Choice of measurement approach for area-level social determinants of health and risk prediction model performance
| Autor: | J. Gutta, Joshua R. Vest, Suranga N. Kasthurirathne, Paul K. Halverson, Ofir Ben-Assuli, W. Ge |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
Nursing (miscellaneous) Social work Referral Social Determinants of Health 010102 general mathematics Health Informatics Primary care 01 natural sciences Random forest 03 medical and health sciences Patient referral 0302 clinical medicine Health Information Management Family medicine Hospital admission medicine Humans 030212 general & internal medicine Overall performance Social determinants of health 0101 mathematics Psychology Social Factors |
| Zdroj: | Informatics for healthsocial care. 47(1) |
| ISSN: | 1753-8165 |
| Popis: | Objective: The objective of this paper is to provide empirical guidance by comparing the performance of six different area-level SDoH measurement approaches in predicting patient referral to a social worker and hospital admission after a primary care visit.Methods: We compared the performance of six area-level SDoH measurement approaches in predicting patient referral to a social worker and hospital admission after a primary care visit using random forest classification algorithm. Data came from 209,605 patient encounters at a federally qualified health center. Models with each area-based measurement approach were compared against the patient-level data only model using area under the curve, sensitivity, specificity, and precision.Results: Addition of area-level features to patient-level data improved the overall performance of models predicting need for a social worker referral. Entering area-level measures as individual features resulted in highest model performance.Conclusion: Researchers seeking to include area-level SDoH measures in risk prediction may be able to forego more complex measurement approaches. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|