving methods to construct clinical measures will support transparency and replicability in approaches, guide interpretation of findings, and facilitate comparisons across studies.