糖心vlog

Key Points

Question听 Do hospitals achieve equitable readmission rates (ie, fewer readmissions with narrow gaps in readmission rates between populations)? What characterizes hospitals with equitable readmissions?

Findings听 Of eligible hospitals, 17% had equitable readmissions by insurance, and 30% had equitable readmissions by race. Hospitals with and without equitable readmissions were characteristically different. Achieving equitable readmissions did not consistently correlate with quality, cost, or value. Many hospitals were not eligible for a disparities assessment due to insufficient numbers of dual-eligible and Black patients.

Meaning听 A minority of hospitals achieve equitable readmissions. Equity-focused outcome measures assess new dimensions of hospital performance distinct from traditional accountability measures.

Importance听 Equity is an essential domain of health care quality. The Centers for Medicare & Medicaid Services (CMS) developed 2 Disparity Methods that together assess equity in clinical outcomes.

Objectives听 To define a measure of equitable readmissions; identify hospitals with equitable readmissions by insurance (dual eligible vs non鈥揹ual eligible) or patient race (Black vs White); and compare hospitals with and without equitable readmissions by hospital characteristics and performance on accountability measures (quality, cost, and value).

Design, Setting, and Participants听 Cross-sectional study of US hospitals eligible for the CMS Hospital-Wide Readmission measure using Medicare data from July 2018 through June 2019.

Main Outcomes and Measures听 We created a definition of equitable readmissions using CMS Disparity Methods, which evaluate hospitals on 2 methods: outcomes for populations at risk for disparities (across-hospital method); and disparities in care within hospitals鈥� patient populations (within-a-single-hospital method).

Exposures听 Hospital patient demographics; hospital characteristics; and 3 measures of hospital performance鈥攓uality, cost, and value (quality relative to cost).

Results听 Of 4638 hospitals, 74% served a sufficient number of dual-eligible patients, and 42% served a sufficient number of Black patients to apply CMS Disparity Methods by insurance and race. Of eligible hospitals, 17% had equitable readmission rates by insurance and 30% by race. Hospitals with equitable readmissions by insurance or race cared for a lower percentage of Black patients (insurance, 1.9% [IQR, 0.2%-8.8%] vs 3.3% [IQR, 0.7%-10.8%], P鈥�<鈥�.01; race, 7.6% [IQR, 3.2%-16.6%] vs 9.3% [IQR, 4.0%-19.0%], P鈥�=鈥�.01), and differed from nonequitable hospitals in multiple domains (teaching status, geography, size; P鈥�<鈥�.01). In examining equity by insurance, hospitals with low costs were more likely to have equitable readmissions (odds ratio, 1.57 [95% CI, 1.38-1.77), and there was no relationship between quality and value, and equity. In examining equity by race, hospitals with high overall quality were more likely to have equitable readmissions (odds ratio, 1.14 [95% CI, 1.03-1.26]), and there was no relationship between cost and value, and equity.

Conclusion and Relevance听 A minority of hospitals achieved equitable readmissions. Notably, hospitals with equitable readmissions were characteristically different from those without. For example, hospitals with equitable readmissions served fewer Black patients, reinforcing the role of structural racism in hospital-level inequities. Implementation of an equitable readmission measure must consider unequal distribution of at-risk patients among hospitals.

Quality measurement in payment and public-reporting programs presents an opportunity to incentivize health care system investment in equity.^1-5 However, quality initiatives without an explicit equity focus risk unintentionally worsening disparities.^6-10 The Centers for Medicare & Medicaid Services (CMS), private payors, and national quality accreditation bodies, have recently launched measurement programs to assess hospital performance in equity.^5,11-13 These programs often stratify existing quality measures by population subgroups to measure disparities in clinical outcomes. For example, CMS confidentially reports results for condition-specific and hospital-wide readmission measures stratified by insurance type and race and ethnicity using CMS Disparity Methods.¹⁴

Improved understanding of hospital performance on stratified outcome measures can facilitate thoughtful implementation in policy and practice. For example, understanding how a hospital鈥檚 patient population relates to performance on equity measures or eligibility for equity measures may identify unintended impacts on safety net hospitals and/or rewards/exclusion for hospitals that do not serve significant numbers of patients at-risk for disparities.^7,15,6 Characterizing hospitals with and without equitable readmissions may identify patient populations most at risk for care inequities based on where they seek care, as well as potential targets for interventions. Furthermore, quantifying the relationship between performance on stratified outcome measures intended to assess disparities in care, and traditional accountability measures such as quality, cost, and value, may reveal redundancy or new, distinct, and meaningful ways to evaluate hospitals. For example, recent work examining the relationship between quality, cost, and overall value demonstrated that quality and value are not synonymous concepts.¹⁶

