Social Determinants Matter For Hospital Readmission Policy: Insights From New York City

The Social Determinants of Health Industry: Two Years On

Social determinants of health (SDOH) have been insufficiently addressed by payers and providers despite increased prioritization at the national level. This led to the development of a separate, for-profit "SDOH industry" found to have a valuation of $18.5 billion (all dollar amounts in U.S. dollars) with $2.4 billion in funding as of July 2021. The purpose of this article is to determine the growth of the industry from 2021 to 2023 and provide a multifaceted explanation for this development. The authors conducted an analysis of 57 SDOH industry companies using a third-party market research platform. Over the previous two-year period, 10 out of 57 (18%) companies were acquired, and the industry gained an additional $1.1 billion (46% increase) in funding and $13.7 billion (74% increase) in valuation. The authors propose four contributing factors to explain the nature of this industry's evolution. They include developments in national health care policy favoring SDOH, standardization of SDOH information as actionable claims data, multi-source investment in SDOH, and improved methods of industry intervention measurement. These trends appear likely to continue, requiring additional scrutiny by all relevant stakeholders to ensure maximum improvement of rampant SDOH disparities that impact millions of individuals daily.

Hospital ownership and admission rates from the emergency department, evidence from Florida

OBJECTIVE

In light of Department of Justice investigations of for-profit chains for over-admitting patients, we sought to evaluate whether for-profit hospitals are more likely to admit patients from the emergency department.

DATA SOURCES

We used statewide visit-level inpatient and emergency department records from Florida's Agency for Healthcare Administration for 2007-2019.

STUDY DESIGN

We calculated differences in admission rates between for-profit and other hospitals, adjusting for patient and hospital characteristics. We also estimated instrumental variables models using differential distance to a for-profit hospital as an instrument.

DATA COLLECTION/EXTRACTION METHODS

Our main analysis focuses on patients ages 65 and older treated in hospitals that primarily serve adults.

PRINCIPAL FINDINGS

Adjusted admission rates among patients ages 65 and older were 7.1 percentage points (95% CI: 5.1-9.1) higher at for-profit hospitals in 2019 (or 18.8% of the sample mean of 37.8%). Differences in admission rates have remained constant since 2009.

CONCLUSION

Our results are consistent with allegations that for-profit hospitals maintain lower admission thresholds to increase occupancy levels.

Social determinants of health predict readmission following COVID-19 hospitalization: a health information exchange-based retrospective cohort study

Introduction

Since February 2020, over 104 million people in the United States have been diagnosed with SARS-CoV-2 infection, or COVID-19, with over 8.5 million reported in the state of Texas. This study analyzed social determinants of health as predictors for readmission among COVID-19 patients in Southeast Texas, United States.

Methods

A retrospective cohort study was conducted investigating demographic and clinical risk factors for 30, 60, and 90-day readmission outcomes among adult patients with a COVID-19-associated inpatient hospitalization encounter within a regional health information exchange between February 1, 2020, to December 1, 2022.

Results and discussion

In this cohort of 91,007 adult patients with a COVID-19-associated hospitalization, over 21% were readmitted to the hospital within 90  days (n = 19,679), and 13% were readmitted within 30  days (n = 11,912). In logistic regression analyses, Hispanic and non-Hispanic Asian patients were less likely to be readmitted within 90  days (adjusted odds ratio [aOR]: 0.8, 95% confidence interval [CI]: 0.7-0.9, and aOR: 0.8, 95% CI: 0.8-0.8), while non-Hispanic Black patients were more likely to be readmitted (aOR: 1.1, 95% CI: 1.0-1.1, p = 0.002), compared to non-Hispanic White patients. Area deprivation index displayed a clear dose-response relationship to readmission: patients living in the most disadvantaged neighborhoods were more likely to be readmitted within 30 (aOR: 1.1, 95% CI: 1.0-1.2), 60 (aOR: 1.1, 95% CI: 1.2-1.2), and 90  days (aOR: 1.2, 95% CI: 1.1-1.2), compared to patients from the least disadvantaged neighborhoods. Our findings demonstrate the lasting impact of COVID-19, especially among members of marginalized communities, and the increasing burden of COVID-19 morbidity on the healthcare system.

