Population Health Management最新文献

A payvider organization provides both payer and provider services and has been linked to administrative and patient cost reduction by improving right-sized utilization of patient care services. A geriatric-focused transitional program was offered to patients covered under a value-based care risk contract formed by the payvider relationship of an integrated health system. This article describes a prospective study comparing utilization and cost metrics of patients enrolled in the transitional care program with the goal of analyzing utilization of services to better understand patient behavior patterns and care needs after hospital admission and consecutive enrollment in the program. Patients enrolled in the program incurred lower costs in all categories but home health care than the reference population. The cost avoidance achieved during the study period was estimated to be over $1.1 million. Individuals participating in the program had similar emergency department visit rates during the 90- and 180-days following the hospital as the reference population but had significantly lower inpatient readmissions (7.8% vs. 15.4%) even with a higher average readmission risk score (66.8 vs. 65.5). The implementation of the transitional care program led to reduced costs and more efficient utilization of services than those not enrolled in the program. The payvider relationship allows systems to think proactively about new initiatives and programs that will better serve their communities, especially when identifying groups with high projected costs and service utilization. Patients benefit from the assurance that the services they are receiving are covered by their insurer and their trusted organization.

Total hip arthroplasty (THA) is a widely performed surgical procedure in the United States, but disparities in THA outcomes related to hospital-level factors, such as safety-net burden, are underexplored. This study expands on previous research by analyzing multicenter, multistate data from 2015 to 2020 to investigate the impact of hospital safety-net burden-defined as the proportion of services billed to Medicaid and uninsured patients-on THA outcomes. This study is a retrospective analysis using data from the State Inpatient Databases for Florida, Kentucky, Maryland, New York, Washington, New Jersey, and North Carolina. The study cohort included 543,814 inpatient primary THA admissions, with patient demographics, comorbidities, and hospital characteristics analyzed across 3 categories of hospital safety-net burden (low, medium, and high). Generalized linear mixed models assessed the association between safety-net burden and in-hospital mortality and postoperative complications, whereas multilevel negative binomial regression evaluated the impact on hospital length of stay. The study findings indicate that patients undergoing THA at hospitals with high safety-net burden had significantly higher odds of in-hospital mortality (adjusted odds ratio [aOR]: 1.20, 95% confidence interval [CI]: 1.02-1.42), postoperative complications (aOR 1.33, 95% CI 1.20-1.48), and longer hospital stays (adjusted incidence rate ratio 1.15, 95% CI 1.10-1.21) compared with those at low-burden hospitals. These results suggest that hospitals with higher safety-net burden, often serving more vulnerable populations, may have suboptimal perioperative processes and protocols, leading to poorer outcomes. The study underscores the need for targeted interventions to improve THA outcomes in these hospitals.

The objective was to examine Medical Advantage (MA) organizations' commitment toward addressing social determinants of health (SDOH) through their health-related social benefit offerings, and the perceived impact of providing supplemental benefits associated with SDOH in their plans. Public reporting documents were reviewed from six of the largest MA firms: Humana, UnitedHealthcare, Cigna, Elevance Health, CVS Health, and Centene. Public reports were obtained from each company's website (eg, from the "Investor Relations" page). Quarterly reports for Q1 2023, annual reports for 2022, and proxy statements for 2023 for all companies were examined. Content analysis of the public reports was conducted under three constructs: (1) Growth of MA in the company, (2) SDOH-related activities in the company, and (3) SDOH-related activities in the MA plans of the company. Each of the three constructs was further analyzed for recurring themes and elements. The findings from content analysis suggests that plans are providing tailored benefits that may address the social needs of vulnerable and underserved populations. Companies that offered supplemental benefits and value-based arrangements that addressed social needs reported beneficiary clinical outcomes resulting in cost savings and increased revenue. Health insurance companies identify MA as a significant growth opportunity and a strategically important market for overall membership and revenue growth. Moreover, companies providing innovative social benefits through their MA plans reported witnessing increased value propositions by underserved and vulnerable populations, leading to increased revenue and cost containment.

Population health research has long demonstrated that where someone lives is highly correlated with health outcomes and quality of life. This study explored if this relationship remained between zip code socioeconomic deprivation index (SDI) and member-reported healthy days among commercially insured adults interacting with virtual care and navigation services between May 1, 2023 and May 31, 2024, offered by Included Health. Members received an SMS-based survey that included the 4 Centers for Disease Control and Prevention Healthy Days questions after interacting with the digital health application. The proportion of members who reported frequent (14 or more) physically unhealthy, mentally unhealthy, and total unhealthy days during the past 30 days was calculated. The adjusted odds ratio for reporting frequent total unhealthy days was estimated by members' zip code SDI quartile, accounting for member demographic characteristics. Of the 6692 survey respondents, 13.7% reported frequent physically unhealthy days, 20.8% reported frequent mentally unhealthy days, and 29.2% reported frequent total unhealthy days. After adjusting for covariates, members in the highest SDI quartile were 1.2 times more likely to report frequent unhealthy days (P = 0.047) than those in the lowest SDI quartile. The results demonstrate the importance of geographic indices, in the absence of other data, to assist employers in identifying members with potentially higher need of digital health services. It also highlights the feasibility of collecting quality of life measures to identify members who could benefit from timely intervention.

