Abstract
Despite recognition of the association between individual social and behavioral determinants of health (SDH) and patient outcomes, little is known regarding the value of SDH in explaining variation in outcomes for high-risk patients. To describe SDH factors among veterans who are at high risk for hospitalization, and to determine whether adding patient-reported SDH measures to electronic health record (EHR) measures improves estimation of 90-day and 180-day all-cause hospital admission. A survey was mailed between April 16 and June 29, 2018, to a nationally representative sample of 10 000 Veterans Affairs (VA) patients whose 1-year risk of hospitalization or death was in the 75th percentile or higher based on a VA EHR-derived risk score. The survey included multiple SDH measures, such as resilience, social support, health literacy, smoking status, transportation barriers, and recent life stressors. The EHR-based characteristics of survey respondents and nonrespondents were compared using standardized differences. Estimation of 90-day and 180-day hospital admission risk was assessed for 3 logistic regression models: (1) a base model of all prespecified EHR-based covariates, (2) a restricted model of EHR-based covariates chosen via forward selection based on minimizing Akaike information criterion (AIC), and (3) a model of EHR- and survey-based covariates chosen via forward selection based on AIC minimization. In total, 4685 individuals (response rate 46.9%) responded to the survey. Respondents were comparable to nonrespondents in most characteristics, but survey respondents were older (eg, >80 years old, 881 [18.8%] vs 800 [15.1%]), comprised a higher percentage of men (4391 [93.7%] vs 4794 [90.2%]), and were composed of more White non-Hispanic individuals (3366 [71.8%] vs 3259 [61.3%]). Based on AIC, the regression model with survey-based covariates and EHR-based covariates better estimated hospital admission at 90 days (AIC, 1947.7) and 180 days (AIC, 2951.9) than restricted models with only EHR-based covariates (AIC, 1980.2 at 90 days; AIC, 2981.9 at 180 days). This result was due to inclusion of self-reported measures such as marital or partner status, health-related locus of control, resilience, smoking status, health literacy, and medication insecurity. Augmenting EHR data with patient-reported social information improved estimation of 90-day and 180-day hospitalization risk, highlighting specific SDH factors that might identify individuals who are at high risk for hospitalization.
Highlights
Augmenting electronic health record (EHR) data with patient-reported social information improved estimation of 90-day and 180-day hospitalization risk, highlighting specific social and behavioral determinants of health (SDH) factors that might identify individuals who are at high risk for hospitalization
In 2014, the Health and Medicine Division of the Institute of Medicine proposed that a battery of SDH measures be universally incorporated into electronic health records (EHRs) in an “interoperable fashion.”[9,10] The Office of the Assistant Secretary for Planning and Evaluation of the Department of Health and Human Services reaffirmed this recommendation, based on an analysis that found that Medicare beneficiaries with social risk factors had worse outcomes than beneficiaries without them.[11]
Our findings contribute to a growing body of literature examining the association between SDH factors and hospitalization, including studies highlighting the critical roles of social support and neighborhood socioeconomic status (SES).[3,50,51,52]
Summary
To be clinically useful and merit integration into EHRs and payment reform, those measures must be brief, validated, and predictive of outcomes important to patients and health systems
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