To address this evidence gap, we applied CMS Disparity Methods to the Hospital-Wide Readmission measure to construct a stratified readmission metric and standard for equitable readmissions. We focused on readmissions for several reasons: they span several care domains (access, effectiveness, transitions); the Disparity Methods have been previously applied to readmission metrics; there are known disparities in readmissions^17,18; effective interventions to reduce hospital readmissions exist^19-25; and there are existing efforts to improve readmission rates through payment and reporting programs. For example, the Hospital-Wide Readmission measure is included in the Inpatient Quality Reporting Program, and adaptations of this measure are included in other acute care and plan-specific accountability programs.^26,27

Our specific objectives were as follows: (1) to define a measure of equitable readmission rates; (2) to identify hospitals with equitable readmission rates; and (3) to compare hospitals with equitable readmission rates to those without equitable readmissions by hospital characteristics and performance on traditional accountability measures (quality, cost, and value).

We used 2 Disparity Methods developed by CMS, as applied to the Hospital-Wide Readmission measure, to define hospitals with equitable readmissions. The across-hospital method compares readmission rates among the subset of patients with a given disparities risk factor (eg, dual eligibility for Medicaid and Medicare) to that of other hospitals; the within-a-single-hospital method estimates differences in outcomes within each hospital between patients with and without the disparities risk factor (eg, between dual-eligible and non鈥揹ual-eligible patients). We defined hospitals as having equitable readmissions if they met threshold scores on each method. Specifically, hospitals were classified as equitable if they achieved criterion 1鈥攁 risk-standardized readmission rate (RSRR) for an at-risk group that was better than the national median for that group (corresponds with the across-hospitals method), and criterion 2鈥攐utcomes for the at-risk group that were within 1 percentage point of outcomes for the non鈥揳t-risk group within their own hospital (within-a-single-hospital method). We selected the threshold of 1 percentage point difference (eg, an absolute adjusted readmission rate difference (ARD) between the group at risk and not at risk of inequities) based on prior methodology,²⁸ clinical significance, and the distribution of ARD. This definition is delineated in eTable 1 in Supplement 1.

We conceptualized criterion 1 as representing high quality and criterion 2 as narrow gaps in care. As such, these dual criteria (eTable 1 in Supplement 1) aim to address 2 underlying mechanisms of disparities: patients may receive care at lower-quality hospitals (criterion 1: the across-hospitals method) or may have different outcomes than other patients at the same hospital (criterion 2: within-a-single-hospital method). These criteria reflect a perspective that equitable care for a group of patients should encompass both high-quality care for that group and small differences in care between that group and others.

We selected 2 demographic risk factors for inequities (insurance as a proxy for income and access; and race and ethnicity as a proxy for racism) and applied the methods separately to evaluate each. To examine inequities by insurance, we compared readmission rates for patients who qualify for both Medicare and Medicaid coverage (dual-eligible patients) with those qualifying for Medicare only (non鈥揹ual-eligible). Dual-eligible patients have higher readmission rates than those who are not dual eligible.¹⁸ To assess racial inequities, we focused on differences between non-Hispanic Black individuals vs non-Hispanic White individuals for 2 reasons: (1) literature demonstrates inequities in readmissions between Black and White patients^17,29; and (2) while Medicare鈥檚 race data are not fully reliable, Black race specifically is more consistent with self-report.^30,31

We conducted a cross-sectional study of all non鈥揤eterans Affairs hospitals in the US included in the CMS Hospital-Wide Readmission measure cohort. Patients eligible for the cohort were aged 65 years and older, enrolled in a Medicare fee-for-service plan Part A and B for 1 year before index admission, and enrolled in Part A during the index admission (to capture prior comorbidities). Patients who died during hospitalization or were discharged against medical advice were excluded. The Hospital-Wide Readmission measure outcome includes readmission to any hospital within 30 days of discharge. Transfers were attributed to the second facility. Observation stays were not included in the numerator or denominator.³²

We identified index admissions using July 2018 through June 2019 inpatient Medicare fee-for-service claims. To identify beneficiaries鈥� Medicare and Medicaid eligibility status and race, we used the CMS Master Beneficiary Summary File and the Medicare enrollment database. To identify high- and low-cost hospitals, we used the CMS Medicare spending per beneficiary³³ total cost of care scores. Additional hospital characteristics were assessed using the CMS provider of service files and the 2018 American Hospital Association annual survey.

