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Activity Number:
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197
- SPEED: Government and Health Policy
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2018 : 10:30 AM to 11:15 AM
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Sponsor:
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Health Policy Statistics Section
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Abstract #332907
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Title:
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New Applications of Machine Learning to Estimating Large Physician Demand Models
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Author(s):
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Bryan Sayer* and William Encinosa
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Companies:
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Social & Scientific Systems, Inc. and Agency for Health Care Quality and Research
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Keywords:
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Machine learning;
semiparametric estimation
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Abstract:
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One of the largest recent transformations in healthcare has been the massive consolidation of physician groups. Little policy work has been done on this due to the lack of physician demand models, which differ from hospital demand models due to their uncommonly large choice sets of docs. Here we develop new methods for estimating physician discrete choice models in Medicare. We compare estimation methods: parametric, semiparametric, and machine learning methods. We also explore methods of randomly reducing the choice sets, similar to McFadden 1978.
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Authors who are presenting talks have a * after their name.
