Personalized Healthcare Outcome Analysis of Cardiovascular Surgical Procedures
成果类型:
Article
署名作者:
Wang, Guihua; Li, Jun; Hopp, Wallace J.
署名单位:
University of Texas System; University of Texas Dallas; University of Michigan System; University of Michigan
刊物名称:
M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
ISSN/ISSBN:
1523-4614
DOI:
10.1287/msom.2023.1227
发表日期:
2023
页码:
1567-1584
关键词:
causal machine learning
personalized healthcare
patient-centric information
摘要:
Problem definition: This study addresses three important questions concerning personalized healthcare: (1) Are outcome differences between hospitals heterogeneous across patients with different features? (2) If they are, how do the best quality hospitals identified using patient-centric information differ from those identified using population average information? (3) How much will hospitals' pay-for-performance reimbursements change if their performance is measured based on patient-centric information? Methodology/results: Using patient-level data from 35 hospitals for six cardiovascular surgeries in New York State, we identify patient groups that exhibit significant differences in outcomes with a recently developed instrumental variable tree approach. We find outcome differences between hospitals are heterogeneous not only across procedure types, but also along other dimensions such as patient age and comorbidities. For around 80% of patients, the best quality hospitals indicated by patient-centric information are different from those indicated as best according to population-average information.Managerial implications: We compare potential outcomes when patients are treated at the best quality hospitals based on the two types of information and find complications could be reduced by using patient centric information instead of population-average information. We also use our model to illustrate how patient-centric information can enhance pay-for-performance programs offered by payers and guide hospitals in targeting quality-improvement efforts.
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