Figure 1 outlines our cohort design and eligibility criteria (eTable 1 in Supplement 1). Hospitals were eligible for the across-hospitals method if they cared for at least 25 patients in the at-risk group (criterion 1) and for the within-a-single-hospital method if they cared for at least 25 patients total and at least 12 patients within each at-risk and not at-risk group (criterion 2A).²⁸ We excluded 6 hospitals in which rates were substantially better for the at-risk group compared with the non鈥揳t-risk group (Figure 1, criterion 2B).

We chose variables based on a conceptual model delineating potential hospital-level factors that influence equitable readmissions (eFigure 1 in Supplement 1) as well as prior work examining drivers of readmissions, readmission disparities, and overall quality.^16,34,35 In this study, we focused on hospital-level factors that are relatively fixed with the goal of characterizing hospitals with equitable readmissions. Patient demographics included mean Disproportionate Share Hospital (DSH) Patient Percentage (defined by the Medicare DSH Adjustment鈥�42 CFR 412.106),³⁶ and mean percentage of patients Medicaid/Medicare dual-eligible and Black.¹⁶ Additional hospital characteristics included ownership status (not for profit, public for profit, and government), teaching status (nonteaching, teaching, and residency), urbanicity (urban vs rural), geographic location (divided into 9 regions: West South Central [AR, LA, OK, TX]; East North Central [IN, IL, MI, OH, WI]; South Atlantic [DE, DC, FL, GA, MD, NC, SC, VA, WV]; Pacific [AK, CA, HI, OR, WA]; East South Central [AL, KY, MS, TN]; West North Central [IA, KS, MN, MO, NE, ND, SD]; Mid Atlantic [NJ, NY, PA]; Mountain [AZ, CO, ID, NM, MT, UT, NV, WY]; New England [CT, ME, MA, NH, RI, VT]), number of staffed beds (as reported in the Medicare provider of service file), and nurse-to-bed ratio (the number of employed full-time equivalent registered nurses divided by the number of staffed beds).

Lastly, we examined 3 external measures of hospital performance: quality, cost, and value, which captures the concept of quality relative to cost.³⁷ High quality was defined as a 4 or 5 overall hospital star rating on Hospital Care Compare, which is determined by a threshold on a summary score encompassing aggregate performance across 5 domains: (1) safety, (2) readmission, (3) mortality, (4) patient experience, and (5) timely and effective care.³⁸ Low cost was defined as a CMS Medicare spending per beneficiary¹⁶ score in the lowest quintile of all hospitals. High value was defined as meeting criteria for both high quality and low cost.¹⁶

Characterizing quality using a threshold of 4 to 5 stars may mask underlying relationships. As a secondary analysis, we examined hospitals鈥� continuous scores on the overall hospital star rating, along with each of the 5 continuous domain scores (see eAppendix in Supplement 1).

We reported the number and percentage of hospitals eligible and ineligible for CMS Disparity Methods for each cohort (Figure 1) and described characteristics of eligible and ineligible hospitals (Table; eTable 2 in Supplement 1). We additionally mapped the percentage of hospitals in each state eligible for the Disparity Methods by both insurance and race (Figure 2).

Among eligible hospitals, we reported the number and proportion that qualified as providing equitable readmissions (by meeting both criterion 1 and 2) by insurance and race separately (Figure 1) and the number and proportion of eligible hospitals with equitable readmissions by both insurance and race. We also reported the number of hospitals that did not qualify for equitable readmissions because they only met criterion 1, only met criterion 2, or met neither criterion 1 nor criterion 2 (Figure 3). We additionally mapped the percentage of eligible hospitals in each state with equitable readmissions by insurance or race (Figure 2).

