Risk Adjustment And Promoting Health Equity In Population-Based Payment: Concepts And Evidence

Risk Adjustment in Capitation Payments to Primary Care Providers: Does It Matter How We Account for Patients' Socioeconomic Status?

BACKGROUND

One of the critical challenges with capitation payment to primary care providers is ensuring that the fixed payments are equitable and adjusted for expected care needs. Patients of lower socioeconomic status (SES) generally have higher health care need. Sweden developed a Care Needs Index, which is used in the capitation payments to primary care providers to account for patient SES.

OBJECTIVES

We aim to examine the potential value of collecting individual-level rather than geographic-level socioeconomic data to support an equitable payment to primary care providers.

RESEARCH DESIGN

We used data from 3 regional administrative care registers, which cover all consultations in publicly funded health care, and Statistics Sweden's registers covering individual background characteristics. We estimated linear regression models and evaluated the model fit using the adjusted R2 with the Care Needs Index at the individual and at the district level. The population consisted of the 3,490,943 individuals residing in the 3 study regions for whom we had complete data.

MEASURES

The main outcome variable was the number of face-to-face consultations with a GP or a nurse at a primary care practice. We use the R2 to compare the predictive power of the models.

RESULTS

The share of the variation explained did not depend on whether the Care Needs Index was measured at the individual level or the small area level.

CONCLUSIONS

SES explains very little variation in primary care visits, and there is no gain from having individual-level information about the individual's SES compared with having district-level information only.

How Do Bundled Payment Initiatives Account for Differences in Patient Risk Profiles? A Systematic Review

OBJECTIVES

Bundled payments (BPs) are increasingly being adopted to enable the delivery of high-value care. For BPs to reach their goals, accounting for differences in patient risk profiles (PRPs) predictive of spending is crucial. However, insight is lacking into how this is done in practice. This study aims to fill this gap.

METHODS

We conducted a systematic review of literature published until February 2024, focusing on BP initiatives in the Organization for Economic Cooperation and Development countries. We collected data on initiatives' general characteristics, details on the (stated reasons for) approaches used to account for PRP, and suggested improvements. Patterns within and across initiatives were analyzed using extraction tables and thematic analysis.

RESULTS

We included 95 documents about 17 initiatives covering various conditions and procedures. Across these initiatives, patient exclusion (n = 14) and risk adjustment (n = 12) of bundle prices were the most applied methods, whereas risk stratification was less common (n = 3). Most authors stated mitigating perverse incentives as the primary reason for PRP accounting. Commonly used risk factors included comorbidities and sociodemographic and condition/procedure-specific characteristics. Our findings show that, despite increasingly sophisticated approaches over time, key areas for improvement included better alignment with value and equity goals, and enhanced data availability for more comprehensive corrections for relevant risk factors.

CONCLUSIONS

BP initiatives use various approaches to account for PRP differences. Despite a trend toward more sophisticated approaches, most remain basic with room for improvement. To enable cross-initiative comparisons and learning, it is important that stakeholders involved in BPs be transparent about the (reasons for) design choices made.

A critical review of the use of R2 in risk equalization research

Nearly all empirical studies that estimate the coefficients of a risk equalization formula present the value of the statistical measure R2. The R2-value is often (implicitly) interpreted as a measure of the extent to which the risk equalization payments remove the regulation-induced predictable profits and losses on the insured, with a higher R2-value indicating a better performance. In many cases, however, we do not know whether a model with R2 = 0.30 reduces the predictable profits and losses more than a model with R2 = 0.20. In this paper we argue that in the context of risk equalization R2 is hard to interpret as a measure of selection incentives, can lead to wrong and misleading conclusions when used as a measure of selection incentives, and is therefore not useful for measuring selection incentives. The same is true for related statistical measures such as the Mean Absolute Prediction Error (MAPE), Cumming's Prediction Measure (CPM) and the Payment System Fit (PSF). There are some exceptions where the R2 can be useful. Our recommendation is to either present the R2 with a clear, valid, and relevant interpretation or not to present the R2. The same holds for the related statistical measures MAPE, CPM and PSF.

