72. A granular approach to optimal and fair patient placement in hospital emergency departments
Invited abstract in session MA-20: Ethics of OR and artificial intelligence , stream OR and Ethics.
Monday, 8:30-10:00Room: 45 (building: 116)
Authors (first author is the speaker)
| 1. | Dessi Pachamanova
|
| Mathematics, Analytics, Science and Technology, Babson College | |
| 2. | Maureen Canellas
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| University of Massachusetts Memorial Medical Center | |
| 3. | Georgia Perakis
|
| MIT Sloan School of Management | |
| 4. | Omar Skali Lami
|
| McKinsey & Co | |
| 5. | Asterios Tsiourvas
|
| Operations Research Center, Massachusetts Institute of Technology |
Abstract
Prolonged emergency department (ED) length of stay (LOS) is associated with detrimental effects on patient care and quality, including increased mortality, increased risk of hospital-acquired infections, and disrupted patient flow. There is also evidence that certain groups of patients experience longer LOS based on their gender or race, especially with regard to the part of LOS that is attributable to waiting to be seen by a clinician. This work tackles the patient prioritization and placement aspects of ED operations with the goal of improving throughput and wait time in a fair, equitable way. We present a novel Mixed Integer Linear Programming predictive-prescriptive formulation that incorporates a breakdown of predicted patient ED LOS into actionable pieces and allows for a more granular model of ED operations. We show how to incorporate considerations for fairness and reformulate the MILP formulation into a compact and computationally tractable formulation that can be solved efficiently in real time. This work was conducted in collaboration with a large US academic medical center. Data from more than 40000 patient visits were used to shape and evaluate the models. We provide an interpretable metamodel trained on the complex model’s recommendations in order to help with the operationalization of the algorithm. The method will be used by the hospital to improve patient flow and quality of care as well as to support more fair and consistent bed allocation decisions.
Keywords
- Health Care
- Algorithms
- Ethics
Status: accepted
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