Henkel, Maurice, Horn, Tobias, Leboutte, Francois, Trotsenko, Pawel, Dugas, Sarah Gina, Sutter, Sarah Ursula, Ficht, Georg, Engesser, Christian, Matthias, Marc, Stalder, Aurelien et al (show 5 more authors)
(2022)
Initial experience with AI Pathway Companion: Evaluation of dashboard-enhanced clinical decision making in prostate cancer screening.
PLOS ONE, 17 (7).
e0271183-.
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Abstract
<h4>Purpose</h4>Rising complexity of patients and the consideration of heterogeneous information from various IT systems challenge the decision-making process of urological oncologists. Siemens AI Pathway Companion is a decision support tool that provides physicians with comprehensive patient information from various systems. In the present study, we examined the impact of providing organized patient information in comprehensive dashboards on information quality, effectiveness, and satisfaction of physicians in the clinical decision-making process.<h4>Methods</h4>Ten urologists in our department performed the entire diagnostic workup to treatment decision for 10 patients in the prostate cancer screening setting. Expenditure of time, information quality, and user satisfaction during the decision-making process with AI Pathway Companion were recorded and compared to the current workflow.<h4>Results</h4>A significant reduction in the physician's expenditure of time for the decision-making process by -59.9% (p < 0,001) was found using the software. System usage showed a high positive effect on evaluated information quality parameters completeness (Cohen's d of 2.36), format (6.15), understandability (2.64), as well as user satisfaction (4.94).<h4>Conclusion</h4>The software demonstrated that comprehensive organization of information improves physician's effectiveness and satisfaction in the clinical decision-making process. Further development is needed to map more complex patient pathways, such as the follow-up treatment of prostate cancer.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Humans, Prostatic Neoplasms, Prostate-Specific Antigen, Decision Making, Artificial Intelligence, Male, Early Detection of Cancer, Clinical Decision-Making |
| Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences > School of Medicine |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 08 Nov 2022 11:54 |
| Last Modified: | 26 Oct 2023 15:44 |
| DOI: | 10.1371/journal.pone.0271183 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3166047 |
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