Financing community partnerships for health equity: Findings and insights from cross-sector professionals

OBJECTIVE

To enhance understanding of financial alignment challenges facing cross-sector partnerships (CSPs) pursuing health equity and offer insights to guide research and practice.

DATA SOURCES AND STUDY SETTING

We collected data through surveys and interviews with cross-sector professionals in 16 states, 2020-2021.

STUDY DESIGN

We surveyed 51 CSP leaders and received 26 responses. Following administration of the surveys to CSP leaders, we also conducted interviews with cross-sector professionals. The data are analyzed descriptively, comparatively, and qualitatively using thematic analysis.

DATA COLLECTION/EXTRACTION METHODS

For quantitative survey data, we compare partnership responses, differentiating perceived levels of alignment among partnerships certified by the Pathways Community HUB Institute (PCHI), partnerships interested in certification, and partnerships without connection to the PCHI® Model of care coordination. For interviews, we engaged CSP professionals and those who fund their work. Two research team members took notes for interviews, which were combined and made available for review by those interviewed. Data were analyzed independently by two team members who met to integrate, identify, and finalize thematic findings.

PRINCIPAL FINDINGS

Our work supports previous findings that financing is a challenge for CSPs, while also suggesting that PCHI-certified partnerships may perceive greater progress in financial alignment than others. We identify four major financial barriers: limited and competitive funding; state health service delivery structures; cultural and practice divides across healthcare, social service, and public health sectors; and needs for further evidence of cross-sector service impacts on client health and costs. We also offer a continuum of measures of financial sustainability progress and identify key issues relating to financial incentivization/accountability.

CONCLUSION

Findings suggest a need for public policy reviews and improvements to aid CSPs in addressing financial alignment challenges. We also offer a measurement framework and ideas to guide research and practice on financial alignment, based on empirical data.

A state-level analysis of macro-level factors associated with hospital readmissions

Investigation of the factors that contribute to hospital readmissions has focused largely on individual level factors. We extend the knowledge base by exploring macrolevel factors that may contribute to readmissions. We point to environmental, behavioral, and socioeconomic factors that are emerging as correlates to readmissions. Data were taken from publicly available reports provided by multiple agencies. Partial Least Squares-Structural Equation Modeling was used to test the association between economic stability and environmental factors on opioid use which was in turn tested for a direct association with hospital readmissions. We also tested whether hospital access as measured by the proportion of people per hospital moderates the relationship between opioid use and hospital readmissions. We found significant associations between Negative Economic Factors and Opioid Use, between Environmental Factors and Opioid Use, and between Opioid Use and Hospital Readmissions. We found that Hospital Access positively moderates the relationship between Opioid Use and Readmissions. A priori assumptions about factors that influence hospital readmissions must extend beyond just individualistic factors and must incorporate a holistic approach that also considers the impact of macrolevel environmental factors.

Racial Disparities in Hospitalization Rates During Long-Term Follow-Up After Deceased-Donor Kidney Transplantation

OBJECTIVE

To compare hospitalization rates between African American (AA) and European American (EA) deceased-donor (DD) kidney transplant (KT) recipients during over a10-year period.

METHOD

Data from the Scientific Registry of Transplant Recipients and social determinants of health (SDoH), measured by the Social Deprivation Index, were used. Hospitalization rates were estimated for kidney recipients from AA and EA DDs who had one kidney transplanted into an AA and one into an EA, leading to four donor/recipient pairs (DRPs): AA/AA, AA/EA, EA/AA, and EA/EA. Poisson-Gamma models were fitted to assess post-transplant hospitalizations.

RESULT

Unadjusted hospitalization rates (95% confidence interval) were higher among all DRP involving AA, 131.1 (122.5, 140.3), 134.8 (126.3, 143.8), and 102.4 (98.9, 106.0) for AA/AA, AA/EA, and EA/AA, respectively, compared to 97.1 (93.7, 100.6) per 1000 post-transplant person-years for EA/EA pairs. Multivariable analysis showed u-shaped relationships across SDoH levels within each DRP, but findings varied depending on recipients' race, i.e., AA recipients in areas with the worst SDoH had higher hospitalization rates. However, EA recipients in areas with the best SDoH had higher hospitalization rates than their counterparts.

CONCLUSIONS

Relationship between healthcare utilization and SDoH depends on DRP, with higher hospitalization rates among AA recipients living in areas with the worst SDoH and among EA recipients in areas with the best SDoH profiles. SDoH plays an important role in driving disparities in hospitalizations after kidney transplantation.

