TY  - JOUR
AU  - Hatef, Elham
AU  - Chang, Hsien-Yen
AU  - Richards, Thomas M
AU  - Kitchen, Christopher
AU  - Budaraju, Janya
AU  - Foroughmand, Iman
AU  - Lasser, Elyse C
AU  - Weiner, Jonathan P
PY  - 2024
DA  - 2024/3/12
TI  - Development of a Social Risk Score in the Electronic Health Record to Identify Social Needs Among Underserved Populations: Retrospective Study
JO  - JMIR Form Res
SP  - e54732
VL  - 8
KW  - AI
KW  - algorithms
KW  - artificial intelligence
KW  - community health
KW  - deep learning
KW  - EHR
KW  - electronic health record
KW  - machine learning
KW  - ML
KW  - population demographics
KW  - population health
KW  - practical models
KW  - predictive analytics
KW  - predictive modeling
KW  - predictive modelling
KW  - predictive models
KW  - predictive system
KW  - public health
KW  - public surveillance
KW  - SDOH
KW  - social determinants of health
KW  - social needs
KW  - social risks
AB  - Background: Patients with unmet social needs and social determinants of health (SDOH) challenges continue to face a disproportionate risk of increased prevalence of disease, health care use, higher health care costs, and worse outcomes. Some existing predictive models have used the available data on social needs and SDOH challenges to predict health-related social needs or the need for various social service referrals. Despite these one-off 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 basis within most US-based health care organizations. Objective: We aimed to retrieve available information in the electronic health record (EHR) relevant to the identification of persons with social needs and to develop a social risk score for use within clinical practice to better identify patients at risk of having future social needs. Methods: We conducted a retrospective study using EHR data (2016-2021) and data from the US Census American Community Survey. We developed a prospective model using current year-1 risk factors to predict future year-2 outcomes within four 2-year cohorts. Predictors of interest included demographics, previous health care use, 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 with 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 study population of 1,852,228 patients included middle-aged (mean age range 53.76-55.95 years), White (range 324,279/510,770, 63.49% to 290,688/488,666, 64.79%), and female (range 314,741/510,770, 61.62% to 278,488/448,666, 62.07%) patients from neighborhoods with high socioeconomic status (mean area deprivation index percentile range 28.76-30.31). Between 8.28% (37,137/448,666) and 11.55% (52,037/450,426) of patients across the study cohorts had at least 1 social need documented in their EHR, with safety issues and economic challenges (ie, financial resource strain, employment, and food insecurity) being the most common documented social needs (87,152/1,852,228, 4.71% and 58,242/1,852,228, 3.14% of overall patients, respectively). The model had an area under the curve of 0.702 (95% CI 0.699-0.705) in predicting prospective social needs in the overall study population. Previous social needs (odds ratio 3.285, 95% CI 3.237-3.335) and emergency department visits (odds ratio 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 to help identify patients with high social needs. 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. 
SN  - 2561-326X
UR  - https://formative.jmir.org/2024/1/e54732
UR  - https://doi.org/10.2196/54732
UR  - http://www.ncbi.nlm.nih.gov/pubmed/38470477
DO  - 10.2196/54732
ID  - info:doi/10.2196/54732
ER  -