Incorporating area-level social drivers of health in predictive algorithms using electronic health record data

OBJECTIVES

The inclusion of social drivers of health (SDOH) into predictive algorithms of health outcomes has potential for improving algorithm interpretation, performance, generalizability, and transportability. However, there are limitations in the availability, understanding, and quality of SDOH variables, as well as a lack of guidance on how to incorporate them into algorithms when appropriate to do so. As such, few published algorithms include SDOH, and there is substantial methodological variability among those that do. We argue that practitioners should consider the use of social indices and factors-a class of area-level measurements-given their accessibility, transparency, and quality.

RESULTS

We illustrate the process of using such indices in predictive algorithms, which includes the selection of appropriate indices for the outcome, measurement time, and geographic level, in a demonstrative example with the Kidney Failure Risk Equation.

DISCUSSION

Identifying settings where incorporating SDOH may be beneficial and incorporating them rigorously can help validate algorithms and assess generalizability.

Supporting Primary Care for Medically and Socially Complex Patients in Medicaid Managed Care

Importance

In 2023, the Massachusetts Medicaid and Children's Health Insurance Program (MassHealth) required accountable care organizations (ACOs) to increase payments to primary care practices and shift to monthly payments, currently calibrated to historical revenues and enhanced practice capabilities, such as being staffed to address behavioral health needs. To prevent rewarding practices for avoiding difficult patients, future payments to primary care practices should reflect their patients' apparent need.

Objective

To describe MassHealth's initiative and a complexity-adjusted payment model.

Design, Setting, and Participants

This cross-sectional study of payment model development and performance was conducted between February 2022 and November 2024. Participants included all 2019 Massachusetts Medicaid managed-care eligible members who were enrolled for 183 days or longer.

Exposures

Medical and social complexity.

Main Outcomes and Measures

For each member, the primary care activity level (PCAL) outcome proxies the resources that primary care clinicians need to provide comprehensive, coordinated care. Models were evaluated via R2 and through ratios of observed-to-expected (ie, estimated by the model) outcomes for selected subgroups, which will be approximately 1.0 when payments and expected costs are well matched. The implications of paying practices using PCAL (vs a model based only on age and sex) were explored by examining financial and practice-level characteristics in high and low deciles of practice-level estimated mean.

Results

Among 1 092 742 MassHealth members enrolled in 3602 primary care practices (1 014 252 person-years; mean [SD] age, 25.9 [18.4] years; 538 065 [53.1%] female), the PCAL model achieved R2 = 69.6% and estimates within 10% of observed PCAL spending for high-risk populations (mental health disorders, substance use disorders, complex chronic conditions, and disabilities) and across racial and ethnic groups. Age-adjusted and sex-adjusted payments would overpay practices in the lowest-need decile by 10% and underpay those in the highest-need decile by 34%, while the PCAL model would match payment to estimated need almost exactly in the lowest decile and underpay by just 6% in the highest decile.

Conclusions and Relevance

MassHealth's 2023 reform invests in primary care. This cross-sectional study developed a risk model that can adjust primary care payments to patient needs. Neither age and sex adjustments nor inflated historical payments would provide adequate resources to primary care practices caring for the most complex patients.

A Framework for the Design of Risk-Adjustment Models in Health care Provider Payment Systems

Prospective payments for health care providers require adequate risk adjustment (RA) to address systematic variation in patients' health care needs. However, the design of RA for provider payment involves many choices and difficult trade-offs between incentives for risk selection, incentives for cost control, and feasibility. Despite a growing literature, a comprehensive framework of these choices and trade-offs is lacking. This article aims to develop such a framework. Using literature review and expert consultation, we identify key design choices for RA in the context of provider payment and subsequently categorize these choices along two dimensions: (a) the choice of risk adjusters and (b) the choice of payment weights. For each design choice, we provide an overview of options, trade-offs, and key references. By making design choices and associated trade-offs explicit, our framework facilitates customizing RA design to provider payment systems, given the objectives and other characteristics of the context of interest.

