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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BioMed Central Ltd
2022
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| Subjects: | |
| Online Access: | View Fulltext in Publisher |