As additional context for the equitable readmission definition, we reported performance on each Disparity Method individually. Performance on the across-hospital method was represented by the distribution of observed readmission rates for dual-eligible and Black patients (see eAppendix in Supplement 1 for explanation of use of observed rates in place of RSRR, the metric used in the across-hospital method) (eFigure 2A and 2B in Supplement 1). We demonstrated performance on the within-a-single-hospital method by reporting the distribution of the ARD within each hospital between dual-eligible vs non鈥揹ual-eligible and Black vs White patients (eFigure 2C in Supplement 1).

We compared patient demographic and characteristics of hospitals with and without equitable readmissions using descriptive statistics and bivariate analyses (蠂²) (Figure 4).

We used logistic regression models to examine the bivariate associations between whether a hospital had equitable readmissions and the following markers of performance: (a) high quality (4-5 star rating), (b) low cost (lowest quintile on the CMS Medicare spending per beneficiary score), and (c) high value (both high quality and low cost). Hospitals without a star rating assigned (due to insufficient number of reportable measures) were excluded from analyses of high value and high quality (Figure 5).

As a secondary analysis, we examined bivariate associations between equitable readmissions and continuous measures of quality鈥攕pecifically the continuous score on the overall star rating and the 5 continuous domain scores: (1) safety, (2) readmission, (3) mortality, (4) patient experience, and (5) timely and effective care (see eAppendix in Supplement 1 and Figure 5).

Of the 4767 hospitals in the Hospital-Wide Readmission measure cohort, 3414 (74%) cared for sufficient numbers of dual-eligible patients and 1962 (42%) cared for sufficient numbers of Black patients to be eligible for the Disparity Methods by insurance and race (Figure 1). Hospitals ineligible for examining disparities by insurance cared for 1.2% of dual-eligible patients and 2.3% of all beneficiaries. Hospitals ineligible for examining disparities by race cared for 2.2% of Black patients and 22.8% of all beneficiaries in the cohort.

Hospitals eligible vs ineligible for the Disparity Methods differed across all hospital and patient demographic characteristics (P鈥�<鈥�.001) including geography (eTable 2 in Supplement 1; Figure 2). The geographic distribution of hospitals that served sufficient dual-eligible patients to be eligible for the Disparity Methods by insurance overlapped with but also differed from the distribution of hospitals with sufficient Black patients to be eligible for the Disparity Methods by race. Eligible hospitals by race were more geographically concentrated than those eligible by insurance.

Among hospitals eligible for the Disparity Methods by insurance, 592 of 3414 (17%) met criterion 1 (high quality/low readmission rates) and 2 (narrow gaps in readmission rates) and were classified as having equitable readmissions (Figure 3).

Of the 2822 hospitals without equitable readmissions, 291 (10%) were disqualified because they did not meet criterion 1 (ie, low quality/high readmissions), 1142 (41%) because they did not meet criterion 2 (ie, had large gaps), and 1389 (49%) because they met neither criterion (Figure 3). Among all hospitals, the median RSRR for dual-eligible patients was 19.5%; therefore, equitable hospitals had to have an RSRR for dual-eligible patients of less than 19.5%. Among eligible hospitals, 883 of 3414 (25.8%) had an ARD of less than 1% between dual-eligible and non鈥揹ual-eligible patients (eFigure 2C in Supplement 1).

Among hospitals eligible for Disparity Methods by race, 596 of 1962 (30%) met criteria 1 and 2 and were classified as having equitable readmissions (Figure 2). Of the 1366 hospitals without equitable readmissions, 409 (29.9%) did not meet criterion 1 and were therefore disqualified, 411 (30.1%) were disqualified because they did not meet criterion 2, and 546 (40%) were disqualified because they met neither criterion (Figure 3). The median RSRR for Black patients was 19.4%. Among eligible hospitals, 1005 (51%) had an ARD less than 1% between Black and White patients (eFigure 2C in Supplement 1).

Only 116 (6%) of the 1917 hospitals eligible for the Disparity Methods by insurance and race had equitable readmissions for both dual-eligible and Black patients.

The geographic distribution of hospitals with equitable readmissions by insurance compared with race varied. In one state, New Mexico, as many as 40% of its 31 hospitals provided equitable readmissions for dual-eligible patients (Figure 2). By race, the few states with a high proportion of hospitals with equitable readmissions also had very few eligible hospitals (eg, Wyoming with only 2 hospitals included in analysis).