Algorithms to Improve Fairness in Medicare Risk Adjustment

Importance

Payment system design creates incentives that impact healthcare spending, access, and outcomes. With Medicare Advantage accounting for more than half of Medicare spending, changes to its risk adjustment algorithm have the potential for broad consequences.

Objective

To develop risk adjustment algorithms that can achieve fair spending targets, and compare their performance to a baseline that emulates the least squares regression approach used by the Centers for Medicare and Medicaid Services.

Design

Retrospective analysis of Traditional Medicare enrollment and claims data between January 2017 and December 2020. Diagnoses in claims were mapped to Hierarchical Condition Categories (HCCs). Algorithms used demographic indicators and HCCs from one calendar year to predict Medicare spending in the subsequent year.

Setting

Data from Medicare beneficiaries with documented residence in the United States or Puerto Rico.

Participants

A random 20% sample of beneficiaries enrolled in Traditional Medicare. Included beneficiaries were aged 65 years and older, and did not have Medicaid dual eligibility. Race/ethnicity was assigned using the Research Triangle Institute enhanced indicator.

Main Outcome and Measures

Prospective healthcare spending by Medicare. Overall performance was measured by payment system fit and mean absolute error. Net compensation was used to assess group-level fairness.

Results

The main analysis included 4,398,035 Medicare beneficiaries with a mean age of 75.2 years and mean annual Medicare spending of $8,345. Out-of-sample payment system fit for the baseline regression was 12.7%. Constrained regression and post-processing both achieved fair spending targets, while maintaining payment system fit values of 12.6% and 12.7%, respectively. Whereas post-processing only increased mean payments for beneficiaries in minoritized racial/ethnic groups, constrained regression increased mean payments for beneficiaries in minoritized racial/ethnic groups and beneficiaries in other groups residing in counties with greater exposure to socioeconomic factors that can adversely affect health outcomes.

Conclusions and Relevance

Constrained regression and post-processing can incorporate fairness objectives in the Medicare risk adjustment algorithm with minimal reduction in overall fit.

Attributing Racial Differences in Care to Health Plan Performance or Selection

Importance

There is increased interest in public reporting of, and linking financial incentives to, the performance of organizations on health equity metrics, but variation across organizations could reflect differences in performance or selection bias.

Objective

To assess whether differences across health plans in sex- and age-adjusted racial disparities are associated with performance or selection bias.

Design, Setting, and Participants

This cross-sectional study leveraged a natural experiment, wherein a southern US state randomly assigned much of its Medicaid population to 1 of 5 plans after shifting to managed care in 2012. Enrollee-level administrative claims and enrollment data from 2011 to 2015 were obtained for self-identified Black and White enrollees. The analyses were limited to Black and White Medicaid enrollees because they accounted for the largest percentages of the population and could be compared with greater statistical power than other groups. Data were analyzed from June 2021 to September 2024.

Exposures

Plan enrollment via self-selection (observational population) vs random assignment (randomized population).

Main Outcomes and Measures

Annual counts of primary care visits, low-acuity emergency department visits, prescription drug fills, and total spending. For observational and randomized populations, models of each outcome were fit as a function of plan indicators, indicators for race, interactions between plan indicators and race, and age and sex. Models estimated the magnitude of racial differences within each plan and tested whether this magnitude varied across plans.

