Development and Validation of Computable Social Phenotypes for Health‐Related Social Needs
Open accessAbstract
Objective: Measurement of health-related social needs (HRSNs) is complex. We sought to develop and validate computable phenotypes (CPs) using structured electronic health record (EHR) data for food insecurity, housing instability, financial insecurity, transportation barriers, and a composite-type measure of these, using human-defined rule-based and machine learning (ML) classifier approaches. Materials and Methods: We collected HRSN surveys as the reference standard and obtained EHR data from 1550 patients in 3 health systems from 2 states. We followed a Delphi-like approach to develop the human-defined rule-based CP. For the ML classifier approach, we trained supervised ML (XGBoost) models using 78 features. Using surveys as the reference standard, we calculated sensitivity, specificity, positive predictive values, and area under the curve (AUC). We compared AUCs using the Delong test and other performance measures using McNemar's test, and checked for differential performance. Results: Most patients (63%) reported at least one HRSN on the reference standard survey. Human-defined rule-based CPs exhibited poor performance (AUCs=.52 to .68). ML classifier CPs performed significantly better, but still poor-to-fair (AUCs = .68 to .75). Significant differences for race/ethnicity were found for ML classifier CPs (higher AUCs for White non-Hispanic patients). Important features included number of encounters and Medicaid insurance. Discussion: Using a supervised ML classifier approach, HRSN CPs approached thresholds of fair performance, but exhibited differential performance by race/ethnicity. Conclusion: CPs may help to identify patients who may benefit from additional social needs screening. Future work should explore the use of area-level features via geospatial data and natural language processing to improve model performance.
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The two studies achieving low PROBAST risk of bias, Patel et al. [22] and Gregory et al. [27], both incorporated multi-site or national data and applied external validation procedures.
“The two studies achieving low PROBAST risk of bias, Patel et al. [22] and Gregory et al. [27], both incorporated multi‐site or national data and applied external validation procedures.”
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 [27]
The Materials and methods (Setting and sample; page unavailable) confirm that Gregory et al. recruited patients from multiple sites across three health systems in two states. The ML classifier phenotype development section describes a random 80% training/20% testing split and five-fold cross-validation, but does not report external validation in a separate setting. The low PROBAST rating is the citing authors’ assessment, not a finding attributed to Gregory et al. (LLM, conf=0.87)
Experimental — accuracy not yet measuredEHR structured data are insufficient for reliable HRSN identification.
“Both approaches showed poor‐to‐fair performance versus patient‐reported reference standards; accuracy lower for non‐White and Hispanic patients; EHR structured data insufficient for reliable HRSN identification”
Both rule-based and ML approaches showed poor-to-fair performance versus patient-reported reference standards for computable social phenotypes.
“Both approaches showed poor‐to‐fair performance versus patient‐reported reference standards; accuracy lower for non‐White and Hispanic patients; EHR structured data insufficient for reliable HRSN identification”
Accuracy was lower for non-White and Hispanic patients.
“Both approaches showed poor‐to‐fair performance versus patient‐reported reference standards; accuracy lower for non‐White and Hispanic patients; EHR structured data insufficient for reliable HRSN identification”
Gregory et al. [27] found that computable social phenotypes derived from structured EHR data performed poorly for non-White and Hispanic patients relative to White patients, even using XGBoost with 78 features.
“Gregory et al. [27] found that computable social phenotypes derived from structured EHR data performed poorly for non‐White and Hispanic patients relative to White patients, even using XGBoost with 78 features.”
Two studies achieved low overall PROBAST risk of bias: Patel et al. [22] and Gregory et al. [27].
“Two studies achieved low overall PROBAST risk of bias: Patel et al. [22] and Gregory et al. [27].”
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 [27]
The Materials and methods > Analyses section describes performance measures and checks for differential performance, but does not report a PROBAST assessment. A search of the full-text copy for PROBAST and overall risk of bias found no such rating for Gregory et al.; the citing paper’s assessment cannot be verified from this source. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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Performance of 4 Methods to Assess Health-Related Social Needs
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- Authors
- Gregory, M. E., Kasthurirathne, S. N., Magoc, T., McNamee, C., Harle, C. A., & Vest, J. R.
- Affiliations
- University of Florida Health (US)
- Regenstrief Institute (US)
- Quality and Patient Safety, College of Medicine, University of Florida , Gainesville, FL 32610,
- Indiana University Indianapolis (US)
- Corresponding author
- Gregory, Megan E
- ORCID
- Venue
- JAMIA Open
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-12-26
- Volume
- 8
- Issue
- 1
- Pages
- ooae150-ooae150
- ISSN
- 2574-2531 (ISSN-L)
- PMC
- PMC11706536
- OpenAlex
- W4406120227
- Cited by
- 2
- References
- 63
- Citations by year
- 2026: 1 · 2025: 1
- FWCI
- 0.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- JAMIA Open (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- IUScholarWorks (Indiana University) (submittedVersion, cc-by-nc)
- Funders
- Indiana University Pervasive Technology Institute
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Social Sciences › Health › Health disparities and outcomes
- Keywords
- health-related social needs, computable phenotypes, food insecurity, housing instability, financial insecurity, transportation barriers, social needs screening, electronic health records, XGBoost classifier, algorithmic fairness, racial and ethnic disparities, Medicaid, social determinants of health
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