Can diverse population characteristics be leveraged in a machine learning pipeline to predict resource intensive healthcare utilization among hospital service areas?

BACKGROUND: Super-utilizers represent approximately 5% of the population in the United States (U.S.) and yet they are responsible for over 50% of healthcare expenditures. Using characteristics of hospital service areas (HSAs) to predict utilization of resource intensive healthcare (RIHC) may offer a...

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Detalles Bibliográficos
Autores principales: Ricket, Iben M., MacKenzie, Todd A., Emond, Jennifer A., Ailawadi, Kusum L., Brown, Jeremiah R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9248096/
https://www.ncbi.nlm.nih.gov/pubmed/35773679
http://dx.doi.org/10.1186/s12913-022-08154-4

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9248096/
https://www.ncbi.nlm.nih.gov/pubmed/35773679
http://dx.doi.org/10.1186/s12913-022-08154-4

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