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 identify the Medicaid eligibility category at delivery and 6 months prior among those with Medicaid and Children's Health Insurance Program (CHIP)-financed births. Descriptive analysis of 2018 national Medicaid claims data. We used the 2018 Transformed Medicaid Statistical Information System Analytic Files to assess Medicaid/CHIP eligibility category at the time of birth and 6 months prior during pregnancy among enrollees with Medicaid/CHIP-paid births in 2018, stratifying by age, race/ethnicity, and state. Just over half (56.2%) of those enrolled in Medicaid/CHIP in 2018 were enrolled in the pregnancy eligibility category at delivery, while 29.5% were enrolled as parents, 8.2% as low-income adults, and 6.1% in other categories. The proportion of pregnant women enrolled via the pregnancy eligibility category varied widely by state, from 11.9% in Kentucky to 97.5% in Texas. Nearly half of pregnant Medicaid/CHIP enrollees were not enrolled via pregnancy Medicaid eligibility when they delivered. It is important for states to be aware of pregnancy status to apply correct eligibility criteria and benefits for pregnant and postpartum enrollees, including the 12 months of extended postpartum coverage newly available and elected in nearly all states.

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.

To measure the accuracy of questions on health insurance premiums and subsidies added to the American Community Survey (ACS) and their utility in categorizing coverage type following the Affordable Care Act (ACA). A reverse record check study where households in Minnesota with individuals enrolled in five different types of coverage-employer-sponsored insurance (ESI), non-group (outside the marketplace), marketplace, Medicaid and MinnesotaCare (a public plan requiring premium contributions from the enrollee)-were administered a telephone survey that included the ACS health insurance module appended with experimental questions on premiums and subsidies. Enrollment records from a private insurer were used as the sample for primary survey data collection in the spring of 2015 using the ACS health insurance module. Survey data were matched back to enrollment records, which indicated coverage status at the time of the survey. The analytic sample includes matched data on about 600 individuals. In total, 100%, 95.3%, and 86.9% of marketplace, non-group, and ESI enrollees, respectively, were correctly reported to have a premium. 74.6% of Medicaid enrollees were correctly reported NOT to have a premium and 77.4% of MinnesotaCare enrollees were correctly reported to HAVE a premium. For the subsidy item, correct reports of no subsidy were 99.1%, 93.8%, and 80.9% for ESI, non-group, and unsubsidized marketplace enrollees, respectively. A total of 72.4% of subsidized marketplace enrollees were correctly reported to have a subsidy. Analysis also indicates that an algorithm leveraging these two new data points can be used to separate the overall "direct purchase" category into two sub-groups: subsidized marketplace and unsubsidized marketplace combined with individual non-group. Results indicate high levels of reporting accuracy for questions about premiums and subsidies. Thus, this post-ACA module of the ACS is capable of rendering more detailed coverage types than previously possible.

To identify state strategies to increase access to medications for opioid use disorder (MOUD) through Section1115 Substance Use Disorder waivers. We conducted a qualitative analysis of 27 waiver applications that were implemented between 2015 and 2020. We identified barriers and proposed strategies for expanding MOUD access and utilization. After excluding five states due to insufficient information, we analyzed 22 applications. We identified six barriers and eight corresponding strategies. Barriers included care delays, limited MOUD facilities, lack of care transition support, limited MOUD access in residential treatment, insufficient care coordination, and prescriber shortages. Commonly proposed strategies were requiring access to MOUD in residential treatment, which was stipulated by the Centers for Medicare & Medicaid Services, addressing prescriber shortages through education and technical assistance, campaigns to address stigma, and increased reimbursement. Other strategies included changes to prior authorization requirements, efforts to increase the number of facilities that offer MOUD, and changes to improve care transitions. States proposed a variety of strategies to expand access to and use of MOUD. Future research could investigate how these approaches, implemented individually or in combination, are associated with outcome change and impact.

