Personalized Healthcare Outcome Analysis of Cardiovascular Surgical Procedures

Guihua Wang (), Jun Li () and Wallace J. Hopp ()
Additional contact information
Guihua Wang: Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75080
Jun Li: Ross School of Business, University of Michigan, Ann Arbor, Michigan 48109
Wallace J. Hopp: Ross School of Business, University of Michigan, Ann Arbor, Michigan 48109

Manufacturing & Service Operations Management, 2023, vol. 25, issue 4, 1567-1584

Abstract: 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.

Keywords: causal machine learning; personalized healthcare; patient-centric information (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/msom.2023.1227 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:25:y:2023:i:4:p:1567-1584

Access Statistics for this article

More articles in Manufacturing & Service Operations Management from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().