Results

Of 118 101 enrollees (mean [SD] age, 9.3 [7.5] years; 53.0% female; 61.4% non-Hispanic Black; and 38.6% non-Hispanic White), 70.2% were included in the randomized population, and 29.8% were included in the observational population. Within-plan differences in primary care visits, low-acuity emergency department visits, prescription drug use, and total spending between Black and White enrollees were large but did not vary substantially and were not statistically significantly different across plans in the randomized population, suggesting minimal effects of plans on racial differences in these measures. In contrast, in the observational population, racial differences varied substantially across plans (standard deviations 2-3 times greater than in the randomized population); this variation was statistically significant after adjustment for multiple testing, except for emergency department visits. Greater between-plan variation in racial differences in the observational population was only partially explained by sampling error. Stratifying by race did not bring observational estimates of plan effects meaningfully closer to randomized estimates.

Conclusions and Relevance

This cross-sectional study showed that selection bias may mischaracterize plans' relative performance on measures of health care disparities. It is critical to address disparities in Medicaid, but adjusting plan payments based on disparity measures may have unintended consequences.

Launching Financial Incentives for Physician Groups to Improve Equity of Care by Patient Race and Ethnicity

Policy Points What are the facilitators and barriers of physician group participation in a performance-based financial incentive program aimed at improving equity of care by patient race and ethnicity? Launching financial incentives to improve racial equity has required extensive organizational change management for participating physician groups, including major investments to improve quality management systems. Carefully designing financial incentives to encourage equity improvement while managing unintended consequences, and considering physician groups' populations served, baseline maturity of quality management systems, and efforts to assess and address patients' social risk factors have been central to prepare physician groups for financial incentives to improve equity of care. Given the major investments required of physician groups to prepare for financial incentives that reward equity improvement, alignment of equity of care measure specifications and reporting requirements across payers could facilitate physician group engagement. Evidence about how baseline physician group capabilities, including the maturity of their quality management systems, impact equity improvement may help health plans prioritize and target their investments to advance equity of care by patient race and ethnicity.

CONTEXT

Blue Cross Blue Shield of Massachusetts (BCBSMA), a large commercial health insurer, is using financial incentives to advance equity of care by patient race and ethnicity. Understanding experiences of this payer and its contracted physician groups can inform efforts elsewhere. We qualitatively assess physician groups' barriers and facilitators of planning and implementing BCBSMA's financial incentives to improve equity of ambulatory care quality by patient race and ethnicity.

METHODS

Key informant interviews (n = 44) of the physician group, BCBSMA, and external stakeholders were conducted, equity initiative meetings were observed, and documents were analyzed to identify barriers and facilitators of designing and preparing for financial incentives to advance racial equity. Physician group experiences of preparing for and responding to financial incentives for equity improvement were assessed.

FINDINGS

Analyses revealed 1) the central importance of valid and reliable equity performance measurement and carefully designed equity improvement incentives for physician group buy-in, 2) that prior to implementing financial incentives for equity improvement, physician groups needed to improve their quality management systems and the accuracy and completeness of patient race and ethnicity data, and 3) physician groups' populations served, baseline maturity of quality management systems, and efforts to assess and address patients' social risk factors were central to consider to plan for physician group financial incentives to improve racial equity.

CONCLUSIONS

Given the major infrastructure investments and organizational change management resources required of physician groups to participate in a financial incentive program designed to reward equity improvement, alignment of equity measurement and performance requirements across payers would facilitate physician groups' engagement in efforts to improve quality of care for racial and ethnic minority patients.

Senior-Focused Primary Care Organizations Increase Access For Medicare Advantage Members, Especially Underserved Groups

Population-based payment in Medicare Advantage (MA) can foster innovation in care delivery by giving risk-bearing providers flexibility and strong incentives to enhance care and engage patients. This may particularly benefit historically underserved groups for whom payments often exceed costs. In this study, using data from Humana MA plans, we examined "senior-focused" primary care organizations that are supported predominantly by population-based payments in contracts with MA plans. We explored whether such organizations supported by such payment are associated with better care and improved equity compared with other primary care organizations receiving other forms of payment in MA. Analyses of data from 462,872 MA beneficiaries in 2021 showed that senior-focused primary care organizations served more Black and dually eligible beneficiaries than other primary care organizations serving MA beneficiaries, and regression-adjusted analysis showed that senior-focused primary care patients received 17 percent more primary care visits. Differences were largest among Black and dual-eligible beneficiaries. These findings suggest that risk-bearing organizations in MA are responding to current payment dynamics and providing enhanced care and access to patients, particularly historically underserved populations.