Hospitals with equitable readmissions by insurance or race were different from hospitals without equitable readmissions on multiple domains: teaching status (P鈥�<鈥�.01 for both insurance and race with equitable hospitals having higher proportions of nonteaching status [insurance, 78% vs 57%; race, 57% vs 45%]), rural location (P鈥�<鈥�.01 for both insurance and race [insurance, 54% vs 30%; race, 21% vs 11%]), and size (P鈥�<鈥�.01 for both insurance and race with equitable hospitals having higher proportions of having 0-99 beds [insurance, 63% vs 30%; race, 19% vs 12%]) (Figure 4). Hospitals with equitable readmissions for dual-eligible patients had different ownership patterns (P鈥�<鈥�.01), with notably different proportions of publicly owned hospitals (25% vs 16%). Hospitals with equitable readmissions by insurance or race were more likely to care for a lower proportion of Black patients (insurance, median 1.9% [IQR, 0.2%-8.8%] vs median 3.3% [IQR, 0.7%-10.8%], P鈥�<鈥�.01; race, median 7.6% [IQR, 3.2%-16.6%] vs median 9.3% [IQR, 4.0%-19.0%], P鈥�=鈥�.01) (Figure 4).

There was no statistically significant relationship between hospitals that provided equitable readmissions for dual-eligible beneficiaries and those that provided high-quality (odds ratio [OR], 1.00 [95% CI, 0.91-1.11]) or high-value care (OR, 1.19 [95% CI, 0.98-1.43]; Figure 5). However, hospitals characterized as providing low-cost care had higher odds of providing equitable readmissions for dual-eligible beneficiaries (OR, 1.57 [95% CI, 1.38-1.77]).

In contrast, hospitals with high-quality care had higher odds of providing equitable readmissions for Black beneficiaries (OR, 1.14 [95% CI, 1.03-1.26]; Figure 5). Equitable readmissions for Black beneficiaries had no statistically significant relationship with low-cost (OR, 1.10 [95% CI, 0.92-1.30]) or high-value care (OR, 1.22 [95% CI, 0.94-1.58]) (Figure 5).

In our secondary analysis, higher continuous scores on the overall star ratings measure conferred higher odds of equitable readmissions (insurance OR, 1.94 [95% CI, 1.62-2.32]; race OR, 1.61 [95% CI, 1.33-1.95]). This relationship was consistent with most individual quality domain scores, including the overall readmission domain (insurance OR, 1.64 [95% CI, 1.48-1.82]); race OR, 1.29 [95% CI, 1.16-1.43]), patient experience, and timely/effective care (Figure 5).

In this cross-sectional study using Medicare administrative claims data, we applied CMS Disparity Methods to identify hospitals that provide equitable readmissions for dual-eligible and Black beneficiaries. We found that among eligible hospitals, 17% provided equitable readmissions for dual-eligible beneficiaries and 30% for Black beneficiaries. Those hospitals had readmission rates at or below the national median for at-risk groups and had narrow gaps in performance between at-risk and not at-risk groups within their own patient population. Notably, however, less than 75% of hospitals cared for sufficient numbers of dual-eligible beneficiaries and less than half cared for sufficient numbers of Black beneficiaries even to be eligible to examine disparities in readmissions. Hospitals with equitable readmissions differed from hospitals that did not provide equitable readmissions as they were more likely to care for a lower percentage of Black patients; to be small, rural, and nonteaching; and to have low nurse-to-bed ratios. Finally, we found that the equitable readmission measure provides a measure of hospital performance distinct from traditional assessments of quality, cost, and value.

Stratified measures are increasingly being used by payors, health care systems, and in public reporting programs to identify disparities in quality. However, little is known about how to design or use these measures to maximize hospital accountability to equity and minimize unintended consequences. Equity weighting has been proposed as a potential method to address shortcomings associated with simple stratification.³⁹ Our study builds on this recent work and advances the field by interrogating hospital performance on the Hospital-Wide Readmission measure using CMS Disparity Methods to stratify outcomes by insurance and race鈥攎ethods that account for both the overall quality of care and gaps in care between populations.

We demonstrated variation in performance on our equitable readmission measure, suggesting potential for improvement at the hospital level. Although patient- and community-level factors influence risk of readmission,^18,35,40-43 prior work demonstrating differential readmission rates for the same patients, with similar risk factors at different hospitals, indicates that hospital quality independently contributes to readmission risk.⁴⁴ Furthermore, prior work has demonstrated that variation in readmission disparities for dual-eligible beneficiaries remains even when controlling for state Medicaid policy, health service availability, and social factors.³⁵ By this logic, inequities in readmission rates are also likely driven by hospital factors, some fixed, but some modifiable to improve inequities in care.

