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 ( < 0.001), with no significant change pre-mandate (0.5-point decline, = 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, < 0.001), whereas no significant change occurred between 2015 and 2018 (aOR = 1.02, 95% CI = 0.89-1.16, = 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 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.

Lack of health care insurance is strongly associated with poor glycemic control in patients with diabetes. However, even among insured patients, achieving glycemic control can be challenging. We investigated whether demographics, physical activity, engagement with health care providers, as well as medical and socioeconomic factors were associated with poor glycemic control (hemoglobin A1c [HbA1c] >8.5%) in patients with type 2 diabetes (T2D) who had employer-sponsored health insurance. We studied data of 2981 employees and spouses with T2D who participated in an annual health assessment in 2019 and had medical insurance benefits for at least 12 consecutive months prior to the assessment. T2D was defined by International Classification of Diseases codes, self-reported physician diagnoses, or test results (fasting glucose >125 mg/dL or HbA1c >6.4%). HbA1c was >7% in 43% of the patients and >8.5% in 16% of patients. Among patients with poor glycemic control, 90% had HbA1c data for at least 2 of the previous 3 years; 76% had poor control in at least 1 of the previous 3 years. Poor glycemic control was associated with demographics (younger age men), disease severity (greater number of diabetes complications and prescription medications), poor engagement with health care providers (eg, more years since last physical exam, less confidence talking with physician), and less physical activity. Thus, lack of glycemic control is persistent and unexpectedly frequent in patients with T2D despite access to health care benefits. Improving physical activity and engagement with providers may improve glycemic control in this population.

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.

There is a global mental health crisis: mental illness is underrecognized, underdiagnosed, and undertreated with adverse effects on mental, physical, and social health. In the United States, there is an insufficient number of traditional psychiatric and psychological resources to provide the mental health care needed to solve this crisis. Community-based interventions could be an important adjunct to traditional mental health care. An evaluation of peer-reviewed articles was performed describing community-based interventions and identified 3 approaches with some evidence of effectiveness: (1) interventions that enhance community mental health literacy to improve recognition of early signs of mental illness for early engagement and provide community, family, and peer support; (2) community clinics providing social, medical, and mental health care and support to transition-age youth (15-25 years); and (3) social networking activities to enhance interactions among elders suffering from social isolation and loneliness. Multisector, multidisciplinary, and multicomponent interventions involving health care providers and community-based organizations had the best evidence of effectiveness and should target transition-age youth and elders.

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 ( = 6 indices), health insurance status ( = 5), commute time, median household income, unemployment, and obesity rates ( = 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.

In response to rising costs associated with providing health care services to Americans over 65 years old, policymakers have called for the expansion of care coordination programs to reduce total spending while improving patient outcomes and provider efficiency. This study uses a Markov Chain model to estimate financial impacts associated with the implementation of a care coordination program across the state of Iowa. Estimates revealed an association between the implementation of the Iowa Return to Community (IRTC) and a reduction in health care service use, which yielded per capita cost savings of $7,920.24 over a 5-year span. Subgroup analysis showed that inclusion of informal care partners enhances these savings, as they contributed to reduced inpatient hospital use and deferred nursing home admissions. The continued expansion of the IRTC appears as a viable strategy to curtail aggregate health care spending while supporting older adults stay at home.

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.

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.

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.

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.

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 ( = 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.

Despite focusing on drivers of health, or social determinants of health, for more than a decade, health care organizations have made minimal progress in improving these factors and associated health outcomes. This data- and theory-driven analysis looks at (1) why that is the case and (2) how organizational leaders and operators can go about correcting it. The authors' research finds that lack of progress is often due to ill-fit, entrenched business models that were optimized for a fee-for-service environment and cannot easily pivot to focus on drivers of health. Additionally, leaders are often unclear about what to change and overwhelmed by how to do it. The authors propose a 5-step strategy and execution process to address these challenges, laying out an end-to-end road map that enables health care leaders to meaningfully improve drivers of health and associated health outcomes for their patients and communities.

Social risks refer to individuals' social and economic conditions shaped by underlying social determinants of health. Health care delivery organizations increasingly screen patients for social risks given their potential impact on health outcomes. However, it can be challenging to meaningfully address patients' needs. Existing frameworks do not comprehensively describe and classify ways in which health care delivery organizations can address social risks after screening. Addressing this gap, the authors developed the Social Risk ACTIONS framework (Actionability Characteristics To Inform Organizations' Next steps after Screening) describing 4 dimensions of actionability: Level of action, Actor, Purpose of action, and Action. First, social risk actions can occur at 3 organizational levels (ie, patient encounter, clinical practice/institution, community). Second, social risk actions are initiated by different staff members, referred to as "actors" (ie, clinical care professionals with direct patient contact, clinical/institutional leaders, and researchers). Third, social risk actions can serve one or more purposes: strengthening relationships with patients, tailoring care, modifying the social risk itself, or facilitating population health, research, or advocacy. Finally, specific actions on social risks vary by level, actor, and purpose. This article presents the Social Risk ACTIONS framework, applies its concepts to 2 social risks (food insecurity and homelessness), and discusses its broader applications and implications. The framework offers an approach for leaders of health care delivery organizations to assess current efforts and identify additional opportunities to address social risks. Future work should validate this framework with patients, clinicians, and health care leaders, and incorporate implementation challenges to social risk action.