Arthroscopy Patients in Medicare Population Became Sicker While Reimbursement Decreased From 2013 to 2020

Purpose

To assess surgeon reimbursement for common arthroscopic procedures, including arthroscopic meniscal debridement and arthroscopic rotator cuff repair, in patients with differing risk profiles within the Medicare population.

Methods

A publicly available Medicare database was used to identify all cases of arthroscopic meniscal debridement and arthroscopic rotator cuff repair procedures billed to Medicare from 2013 to 2020. The surgeon reimbursement from Medicare was collected and adjusted for inflation. All procedure episodes were split into 2 cohorts; those with a hierarchical condition category (HCC) risk score ≥1.5, and those with patient HCC risk scores <1.5. Reimbursement rates were compared between groups.

Results

From 2013 to 2020, a total of 624,077 meniscal debridement procedures and 567,794 arthroscopic rotator cuff repairs were billed to Medicare Part B. During this time, the mean adjusted surgeon reimbursement for arthroscopic rotator cuff repair decreased by 9.2% from 2013 to 2020. During the same time period, the adjusted mean surgeon reimbursement for arthroscopic both compartment meniscal debridement and single compartment meniscal debridement decreased by 7.9% and 9.9%, respectively. Throughout the study period, the mean HCC risk score increased from 1.19 in 2013 to 1.31 in 2020 (P < .001). Across all years in the study, the sicker cohort had a significantly greater rate of all comorbidities and a greater mean body mass index (P < .001 for all variables). The mean reimbursement across this cohort was lower for both rotator cuff repair (P = .037) and meniscal debridement procedures (P < .001) compared with the healthier cohort.

Conclusions

This study demonstrates that from 2013 to 2020, inflation-adjusted surgeon reimbursement for arthroscopic rotator cuff repair and meniscal debridement decreased while patient complexity increased. Further, mean surgeon reimbursement was lower among patients with more complexity in comparison with their healthier counterparts for such procedures.

Level of Evidence

Level III, retrospective cohort study.

Health Equity Adjustment and Hospital Performance in the Medicare Value-Based Purchasing Program

Importance

Medicare's Hospital Value-Based Purchasing (HVBP) program will provide a health equity adjustment (HEA) to hospitals that have greater proportions of patients dually eligible for Medicare and Medicaid and that offer high-quality care beginning in fiscal year 2026. However, which hospitals will benefit most from this policy change and to what extent are unknown.

Objective

To estimate potential changes in hospital performance after HEA and examine hospital patient mix, structural, and geographic characteristics associated with receipt of increased payments.

Design, Setting, and Participants

This cross-sectional study analyzed all 2676 hospitals participating in the HVBP program in fiscal year 2021. Publicly available data on program performance and hospital characteristics were linked to Medicare claims data on all inpatient stays for dual-eligible beneficiaries at each hospital to calculate HEA points and HVBP payment adjustments.

Exposures

Hospital Value-Based Purchasing program HEA.

Main Outcomes and Measures

Reclassification of HVBP bonus or penalty status and changes in payment adjustments across hospital characteristics.

