Accountable Care Research Articles (Page 1)

To define measures of Medicare diagnosis coding intensity that capture the dynamics of changes in coding practices. Retrospective analysis of coding for risk adjustment using observational claims data from Medicare beneficiaries. Enrollment and claims data from 2017 and 2018 of a random 20% sample of Medicare beneficiaries were subset to those assigned to an Accountable Care Organization in 2018. We decompose the prevalence of a diagnosis code into incidence (proportion of beneficiaries that newly have the code) and persistence (proportion of beneficiaries who previously had the code and continue to do so). Together these define steady-state prevalence, the hypothetical long-run prevalence implied by no changes in current rates of incidence and persistence of coding. Steady-state prevalence can help explain why observed prevalence tends to grow over time without continued behavioral change. For example, our measures suggest that the prevalence of the Specified Heart Arrhythmias diagnosis would continue to rise from 18.7% in 2018 to 28.0% without changes in coding practices. Researchers and policymakers can better understand why changes in coding practices can take years to be fully reflected in data and monitor coding behavior by using our proposed measures.

To compare chronic condition specialists to primary care providers (PCPs) on rates of serving as the usual provider of care (UPC, defined as providing the most visits) versus being accountable under "PCP-first" assignment used in accountable care organization (ACO) programs, and to compare risk-based ACO participation. We conducted a retrospective cohort study of PCP versus chronic condition specialty clinicians on their rates of serving as UPC for patients with complex chronic conditions, patient assignment under a "PCP-first" assignment mechanism, and participation in risk-based ACOs. We then estimated linear probability models predicting clinician participation in risk-based ACOs as a function of their rates of serving as the UPC. We used 100% traditional fee-for-service Medicare (TM) clinician data and beneficiary claims from 2017 to 2022. The study included 2,065,755 and 254,918 clinician-years for PCPs and chronic condition specialists (cardiology, endocrinology, nephrology, pulmonology), respectively. Specialists more often served as the UPC than they were accountable under PCP-first assignment algorithms (7.9% UPC vs. 3.3% PCP-first assignment); the opposite was true of PCPs (19.2% vs. 29.8%). Specialists in the top quintile for serving as UPC were 19.0% less likely (4.4 percentage point [pp] absolute difference, 95% CI, 3.7-5.1 pp) to participate in risk-based ACOs than specialists in the bottom quintile. PCPs in the top UPC quintile were 18.7% more likely (3.8 pp. absolute difference, 95% CI, 3.6-4.1 pp) to participate in risk-based ACOs than PCPs in the bottom quintile. Existing assignment mechanisms in Medicare ACOs may undervalue specialists' care for patients with chronic conditions. More efforts are needed to engage specialists in accountable care.

Background: Accountable Care Organizations (ACOs) assume financial risk for total cost of care under value-based payment models and require scalable interventions to reduce high-cost utilization. Remote patient monitoring (RPM) has demonstrated potential to decrease emergency department (ED) visits and hospitalizations in single-disease cohorts, but real-world economic evaluations within fully risk-bearing ACOs remain limited. Hypothesis: We hypothesize that ACO patients enrolled in an RPM program will experience significantly lower per-member-per-month (PMPM) healthcare expenditures, reduced utilization of high-cost services (including ED visits, inpatient admissions, and readmissions) compared to ACO patients not participating in RPM. Methods: We conducted a retrospective cohort analysis of Medicare Fee-for-Service beneficiaries attributed to the Mount Sinai ACO between January and October 2024. Patients with ≥2 structured RPM encounters in Epic (n = 255) were compared to non-RPM controls (n = 4,245) over a 12-month follow-up from each patient’s index date. Outcomes included mean annual claims expense per patient, PMPM costs, and utilization-driven cost components (ED visits, admissions, readmissions, length of stay). Results: RPM participants incurred a mean annual claims expense of $3,223.90 versus $4,333.93 for non-RPM patients, yielding $1,110 (26%) savings per patient-year (p<0.001). On a PMPM basis, costs were $268.66 (RPM) versus $361.16 (non-RPM), for a $92.50 monthly savings (p<0.001). Utilization analyses (per 1,000 patients annually) showed ED visits decreased by 13.3% (452.5 to 392.2), inpatient admissions decreased by 44.7% (198.6 to 109.8), readmissions decreased by 36.0% (24.5 to 15.7), and average length of stay decreased by 11.0% (5.25 to 4.67 days). Preventable ED visits rose by 27.6% (67.4 to 86.0). Conclusions: RPM enrollment was associated with significant reductions in annual and PMPM healthcare expenditures, driven primarily by fewer hospitalizations, and ED visits. These findings support RPM as a scalable, evidence-based strategy to enhance patient engagement and curb avoidable costs under value-based care.

