Emergency department visit frequency and health care costs following implementation of an integrated practice unit for frequent utilizers.

OBJECTIVES: The integrated practice unit (IPU) aims to improve care for patients with complex medical and social needs through care coordination, medication reconciliation, and connection to community resources. This study examined the effects of IPU enrollment on emergency department (ED) utilization and health care costs among frequent ED utilizers with complex needs. METHODS: We extracted electronic health records (EHR) data from patients in a large health care system who had at least four distinct ED visits within any 6-month period between March 1, 2018, and May 30, 2021. Interrupted time series (ITS) analyses were performed to evaluate the impact of IPU enrollment on monthly ED visits and health care costs. A control group was matched to IPU patients using a propensity score at a 3:1 ratio. RESULTS: We analyzed EHRs of 775 IPU patients with a control group of 2325 patients (mean [±SD] age 43.6 [±17]; 45.8% female; 50.9% White, 42.3% Black). In the single ITS analysis, IPU enrollment was associated with a decrease of 0.24 ED visits (p < 0.001) and a cost reduction of $466.37 (p = 0.040) in the first month, followed by decreases of 0.11 ED visits (p < 0.001) and $417.61 in costs (p < 0.001) each month over the subsequent year. Our main results showed that, compared to the matched control group, IPU patients experienced 0.20 more ED visits (p < 0.001) after their fourth ED visit within 6 months, offset by a reduction of 0.02 visits (p < 0.001) each month over the next year. No significant immediate or sustained increase in costs was observed for IPU-enrolled patients compared to the control group. CONCLUSIONS: This quasi-experimental study of frequent ED utilizers demonstrated an initial increase in ED visits following IPU enrollment, followed by a reduction in ED utilization over subsequent 12 months without increasing costs, supporting IPU's effectiveness in managing patients with complex needs and limited access to care.

Isolating the Drivers of Racial Inequities in Prostate Cancer Treatment.

BACKGROUND: Black individuals in the United States are less likely than White individuals to receive curative therapies despite a 2-fold higher risk of prostate cancer death. While research has described treatment inequities, few studies have investigated underlying causes. METHODS: We analyzed a cohort of 40,137 Medicare beneficiaries (66 and older) linked to the Surveillance Epidemiology and End Results (SEER) cancer registry who had clinically significant, non-metastatic (cT1-4N0M0, grade group 2-5) prostate cancer (diagnosed 2010-2015). Using the Kitagawa-Oaxaca-Blinder decomposition, we assessed the contributions of patient health and health care delivery on the racial difference in localized prostate cancer treatments (radical prostatectomy or radiation). Patient health consisted of comorbid diagnoses, tumor characteristics, SEER site, diagnosis year, and age. Health care delivery was captured as a prediction model with these health variables as predictors of treatment, reflecting current treatment patterns. RESULTS: A total of 72.1% and 78.6% of Black and White patients received definitive treatment, respectively, a difference of 6.5 percentage points. An estimated 15% [95% confidence interval (CI): 6-24] of this treatment difference was explained by measured differences in patient health, leaving the remaining estimated 85% (95% CI: 74-94) attributable to a potentially broad range of health care delivery factors. Limitations included insufficient data to explore how specific health care delivery factors, including structural racism and social determinants, impact differential treatment. CONCLUSIONS: Our results show the inadequacy of patient health differences as an explanation of the treatment inequity. IMPACT: Investing in studies and interventions that support equitable health care delivery for Black individuals with prostate cancer will contribute to improved outcomes.

Impact of the Affordable Care Act on participation in the Supplemental Nutrition Assistance Program among low-income older Medicare beneficiaries.

BACKGROUND: The Affordable Care Act (ACA) provisions, especially Medicaid expansion, are believed to have "spillover effects," such as boosting participation in the Supplemental Nutrition Assistance Program (SNAP) among eligible individuals in the United States (US). However, little empirical evidence exists about the impact of the ACA, with its focus on the dual eligible population, on SNAP participation. The current study investigates whether the ACA, under an explicit policy aim of enhancing the interface between Medicare and Medicaid, has improved participation in the SNAP among low-income older Medicare beneficiaries. METHODS: We extracted 2009 through 2018 data from the US Medical Expenditure Panel Survey (MEPS) for low-income (≤ %138 Federal Poverty Level [FPL]) older Medicare beneficiaries (n = 50,466; aged ≥ 65), and low-income (≤ %138 FPL) younger adults (aged 20 to < 65 years, n = 190,443). MEPS respondents of > %138 FPL incomes, younger Medicare and Medicaid beneficiaries, and older adults without Medicare were excluded from this study. Using a quasi-experimental comparative interrupted time-series design, we examined (1) whether ACA's support for the Medicare-Medicaid dual-eligible program, through facilitating the online Medicaid application process, was associated with an increase in SNAP uptake among low-income older Medicare beneficiaries, and (2) in the instance of an association, to assess the magnitude of SNAP uptake that can be explicitly attributed to the policy's implementation. The outcome, SNAP participation, was measured annually from 2009 through 2018. The year 2014 was set as the intervention point when the Medicare-Medicaid Coordination Office started facilitating Medicaid applications online for eligible Medicare beneficiaries. RESULTS: Overall, the change in the probability of SNAP enrollment from the pre- to post-intervention period was 17.4 percentage points higher among low-income older Medicare enrollees, compared to similarly low-income, SNAP-eligible, younger adults (ß = 0.174, P < .001). This boost in SNAP uptake was significant and more apparent among older White (ß = 0.137, P = .049), Asians (ß = 0.408, P = .047), and all non-Hispanic adults (ß = 0.030, P < .001). CONCLUSIONS: The ACA had a positive, measurable effect on SNAP participation among older Medicare beneficiaries. Policymakers should consider additional approaches that link enrollment to multiple programs to increase SNAP participation. Further, there may be a need for additional, targeted efforts to address structural barriers to uptake among African Americans and Hispanics.

