Abstract
While a growing number of machine learning (ML) systems have been deployed in clinical settings with the promise of improving patient care, many have struggled to gain adoption and realize this promise. Based on a qualitative analysis of coded interviews with clinicians who use an ML-based system for sepsis, we found that, rather than viewing the system as a surrogate for their clinical judgment, clinicians perceived themselves as partnering with the technology. Our findings suggest that, even without a deep understanding of machine learning, clinicians can build trust with an ML system through experience, expert endorsement and validation, and systems designed to accommodate clinicians’ autonomy and support them across their entire workflow.
| Original language | English (US) |
|---|---|
| Article number | 97 |
| Journal | npj Digital Medicine |
| Volume | 5 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 2022 |
Funding
We would like to thank Catherine Miller, Jane Scanlon, Jeanette Nazarian, Cynthia Balmaceda, Lisa Grubb, and Mojgan Azadi, whose help and advice made this work possible. Further, we wish to thank Renee Demski, Karen D’Souza, Allen Kachalia, Allen Chen, and clinical and quality stakeholders who contributed to tool deployment, education, and championing the work. This work was supported by funding from the Gordon and Betty Moore Foundation (Grant no. 3186.01), the National Science Foundation (Grant no. 1840088), and the Sloan Foundation. This information or content and conclusions are those of the authors and should neither be construed as the official position or policy of, nor should any endorsements be inferred by the NSF the U.S. Government. We would like to thank Catherine Miller, Jane Scanlon, Jeanette Nazarian, Cynthia Balmaceda, Lisa Grubb, and Mojgan Azadi, whose help and advice made this work possible. Further, we wish to thank Renee Demski, Karen D’Souza, Allen Kachalia, Allen Chen, and clinical and quality stakeholders who contributed to tool deployment, education, and championing the work. This work was supported by funding from the Gordon and Betty Moore Foundation (Grant no. 3186.01), the National Science Foundation (Grant no. 1840088), and the Sloan Foundation. This information or content and conclusions are those of the authors and should neither be construed as the official position or policy of, nor should any endorsements be inferred by the NSF the U.S. Government. Under a license agreement between Bayesian Health and the Johns Hopkins University, Dr. Henry, Dr. Saria, and Johns Hopkins University are entitled to revenue distributions. Additionally, the University owns equity in Bayesian Health. This arrangement has been reviewed and approved by the Johns Hopkins University in accordance with its conflict of interest policies. S.S. also has grants from Gordon and Betty Moore Foundation, the National Science Foundation, the National Institutes of Health, Defense Advanced Research Projects Agency, the Food and Drug Administration, and the American Heart Association; she is a founder of and holds equity in Bayesian Health; she is the scientific advisory board member for PatientPing; and she has received honoraria for talks from a number of biotechnology, research, and health-tech companies. The remaining authors declare no competing interests.
ASJC Scopus subject areas
- Health Information Management
- Health Informatics
- Medicine (miscellaneous)
- Computer Science Applications