Invasive prenatal diagnostic tests have higher detection rates (DR) than screening tests. Cell-free (cf)DNA screening has higher detection than Combined testing (CT). We estimated cost per cytogenetic abnormality diagnosed for prenatal testing and screening methods (USA data). We compared seven strategies: universal diagnosis with microarray; with karyotype; cfDNA screening with four methodologies (DANSR, MPSS, SNPs and RCA); and CT. Five abnormality groupings were considered: common autosomal trisomies (21,18,13); sex chromosome aneusomies; triploidy; clinically significant sub-chromosome changes; and CNVs. Prevalence, detection, false-positive rates, and unit costs (literature) were obtained. DR was aggregated across groups as were costs, including diagnostic tests following positive screening results. All six high detection strategies had greater cost per abnormality diagnosed than CT ($19,600). Costliest was cfDNA using MPSS ($96,100), followed by universal karyotyping ($95,900), cfDNA using SNPs, DANSR ($94,800 - $92,800), universal microarray ($56,600) and cfDNA with RCA ($32,200). The additional cost per extra abnormality diagnosed compared to CT ('marginal' cost) was lowest for universal microarray ($62,100); others were considerably higher ($170,900-$238,300). Preferred prenatal strategies vary among stakeholders. As monopolies (large systems and Accountable Care Organizations) now control many American healthcare decisions, lowest cost cfDNA screening will likely prevail in decision making.

Social and structural factors drive health disparities in children. Hospitalization of a child is stressful for families, especially for low-income families who may experience exacerbation of food insecurity and other financial hardships. There is a need to evaluate interventions to support low-income families after hospitalization to improve child health and family well-being and ultimately to reduce health disparities. In this randomized control trial (RCT), we will compare the effectiveness of a post-discharge financial support intervention with standard resources on the primary outcomes of food insecurity and urgent healthcare reutilization over 12 months among low-income families of hospitalized children. Families are eligible to participate if their child is hospitalized on our main Hospital Medicine unit between September 2024 and July 2025, <18 years old, and enrolled in our institution's Medicaid accountable care organization. Our planned sample size is 400, with a 3:1 control to intervention randomization. The intervention is monthly $100 gift cards to a grocery store chain, provided for 12 months after hospital discharge. Intervention and control participants are offered standard resources (i.e., social work referral) as needed. We will assess food insecurity through longitudinal surveys and urgent healthcare reutilization with claims data. Our secondary outcomes include family-centered measures, primary care utilization, and cost. We expect to complete primary analyses in the spring of 2027. In this RCT, we will evaluate the effectiveness of a post-discharge financial support intervention among low-income families. Findings may inform future high-value interventions, programs, and policies to support low-income families and children.

Objectives: Urinary tract infections (UTIs) are the most common healthcare-associated infections in Skilled Nursing Facilities (SNFs); they are associated with longer lengths of stay, higher levels of care, increased treatment costs, and higher mortality rates. This study aimed to develop a machine learning classification model to predict the risk of high catheter-associated urinary tract infection rates based on SNF characteristics. Methods: We analyzed 94,877 total SNF-year observations from 2019 to 2024, not unique facilities; thus, individual SNFs may appear in multiple years. The factor variables were average length of stay in days, number of staffed beds, total nurse and total physical therapy staffing hours per resident per day, facility ownership, geographic classification, facility accreditation, Accountable Care Organization affiliations, Centers for Medicare and Medicaid Services SNF Overall Star Rating, and the SNF-year of the observations. We utilized three machine learning models for this analysis: Random Forest, XGBoost, and LightGBM. We used Shapley Additive exPlanations to interpret the best-performing machine learning model by visualizing feature importance and examining the relationship between key predictors and the outcome. Results: We found that machine learning models outperformed traditional logistic regression in predicting UTIs in skilled nursing facilities. Using the best-performing model, Random Forest, we identified rural SNFs, and the number of staffed beds as the most influential predictors of high UTI rates, followed by average length of stay, and geographic location. Conclusions: This study demonstrates the value of using facility-level characteristics to predict the risk of UTIs in SNFs with machine learning models. Results from this study can inform infection prevention efforts in post-acute care settings.