Screening for Health-Related Social Needs and Collaboration With External Partners Among US Hospitals

Importance

In recent years, hospitals and health systems have reported increasing rates of screening for patients' individual and community social needs, but few studies have explored the national landscape of screening and interventions directed at addressing health-related social needs (HRSNs) and social determinants of health (SDOH).

Objective

To evaluate the associations of hospital characteristics and area-level socioeconomic indicators to quantify the presence and intensity of hospitals' screening practices, interventions, and collaborative external partnerships that seek to measure and ameliorate patients' HRSNs and SDOH.

Design, Setting, and Participants

This cross-sectional study used national data from the American Hospital Association Annual Survey Database for fiscal year 2020. General-service, acute-care, nonfederal hospitals were included in the study's final sample, representing nationally diverse hospital settings. Data were analyzed from July 2022 to February 2023.

Exposures

Organizational characteristics and area-level socioeconomic indicators.

Main Outcomes and Measures

The outcomes of interest were hospital-reported patient screening of and strategies to address 8 HRSNs and 14 external partnership types to address SDOH. Composite scores for screening practices and external partnership types were calculated, and ordinary least-square regression analyses tested associations of organizational characteristics with outcome measures.

Results

Of 2858 US hospital respondents (response rate, 67.0%), most hospitals (79.2%; 95% CI, 77.7%-80.7%) reported screening patients for at least 1 HRSN, with food insecurity or hunger needs (66.1%; 95% CI, 64.3%-67.8%) and interpersonal violence (66.4%; 95% CI, 64.7%-68.1%) being the most commonly screened social needs. Most hospitals (79.4%; 95% CI, 66.3%-69.7%) reported having strategies and programs to address patients' HRSNs; notably, most hospitals (52.8%; 95% CI, 51.0%-54.5%) had interventions for transportation barriers. Hospitals reported a mean of 4.03 (95% CI, 3.85-4.20) external partnership types to address SDOH and 5.69 (5.50-5.88) partnership types to address HRSNs, with local or state public health departments and health care practitioners outside of the health system being the most common. Hospitals with accountable care contracts (ACCs) and bundled payment programs (BPPs) reported higher screening practices (ACC: β = 1.03; SE = 0.13; BPP: β = 0.72; SE = 0.14), interventions (ACC: β = 1.45; SE = 0.12; BPP: β = 0.61; SE = 0.13), and external partnership types to address HRSNs (ACC: β = 2.07; SE = 0.23; BPP: β = 1.47; SE = 0.24) and SDOH (ACC: β = 2.64; SE = 0.20; BPP: β = 1.57; SE = 0.21). Compared with nonteaching, government-owned, and for-profit hospitals, teaching and nonprofit hospitals were also more likely to report more HRSN-directed activities. Patterns based on geographic and area-level socioeconomic indicators did not emerge.

Conclusions and Relevance

This cross-sectional study found that most US hospitals were screening patients for multiple HRSNs. Active participation in value-based care, teaching hospital status, and nonprofit status were the characteristics most consistently associated with greater overall screening activities and number of related partnership types. These results support previously posited associations about which types of hospitals were leading screening uptake and reinforce understanding of the role of hospital incentives in supporting health equity efforts.

The geography of Medicare's hospital value-based purchasing in relation to market demographics

OBJECTIVE

To illustrate the association between the sociodemographic characteristics of hospital markets and the geographic patterns of Medicare hospital value-based 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 (ß = -12.79, p < 0.0001), followed by the percent of the population coded to "non-Hispanic, some other race" (ß = -2.59, p < 0.0023), and the percent of families in poverty (ß = -0.26, p < 0.0001). Percent of the population was non-Hispanic American Indian/Alaskan Native (ß = 0.35, p < 0.0001) and percent non-Hispanic Asian (ß = 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.

Why Did New York State Hospitals Rank So Poorly?

BACKGROUND

According to the Centers for Medicare and Medicaid Services star ratings, New York State (NYS) hospitals are relatively poor performers, with 33% achieving 1 star compared with 5% of hospitals across the United States.