Results

Of 2676 hospitals participating in the HVBP program in fiscal year 2021, 1470 (54.9%) received bonuses and 1206 (45.1%) received penalties. After HEA, 102 hospitals (6.9%) were reclassified from bonus to penalty status, whereas 119 (9.9%) were reclassified from penalty to bonus status. At the hospital level, mean (SD) HVBP payment adjustments decreased by $4534 ($90 033) after HEA, ranging from a maximum reduction of $1 014 276 to a maximum increase of $1 523 765. At the aggregate level, net-positive changes in payment adjustments were largest among safety net hospitals ($28 971 708) and those caring for a higher proportion of Black patients ($15 468 445). The likelihood of experiencing increases in payment adjustments was significantly higher among safety net compared with non-safety net hospitals (574 of 683 [84.0%] vs 709 of 1993 [35.6%]; adjusted rate ratio [ARR], 2.04 [95% CI, 1.89-2.20]) and high-proportion Black hospitals compared with non-high-proportion Black hospitals (396 of 523 [75.7%] vs 887 of 2153 [41.2%]; ARR, 1.40 [95% CI, 1.29-1.51]). Rural hospitals (374 of 612 [61.1%] vs 909 of 2064 [44.0%]; ARR, 1.44 [95% CI, 1.30-1.58]), as well as those located in the South (598 of 1040 [57.5%] vs 192 of 439 [43.7%]; ARR, 1.25 [95% CI, 1.10-1.42]) and in Medicaid expansion states (801 of 1651 [48.5%] vs 482 of 1025 [47.0%]; ARR, 1.16 [95% CI, 1.06-1.28]), were also more likely to experience increased payment adjustments after HEA compared with their urban, Northeastern, and Medicaid nonexpansion state counterparts, respectively.

Conclusions and Relevance

Medicare's implementation of HEA in the HVBP program will significantly reclassify hospital performance and redistribute program payments, with safety net and high-proportion Black hospitals benefiting most from this policy change. These findings suggest that HEA is an important strategy to ensure that value-based payment programs are more equitable.

A Novel Machine Learning Algorithm for Creating Risk-Adjusted Payment Formulas

Importance

Models predicting health care spending and other outcomes from administrative records are widely used to manage and pay for health care, despite well-documented deficiencies. New methods are needed that can incorporate more than 70 000 diagnoses without creating undesirable coding incentives.

Objective

To develop a machine learning (ML) algorithm, building on Diagnostic Item (DXI) categories and Diagnostic Cost Group (DCG) methods, that automates development of clinically credible and transparent predictive models for policymakers and clinicians.

Design, Setting, and Participants

DXIs were organized into disease hierarchies and assigned an Appropriateness to Include (ATI) score to reflect vagueness and gameability concerns. A novel automated DCG algorithm iteratively assigned DXIs in 1 or more disease hierarchies to DCGs, identifying sets of DXIs with the largest regression coefficient as dominant; presence of a previously identified dominating DXI removed lower-ranked ones before the next iteration. The Merative MarketScan Commercial Claims and Encounters Database for commercial health insurance enrollees 64 years and younger was used. Data from January 2016 through December 2018 were randomly split 90% to 10% for model development and validation, respectively. Deidentified claims and enrollment data were delivered by Merative the following November in each calendar year and analyzed from November 2020 to January 2024.

Main Outcome and Measures

Concurrent top-coded total health care cost. Model performance was assessed using validation sample weighted least-squares regression, mean absolute errors, and mean errors for rare and common diagnoses.

Results

This study included 35 245 586 commercial health insurance enrollees 64 years and younger (65 901 460 person-years) and relied on 19 clinicians who provided reviews in the base model. The algorithm implemented 218 clinician-specified hierarchies compared with the US Department of Health and Human Services (HHS) hierarchical condition category (HCC) model's 64 hierarchies. The base model that dropped vague and gameable DXIs reduced the number of parameters by 80% (1624 of 3150), achieved an R2 of 0.535, and kept mean predicted spending within 12% ($3843 of $31 313) of actual spending for the 3% of people with rare diseases. In contrast, the HHS HCC model had an R2 of 0.428 and underpaid this group by 33% ($10 354 of $31 313).