To develop and validate a hierarchical algorithm for assigning prenatal care (PNC) encounters using claims data while ensuring continuity of care. We conducted a retrospective cohort study among South Carolina Medicaid beneficiaries. Using a six-step hierarchical algorithm-incorporating specialty designations, diagnostic/procedure codes, and adjustments for inpatient stays and supplemental visits-we assigned PNC encounters and identified predominant PNC providers. To assess predictive validity, we examined associations between predominant provider status and adverse birth outcomes (obtained from linked birth certificates and claims data) using logit-binomial generalized estimating equations with robust standard errors, and we compared models' performance using both model fit statistics and 10-fold cross-validation. We used South Carolina Medicaid data on live-birth pregnancies from 2016 to 2021. We followed participants from conception until delivery. Initial screening identified 302 package/bundle payment claims, leading to the exclusion of 299 pregnancies (0.3%) from further analysis. The final analytic dataset contained 1,072,615 confirmed PNC encounters for 90,581 (97%) pregnancies. This study identified predominant providers for 87,573 pregnancies (98% of cases with at least two PNC encounters). The analysis of predictive validity revealed significant protective associations for two outcomes when comparing pregnancies with versus without predominant providers: preterm birth (adjusted RR: 0.68, 95% CI: 0.59-0.77) and low-birth-weight (adjusted RR: 0.68, 95% CI: 0.57-0.80). This study developed and validated a claims-based algorithm to identify PNC utilization in South Carolina Medicaid data. Predictive validity tests revealed that predominant provider status was associated with reduced adverse birth outcomes, suggesting care continuity may improve perinatal health. Future research could apply this algorithm to examine causal relationships between predominant provider status and specific outcomes (e.g., preterm birth, low birth weight), while accounting for institutional and socioeconomic confounders. These findings offer a foundation for optimizing PNC delivery through continuity-focused interventions.

To enhance National Provider Identifier (NPI) and specialty information available in Medicare Advantage (MA) encounter data and use the enhanced data to evaluate methods for retrospective attribution of the patient's usual clinician, comparing results across MA and Traditional Medicare (TM) populations. We fill in missing clinician identifiers and specialty codes in MA encounter data using Centers for Medicare and Medicaid Services (CMS) and publicly available provider datasets. We attributed patients to the usual clinician using 16 methodological options, comparing the performance of these attribution methods in MA and TM. We used a 20% sample of MA encounter data and TM claims data for 2016-2022, incorporating information from CMS's Medicare Data on Provider Practice and Specialty, archived data from the National Plan and Provider Enumeration System, and specialty-taxonomy crosswalks derived from CMS publications. For MA, we identified individual NPIs for 83% of medical claims in 2016, improving to 89% in 2022. Among MA medical claims billed by physicians and advanced practice providers, 95% of NPIs were for individual clinicians by 2022. In total, we identified individual or organization NPIs and specialty codes for over 99% of medical encounters in both TM and MA in all years. Rates of patient attribution were stable over time, and most methods had similar performance in MA and TM. We recommend a hierarchical attribution method that resulted in the highest fraction attributed with good consistency of attributed clinician year over year. Published reference files and SAS code make these NPI identification and patient attribution methods accessible. Our methods allow researchers to identify provider NPIs that can be matched to external clinician data, used to attribute patients to a usual source of care, or to fit clinician fixed effects in studies of MA and TM.

