Population Health Management最新文献

This study explored a large segment of Medicare claims data to evaluate the association between Accountable Care Organization (ACO) attribution and 30-day all-cause hospital readmissions. ACOs deliver value-based care to attributed patient populations, aiming to enhance care coordination and transitional care outcomes. Initiatives such as the Medicare Shared Savings Program (MSSP) incentivize health care systems to reduce readmissions and the total cost of care. The study included all Medicare inpatient discharges across 50 US states from January 1, 2022, to December 1, 2024. The primary measure of interest was 30-day all-cause readmissions. Hospitalizations for ACO-attributed beneficiaries (readmitted vs. not readmitted) were compared with hospitalizations for non-ACO-attributed beneficiaries. Subgroup and sensitivity analyses were conducted to explore ACO readmission performance with cohorts of beneficiaries with higher levels of clinical complexity and single or multiple hospital admissions. MSSP ACO beneficiaries had a 6% lower rate of 30-day all-cause readmissions. When restricting the cohorts to beneficiaries with higher levels of clinical complexity, MSSP ACO participants had significantly lower readmission rates. Sensitivity analyses adjusting for unequal sample sizes, differences in clinical complexity, and excess zeros (statistical overinflation) indicated that despite the positive effect of multiple hospitalizations, assignment to an ACO was significantly associated with lower readmission risk. The ACO care delivery model is a high-performing care coordination model that exemplifies best practices in addressing transitional care challenges, providing actionable insights for other health care organizations seeking to advance their transitional care strategies within value-based programs.

Community health workers (CHW) play a unique role as trusted frontline public health workers who connect underserved populations with health and social services. In addition, CHWs have local insights on underserved patients and families, which can help to reduce information gaps and enhance the capacity of health care systems to understand health-related social needs. However, prior reviews have included studies of varying quality, which makes it difficult to assess rigorous evidence from randomized control trial (RCT) studies. Also, many CHW intervention studies do not clearly specify in which organizational setting a CHW is employed. This scoping review of US studies published in the peer-reviewed literature from 2000 to 2023 focuses on RCT studies of CHW interventions by type of organization. A total of 39 studies met all inclusion criteria. Most RCT studies were conducted in health care systems and among safety-net providers, including community health centers. However, only a handful of rigorous RCT studies of CHW interventions were conducted in public health agencies or payer settings (managed care organizations). Overall, most RCT studies of CHW interventions found consistent evidence of improved outcomes. Health care organizations can enhance their efforts to address resource gaps by hiring CHWs or partnering with organizations that employ CHWs. Finally, future RCT studies on CHWs employed by health plans (payers) or public health agencies are needed to bolster the growing body of rigorous evidence that CHWs are highly effective in improving patient outcomes across multiple organizational settings.

Social determinants of health (SDOH) have a greater impact on health outcomes than clinical care. It is essential to address SDOH to improve population health outcomes and achieve success in value-based care models. Primary care delivery models have increased the focus on screening for SDOH to meet these needs. However, there are no publications regarding SDOH screening or addressing social needs in gastroenterology practice. Furthermore, there is no evidence regarding the impact of SDOH screening on the business of gastroenterology practice. This study surveyed community gastroenterologists to explore the potential benefits of addressing SDOH in gastrointestinal specialty care.

Providing value-based care (VBC) training to relevant stakeholders promotes broader adoption of VBC principles, which in turn can drive improvements in care coordination, patient outcomes, and cost efficiency across the health system. This study assessed the impact of VBC training on learners' self-reported knowledge and examined how learner characteristics influenced the implementation of VBC principles in professional practice post-training. A 12-week, open online VBC course with 6 modules was developed collaboratively by an academic institution and industry partners. Learners were invited to complete pre- and post-course surveys, and to self-report changes in their knowledge and implementation of VBC principles following course completion. Independent variables included age, geographic residence, education level, biological sex, race/ethnicity, student status, employment status, prior VBC experience, and health care work experience. A linear regression model was used to examine factors associated with increased self-reported knowledge, while logistic regression assessed the relationship between independent variables and the likelihood of learners implementing the course concepts learned in practice. The analytic sample included 715 pre- and post- survey responses. Self-reported knowledge and confidence in VBC concepts increased by 60% by course completion, with 63% of learners reporting early implementation of VBC concepts. Greater increases in self-reported were observed among learners with prior clinical experience and those without prior VBC experience. Learners with higher rates of self-reported VBC implementation were more likely to be female, in full-time employment (35+ hours a week), have prior VBC experience as providers, and undergraduate students. Online VBC education can improve self-reported knowledge and confidence in VBC concepts for a myriad of learners, which translates to increased implementation in health care environments.

Diabetes accounts for 1 in 4 health care dollars spent. Succeeding in value-based payment models depends on identifying those at risk for high costs and providing them with appropriate treatment. The objective was to determine factors associated with type 2 diabetes mellitus costs. In this cohort study, this study used longitudinal data from a national insurer between 2016 and 2020. The authors included individuals aged 65 and older with type 2 diabetes mellitus with at least 12 months of continuous enrollment in Medicare Advantage. Exclusions included those who died during the study period or had incomplete data. Factors included study year, demographics (age, sex, race/ethnicity, language, dual eligibility, rurality), and diabetes complications (Diabetes Complications Severity Index). The outcomes of interest were medical and prescription costs. The study included 49,843 individuals. Diabetes complications (coefficient = $3582.11, P < 0.001), year (coefficient = $1003.22, P < 0.001, 2020 vs. 2016), sex (coefficient = $238.35, P < 0.001, female vs. male), dual eligibility (coefficient = $618.61, P < 0.001, yes vs. no), and rurality (coefficient = $1242.38, P < 0.001, yes vs. no) were associated with higher medical costs. Age (coefficient = $-2851.67, P < 0.001), race/ethnicity (coefficient = $-1458.03, P < 0.001, Black vs. White; coefficient = $-1679.81, P < 0.001, Hispanic vs. White), and language (coefficient = $-2523.29, P < 0.001, Spanish vs. English) were associated with lower medical costs. Individuals who had complications, were female, were dually eligible, and lived in rural communities had higher medical costs. Black, Hispanic, and Spanish-speaking individuals had lower medical costs, mirroring well-known disparities. Policy makers and health care organizations can use these data to more efficiently deliver care to some while ensuring adequate access for others.

Devastating mortality, morbidity, economic, and quality of life impacts have resulted from disasters in the United States. This study aimed to validate a preexisting machine learning (ML) model of household disaster preparedness. Data from 2021 to 23 Federal Emergency Management Agency's National Household Surveys (n = 21,294) were harmonized. Importance features from the preexisting random forest ML model were transferred and tested in multiple linear and logistic regression models with updated datasets. Multiple regression models explained 42%-53% of the variance in household disaster preparedness. Features that improved the odds of overall disaster preparedness included detailed evacuation plans (odds ratios [OR] = 3.5-5.5), detailed shelter plans (OR = 4.3-11.0), having flood insurance (OR = 1.5-2.0), and higher educational attainment (OR = 1.1). Having no specified source of disaster information lowered preparedness odds (OR = 0.11-0.53). When stratified further by older adults with Black racial identities (n = 350), television as a main source of disaster-related information demonstrated associations with increased preparedness odds (OR = 2.2). These results validate the importance of detailed evacuation and shelter planning and the need to consider flood insurance subsidies in population health management to prepare for disasters. Tailored preparedness education for older adults with low educational attainment and targeted television media for subpopulation disaster-related information are indicated. By demonstrating a feasible use case to import ML model findings for regression testing in new datasets, this process promises to enhance population management health equity for those in sites that do not yet utilize local ML.