Predicting health-related social needs in Medicaid and Medicare populations using machine learning
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 fro...
Main Authors: | Jennifer Holcomb, Luis C. Oliveira, Linda Highfield, Kevin O. Hwang, Luca Giancardo, Elmer Victor Bernstam |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2022-03-01
|
Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-022-08344-4 |
Similar Items
-
Unmet Long-Term Care Needs: An Analysis of Medicare-Medicaid Dual Eligibles
by: Harriet L. Komisar, et al.
Published: (2005-05-01) -
Medicaid and Medicare Part B spending on immunomodulators and biosimilars
by: Alyssa M. Thompson, et al.
Published: (2022-04-01) -
The Center for Medicare and Medicaid Innovation: The Case for Reform
by: Doug Badger MDiv
Published: (2022-08-01) -
The Limited English Proficient Population: Describing Medicare, Medicaid, and Dual Beneficiaries
by: Kimberly Proctor, et al.
Published: (2018-05-01) -
Medicare and medicaid spending trends for immunomodulators prescribed for dermatologic conditions
by: Kyla N. Price, et al.
Published: (2022-01-01)