OBJECTIVES

We compared NYS hospitals to all United States hospitals using Centers for Medicare and Medicaid Services Hospital Value-Based Purchasing (HVBP) and star ratings component measures. We perform risk adjustment for hospital and market characteristics associated poor performance.

RESEARCH DESIGN

This was a cross-sectional observational study.

SUBJECTS

All acute care hospitals in the United States which had HVBP scores for 2019 in April 21, 2021, Hospital Care Compare database.

MEASURES

Analysis of variance was used to compare NYS hospitals to all United States hospitals. Multivariable-based risk adjustment was applied to NYS hospitals with adjustment for hospital characteristics (eg, occupancy, size), hospital fiscal ratios (eg, operating margin), and market characteristics (eg, percent of hospital market that has a high school diploma).

RESULTS

NYS hospitals averaged lower patient satisfaction and higher readmissions. These domains were statistically significantly associated with lower socioeconomic status in the hospital market area. Risk adjustment reduced but did not eliminate these differences. NYS also performed poorly on pressure ulcers and deep vein thrombosis/pulmonary embolism prevention. NYS hospitals were similar to the United States in mortality and hospital-acquired infections.

CONCLUSIONS

Differences in the demographic makeup of hospital markets account for some of the poor performance of NYS hospitals. Some aspects, such as long length of stay, may be associated with wider regional trends.

Characterizing the relative frequency of clinician engagement with structured social determinants of health data

OBJECTIVE

Electronic health records (EHRs) are increasingly used to capture social determinants of health (SDH) data, though there are few published studies of clinicians' engagement with captured data and whether engagement influences health and healthcare utilization. We compared the relative frequency of clinician engagement with discrete SDH data to the frequency of engagement with other common types of medical history information using data from inpatient hospitalizations.

MATERIALS AND METHODS

We created measures of data engagement capturing instances of data documentation (data added/updated) or review (review of data that were previously documented) during a hospitalization. We applied these measures to four domains of EHR data, (medical, family, behavioral, and SDH) and explored associations between data engagement and hospital readmission risk.

RESULTS

SDH data engagement was associated with lower readmission risk. Yet, there were lower levels of SDH data engagement (8.37% of hospitalizations) than medical (12.48%), behavioral (17.77%), and family (14.42%) history data engagement. In hospitalizations where data were available from prior hospitalizations/outpatient encounters, a larger proportion of hospitalizations had SDH data engagement than other domains (72.60%).

DISCUSSION

The goal of SDH data collection is to drive interventions to reduce social risk. Data on when and how clinical teams engage with SDH data should be used to inform informatics initiatives to address health and healthcare disparities.

CONCLUSION

Overall levels of SDH data engagement were lower than those of common medical, behavioral, and family history data, suggesting opportunities to enhance clinician SDH data engagement to support social services referrals and quality measurement efforts.

Influence of Social Determinants of Health on Heart Failure Outcomes: A Systematic Review

Background Prior research suggests an association between clinical outcomes in heart failure (HF) and social determinants of health (SDoH). Because providers should identify and address SDoH in care delivery, we evaluated how SDoH have been defined, measured, and evaluated in studies that examine HF outcomes. Methods and Results Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, databases were searched for observational or interventional studies published between 2009 and 2021 that assessed the influence of SDoH on outcomes. Selected articles were assessed for quality using a validated rating scheme. We identified 1373 unique articles for screening; 104 were selected for full-text review, and 59 met the inclusion criteria, including retrospective and prospective cohort, cross-sectional, and intervention studies. The majority examined readmissions and hospitalizations (k=33), mortality or survival (k=29), and success of medical devices and transplantation (k=8). SDoH examined most commonly included race, ethnicity, age, sex, socioeconomic status, and education or health literacy. Studies used a range of 1 to 9 SDoH as primary independent variables and 0 to 7 SDoH as controls. Multiple data sources were employed and frequently were electronic medical records linked with national surveys and disease registries. The effects of SDoH on HF outcomes were inconsistent because of the heterogeneity of data sources and SDoH constructs. Conclusions Our systematic review reveals shortcomings in measurement and deployment of SDoH variables in HF care. Validated measures need to be prospectively and intentionally collected to facilitate appropriate analysis, reporting, and replication of data across studies and inform the design of appropriate, evidence-based interventions that can ameliorate significant HF morbidity and societal costs.

