Predicting health-related social needs in Medicaid and Medicare populations using machine learning.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
16 03 2022
16 03 2022
Historique:
received:
24
09
2021
accepted:
03
03
2022
entrez:
17
3
2022
pubmed:
18
3
2022
medline:
6
5
2022
Statut:
epublish
Résumé
Providers currently rely on universal screening to identify health-related social needs (HRSNs). Predicting HRSNs using EHR and community-level data could be more efficient and less resource intensive. Using machine learning models, we evaluated the predictive performance of HRSN status from EHR and community-level social determinants of health (SDOH) data for Medicare and Medicaid beneficiaries participating in the Accountable Health Communities Model. We hypothesized that Medicaid insurance coverage would predict HRSN status. All models significantly outperformed the baseline Medicaid hypothesis. AUCs ranged from 0.59 to 0.68. The top performance (AUC = 0.68 CI 0.66-0.70) was achieved by the "any HRSNs" outcome, which is the most useful for screening prioritization. Community-level SDOH features had lower predictive performance than EHR features. Machine learning models can be used to prioritize patients for screening. However, screening only patients identified by our current model(s) would miss many patients. Future studies are warranted to optimize prediction of HRSNs.
Identifiants
pubmed: 35296719
doi: 10.1038/s41598-022-08344-4
pii: 10.1038/s41598-022-08344-4
pmc: PMC8927567
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
4554Subventions
Organisme : NCATS NIH HHS
ID : UL1TR000371
Pays : United States
Organisme : NCATS NIH HHS
ID : U01 TR002393
Pays : United States
Informations de copyright
© 2022. The Author(s).
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