Assessing the Capacity of Social Determinants of Health Data to Augment Predictive Models Identifying Patients in Need of Wraparound Social Services
Open accessAbstract
Introduction: A growing variety of diverse data sources is emerging to better inform health care delivery and health outcomes. We sought to evaluate the capacity for clinical, socioeconomic, and public health data sources to predict the need for various social service referrals among patients at a safety-net hospital. Materials and Methods: We integrated patient clinical data and community-level data representing patients’ social determinants of health (SDH) obtained from multiple sources to build random forest decision models to predict the need for any, mental health, dietitian, social work, or other SDH service referrals. To assess the impact of SDH on improving performance, we built separate decision models using clinical and SDH determinants and clinical data only. Results: Decision models predicting the need for any, mental health, and dietitian referrals yielded sensitivity, specificity, and accuracy measures ranging between 60% and 75%. Specificity and accuracy scores for social work and other SDH services ranged between 67% and 77%, while sensitivity scores were between 50% and 63%. Area under the receiver operating characteristic curve values for the decision models ranged between 70% and 78%. Models for predicting the need for any services reported positive predictive values between 65% and 73%. Positive predictive values for predicting individual outcomes were below 40%. Discussion: The need for various social service referrals can be predicted with considerable accuracy using a wide range of readily available clinical and community data that measure socioeconomic and public health conditions. While the use of SDH did not result in significant performance improvements, our approach represents a novel and important application of risk predictive modeling.
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Community-level SDOH added meaningful predictive value beyond clinical variables alone.
“Random forest predicted social service referral needs with considerable accuracy; community‐level SDOH added meaningful predictive value beyond clinical variables alone; among earliest demonstrations of SDOH‐augmented prediction in clinical settings”
From Artificial Intelligence and Machine Learning for Identifying Social Determinants of Health in Low‐Income Populations Within United States Health Systems: A Scoping Review (2026) · cites this paper as [34]
The Discussion states that adding socioeconomic and public health data did not produce a statistically significant performance improvement in any predictive model. The Abstract likewise says that SDH did not result in significant performance improvements, contrary to the claim that community-level SDOH added meaningful predictive value beyond clinical variables alone. (LLM, conf=0.94)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredSDOH-enriched models improve social service referral prediction.
“one of the earliest demonstrations that SDOH‐enriched models improve social service referral prediction”
From Artificial Intelligence and Machine Learning for Identifying Social Determinants of Health in Low‐Income Populations Within United States Health Systems: A Scoping Review (2026) · cites this paper as [34]
The Discussion says that adding socioeconomic and public health data did not produce a statistically significant performance improvement in any predictive model. The study demonstrated that social service referral needs could be predicted, but not that SDOH enrichment improved prediction over clinical data alone. (LLM, conf=0.92)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredMethods included gradient boosting, random forest, ensemble models, XGBoost, and logistic regression-based ML pipelines with fairness optimization.
“Methods included gradient boosting [32], random forest [26, 34], ensemble models [26], XGBoost [27], and logistic regression‐based ML pipelines with fairness optimization [21].”
Random forest predicted social service referral needs with considerable accuracy.
“Random forest predicted social service referral needs with considerable accuracy; community‐level SDOH added meaningful predictive value beyond clinical variables alone; among earliest demonstrations of SDOH‐augmented prediction in clinical settings”
Kasthurirathne et al. [34] augmented clinical EHR data with community-level socioeconomic and public health datasets.
“Kasthurirathne et al. [34] augmented clinical EHR data with community‐level socioeconomic and public health datasets”
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Self-citationPredicting Social Determinants of Health in Patient Navigation: Case Study
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Facilitating Inclusion of Geocoded Pollution Data into Health Studies.
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Self-citationUse of Stratified Cascade Learning to predict hospitalization risk with only socioeconomic factors
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Burnout in Global Healthcare Systems
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- Authors
- Kasthurirathne, S. N., Vest, J. R., Menachemi, N., Halverson, P. K., & Grannis, S. J.
- Affiliations
- Indiana University Indianapolis (US)
- Regenstrief Institute (US)
- Corresponding author
- Kasthurirathne, Suranga N
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-11-21
- Volume
- 25
- Issue
- 1
- Pages
- 47-53
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC7647142
- OpenAlex
- W2770831828
- Cited by
- 71
- References
- 50
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 9 · 2023: 7 · 2022: 7 · 2021: 10 · 2020: 14 · 2019: 12 · 2018: 7 · 2017: 1
- FWCI
- 5.55
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Journal of the American Medical Informatics Association (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- IUScholarWorks (Indiana University) (submittedVersion)
- Purdue University Indianapolis (Indiana University) (submittedVersion)
- Funders
- Systems for Action National Coordinating Center (73485)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Social Sciences › Health › Health disparities and outcomes
- Keywords
- social determinants of health, social services referral, safety-net hospitals, risk prediction, data integration, socioeconomic data, public health data, mental health referral, dietitian referral, social work referral, random forest, prediction model performance
- MeSH terms
- Supervised Machine Learning, Adult, Decision Support Techniques, Female, Humans, Indiana, Male, Primary Health Care, ROC Curve, Sensitivity and Specificity, Social Work, Safety-net Providers, Social Determinants of Health
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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