When performance is not enough—A multidisciplinary view on clinical decision support

Language
en
Document Type
Article
Issue Date
2023-06-15
First published
2023-04-24
Issue Year
2023
Authors
Roller, Roland
Burchardt, Aljoscha
Samhammer, David
Ronicke, Simon
Duettmann, Wiebke
Schmeier, Sven
Möller, Sebastian
Dabrock, Peter
Budde, Klemens
Mayrdorfer, Manuel
Editor
Abstract

Scientific publications about the application of machine learning models in healthcare often focus on improving performance metrics. However, beyond often short-lived improvements, many additional aspects need to be taken into consideration to make sustainable progress. What does it take to implement a clinical decision support system, what makes it usable for the domain experts, and what brings it eventually into practical usage? So far, there has been little research to answer these questions. This work presents a multidisciplinary view of machine learning in medical decision support systems and covers information technology, medical, as well as ethical aspects. The target audience is computer scientists, who plan to do research in a clinical context. The paper starts from a relatively straightforward risk prediction system in the subspecialty nephrology that was evaluated on historic patient data both intrinsically and based on a reader study with medical doctors. Although the results were quite promising, the focus of this article is not on the model itself or potential performance improvements. Instead, we want to let other researchers participate in the lessons we have learned and the insights we have gained when implementing and evaluating our system in a clinical setting within a highly interdisciplinary pilot project in the cooperation of computer scientists, medical doctors, ethicists, and legal experts.

Journal Title
PLoS ONE
Volume
18
Issue
4
Citation
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