183. ICU capacity management: An AI-based transparent scoring model for integrated clinical decision support
Invited abstract in session MD-3: ED and ICU, stream Sessions.
Monday, 13:30-15:00Room: NTNU, Realfagbygget R9
Authors (first author is the speaker)
| 1. | Christina Bartenschlager
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| Nürnberg School of Health, Technical University of Applied Sciences Nürnberg |
Abstract
The intensive care unit (ICU) is a critical and costly asset within hospital settings. Effective and efficient ICU management strategies are paramount for patient care. A large subset of ICU admissions arises from elective surgeries. Artificial intelligence (AI) and analytics as decision support tools are promising to support capacity management challenges. However, the practical implementation of such systems remains limited, hindered by factors including digitization gaps and skepticism surrounding AI transparency. This work presents the development and validation of a transparent scoring model utilizing AI and analytics to provide decision support. Focused on the decision of post-surgery ICU transfer for elective patients, our model aims to aid physicians, especially those with less experience, while enhancing capacity planning efficiency. Drawing from existing research on AI-based decision support systems, we propose a novel approach that integrates machine learning (ML) algorithms to identify key features influencing the need for post-operative ICU care. Through rigorous experimentation and validation, our clinical decision support system (CDSS) demonstrates its potential to accurately predict ICU requirements, thus optimizing resource utilization and enhancing patient care. The practical relevance and usability of our transparent scoring model are evaluated through a comprehensive experiment involving physicians. By addressing the gap between AI innovation and practical
Keywords
- Artificial Intelligence
- Analytics
- Decision support
Status: accepted
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