Emergency department returns and early follow-up visits after heart failure hospitalization: Cohort study examining the role of race

Health disparities in heart failure (HF) show that Black patients face greater ED utilization and worse clinical outcomes. Transitional care post-HF hospitalization, such as 7-day early follow-up visits, may prevent ED returns. We examine whether early follow-up is associated with lower ED returns visits within 30 days and whether Black race is associated with receiving early follow-up after HF hospitalization. This was a retrospective cohort analysis of all Black and White adult patients at 13 hospitals in Michigan hospitalized for HF from October 1, 2017, to September 30, 2020. Adjusted risk ratios (aRR) were estimated from multivariable logistic regressions. The analytic sample comprised 6,493 patients (mean age = 71 years (SD 15), 50% female, 37% Black, 9% Medicaid). Ten percent had an ED return within 30 days and almost half (43%) of patients had 7-day early follow-up. Patients with early follow-up had lower risk of ED returns (aRR 0.85 [95%CI, 0.71-0.98]). Regarding rates of early follow-up, there was no overall adjusted association with Black race, but the following variables were related to lower follow-up: Medicaid insurance (aRR 0.90 [95%CI, 0.80-1.00]), dialysis (aRR 0.86 [95%CI, 0.77-0.96]), depression (aRR 0.92 [95%CI, 0.86-0.98]), and discharged with opioids (aRR 0.94 [95%CI, 0.88-1.00]). When considering a hospital-level interaction, three of the 13 sites with the lowest percentage of Black patients had lower rates of early follow-up in Black patients (ranging from 15% to 55% reduced likelihood). Early follow-up visits were associated with a lower likelihood of ED returns for HF patients. Despite this potentially protective association, certain patient factors were associated with being less likely to receive scheduled follow-up visits. Hospitals with lower percentages of Black patients had lower rates of early follow-up for Black patients. Together, these may represent missed opportunities to intervene in high-risk groups to prevent ED returns in patients with HF.

Accounting for the Growth of Observation Stays in the Assessment of Medicare's Hospital Readmissions Reduction Program

IMPORTANCE

Decreases in 30-day readmissions following the implementation of the Medicare Hospital Readmissions Reduction Program (HRRP) have occurred against the backdrop of increasing hospital observation stay use, yet observation stays are not captured in readmission measures.

OBJECTIVE

To examine whether the HRRP was associated with decreases in 30-day readmissions after accounting for observation stays.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study included a 20% sample of inpatient admissions and observation stays among Medicare fee-for-service beneficiaries from January 1, 2009, to December 31, 2015. Data analysis was performed from November 2021 to June 2022. A differences-in-differences analysis assessed changes in 30-day readmissions after the announcement of the HRRP and implementation of penalties for target conditions (heart failure, acute myocardial infarction, and pneumonia) vs nontarget conditions under scenarios that excluded and included observation stays.

MAIN OUTCOMES AND MEASURES

Thirty-day inpatient admissions and observation stays.

RESULTS

The study included 8 944 295 hospitalizations (mean [SD] age, 78.7 [8.2] years; 58.6% were female; 1.3% Asian; 10.0% Black; 2.0% Hispanic; 0.5% North American Native; 85.0% White; and 1.2% other or unknown). Observation stays increased from 2.3% to 4.4% (91.3% relative increase) of index hospitalizations among target conditions and 14.1% to 21.3% (51.1% relative increase) of index hospitalizations for nontarget conditions. Readmission rates decreased significantly after the announcement of the HRRP and returned to baseline by the time penalties were implemented for both target and nontarget conditions regardless of whether observation stays were included. When only inpatient hospitalizations were counted, decreasing readmissions accrued into a -1.48 percentage point (95% CI, -1.65 to -1.31 percentage points) absolute reduction in readmission rates by the postpenalty period for target conditions and -1.13 percentage point (95% CI, -1.30 to -0.96 percentage points) absolute reduction in readmission rates by the postpenalty period for nontarget conditions. This reduction corresponded to a statistically significant differential change of -0.35 percentage points (95% CI, -0.59 to -0.11 percentage points). Accounting for observation stays more than halved the absolute decrease in readmission rates for target conditions (-0.66 percentage points; 95% CI, -0.83 to -0.49 percentage points). Nontarget conditions showed an overall greater decrease during the same period (-0.76 percentage points; 95% CI, -0.92 to -0.59 percentage points), corresponding to a differential change in readmission rates of 0.10 percentage points (95% CI, -0.14 to 0.33 percentage points) that was not statistically significant.

