Predicting health-related social needs in Medicaid and Medicare populations using machine learning.
Sci Rep
; 12(1): 4554, 2022 03 16.
Article
em En
| MEDLINE
| ID: mdl-35296719
ABSTRACT
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Medicare
/
Medicaid
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
/
Screening_studies
Aspecto:
Determinantes_sociais_saude
/
Equity_inequality
Limite:
Aged
/
Humans
País/Região como assunto:
America do norte
Idioma:
En
Revista:
Sci Rep
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Estados Unidos