In recent decades, the integration of artificial intelligence (AI) into health care has revolutionized diagnostics, treatment customization, and delivery. In low-resource settings, AI offers significant potential to address health care disparities exacerbated by shortages of medical professionals and other resources. However, implementing AI effectively and responsibly in these settings requires careful consideration of context-specific needs and barriers to equitable care. This article explores the practical deployment of AI in low-resource environments through a review of existing literature and interviews with experts, ranging from health care providers and administrators to AI tool developers and government consultants. The authors highlight 4 critical areas for effective AI deployment: infrastructure requirements, deployment and data management, education and training, and responsible AI practices. By addressing these aspects, the proposed framework aims to guide sustainable AI integration, minimizing risk, and enhancing health care access in underserved regions.

Lung cancer screening (LCS) rates are low, and lung cancer mortality is high in the United States. This report describes a strategy that health systems can use to identify LCS areas of need and engage associated primary care providers and patients in screening. A research team from Jefferson Health (JH), a large, urban health system, used geocoded standardized lung cancer mortality rates (SMRs) to identify zip codes in Philadelphia where lung cancer mortality is high. In addition, health system electronic medical record data were used to identify primary care practices serving these areas. The study also developed an online program to train providers in shared decision making (SDM) about LCS. Finally, primary care leaders were interviewed to learn about training obstacles and opportunities. The JH research team identified 8 high-SMR zip codes and 8 practices with patients from those areas. Working with the American College of Chest Physicians and the National Lung Cancer Round Table, the authors developed a free, online, accredited course to train providers in patient education, values elicitation, and decision support for LCS. Interview analyses with practice leaders encouraged the health system to incentivize provider training and use of SDM tools in practice. Health systems can implement a systematic approach to identify LCS areas of need and train primary care providers to engage patients in SDM about LCS. Research is needed to implement such an approach and evaluate the program's impact on patient engagement, screening, and related outcomes among patients' diverse populations.

Composite health indicators are valuable tools to assess population health over time and identify areas for intervention. This scoping review (ScR) aimed to map the literature describing comprehensive health-related metrics used to assess community health. The Arksey and O'Malley framework was used to conduct the ScR, using the following steps: identifying the research question, identifying relevant studies, charting the data, collating and reporting results. United States-based studies that developed/utilized a composite health index using geographic information system (GIS) mapping capabilities to assess community health at the county level or more granular were identified through literature searches conducted in PubMed and EMBASE databases. Literature searches identified 5112 articles; of these, 8 studies describing composite health indices were included. The number of indicators used in each index ranged from 4 to 75 (median: 22). Health outcomes, health behaviors, education, and economics were incorporated into most indices. High school graduation rate (n = 6 indices), health insurance status (n = 5), commute time, median household income, unemployment, and obesity rates (n = 4 each) were the most common indicators across indices. All indicators were derived from publicly available data sources, such as the American Community Survey and US Census Bureau. Although a limited number of community health indices were identified in the ScR, the indices included a broad range of indicators covering both health outcomes and factors contributing to health vulnerabilities. The public data sources and GIS integration of the indices provide potential for broad, insightful applications to various contexts across the United States.

Cardiovascular disease (CVD) remains a major national health challenge with significant disparities linked to socioeconomic status, race, ethnicity, sex, and geography, prompting federal efforts to build statewide primary care quality improvement (QI) cooperatives to improve heart health. To be effective, cooperatives require high levels of member engagement and leaders need ways to assess engagement. The objective of this study was to develop and validate a novel Cooperative Member Survey to assess cooperative member engagement and assess the value of the cooperative to members across three statewide heart health QI cooperatives. The 14-item survey included fixed-response and open-ended questions and was developed through multiple iterative rounds across the three cooperatives to gain consensus on the wording of final items using a Delphi process. The survey then was administered to the key stakeholders in the three cooperatives. Findings from both the quantitative and qualitative items were analyzed and reported based on frequencies and emerging themes. The survey was then analyzed to determine factor structure and validity. Analysis revealed a two-factor structure which the research team identified as: (1) Cooperative Engagement, consisting of 11 items that measured how well the cooperative functioned overall, and (2) Cooperative Value, consisting of two items that assessed the perceived value of mutual learning and respect within the cooperative. This two-factor structure indicated that the Cooperative Member Survey successfully captured both the practical aspects of how the cooperative operates and the members' perceived benefits of their involvement. Successful QI cooperatives not only require efficient operations but also a sense of shared value among members. These findings suggest that cooperatives designed to improve public health outcomes may benefit from focusing not only on practical aspects of engagement but also on cultivating mutual respect and collective learning.