A Causal Tree Approach for Personalized Health Care Outcome Analysis
dc.contributor.author | Wang, Guihua | |
dc.contributor.author | Li, Jun | |
dc.contributor.author | Hopp, Wallace J. | |
dc.date.accessioned | 2017-02-17T13:30:27Z | |
dc.date.available | 2017-02-17T13:30:27Z | |
dc.date.issued | 2016-12 | |
dc.identifier | 1336 | en_US |
dc.identifier.citation | submitted to Management Science | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/136093 | |
dc.description.abstract | Using patient-level data from 35 hospitals for 6 cardiovascular surgeries in New York, we provide empirical evidence that outcome differences between health care providers are heterogeneous across different groups of patients. We then use a causal tree approach to identify patient groups that exhibit significant differences in outcome. By quantifying these differences, we demonstrate that a large majority of patients can achieve better expected outcomes by selecting providers based on patient-centric outcome information. We also show how patient-centric outcome information can help providers to improve their processes and payers to design effective pay-for-performance programs. | en_US |
dc.subject | Health care | en_US |
dc.subject | patient-centric | en_US |
dc.subject | quality information | en_US |
dc.subject | machine learning | en_US |
dc.subject | medical outcomes | en_US |
dc.subject | hospital ratings | en_US |
dc.subject | data analytics | en_US |
dc.subject.classification | Operations and Management Science | en_US |
dc.title | A Causal Tree Approach for Personalized Health Care Outcome Analysis | en_US |
dc.type | Working Paper | en_US |
dc.subject.hlbsecondlevel | Business (General) | en_US |
dc.subject.hlbtoplevel | Business | |
dc.contributor.affiliationum | Ross School of Business | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/136093/1/1336_Wang.pdf | |
dc.owningcollname | Business, Stephen M. Ross School of - Working Papers Series |
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