An analytics approach to problems in health care
Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2017.
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| Other Authors: | |
| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2017
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| Online Access: | http://hdl.handle.net/1721.1/112358 |
| _version_ | 1811083675359510528 |
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| author | Kung, Jerry Lai |
| author2 | Dimitris Bertsimas. |
| author_facet | Dimitris Bertsimas. Kung, Jerry Lai |
| author_sort | Kung, Jerry Lai |
| collection | MIT |
| description | Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2017. |
| first_indexed | 2024-09-23T12:37:16Z |
| format | Thesis |
| id | mit-1721.1/112358 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T12:37:16Z |
| publishDate | 2017 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1123582019-04-10T14:02:51Z An analytics approach to problems in health care Kung, Jerry Lai Dimitris Bertsimas. Massachusetts Institute of Technology. Operations Research Center. Massachusetts Institute of Technology. Operations Research Center. Operations Research Center. Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2017. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 99-102). Health care expenditures in the United States have been increasing at unsustainable rates for more than thirty years with no signs of abating. Decisions to accept or reject deceased-donor kidneys offered to patients on the kidney transplantation wait-list currently rely on physician experience and intuition. Scoring rules to determine which end-stage liver disease patients are in most dire need of immediate transplantation have been haphazardly designed and reactively modified in an attempt to decrease waitlist mortality and increase fairness for cancer patients. For each of the above problem settings, we propose a framework that takes real-world data as input and draws upon modern data analytics methods ranging from mixed integer linear optimization to predictive machine learning to yield actionable insights that can add a significant edge over current practice. We describe an approach that, given insurance claims data, leads conservatively to a 10% reduction in health care costs in a study involving a large private US employer. Using historical data for patients on the kidney waitlist and organ match runs, we build a model that achieves an out-of-sample AUC of 0.87 when predicting whether or not a patient will receive a kidney of a particular quality within three, six, or twelve months. Given historical data for patients on the liver waitlist, we create a unified model that is capable of averting an additional 25% of adverse events in simulation compared to current practice without disadvantaging cancer patients. by Jerry Lai Kung. Ph. D. 2017-12-05T16:24:22Z 2017-12-05T16:24:22Z 2017 2017 Thesis http://hdl.handle.net/1721.1/112358 1008591884 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 102 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Operations Research Center. Kung, Jerry Lai An analytics approach to problems in health care |
| title | An analytics approach to problems in health care |
| title_full | An analytics approach to problems in health care |
| title_fullStr | An analytics approach to problems in health care |
| title_full_unstemmed | An analytics approach to problems in health care |
| title_short | An analytics approach to problems in health care |
| title_sort | analytics approach to problems in health care |
| topic | Operations Research Center. |
| url | http://hdl.handle.net/1721.1/112358 |
| work_keys_str_mv | AT kungjerrylai ananalyticsapproachtoproblemsinhealthcare AT kungjerrylai analyticsapproachtoproblemsinhealthcare |