Conclusions and Relevance

In this study, by automating DXI clustering within clinically specified hierarchies, this algorithm built clinically interpretable risk models in large datasets while addressing diagnostic vagueness and gameability concerns.

Medicaid and the Children's Health Insurance Program: Optimization to Promote Equity in Child and Young Adult Health

The American Academy of Pediatrics envisions a child and adolescent health care system that provides individualized, family-centered, equitable, and comprehensive care that integrates with community resources to help each child and family achieve optimal growth, development, and well-being. All infants, children, adolescents, and young adults should have access to this system. Medicaid and the Children's Health Insurance Program (CHIP) provide critical support and foundation for this vision. Together, the programs currently serve about half of all children, many of whom are members of racial and ethnic minoritized populations or have complex medical conditions. Medicaid and CHIP have greatly improved the health and well-being of US infants, children, adolescents, and young adults. This statement reviews key program aspects and proposes both program reforms and enhancements to support a higher-quality, more comprehensive, family-oriented, and equitable system of care that increases access to services, reduces disparities, and improves health outcomes into adulthood. This statement recommends foundational changes in Medicaid and CHIP that can improve child health, achieve greater equity in health and health care, further dismantle structural racism within the programs, and reduce major state-by-state variations. The recommendations focus on (1) eligibility and duration of coverage; (2) standardization of covered services and quality of care; and (3) program financing and payment. In addition to proposed foundational changes in the Medicaid and CHIP program structure, the statement indicates stepwise, coordinated actions that regulation from the Centers for Medicare and Medicaid Services or federal legislation can accomplish in the shorter term. A separate technical report will address the origins and intents of the Medicaid and CHIP programs; the current state of the program including variations across states and payment structures; Medicaid for special populations; program innovations and waivers; and special Medicaid coverage and initiatives.

A Formal Framework For Incorporating Equity Into Health Care Quality Measurement

Quality measurement is an important tool for incentivizing improvement in the quality of health care. Most quality measurement efforts do not explicitly target health equity. Although some measurement approaches may intend to realign incentives to focus quality improvement efforts on underserved groups, the extent to which they accomplish this goal is understudied. We posit that tying incentives to approaches on the basis of stratification or disparities may have unintended consequences or limited effects. Such approaches might not reduce existing disparities because addressing one aspect of equity may be in competition with addressing others. We propose equity weighting, a new measurement framework to advance equity on multiple fronts that addresses the shortcomings of existing approaches and explicitly calibrates incentives to align with equity goals. We use colorectal cancer screening data derived from 2017 Medicare claims to illustrate how equity weighting fixes unintended consequences in other methods and how it can be adapted to policy goals.

Paying for Medical and Social Complexity in Massachusetts Medicaid

Importance

The first MassHealth Social Determinants of Health payment model boosted payments for groups with unstable housing and those living in socioeconomically stressed neighborhoods. Improvements were designed to address previously mispriced subgroups and promote equitable payments to MassHealth accountable care organizations (ACOs).

Objective

To develop a model that ensures payments largely follow observed costs for members with complex health and/or social risks.

Design, Setting, and Participants

This cross sectional study used administrative data for members of the Massachusetts Medicaid program MassHealth in 2016 or 2017. Participants included members who were eligible for MassHealth's managed care, aged 0 to 64 years, and enrolled for at least 183 days in 2017. A new total cost of care model was developed and its performance compared with 2 earlier models. All models were fit to 2017 data (most recent available) and validated on 2016 data. Analyses were begun in February 2019 and completed in January 2023.

Exposures

Model 1 used age-sex categories, a diagnosis-based morbidity relative risk score (RRS), disability, serious mental illness, substance use disorder, housing problems, and neighborhood stress. Model 2 added an interaction for unstable housing with RRS. Model 3 added rurality and updated diagnosis-based RRS, medication-based RRS, and interactions between sociodemographic characteristics and morbidity.