Screening for social determinants of health (SDOH) has been mandated by health systems nationwide. However, a gap exists in closed-loop referral for care coordination between health care and social services. This article presents the framework of a technology-based project to facilitate closed-loop referral between health care and social service agencies in Greater Houston by leveraging and connecting the existing care coordination technology infrastructure. Ten health care and social service organizations in Greater Houston participated in the demonstration project initiated in January 2023. The authors leveraged and linked regional health information exchange (HIE) technology with a master patient index of >18 million, and sector-specific care coordination platforms to build closed-loop referral capacity between HIE-participating health care organizations and social service organizations to meet patient SDOH needs. Evaluation efforts will assess the reach, adoption, implementation, and the effectiveness of the closed-loop framework in improving social and health outcomes. The framework comprised the following 4 components: (1) establishment of collaborative governance for shared decision-making processes, fostering trust, alignment, and transparency among organizations; (2) development of technology linkages between existing platforms to facilitate seamless referrals between organizations and ensure visibility of referral outcomes; (3) integration of regional resource directories into technology infrastructure to ensure resource accessibility/quality; and (4) evaluation of the system's impact on health equity, efficiency, and cost reduction. This project aimed to close the loop for care coordination between health care and social service agencies, enable data evaluation to determine care coordination effectiveness, and lay the foundation for SDOH-related research/practice equitably.

A majority of patients with heart failure (HF) do not receive adequate medical therapy as recommended by clinical guidelines. One major obstacle encountered by population health management (PHM) programs to improve medication usage is the substantial burden placed on clinical staff who must manually sift through electronic health records (EHRs) to ascertain patients' eligibility for the guidelines. As a potential solution, the study team developed a rule-based system (RBS) that automatically parses the EHR for identifying patients with HF who may be eligible for guideline-directed therapy. The RBS was deployed to streamline a PHM program at Brigham and Women's Hospital wherein the RBS was executed every other month to identify potentially eligible patients for further screening by the program staff. The study team evaluated the performance of the system and performed an error analysis to identify areas for improving the system. Of approximately 161,000 patients who have an echocardiogram in the health system, each execution of the RBS typically identified around 4200 patients. A total 5460 patients were manually screened, of which 1754 were found to be truly eligible with an accuracy of 32.1%. An analysis of the false-positive cases showed that over 38% of the false positives were due to incorrect determination of symptomatic HF and medication history of the patients. The system's performance can be potentially improved by integrating information from clinical notes. The RBS provided a systematic way to narrow down the patient population to a subset that is enriched for eligible patients. However, there is a need to further optimize the system by integrating processing of clinical notes. This study highlights the practical challenges of implementing automated tools to facilitate guideline-directed care.

Behavioral health integration (BHI) is increasingly implemented to expand capacity to address behavioral health conditions within primary care. Survey and claims data from the evaluation of the Public Hospital Redesign and Incentives in Medi-Cal program were used to examine the relationship between BHI and alcohol-related outcomes among Medicaid patients within 17 public hospitals in California. Key informant survey data measured hospital-level BHI at 3 levels (overall composite, infrastructure, and process domains, 10 themes). Multilevel logistic regression models estimated the relationship between BHI and outcomes indicating receipt of appropriate alcohol-related care (any primary care visit, any detoxification, timely initiation, timely engagement) and acute care (any emergency department [ED] visit or hospitalization, classified as alcohol-related or all-cause) in the year following an alcohol-related index encounter. Of 6196 patients, some had an alcohol-related primary care visit (33%), detoxification (16%), timely initiation (14%), or engagement in treatment (7%). ED visits resulting in discharge were more common (40% alcohol-related, 64% all-cause) than hospitalizations (15% alcohol-related, 26% all-cause). Controlling for patient-level characteristics, no significant relationships between overall BHI and these outcomes were observed. However, greater BHI infrastructure was associated with alcohol-related (odds ratio [OR] 1.86, 95% confidence interval [CI] 1.14-3.05) and all-cause hospitalization (OR 1.25, 95% CI 1.01-1.55). Associations emerged between BHI themes (eg, related to support of providers) and greater likelihood of alcohol-related detoxification, primary care visit, timely initiation, and acute care utilization. Findings suggest that implementing specific BHI components may improve receipt of alcohol-related treatment, and warrant future research into these relationships.

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.