CONCLUSIONS AND RELEVANCE

The findings of this study suggest that the reduction of readmissions associated with the implementation of the HRRP was smaller than originally reported. More than half of the decrease in readmissions for target conditions appears to be attributable to the reclassification of inpatient admission to observation stays.

Society of General Internal Medicine Position Statement on Social Risk and Equity in Medicare's Mandatory Value-Based Payment Programs

The Affordable Care Act (2010) and Medicare Access and CHIP Reauthorization Act (2015) ushered in a new era of Medicare value-based payment programs. Five major mandatory pay-for-performance programs have been implemented since 2012 with increasing positive and negative payment adjustments over time. A growing body of evidence indicates that these programs are inequitable and financially penalize safety-net systems and systems that care for a higher proportion of racial and ethnic minority patients. Payments from penalized systems are often redistributed to those with higher performance scores, which are predominantly better-financed, large, urban systems that serve less vulnerable patient populations - a "Reverse Robin Hood" effect. This inequity may be diminished by adjusting for social risk factors in payment policy. In this position statement, we review the literature evaluating equity across Medicare value-based payment programs, major policy reports evaluating the use of social risk data, and provide recommendations on behalf of the Society of General Internal Medicine regarding how to address social risk and unmet health-related social needs in these programs. Immediate recommendations include implementing peer grouping (stratification of healthcare systems by proportion of dual eligible Medicare/Medicaid patients served, and evaluation of performance and subsequent payment adjustments within strata) until optimal methods for accounting for social risk are defined. Short-term recommendations include using census-based, area-level indices to account for neighborhood-level social risk, and developing standardized approaches to collecting individual socioeconomic data in a robust but sensitive way. Long-term recommendations include implementing a research agenda to evaluate best practices for accounting for social risk, developing validated health equity specific measures of care, and creating policies to better integrate healthcare and social services.

Social Risk Adjustment In The Hospital Readmissions Reduction Program: A Systematic Review And Implications For Policy

Value-based payment programs adjust payments to providers based on spending, quality, or health outcomes. Concern that these programs penalize providers disproportionately serving vulnerable patients prompted calls to adjust performance measures for social risk factors. We reviewed fourteen studies of social risk adjustment in Medicare's Hospital Readmissions Reduction Program (HRRP), a value-based payment model that initially did not adjust for social risk factors but subsequently began to do so. Seven studies found that adding social risk factors to the program's base risk-adjustment model (which adjusts only for age, sex, and comorbidities) reduced differences in risk-adjusted readmissions and penalties between safety-net hospitals and other hospitals. Three studies found that peer grouping, the HRRP's current approach to social risk adjustment, reduced penalties among safety-net hospitals. Two studies found that differences in risk-adjusted readmissions and penalties were further narrowed when augmentation of the base model was combined with peer grouping. Two studies showed that it is possible to adjust for social risk factors without obscuring quality differences between hospitals. These findings support the use of social risk adjustment to improve provider payment equity and highlight opportunities to enhance social risk adjustment in value-based payment programs.

The Assessment of Social Determinants of Health in Postsepsis Mortality and Readmission: A Scoping Review

To summarize knowledge and identify gaps in evidence about the relationship between social determinants of health (SDH) and postsepsis outcomes.

DATA SOURCES

We conducted a comprehensive search of PubMed/Medical Literature Analysis and Retrieval System Online, Excerpta Medica database, and the Cochrane Library.

STUDY SELECTION

We identified articles that evaluated SDH as risk factors for mortality or readmission after sepsis hospitalization. Two authors independently screened and selected articles for inclusion.

DATA EXTRACTION

We dual-extracted study characteristics with specific focus on measurement, reporting, and interpretation of SDH variables.