Different patterns and characteristics of hospitals with equitable readmissions by insurance vs race highlight the central role of structural racism as a key driver of hospital-level inequities in care. The impact of patient-, community-, and hospital-level factors on the complex outcome of readmission are all fundamentally driven by structural determinants of health. These include social hierarchies that dictate access to power and resources, and structural racism, defined by Lorraine Dean as the 鈥渢otality of ways鈥� that multiple systems and institutions interact to assert racist policies, practices, and beliefs about people in a racialized groups.鈥�^36,45,46 While interpersonal and institutional racism within health care is critical, the health care system sits within a complex sociopolitical ecosystem, and racism acts across multiple levels.^47,48

Hospitals with equitable readmissions by insurance cared for a higher proportion of patients who were dually eligible for Medicaid and Medicare. In contrast, hospitals with equitable readmissions by insurance or race cared for fewer, not more, patients identifying as Black. Understanding differences in why hospitals did not meet equitable readmissions criteria helps to unpack these findings. Failure to meet criteria for equitable readmissions by insurance was driven by within-a-single-hospital disparities鈥攊e, large gaps in quality between groups. In contrast, failure to meet criteria for equitable readmissions by race was driven by across-hospitals disparities鈥攊e, low quality for the population atrisk compared with other hospitals. These findings reinforce the structurally determined fact that hospitals serving higher proportions of Black patients often provide lower-quality care.^49-52 This phenomenon reflects structural racism embedded in the unequal reimbursement of Medicaid compared to other public and private payors, and systemic disinvestment in both hospitals and communities that predominantly serve Black individuals.^53,54 As such, hospitals without equitable readmissions may be underresourced to address inequities in care. Beyond individual hospital-level quality improvement, we need structural interventions that disrupt racist policies and practices and explicitly invest in hospitals that predominantly serve Black individuals.^55-57

Furthermore, equity measures, like traditional quality measures, risk disproportionately rewarding hospitals that care for fewer Black patients and perpetuating these inequities in care⁵⁸ if patient population is not considered in their implementation. Many hospitals were excluded from the equitable readmissions measure entirely because they cared for insufficient dual-eligible and Black patients. Hospitals excluded from the equitable readmission measure by race encompassed 20% of all Medicare admissions鈥攁 finding reflective of known care segregation鈥攐ne quarter of hospitals care for nearly 90% of Black Medicare beneficiaries nationally.⁵⁹ However, it is important to consider why hospitals care for a low proportion of dual-eligible or Black patients. Differences in patient population may reflect differences in catchment area demographics, secondary to longstanding income and racial segregation. However, a hospital鈥檚 payor and racial mix may also represent systematic exclusion of dual-eligible and Black patients.^60-62 CMS proposed health equity adjustment, which incorporates care for a high proportion of underserved and/or dual-eligible beneficiaries into criteria for payment programs, is a potential policy solution to address these challenges.^63,64

Measuring equity in outcomes is a relatively new concept in quality measurement. We found that equity was associated with, but not identical to, measures of quality, cost, and value. For instance, while hospitals with equitable readmissions by race were more likely to have high quality (achieving 4-5 star scores), only 34% of hospitals receiving 4 to 5 stars also provided equitable readmissions for Black patients. We found no statistically significant relationship between equitable readmissions and value (quality relative to cost). These findings provide some external validation that the measurement concepts of equity in outcomes, quality, and value are each distinct, building on prior literature.^16,65-67 Furthermore, achieving low and equitable readmissions may be costly. Embedding equity as an essential or balancing measure within the rapid increase in value-based payment programs, will be critical.

