JMIR Publications

ABSTRACT

Background:

Some existing predictive models have utilized the available data on social needs and/or social determinants of health (SDOH) challenges to predict health-related social needs or the need for various social service referrals. Despite these one-off research and pilot efforts, the work to date suggests that many technical and organizational challenges must be surmounted before SDOH-integrated solutions can be implemented on an ongoing, wide scale within most US healthcare organizations.

Objective:

To retrieve available information in the electronic health record (EHR) relevant to the identification of persons with social needs, to link these with community-based measures, and to develop a social risk score for use within clinical practice to better identify patients at risk of having future social needs.

Methods:

A retrospective study using EHR data (2016-2021) and data from the U.S. Census, American Community Survey. Predictors of interest included demographics, previous healthcare utilization, diagnostic/comorbidity, previously identified social needs, and neighborhood characteristics as reflected by the area deprivation index. The outcome variable was a binary indicator reflecting the likelihood of the presence of a patient having social needs. We applied a generalized estimating equation approach, adjusting for patient-level risk factors, the possible effect of geographically clustered data, and the effect of multiple visits for each patient.

Results:

The model performance in predicting prospective social needs was acceptable (AUC:0.702, 95% CI: 0.699-0.705). Previous social needs (OR: 3.285, 95% CI: 3.237-3.335) and emergency department visits (OR: 1.659, 95% CI: 1.634-1.684) were the strongest predictors of future social needs.

Conclusions:

Our model provides an opportunity to make use of available EHR data in conjunction with community-level data, to help identify patients with high social needs, for further assessment and or/referral to community-based organizations. Our proposed social risk score could help identify the subset of patients who would most benefit from further social needs screening and data collection to avoid potentially more burdensome primary data collection on all patients in a target population of interest.