DATA SYNTHESIS

Of 2,077 articles screened, 103 articles assessed risk factors for postsepsis mortality or readmission. Of these, 28 (27%) included at least one SDH variable. Inclusion of SDH in studies assessing postsepsis adverse outcomes increased over time. The most common SDH evaluated was race/ethnicity (n = 21, 75%), followed by payer type (n = 10, 36%), and income/wealth (n = 9, 32%). Of the studies including race/ethnicity, nine (32%) evaluated no other SDH. Only one study including race/ethnicity discussed the use of this variable as a surrogate for social disadvantage, and none specifically discussed structural racism. None of the studies specifically addressed methods to validate the accuracy of SDH or handling of missing data. Eight (29%) studies included a general statement that missing data were infrequent. Several studies reported independent associations between SDH and outcomes after sepsis discharge; however, these findings were mixed across studies.

CONCLUSIONS

Our review suggests that SDH data are underutilized and of uncertain quality in studies evaluating postsepsis adverse events. Transparent and explicit ontogenesis and data models for SDH data are urgently needed to support research and clinical applications with specific attention to advancing our understanding of the role racism and racial health inequities in postsepsis outcomes.

Bounce-Back: Predicting Acute Readmission From Inpatient Rehabilitation for Patients With Stroke

OBJECTIVE

The aim of the study was to identify demographic, medical, and functional risk factors for discharge to an acute hospital before completion of an inpatient rehabilitation program and 7- and 30-day readmissions after completion of an inpatient rehabilitation program.

DESIGN

This cohort study included 138,063 fee-for-service Medicare beneficiaries with a primary diagnosis of new onset stroke discharged from an inpatient rehabilitation facility from June 2009 to December 2011. Multivariate models examined readmission outcomes and included data from 6 mos before onset of the stroke to 30 days after discharge from the inpatient rehabilitation facility.

RESULTS

In the acute discharge model (n = 9870), comorbidities and complications added risk, and the longer the stroke onset to admission to inpatient rehabilitation facility, the more likely discharge to the acute hospital. In the 7-day (n = 4755) and 30-day (n = 9861) readmission models, patients who were more complex with comorbidities, were black, or had managed care Medicare were more likely to have a readmission. Functional status played a role in all three models.

CONCLUSIONS

Results suggest that certain demographic, medical, and functional characteristics are associated differentially with rehospitalization after completion inpatient rehabilitation. The strongest model was the discharge to the acute hospital model with concordance statistic (c-statistic) of 0.87.

Impact of social determinants of health on improving the LACE index for 30-day unplanned readmission prediction

Objective

Early and accurate prediction of patients at risk of readmission is key to reducing costs and improving outcomes. LACE is a widely used score to predict 30-day readmissions. We examine whether adding social determinants of health (SDOH) to LACE can improve its predictive performance.

Methods

This is a retrospective study that included all inpatient encounters in the state of Maryland in 2019. We constructed predictive models by fitting Logistic Regression (LR) on LACE and different sets of SDOH predictors. We used the area under the curve (AUC) to evaluate discrimination and SHapley Additive exPlanations values to assess feature importance.

Results

Our study population included 316 558 patients of whom 35 431 (11.19%) patients were readmitted after 30 days. Readmitted patients had more challenges with individual-level SDOH and were more likely to reside in communities with poor SDOH conditions. Adding a combination of individual and community-level SDOH improved LACE performance from AUC = 0.698 (95% CI [0.695–0.7]; ref) to AUC = 0.708 (95% CI [0.705–0.71]; P < .001). The increase in AUC was highest in black patients (+1.6), patients aged 65 years or older (+1.4), and male patients (+1.4).

Discussion

We demonstrated the value of SDOH in improving the LACE index. Further, the additional predictive value of SDOH on readmission risk varies by subpopulations. Vulnerable populations like black patients and the elderly are likely to benefit more from the inclusion of SDOH in readmission prediction.

Conclusion

These findings provide potential SDOH factors that health systems and policymakers can target to reduce overall readmissions.

Information Extraction From Electronic Health Records to Predict Readmission Following Acute Myocardial Infarction: Does Natural Language Processing Using Clinical Notes Improve Prediction of Readmission?

Background

Social risk factors influence rehospitalization rates yet are challenging to incorporate into prediction models. Integration of social risk factors using natural language processing (NLP) and machine learning could improve risk prediction of 30‐day readmission following an acute myocardial infarction.