Main Outcome and Measures

Total 2017 annual cost was modeled and overall model performance (R2) and fair pricing of subgroups evaluated using observed-to-expected (O:E) ratios.

Results

Among 1 323 424 members, mean (SD) age was 26.4 (17.9) years, 53.4% were female (46.6% male), and mean (SD) 2017 cost was $5862 ($15 417). The R2 for models 1, 2, and 3 was 52.1%, 51.5%, and 60.3%, respectively. Earlier models overestimated costs for members without behavioral health conditions (O:E ratios 0.94 and 0.93 for models 1 and 2, respectively) and underestimated costs for those with behavioral health conditions (O:E ratio >1.10); model 3 O:E ratios were near 1.00. Model 3 was better calibrated for members with housing problems, those with children, and those with high morbidity scores. It reduced underpayments to ACOs whose members had high medical and social complexity. Absolute and relative model performance were similar in 2016 data.

Conclusions and Relevance

In this cross-sectional study of data from Massachusetts Medicaid, careful modeling of social and medical risk improved model performance and mitigated underpayments to safety-net systems.

Beyond demonstrations: implementing a primary care hybrid payment model in Medicare

The National Academies of Sciences, Engineering, and Medicine's (NASEM's) 2021 report on primary care called for a hybrid payment approach-a mix of fee-for-service and population-based payment-with performance accountability to strike the proper balance for desired practice transformation and to support primary care's important and expanding role. The NASEM report also proposed substantial increases to primary care payment and reforms to the Medicare Physician Fee Schedule. This paper addresses pragmatic ways to implement these recommendations, describing and proposing solutions to the main implementation challenges. The urgent need for primary care payment reform calls for adopting a hybrid model within the Medicare fee schedule rather than engaging in another round of demonstrations, despite legal and practical obstacles to adoption. The paper explores reasons for adopting a roughly 50:50 blend of fee-for-service and population-based payment and addresses other design features, presenting reasons why spending accountability should rely on utilization measures under primary care control rather than performance on total cost of care, and proposes a fresh approach to quality, emphasizing that quality measures should be parsimonious, focused on important outcomes with demonstrated quality improvement.

Burden Of Health-Related Social Needs Among Dual- And Non-Dual-Eligible Medicare Advantage Beneficiaries

Little information exists to inform stakeholders' efforts to screen for, address, and risk-adjust for the health-related social needs (HRSNs) of Medicare Advantage (MA) enrollees, particularly those not dually Medicaid-Medicare eligible and those younger than age sixty-five. HRSNs can include food insecurity, housing instability, transportation issues, and other factors. We examined the prevalence of HRSNs in 2019 among 61,779 enrollees in a large, national MA plan. Although HRSNs were more common among dual-eligible beneficiaries, with 80 percent reporting at least one (average, 2.2 per beneficiary), 48 percent of non-dual-eligible beneficiaries reported one or more, indicating that dual eligibility alone would have inadequately captured HRSN risk. HRSN burden was unequally distributed across multiple beneficiary characteristics, notably with beneficiaries younger than age sixty-five more likely than those ages sixty-five and older to report having an HRSN. We also found that some HRSNs were more strongly associated with hospitalizations, emergency department visits, and physician visits than others. These findings suggest the importance of considering the HRSNs of dual- and non-dual-eligible beneficiaries, as well as those of beneficiaries of all ages, when exploring how to address HRSNs in the MA population.

The Better Care Plan: a blueprint for improving America's healthcare system

The United States falls far short of its potential for delivering care that is effective, efficient, safe, timely, patient-centered, and equitable. We put forward the Better Care Plan, an overarching blueprint to address the flaws in our current system. The plan calls for continuously improving care, moving all payers to risk-adjusted prospective payment, and creating national entities for collecting, analyzing, and reporting patient safety and quality-of-care outcomes data. A number of recommendations are made to achieve these goals.