Structured Objective.To define measures of Medicare diagnosis coding intensity that capture the dynamics of changes in coding practices.Study setting and design.Retrospective analysis of coding for risk adjustment using observational claims data from Medicare beneficiaries.Data sources.Enrollment and claims data from 2017–2018 of a random 20 percent sample of Medicare beneficiaries were subset to those assigned to an Accountable Care Organization in 2018.Principal findings.We decompose prevalence of a diagnosis code into incidence (proportion of beneficiaries that newly have the code) and persistence (proportion of beneficiaries who previously had the code and continue to do so). Together these define steady-state prevalence, the hypothetical long-run prevalence implied by no changes in current rates of incidence and persistence of coding. Steady-state prevalence can help explain why observed prevalence tends to grow over time without continued behavioral change. For example, our measures suggest that the prevalence of the Specified Heart Arrhythmias diagnosis would continue to rise from 18.7% in 2018 to 28.0% without changes in coding practices.Conclusions.Researchers and policymakers can better understand why changes in coding practices can take years to be fully reflected in data and monitor coding behavior by using our proposed measures.

This evaluation highlights potential strategies to decrease unnecessary skilled-nursing facility (SNF) utilization. We evaluated the impact of a SNF network and transitional care management program (TCM) on SNF length of stay (LOS) for beneficiaries in a Medicare Shared Savings Plan (MSSP) Accountable Care Organization (ACO). Design: Retrospective claims-based cohort study. Mass General Brigham (MGB) is a large integrated healthcare system. MGB Population Health develops value-based strategies to improve performance in shared savings ACOs, including nearly 130,000 Medicare beneficiaries. Adult MSSP ACO beneficiaries admitted to a SNF between January 2022 and September 2023. Exclusions included death, admission to hospice or long-term care hospital; patients were excluded from the SNF LOS analysis if they experienced hospital readmission. The MGB SNF Collaborative Network included 60 SNFs in 2022 and 63 SNFs in 2023 based on quality metrics and collaboration with MGB hospitals. The TCM Program included 14 care managers, who optimize SNF utilization for beneficiaries discharged to MGB Network SNFs. The primary outcome of interest was SNF LOS. We conducted univariate regression analyses with variables that could impact SNF LOS; covariates with a p value ≤ 0.1 were included in the multivariable regression model. We assessed for collinearity with model diagnostics, including variance of inflation measures. Multivariable regression demonstrated that admission to a SNF network facility without TCM was associated with a reduction in LOS of 4 days, and admission to a SNF network facility with TCM resulted in a 5-day LOS reduction. Based on LOS reduction, we estimate a programmatic impact of $556 per beneficiary per day [1], with savings of $2224-$2780 per admission. SNF networks and care management can mitigate unnecessary SNF utilization, offering a high-value approach for ACOs and other value-based care organizations.

There is significant heterogeneity in how Medicaid accountable care organizations (ACOs) are designed, but little is known about the association between different Medicaid ACO model designs and maternal health. In 2018, Massachusetts initiated a Medicaid ACO program with 2 distinct ACO models-model A (health system-managed care organization partnership) and model B (primary care practice [PCP]-led)-which may differentially affect coordination, integration, and quality for pregnant and postpartum patients. To estimate the differential association between Medicaid ACO model designs and maternal health care measures. This cohort study used the 2014 to 2020 Massachusetts All Payer Claims Database on Medicaid-covered deliveries among pregnant patients aged 18 years or older, indexed to the delivery-quarter. Data were analyzed September 2024 to March 2025. Attribution to Medicaid ACO model A vs model B vs a non-ACO. Six quality of care-sensitive maternal health measures (severe maternal morbidity, preterm birth, cesarean delivery, timely postpartum care, postpartum depression screening, and postpartum glucose screening) and 2 health care utilization measures (office visits, emergency department [ED] visits) were measured during the prenatal period and at 60 days and 6 months post partum. Using a difference-in-differences (DID) approach, study measures were compared before (2016-2017) vs after (2018-2020) Medicaid ACO implementation among ACO model A vs model B vs non-ACO patients. Models adjusted for patient-level characteristics and included hospital, county, and delivery-month fixed effects. Among 67 204 Medicaid-insured deliveries (mean [SD] maternal age, 28.1 [5.7] years), both ACO model A and model B were associated with increased probability of postpartum depression screening (DID for model A, 6.11 percentage points [pp]; 95% CI. 4.34-7.88 pp; DID for model B, 5.58 pp; 95% CI, 3.83-7.33 pp) compared with Medicaid non-ACO deliveries. Model A was associated with increased probability of a timely postpartum visit (DID, 5.18 pp, 95% CI, 4.34-7.88 pp) and decreased rates of prenatal ED visits (DID incidence rate ratio [IRR], 0.91, 95% CI, 0.84-0.98), whereas model B was associated with increased office visits during the prenatal and postpartum periods (eg, DID IRR for prenatal period, 1.10; 95% CI, 1.07-1.12). Changes in cesarean deliveries were inconclusive, with no other statistically significant changes in outcomes. In this cohort study of Medicaid-insured deliveries, there was heterogeneity in the association between Medicaid ACOs and maternal health care measures by ACO model type. States should consider the differential implications of ACO design types and how to best optimize model designs to improve care and outcomes for pregnant and postpartum patients.

