Design and rationale for the supermarket and web-based intervention targeting nutrition (SuperWIN) for cardiovascular risk reduction trial.

PURPOSE: The prevalence of chronic diseases is increasing largely due to suboptimal dietary habits. It is not known whether individualized, supermarket-based, nutrition education delivered by registered dietitians, utilizing the advantages of the in-store and online environments, and electronically collected purchasing data, can increase dietary quality. METHODS AND RESULTS: The supermarket and web-based intervention targeting nutrition (SuperWIN) for cardiovascular risk reduction trial is a randomized, controlled dietary intervention study. Adults identified from a primary care network with 1 or more risk factors were randomized at their preferred store to: (1) standard of care plus individualized, point- of-purchase nutrition education; (2) standard of care plus individualized, point- of-purchase nutrition education enhanced with online shopping technologies and training; or (3) standard of care alone. Educational sessions within each store's clinic and aisles, emphasized the dietary approaches to stop hypertension (DASH) diet. The primary assessment was an intention-to-treat comparison on the effects of the dietary interventions on mean change in DASH score (90-point range) from baseline to 3 months (post-intervention). Additional outcomes included blood pressure, lipids, weight, purchasing behavior, food literacy, and intervention feedback. Between April 2019 to February 2021, 267 participants were randomized (20 excluded due to coronavirus disease pandemic). Median age was 58 years, 69% were female, 64% had a college degree, 53% worked full-time, 64% were obese, 73% were treated with blood pressure and 42% with cholesterol medications, and most had low-to-moderate diet quality. CONCLUSION: The SuperWIN trial was designed to provide a rigorous evaluation of the efficacy of 2 novel, comprehensive, supermarket-based dietary intervention programs.

Determining the Number of Bariatric Beds Needed in a U.S. Acute Care Hospital.

AIM: This project used historical hospital data to forecast demand for specialized bariatric beds. Models were evaluated that determined the relationship between the number of bariatric beds owned and service level for patients of size requiring these beds. A calculator was developed for minimizing the equipment costs of meeting demand. BACKGROUND: Failing to provide enough bariatric beds may negatively affect outcomes for patients of size and healthcare workers, whereas owning more bariatric beds than required to meet demand means unnecessary cost. With rising rates of obesity increasing care costs, minimizing equipment costs is increasingly important. METHOD: One year of hospital admissions data were used to determine arrival rates and lengths of stay for patients of size. Two subsequent years verified the consistency of these rates. Simulations modeled the flow of patients of size through the hospital and the service level associated with the number of beds owned. A minimization function determined the optimal number of bariatric beds to be provided. A simplified, generalizable model was compared to the simulation. RESULTS: The simplified model produced similar results to more complex simulation. The optimization was robust, or insensitive to small changes in inputs, and identified substantial opportunity for savings if demand for beds was substantially over- or underestimated. CONCLUSIONS: The simplified model and cost optimization could be used in many situations to prevent costly errors in equipment planning. However, hospitals should consider customized simulation to estimate demand for high-cost equipment or unique circumstances not fitting the assumptions of these models.

FASStR: a framework for ensuring high-quality operational metrics in health care.

OBJECTIVES: Poorly defined measurement impairs interinstitutional comparison, interpretation of results, and process improvement in health care operations. We sought to develop a unifying framework that could be used by administrators, practitioners, and investigators to help define and document operational performance measures that are comparable and reproducible. STUDY DESIGN: Retrospective analysis. METHODS: Health care operations and clinical investigators used an iterative process consisting of (1) literature review, (2) expert assessment and collaborative design, and (3) end-user feedback. We sampled the literature from the medical, health systems research, and health care operations (business and engineering) disciplines to assemble a representative sample of studies in which outpatient health care performance metrics were used to describe the primary or secondary outcome of the research. RESULTS: We identified 2 primary deficiencies in outpatient performance metric definitions: incompletion and inconsistency. From our review of performance metrics, we propose the FASStR framework for the Focus, Activity, Statistic, Scale type, and Reference dimensions of a performance metric. The FASStR framework is a method by which performance metrics can be developed and examined from a multidimensional perspective to evaluate their comprehensiveness and clarity. The framework was tested and revised in an iterative process with both practitioners and investigators. CONCLUSIONS: The FASStR framework can guide the design, development, and implementation of operational metrics in outpatient health care settings. Further, this framework can assist investigators in the evaluation of the metrics that they are using. Overall, the FASStR framework can result in clearer, more consistent use and evaluation of outpatient performance metrics.