Better prediction through design : new tools for learning health systems
Abstract/Contents
- Abstract
- The act of prediction is ubiquitous in health care--be it via clinician gestalt, or through algorithms. Increasingly, predictive algorithms are beginning to displace clinician decision-making and to drive interventions of all types. Underscoring their importance, just one category of clinical predictive algorithms alone--those used for case management--is used to drive care for over 200 million individuals in the U.S. each year. However, evidence remains scant that algorithm deployments for this and other use cases actually improve clinical outcomes. This evidence gap is further compounded by the particular impracticality of conducting randomized controlled trials to study these algorithms' impacts, and by the lack of alternative design approaches. Moreover, there also exists a currently-unmet need for approaches to iteration on preexisting algorithmic deployments, so that an intervention can be re-prioritized to the patients most likely to benefit. This thesis attempts to fill this methodological vacuum using established tools from causal inference, with the problem of hospital readmission prediction and prevention as a test case.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Marafino, Ben Joseph |
|---|---|
| Degree supervisor | Baiocchi, Michael |
| Thesis advisor | Baiocchi, Michael |
| Thesis advisor | Geldsetzer, Pascal |
| Thesis advisor | Liu, Vincent |
| Thesis advisor | Owen, Art B |
| Degree committee member | Geldsetzer, Pascal |
| Degree committee member | Liu, Vincent |
| Degree committee member | Owen, Art B |
| Associated with | Stanford University, Program in Biomedical Informatics |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Ben Marafino. |
|---|---|
| Note | Submitted to the Program in Biomedical Informatics. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/yv248hh5400 |
Access conditions
- Copyright
- © 2022 by Ben Joseph Marafino
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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