Purpose Medical-dental integration models in primary care settings offer opportunities to increase access to oral health care, particularly for vulnerable populations. The purpose of this study was to assess dental care utilization and measure patient interest in accessing integrated dental services within the primary care setting, among patients attending a primary care practice participating in MassHealth's Accountable Care Organization program.Methods Convenience sampling was used to recruit participants from a safety-net health care system serving the greater Boston area. A 10-item survey was used to collect data on dental care utilization and measure interest in accessing integrated dental services at one primary care practice. Descriptive statistics, in addition to bivariate and multivariate logistic regression models were used to characterize the sample and explore dental care utilization associations.Results A total of 149 individuals participated, with over half (52%) reporting dental care utilization in the past year. Participants with public insurance were less likely to have seen a dental provider compared to those with private insurance (OR=0.35, 95% CI [0.12, 0.99]). Most patients (77.2%) expressed interest in accessing both in-person appointments with dental hygienists and teledentistry appointments with dentists (63.8%). When asked to choose between the two services, integrated dental hygienist appointments emerged as the more popular option for a majority of respondents (83.9%) Interest in accessing teledentistry appointments was higher among non-English speakers (OR=2.76, 95% CI [1.19, 6.40]).Conclusion Patients with low dental care utilization showed high interest in accessing integrated dental services at a primary care practice that participates in MassHealth's Accountable Care Organization program, supporting moving forward with pilot initiatives to evaluate implementation strategies.

Improving children's use of care in Oregon: The role of a novel dental accountable care organization.

The Three-fer: How Biosimilars Reduce Cost, Expand Access, and Improve Margin.

Oral health quality measurement and improvement in Medicaid and Children's Health Insurance Programs: A qualitative analysis of oral health-related performance improvement projects.

Hand Surgeons and Accountable Care Organizations: Increasing Reimbursement Through Value.

Health care delivery organizations increasingly employ advanced practice professionals (APPs) and participate in alternative payment models such as accountable care organizations (ACOs). Given the former's incentive to constrain spending, APPs' practice patterns may vary in ACO-participating versus non-ACO practices. To compare outpatient care provided by APPs and physicians through ACO participation. We used multivariate linear regression to compare measures of workload allocation and billing across ACO-participating and non-ACO practices in 2022, controlling for practice size and market. A total of 91,149 practices, 12,072 in a Medicare Shared Savings Program ACO in 2022. We used 100% fee-for-service Medicare claims to identify ACO-participating and non-ACO practices. For every practice, we calculated the share of outpatient encounters provided by APPs rather than physicians and the share of APP-provided encounters billed indirectly to Medicare. We also calculated the share of annual wellness visits, chronic condition care management services, transitional care management services, and postoperative visits provided by APPs. APPs provided a smaller share of outpatient encounters at ACO-participating versus non-ACO practices, but were more likely to bill indirectly. Among most categories of routine services (eg, annual wellness visits and chronic condition management), APPs provided a smaller share of services at ACO-participating versus non-ACO practices. In the largest quartile of practices, APP practice patterns were more similar across ACO-participation status, and indirect billing was less likely within ACOs. Findings provide little evidence that ACOs deploy their APP workforce in a more cost-conscious manner than non-ACOs.

In the face of escalating sociopolitical hostility toward diversity, equity, and inclusion (DEI) efforts, trans and queer (TQ) center(ed) diversity workers in higher education are navigating increasingly precarious professional landscapes. This study explores the lived experiences of TQ-center(ed) diversity workers through a general qualitative design informed by participatory action research (PAR). Drawing on the concept of “burn through,” critiquing the role of institutions in the exhaustion of practitioners, and the theory of tempered radicalism, describing the fine line diversity workers must navigate to advocate for change within oppressive systems, we examine how these practitioners persist amid institutional neglect, emotional labor, and political antagonism. Findings from interviews with eight participants reveal three central themes: the systemic nature of burn through, the protective power of community, and the multifaceted role of liberation in TQ-center(ed) diversity work. Participants described both the toll and the transformative potential of their roles, highlighting community as a critical site of resistance and renewal. This study contributes to the growing literature on TQ advocacy in higher education and underscores the need for institutional accountability and collective care in sustaining liberatory futures.

