An AI-Based Clinical Decision Support System for Antibiotic Therapy in Sepsis (KINBIOTICS): Use Case Analysis
Early integration of users is beneficial for both the identification of relevant context factors and the further development of an effective CDSS. Overall, the potential of AI-based CDSSs is offset by inhibiting contextual conditions that impede its acceptance and implementation. The advancement of AI-based CDSSs and the mitigation of these inhibiting conditions are crucial for the realization of its full potential.
Authors
- Wolfgang Greiner (ORCID: https://orcid.org/0000-0001-9552-6969)
- Sebastian Rehberg (ORCID: https://orcid.org/0000-0002-5835-7083)
- M Kirchner
- Svenja Elkenkamp (ORCID: https://orcid.org/0000-0001-8704-1208)
- Rainer Borgstedt (ORCID: https://orcid.org/0000-0002-2870-745X)
- Philipp Cimiano (ORCID: https://orcid.org/0000-0002-4771-441X)
- Christoph Düsing (ORCID: https://orcid.org/0000-0002-7817-9448)
- David Lampe (ORCID: https://orcid.org/0000-0003-3400-0861)
- Christiane Fuchs (ORCID: https://orcid.org/0000-0003-3565-8315)
- Juliane Düvel (ORCID: https://orcid.org/0000-0001-5700-4852)
- Hannah Marchi (ORCID: https://orcid.org/0000-0003-0607-6033)
- Sophie Schmiegel (ORCID: https://orcid.org/0000-0001-9632-3426)
- Simon Claßen
- K. Meier
Institutions
- Bielefeld University (DE)
- Hochschule Bielefeld (DE)
- Evangelisches Krankenhaus Bielefeld (DE)
- Klinikum Bielefeld (DE)
- Bethel University (US)
Publication Details
- Journal
- JMIR Human Factors
- Published
- 2024-12-31
- DOI
- https://doi.org/10.2196/66699
- Citations
- 14
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 1.68