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Objective: To test whether differences in hospital interoperability are related to the extent to which hospitals treat groups that have been economically and socially marginalized. Data Sources and Study Setting: Data on 2393 non-federal acute care hospitals in the United States from the American Hospital Association Information Technology Supplement fielded in 2021, the 2019 Medicare Cost Report, and the 2019 Social Deprivation Index. Study Design: Cross-sectional analysis. Data Collection/Extraction Methods: We identified five proxy measures related to marginalization and assessed the relationship between those measures and the likelihood that hospitals engaged in all four domains of interoperable information exchange and participated in national interoperability networks in cross-sectional analysis. Principal Findings: In unadjusted analysis, hospitals that treated patients from zip codes with high social deprivation were 33% less likely to engage in interoperable exchange (Relative Risk = 0.67, 95% CI: 0.58-0.76) and 24% less likely to participate in a national network than all other hospitals (RR = 0.76; 95% CI: 0.66-0.87). Critical Access Hospitals (CAH) were 24 percent less likely to engage in interoperable exchange (RR = 0.76; 95% CI: 0.69-0.83) but not less likely to participate in a national network (RR = 0.97; 95% CI: 0.88-1.06). No difference was detected for 2 measures [high Disproportionate Share Hospital percentage and Medicaid case mix) while 1 was associated with a greater likelihood to engage (high uncompensated care burden). The association between social deprivation and interoperable exchange persisted in an analysis examining metropolitan and rural areas separately and in adjusted analyses accounting for hospital characteristics. Conclusions: Hospitals that treat patients from areas with high social deprivation were less likely to engage in interoperable exchange than other hospitals, but other measures were not associated with lower interoperability. The use of area deprivation data may be important to monitor and address hospital clinical data interoperability disparities to avoid related health care disparities. KEYWORDS health equity, health information exchange, health system, hospitals, interoperability What is known on this topic * Several hospital characteristics reflecting smaller scale or reduced resources have been shown to relate to lower rates of hospital engagement in the interoperable exchange of health information, an important policy goal. * Numerous proxy measures exist for describing the patient populations hospitals treat, and measures are not closely correlated, complicating descriptions of the extent that hospitals treat populations that have been marginalized. What this study adds * Only one of five proxy measures of the extent to which hospitals treat populations that have been marginalized--the area social deprivation index--was associated with differences in hospital interoperability. * Differences by social deprivation index persist when separately examining rural and urban hospitals and adjusted analysis, indicating this metric is capturing information beyond geographic divides., 1 | INTRODUCTION Interoperable exchange--the sharing and integration of health information between organizations--provides important benefits to patients. (1) While hospital engagement in interoperable exchange and participation in national networks has [...]

Objective: To illustrate the association between the sociodemographic characteristics of hospital markets and the geographic patterns of Medicare hospital valuebased purchasing (HVBP) scores. Data Sources and Study Setting: This is a secondary analysis of United States hospitals with a HVBP Total Performance Score (TPS) for 2019 in the Centers for Medicare and Medicaid Services (CMS) Hospital Compare database (4/2021 release) and American Community Survey (ACS) data for 2015-2019. Study Design: This is a cross-sectional study using spatial multivariable autoregressive models with HVBP TPS and component domain scores as dependent variables and hospital market demographics as the independent variables. Data Collection/Extraction Methods: We calculated hospital market demographics using ZIP code level data from the ACS, weighted the 2019 CMS inpatient Hospital Service Area file. Principal Findings: Spatial autoregressive models using eight nearest neighbors with diversity index, race and ethnicity distribution, families in poverty, unemployment, and lack of health insurance among residents ages 19-64 years provided the best model fit. Diversity index had the highest statistically significant contribution to lower TPS ([beta] = -12.79, p < 0.0001), followed by the percent of the population coded to 'non-Hispanic, some other race' ([beta] = -2.59, p < 0.0023), and the percent of families in poverty ([beta] = -0.26, p < 0.0001). Percent of the population was non-Hispanic American Indian/Alaskan Native ([beta] = 0.35, p < 0.0001) and percent non-Hispanic Asian ([beta] = 0.12, p < 0.02071) were associated with higher TPS. Lower predicted TPS was observed in large urban cities throughout the US as well as in states throughout the Southeastern US. Similar geographic patterns were observed for the predicted Patient Safety, Person and Community Engagement, and Efficiency and Cost Reduction domain scores but are not for predicted Clinical Outcomes scores. Conclusions: The lower predicted scores seen in cities and in the Southeastern region potentially reflect an inherent-that is, structural-association between market sociodemographics and HVBP scores. KEYWORDS hospitals, patient satisfaction, pay for performance, quality of healthcare, spatial analysis, systemic racism What is known on this topic * Medicare's hospital value-based purchasing program scores have been shown to be lower for safety-net hospitals, resulting in reduced Medicare reimbursement. * There has been a call to adjust the scores for the socioeconomic status of patients seen by each hospital, but regulators have resisted holding these hospitals to a lower standard. * The local and regional geographic impact of these policies across the United States has not been explored. What this study adds * We created maps showing how the hospital scores were potentially affected by the sociodemographic characteristics of their market areas. * Hospitals in the Southeastern states and larger cities with racial and ethnic diversity had lower predicted scores, potentially representing an increased risk of lower hospital valuebased purchasing program scores. * Patient experience, patient safety, and hospitalization related costs contributed to the lower predicted scores. There was less evidence of regional geographic patterns in clinical quality., 1 | INTRODUCTION The Centers for Medicare and Medicaid Services (CMS) implemented hospital value-based purchasing (HVBP) for acute care hospitals starting with 2012 discharges as part of the Affordable Care [...]