A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 1-Data From Wearable Devices.

OBJECTIVES: With the emerging use of machine learning (ML) techniques, there has been particular interest in using wearable data for health economics and outcomes research (HEOR). We aimed to understand the emerging patterns of how ML has been applied to wearable data in HEOR. METHODS: We identified studies published in PubMed between January 2016 and March 2021. Studies that included at least 1 HEOR-related Medical Subject Headings term, applied an ML, and used wearable data were eligible for inclusion. Two reviewers abstracted information including ML application types and data on which ML was applied and analyzed them using descriptive analyses. RESULTS: A total of 148 studies were identified from PubMed, among which 32 studies met the inclusion criteria. There has been an increase over time in the number of ML studies using wearable data. ML has been more frequently used for monitoring events in real time (78%) than to predict future events (22%). There has been a wide range of outcomes examined, ranging from general physical or mental health (24%) to more disease-specific outcomes (eg, disease incidence [19%] and progression [13%]) and treatment-related outcomes (eg, treatment adherence [9%] and outcomes [9%]). Data for ML models were more often derived from wearable devices with specific medical purposes (60%) than those without (40%). CONCLUSION: There has been a wide range of applications of ML to wearable data. Both medical and nonmedical wearable devices have been used as a data source, showing the potential for providing rich data for ML studies in HEOR.

Hospital value-based payment programs and disparity in the United States: A review of current evidence and future perspectives.

Beginning in the early 2010s, an array of Value-Based Purchasing (VBP) programs has been developed in the United States (U.S.) to contain costs and improve health care quality. Despite documented successes in these efforts in some instances, there have been growing concerns about the programs' unintended consequences for health care disparities due to their built-in biases against health care organizations that serve a disproportionate share of disadvantaged patient populations. We explore the effects of three Medicare hospital VBP programs on health and health care disparities in the U.S. by reviewing their designs, implementation history, and evidence on health care disparities. The available empirical evidence thus far suggests varied impacts of hospital VBP programs on health care disparities. Most of the reviewed studies in this paper demonstrate that hospital VBP programs have the tendency to exacerbate health care disparities, while a few others found evidence of little or no worsening impacts on disparities. We discuss several policy options and recommendations which include various reform approaches and specific programs ranging from those addressing upstream structural barriers to health care access, to health care delivery strategies that target service utilization and health outcomes of vulnerable populations under the VBP programs. Future studies are needed to produce more explicit, conclusive, and consistent evidence on the impacts of hospital VBP programs on disparities.

A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 2-Data From Nonwearables.

OBJECTIVES: Despite the increasing interest in applying machine learning (ML) methods in health economics and outcomes research (HEOR), stakeholders face uncertainties in when and how ML can be used. We reviewed the recent applications of ML in HEOR. METHODS: We searched PubMed for studies published between January 2020 and March 2021 and randomly chose 20% of the identified studies for the sake of manageability. Studies that were in HEOR and applied an ML technique were included. Studies related to wearable devices were excluded. We abstracted information on the ML applications, data types, and ML methods and analyzed it using descriptive statistics. RESULTS: We retrieved 805 articles, of which 161 (20%) were randomly chosen. Ninety-two of the random sample met the eligibility criteria. We found that ML was primarily used for predicting future events (86%) rather than current events (14%). The most common response variables were clinical events or disease incidence (42%) and treatment outcomes (22%). ML was less used to predict economic outcomes such as health resource utilization (16%) or costs (3%). Although electronic medical records (35%) were frequently used for model development, claims data were used less frequently (9%). Tree-based methods (eg, random forests and boosting) were the most commonly used ML methods (31%). CONCLUSIONS: The use of ML techniques in HEOR is growing rapidly, but there remain opportunities to apply them to predict economic outcomes, especially using claims databases, which could inform the development of cost-effectiveness models.