"Wait for it" : toward optimal emergency department wait time prediction
Abstract/Contents
- Abstract
- This thesis explores two problems in the field of Healthcare Operations Management, specifically in the emergency department (ED) setting. We first study the problem of how to accurately estimate the wait times of incoming patients to an ED. We introduce the Q-Lasso method for wait time prediction, which combines statistical learning with fluid model estimators. We implement Q-Lasso on an external website and in a partner ED's triage room, and observe that it achieves over 30% lower mean squared prediction error than would occur with the best rolling average method. Next, we study the problem of how to quantify and mitigate the extra waiting imposed on high-acuity patients by low-acuity patients in an ED, or "ExWAH." We find that an arriving low-acuity patient on average increases the subsequent aggregate wait times of high-acuity patients by 55 minutes. We investigate what operational mechanisms drive this effect, and propose managerial interventions EDs can use to reduce it.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2017 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Kwasnick, Sara Elizabeth |
|---|---|
| Associated with | Stanford University, Graduate School of Business. |
| Primary advisor | Bayati, Mohsen |
| Primary advisor | Plambeck, Erica L |
| Thesis advisor | Bayati, Mohsen |
| Thesis advisor | Plambeck, Erica L |
| Thesis advisor | Xu, Kuang |
| Advisor | Xu, Kuang |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Sara Elizabeth Kwasnick. |
|---|---|
| Note | Submitted to the Graduate School of Business. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2017. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2017 by Sara Elizabeth Kwasnick
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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