We recognize several limitations in this study. Limitations inherent to observational studies of administrative data include an inability to make causal inferences. Medicare administrative data have relatively high validity for Black race³¹ compared with other racial groups. However, racial identities, as reported in administrative claims overall, have substantial limitations in validity and accuracy and fail to acknowledge individuals with multiple intersectional identities. As a result, they are a proxy but not a precise measure of the social construct of race and the impact of racism on health care and health care outcomes. Defining high quality, low cost, and high value dichotomously may mask more subtle relationships, which we found in our secondary analysis that used continuous measures of quality. We only examined select hospital characteristics previously associated with quality and value but did not assess more modifiable factors such as services delivered or explanatory factors such as community characteristics.⁴²

Our measure of equitable readmissions provides an important assessment of hospital performance distinct from traditional accountability measures. Although hospitals with equitable readmissions generally have higher quality, high-quality hospitals do not necessarily confer good outcomes for dual-eligible and Black beneficiaries. Evidence of hospital-level variation in performance suggests that equitable readmissions is a modifiable outcome with potential as a measure to promote equitable care. However, stratified outcome measures, similar to traditional performance measures, may reward hospitals serving fewer Black patients, reinforcing the role of structural racism in hospital-level inequities. Addressing these limitations will require thoughtful policy design to promote and not reinforce inequities.

Accepted for Publication: November 13, 2023.

Corresponding Author: Katherine A. Nash, MD, MHS, Columbia University Vagelos College of Physicians and Surgeons, 622 W 168th St, Vanderbilt Clinic-4, New York, NY 10032 (kan2123@cumc.columbia.edu).

Author Contributions: Drs Yu and Lin had full access to all of the data in the study; Drs Nash, Weerahandi, and Bernanheim take responsibility for the integrity of the data and the accuracy of the data analysis. Drs Nash and Weerahandi, co-first authors of the manuscript, contributed equally to this work.

Concept and design: Nash, Weerahandi, Venkatesh, Ross, Herrin, Horwitz, Bernheim.

Acquisition, analysis, or interpretation of data: Weerahandi, Yu, Holaday, Lin, Ross, Herrin, Horwitz, Bernheim.

Drafting of the manuscript: Nash, Weerahandi, Bernheim.

Critical review of the manuscript for important intellectual content: Weerahandi, Yu, Venkatesh, Holaday, Lin, Ross, Herrin, Horwitz, Bernheim.

Statistical analysis: Yu, Lin.

Obtained funding: Herrin, Horwitz.

Administrative, technical, or material support: Weerahandi, Horwitz.

Supervision: Nash, Weerahandi, Venkatesh, Lin, Horwitz, Bernheim.

Conflict of Interest Disclosures: Dr Venkatesh reported grants from the Centers for Medicare & Medicaid Services (CMS) during the conduct of the study; and grants from the Society for Academic Emergency Medicine, the Elevance Foundation, and the Moore Foundation outside the submitted work. Dr Holaday reported grants from the National Institute on Aging (NIA) outside the submitted work. Dr Lin reported working under contracts with CMS to develop quality measures. Dr Ross reported grants from the Agency for Healthcare Research and Quality (AHRQ, R01HS022882) during the conduct of the study; grants from the US Food and Drug Administration, Johnson & Johnson, Medical Devices Innovation Consortium, grants from the National Institutes of Health/National Heart, Lung, and Blood Institute (NIH/NHLBI), and Arnold Ventures outside the submitted work; and serving as an expert witness at the request of Relator鈥檚 attorneys, the Greene Law Firm, in a qui tam suit alleging violations of the False Claims Act and Anti-Kickback Statute against Biogen Inc that was settled September 2022. Dr Herrin reported grants from AHRQ during the conduct of the study. Dr Horwitz reported grants from AHRQ (No. HS022882) during the conduct of the study. Dr Bernheim reported previous receipt of funding from CMS to develop outcomes quality measures. No other disclosures were reported.

Funding/Support: This work is supported through a grant (R01HS022882) from AHRQ. Dr Weerahandi is supported by a grant from NIH/NHLBI (K23HL145110). Dr Holaday currently receives research support through NIA/NIH (T32AG066598), NIA (R24AG065175 [The Aging Research in Criminal Justice Health Network]), and the National Institute on Drug Abuse (R25DA037190 [Lifespan/Brown Criminal Justice Research Program on Substance Use and HIV]).

Role of the Funder/Sponsor: AHRQ and the NIH had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: Dr Ross is a Deputy Editor at JAMA but was not involved in decisions regarding this manuscript. The views presented are those of the individual authors and do not represent the views of the US federal government.

Data Sharing Statement: See Supplement 2.

Additional Contributions: We thank Sarah Tsuruo, BA, Division of Healthcare Delivery Science, Department of Population Health, New York University Grossman School of Medicine, for her contributions to this article. Ms Tsuruo received compensation through funding from the grant from AHRQ.