Adding Social Determinants in the Electronic Health Record in Clinical Care in Hawai'i: Supporting Community-Clinical Linkages in Patient Care.

Social and behavioral determinants of health, such as poverty, homelessness, and limited social support, account for an estimated 40% of health burdens and predict critical health outcomes. Many clinical-community linkages specifically focus on addressing such challenges. Given its distinctive history, culture, and location, Hawai'i has unique social factors impacting population health. Local health systems are striving to address these issues to meet their patients' health needs. Yet the evidence on precisely how health care systems and communities may work together to achieve these goals are limited both generally and specifically in the Hawai'i context. This article describes real-world efforts by 3 local health care delivery systems that integrate the identification of social needs into clinical care using the electronic health record (EHR). One health care system collects and assesses social challenges and interpersonal needs to improve the care for its frail seniors (aged 65 and older). Another system added key data fields around social support and inpatient mobility in the EHR to identify whether patients needed additional help during hospitalization and post-discharge. A third added a social needs screening tool (eg, housing instability, food insecurity, transportation needs) to its EHR to ensure that patient-specific needs can be appropriately addressed by the care team. Successful integration of this information into the EHR can identify, direct, and support clinical-community linkages and integrate such relationships into the care team. Many lessons can be learned from the implementation of these programs, including the importance of clinical relevance and ensuring capacity for social work liaisons trained for this work to address identified needs.

Living Alone and Homelessness as Predictors of 30-Day Potentially Preventable Hospital Readmission.

INTRODUCTION: The effect of social factors on health care outcomes is widely recognized. Health care systems are encouraged to add social and behavioral measures to electronic health records (EHRs), but limited research demonstrates how to leverage this information. We assessed 2 social factors collected from EHRs - social isolation and homelessness - in predicting 30-day potentially preventable readmissions (PPRs) to hospital. METHODS: EHR data were collected from May 2015 through April 2017 from inpatients at 2 urban hospitals on O'ahu, Hawai'i (N = 21,274). We performed multivariable logistic regression models predicting 30-day PPR by living alone versus living with others and by documented homelessness versus no documented homelessness, controlling for relevant factors, including age group, race/ethnicity, sex, and comorbid conditions. RESULTS: Among the 21,274 index hospitalizations, 16.5% (3,504) were people living alone and 11.2% (2,385) were homeless; 4.2% (899) hospitalizations had a 30-day PPR. In bivariate analysis, living alone did not significantly affect likelihood of a 30-day PPR (16.6% [3,376 hospitalizations] without PPR vs 14.4% [128 hospitalizations] with PPR; P = .09). However, documented homelessness did show a significant effect on the likelihood of 30-day PPR in the bivariate analysis (11.1% [2,259 hospitalizations] without PPR vs 14.1% [126 hospitalizations] with PPR; P = .006). In multivariable models, neither living alone nor homelessness was significantly associated with PPR. Factors that were significantly associated with PPR were comorbid conditions, discharge disposition, and use of an assistive device. CONCLUSION: Homelessness predicted PPR in descriptive analyses. Neither living alone nor homelessness predicted PPR once other factors were controlled. Instead, indicators of physical frailty (ie, use of an assistive device) and medical complexity (eg, hospitalizations that required assistive care post-discharge, people with a high number of comorbid conditions) were significant. Future research should focus on refining, collecting, and applying social factor data obtained through acute care EHRs.