Accountable Care Organization Assignment and Outcomes and Spending for TEAM Surgical Conditions

Access to and quality of care vary substantially by area socioeconomic status. Expanding hospital health information technology (HIT) adoption may help reduce these disparities, given hospitals' central role in serving underserved populations. To examine variations in US hospital adoption of telehealth and health information exchange (HIE) functionalities by hospital service area (HSA) socioeconomic deprivation. This cross-sectional study links data from the 2018-2023 American Hospital Association Annual Survey and Information Technology Survey with HSA-level area deprivation index. Nonfederal acute care hospitals with complete data on HIT outcomes, comprising 16 646 observations for the telehealth outcomes and 9218 observations for the HIE outcomes across 6 years, were included. Data were analyzed from February 2024 to February 2025. HSA-level area deprivation index in quartiles. Hospital adoption of treatment-stage telehealth and postdischarge telehealth services and HIE infrastructure supporting electronic data query and availability. Descriptive, regression, and Blinder-Oaxaca decomposition analyses and visualized time trends in hospital HIT adoption were used in analyses. This study included 16 646 hospital-level observations and 9218 observations for health information exchange functionalities. Hospitals in the most socioeconomically deprived HSAs were significantly less likely to adopt HIT compared with those in the least deprived areas (treatment-stage telehealth: marginal effect [ME], -0.03; 95% CI, -0.06 to -0.01; postdischarge telehealth: ME, -0.03; 95% CI, -0.07 to 0.01; electronic data query capability: ME, -0.03; 95% CI, -0.06 to -0.01; electronic data availability: ME, -0.06; 95% CI, -0.11 to -0.01). Year fixed effects indicated significant increases in HIT adoption from 2018 to 2023, regardless of HSA deprivation level. Decomposition analyses showed that differences in hospital bed size, urban/rural location, and accountable care organization participation explained a substantial portion of the disparities by HSA deprivation. In this study, hospitals in more socioeconomically disadvantaged HSAs remained likely to adopt telehealth and HIE functionalities. Nevertheless, HIT adoption has grown steadily over time. Accountable care organization participation may support HIT infrastructure and help reduce geographic disparities in adoption and access to care.

To assess the relationship between the changing Accountable Care Organizations-ACO workforce and ACOs' shared savings earnings and quality performance. Medicare Shared Savings Program-MSSP provider-level research identifiable files, performance year financial and quality report public use files, and National Physician Compare data (2013-2021). We characterized 865 MSSPs, separately pre- (2013-2019) and post-pandemic (2020-2021) according to the percentage of primary care physicians (PCPs), non-physicians, specialists, and other specialty, financial risk model, assigned Medicare beneficiary demographics, clinical risk factors, and provider supply by specialty within the MSSP's primary service state, (total and per-capita) shared savings earnings/losses owed and quality score. Longitudinal ordinary least-squares regressions with random effects were estimated to assess the association between MSSP provider specialty mix and annual (1) per-capita shared savings/losses and (2) quality score, controlling for risk model, beneficiary characteristics, provider supply, and year factors. We also compared outcomes across MSSPs, 32 Pioneers and 62 Next Generation-NGACOs. PCPs represented 33.9% of MSSP's workforce, on average. Higher percentages of PCPs and non-physicians were associated with higher per-capita earned shared savings and quality scores among MSSPs. A 1-percentage-point (ppt) increase in PCPs and non-physicians was associated with higher per-capita shared savings of $2.25 (p < 0.01) and $1.82 (p = 0.03), respectively, pre-COVID, and $2.73 (p < 0.01) and $1.81 (p = 0.14) post-COVID. We estimated increases in quality scores among MSSPs of ~0.1 ppt with a 1 ppt increase in PCPs, non-physicians, and specialists only pre-pandemic. No statistically significant relationships were estimated between provider specialty mix and performance measures in Pioneers and NGACOs. Higher percentages of PCPs and non-physicians were associated with higher per-capita shared savings earnings and quality scores among MSSPs. As new federal initiatives continue to unfold, value-based payment models increasing incentives for primary care should be monitored to determine their ability to further improve care efficiency.

Assessing the feasibility, acceptability, and efficacy of a pragmatic pilot and full-scale trial to improve care for older adults with complex care needs: The SPIRE study.