How do machine learning algorithms perform in predicting hospital choices? evidence from changing environments

Researchers have found that machine learning methods are typically better at prediction than econometric models when the choice environment is stable. We study hospital demand models, and evaluate the relative performance of machine learning algorithms when the choice environment changes substantial...

Full description

Saved in:
Bibliographic Details
Published in:Journal of health economics 2021-07, Vol.78, p.102481-102481, Article 102481
Main Authors: Raval, Devesh, Rosenbaum, Ted, Wilson, Nathan E.
Format: Article
Language:English
Subjects:
Algorithms
Changing environments
Choice learning
Econometrics
Environment models
Health administration
Health economics
Hospitals
Learning algorithms
Machine learning
Natural disasters
Natural experiment
Patient choice
Performance prediction
Prediction
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Researchers have found that machine learning methods are typically better at prediction than econometric models when the choice environment is stable. We study hospital demand models, and evaluate the relative performance of machine learning algorithms when the choice environment changes substantially due to natural disasters that closed previously available hospitals. While machine learning algorithms outperform traditional econometric models in prediction, the gain they provide shrinks when patients’ choice sets are more profoundly affected. We show that traditional econometric methods provide important additional information when there are major changes in the choice environment.
ISSN:0167-6296
1879-1646
DOI:10.1016/j.jhealeco.2021.102481