Methods and Results

Patients were enrolled into derivation and validation cohorts. The derivation cohort included inpatient discharges from Vanderbilt University Medical Center between January 1, 2007, and December 31, 2016, with a primary diagnosis of acute myocardial infarction, who were discharged alive, and not transferred from another facility. The validation cohort included patients from Dartmouth‐Hitchcock Health Center between April 2, 2011, and December 31, 2016, meeting the same eligibility criteria described above. Data from both sites were linked to Centers for Medicare & Medicaid Services administrative data to supplement 30‐day hospital readmissions. Clinical notes from each cohort were extracted, and an NLP model was deployed, counting mentions of 7 social risk factors. Five machine learning models were run using clinical and NLP‐derived variables. Model discrimination and calibration were assessed, and receiver operating characteristic comparison analyses were performed. The 30‐day rehospitalization rates among the derivation (n=6165) and validation (n=4024) cohorts were 15.1% (n=934) and 10.2% (n=412), respectively. The derivation models demonstrated no statistical improvement in model performance with the addition of the selected NLP‐derived social risk factors.

Conclusions

Social risk factors extracted using NLP did not significantly improve 30‐day readmission prediction among hospitalized patients with acute myocardial infarction. Alternative methods are needed to capture social risk factors.

AI Models for Predicting Readmission of Pneumonia Patients within 30 Days after Discharge

A model with capability for precisely predicting readmission is a target being pursued worldwide. The objective of this study is to design predictive models using artificial intelligence methods and data retrieved from the National Health Insurance Research Database of Taiwan for identifying high-risk pneumonia patients with 30-day all-cause readmissions. An integrated genetic algorithm (GA) and support vector machine (SVM), namely IGS, were used to design predictive models optimized with three objective functions. In IGS, GA was used for selecting salient features and optimal SVM parameters, while SVM was used for constructing the models. For comparison, logistic regression (LR) and deep neural network (DNN) were also applied for model construction. The IGS model with AUC used as the objective function achieved an accuracy, sensitivity, specificity, and area under ROC curve (AUC) of 70.11%, 73.46%, 69.26%, and 0.7758, respectively, outperforming the models designed with LR (65.77%, 78.44%, 62.54%, and 0.7689, respectively) and DNN (61.50%, 79.34%, 56.95%, and 0.7547, respectively), as well as previously reported models constructed using thedata of electronic health records with an AUC of 0.71–0.74. It can be used for automatically detecting pneumonia patients with a risk of all-cause readmissions within 30 days after discharge so as to administer suitable interventions to reduce readmission and healthcare costs.

Did the Hospital Readmissions Reduction Program Reduce Readmissions without Hurting Patient Outcomes at High Dual-Proportion Hospitals Prior to Stratification?

Since the implementation of Medicare’s Hospital Readmissions Reduction Program (HRRP), safety-net hospitals have received a disproportionate share of financial penalties for excess readmissions, raising concerns about the fairness of the policy. In response, the HRRP now stratifies hospitals into five quintiles by low-income Medicare (dual Medicare–Medicaid eligible) stay proportion and compares readmission rates within quintiles. To better understand the potential effects of the revised policy, we used difference-in-differences models to compare changes in 30-day readmission, 30-day mortality, and 90th-day community-dwelling rates after discharge of fee-for-service Medicare beneficiaries hospitalized for acute myocardial infarction, heart failure and pneumonia during 2007-2014, for hospitals in the highest (N = 677) and lowest (N = 678) dual-proportion quintiles before and after the original HRRP implementation in fiscal year 2013. We find that high dual-proportion hospitals lowered readmissions for all three conditions, while their patients’ health outcomes remained largely stable. We also find that for heart failure, high dual-proportion hospitals reduced readmissions more than low dual-proportion hospitals, albeit with a relative increase in mortality. Contrary to concerns about fairness, our findings imply that, under the original HRRP, high dual-proportion hospitals improved readmissions performance generally without adverse effects on patients’ health. Whether these gains could be retained under the new policy should be closely monitored.

Social Determinants of Health in Neurology

Neurologic health disparities are created and perpetuated by structural and social determinants of health. These factors include, but are not limited to, interpersonal bias, institutional factors that lead to disparate access to care, and neighborhood-level factors, such as socioeconomic status, segregation, and access to healthy food. Effects of these determinants of health can be seen throughout neurology, including in stroke, epilepsy, headache, amyotrophic lateral sclerosis, multiple sclerosis, and dementia. Interventions to improve neurologic health equity require multilayered approaches to address these interdependent factors that create and perpetuate disparate neurologic health access and outcomes.