Population Health Management最新文献

The Affordable Care Act (ACA) expanded health care access in the United States. This study examines the long-term impact of the ACA on private health insurance enrollment using National Health Interview Survey (NHIS) data. A repeated cross-sectional study using NHIS data from 2015 to 2022 was analyzed. Given the repeal of the ACA's individual mandate in 2019, stratified analyses compared Marketplace enrollment before (2015, 2018) and after (2019, 2022) the repeal. The study included US adults aged 26-64 years. Unadjusted enrollment rates were compared across age, sex, race/ethnicity, social determinants of health (SDOH), chronic conditions, body mass index, and smoking. Multivariable logistic regression assessed enrollment trends and associated factors. Marketplace enrollment increased by 1.4 percentage points post-mandate (P < 0.001), with no significant change pre-mandate (0.5-point decline, P = 0.235). Some subgroups (ages 26-39, Midwest, West) saw declines pre-mandate, while many experienced increased enrollments post-mandate. After adjustment, individuals in 2022 had 27% higher odds of enrollment than in 2019 (adjusted odds ratio [aOR] = 1.27, 95% confidence interval [CI] = 1.13-1.43, P < 0.001), whereas no significant change occurred between 2015 and 2018 (aOR = 1.02, 95% CI = 0.89-1.16, P = 0.818). Age, racial minority status, and unfavorable SDOH were associated with higher post-mandate enrollment odds. Marketplace enrollment grew post-mandate, particularly among vulnerable populations. While the repeal of the individual mandate may have contributed, other policy changes-expanded enrollment windows, increased subsidies, enhanced outreach, and streamlined applications-likely played a role, particularly in response to COVID-19.

The authors aimed to investigate potential differences between health care use and related payments for patients with complex needs and high costs in Health Resources and Services Administration-funded health centers (HCs) and with other safety net primary care providers. The authors used data from the California Health Homes Program that was designed to improve health outcomes and reduce expenditures of such Medicaid managed care beneficiaries. The authors used 2018 data prior to program implementation and conducted propensity score-matched regressions. The authors then estimated predicted rates of use across seven service categories and payment values for each category and for overall payments. The authors found that 29% of the sample were HC patients and had lower estimated average total payment values ($21,220) than group provider patients ($23,180). HC patients also had lower values for hospitalizations and long-term facility stays and higher values for primary and mental health services than all other providers. Payment differences were generally consistent with differences in predicted rates of use. These findings suggest that HC approaches to managing patient care access and integrated mental health services may explain these differences in use and payment patterns.

The aim was to compare clinical outcomes for older adults with diabetes who received telehealth (TH) as an adjunct to in-person care (F2F) compared with those who received in-person only care (F2F). Systematic literature search was performed using the following databases: Ovid MEDLINE, Embase, Scopus, Web of Science, Cochrane, CINAHL, and ClinicalTrials.gov to include studies involving TH care for older adults with diabetes. Two authors independently reviewed the full text of shortlisted articles. A total of four studies that met the eligibility criteria were included. One study showed slight worsening in glycemic control in the TH group, but the remaining three showed improvement or no difference between the two groups. This review shows that TH modality, when utilized as an adjunct to F2F care, has comparability to F2F alone, with similar or better glycemic control for older adults with type II diabetes, especially those residing in rural communities, those older than age 75, and those with multiple comorbidities who had multiple clinical encounters.

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.