To provide a comprehensive overview of the different types of hospital discharge planning (DP) interventions and outcomes examined in systematic reviews, and to assess the strength of evidence (SoE) for the associations between DP and these outcomes. Umbrella review ("review of systematic reviews"). We searched five databases (PubMed, CINAHL, Web of Science, Cochrane, and Business Source Complete) from inception through February 2024 for systematic reviews examining associations between hospital DP and various outcomes. We conducted backward and forward citation searches to identify additional systematic reviews. Altogether, these searches yielded 1817 records, of which 34 met the inclusion criteria. We assessed the methodological quality of the included reviews using the AMSTAR 2 tool, summarized DP intervention types and the reviews' subgroup analyses narratively, and evaluated the SoE for 19 outcomes using a recently developed method. We identified 20 distinct DP intervention types which we grouped into six intervention categories. Patient education was the most frequently investigated type. We rated SoE as high for five outcomes, moderate for eight, and low for six. We found the strongest evidence for associations between hospital DP and reduced readmissions, fewer medication discrepancies, and greater patient satisfaction. Evidence for associations with quality of life, emergency department visits, mortality, and overall patient health, however, was weak or lacking. Our synthesis of the reviews' subgroup analyses indicated that the effects of hospital DP varied across patient populations and intervention types. Overall, the most effective interventions appeared to be high-intensity, bundled programs, incorporating medication-related interventions and follow-ups, particularly for reducing readmissions. This umbrella review synthesizes evidence on associations between hospital DP and various outcomes. The findings support the development of tailored DP strategies and point to research gaps. Future studies should prioritize standardizing intervention definitions, outcome measures, and subgroup classifications, and investigate unexamined causal mechanisms.

To understand perceived successes and challenges of the HEART payment, and opportunities for similar value-based payment mechanisms aiming to address health-related social needs. This study analyzes perceptions of primary care practices participating in the Maryland Primary Care Program (MDPCP) on the HEART payment, a value-based payment designed to support patients' social and medical needs. After a year of payment implementation, we gathered feedback through participant surveys and focus groups. From February to March 2023, we administered a survey with 112 responses and held seven focus groups to collect primary data. For quantitative survey data, we summarized descriptive statistics and performed regression analyses to determine predictors of perceived value of the HEART payment. For qualitative focus group data, we coded and analyzed data to understand key themes on success factors and barriers to HEART payment implementation. The HEART payment was rated as high value for 61.3% of survey respondents. In bivariate regression analysis, the level of funds received and affiliation with a Care Transformation Organization (CTO) were associated with perceived value of the HEART payment; however, these associations were not significant in multivariate models. In focus groups, we found that the biggest perceived success of HEART was its unique ability to enable direct support for patients' health-related social needs, with practices using the payment to provide patients with resources such as transportation, medically necessary home remediations, and support for loneliness. Perceived challenges included the need for more precise patient eligibility targeting and administrative burdens. The HEART payment is a promising new payment model that enables primary care practices to directly address patients' social needs. Future value-based payment models that incorporate social risk adjustments in provider payments may consider alternate methods to identify patients with a high burden of health-related social needs. This may include adjusting data points used to identify beneficiaries or allowing providers to directly identify patients.

To examine which hospital-payer contracts include Diagnosis Related Group (DRG) codes and whether they set prices as a consistent multiple of hospital list prices or Medicare's DRG fee schedule. We study the cash rates and negotiated contracts (including commercial group, Medicare Advantage, Medicaid Managed Care, and individual market health plans) of US general and surgical acute care hospitals. We develop bunching and regression-based methods to classify the pricing bases of DRGs within contracts. We show the unadjusted and regression-adjusted variation in DRG inclusion and pricing across hospital and insurer characteristics. Hospital price transparency data from Turquoise Health (May 2024) is joined with hospital characteristics from the American Hospital Association, insurer market concentration from Clarivate, and Medicare DRG rates. We observe 4033 hospitals with 157,313 hospital-health plan contracts and 3902 sets of cash rates. About 17% of hospitals do not include DRGs in any of their negotiated contracts or cash rates, while 54% include them in some, but not all contracts. Nearly half (48%) of hospitals exclude DRGs from their cash rates. Among commercial group contracts with DRGs, 25%-27% benchmark their DRG prices to hospital list prices, while 32%-36% are based on Medicare's fee schedule. Medicare Advantage contracts are more likely to be benchmarked to Medicare (64%), while most hospitals base their cash rates on list prices (85%). Hospitals facing less competition had lower rates of DRG contracting but were observed to be more likely to negotiate prices based on list prices conditional on including DRGs. Our findings suggest that hospital market power may influence hospital-health plan negotiations beyond the average price levels. Policies aimed at standardizing these contracts must account for the wide variation in payment and pricing bases currently used in the private market.