Development of an Automated Triage System for Longstanding Dizzy Patients Using Artificial Intelligence.

Autor: Romero-Brufau S; Department of Otolaryngology-Head and Neck Surgery Mayo Clinic Rochester Minnesota USA.; Department of Biostatistics, Harvard T. H. Chan School of Public Health Harvard University Boston Massachusetts USA., Macielak RJ; Department of Otolaryngology-Head and Neck Surgery Mayo Clinic Rochester Minnesota USA., Staab JP; Department of Otolaryngology-Head and Neck Surgery Mayo Clinic Rochester Minnesota USA.; Department of Psychiatry Mayo Clinic Rochester Minnesota USA., Eggers SDZ; Department of Neurology Mayo Clinic Rochester Minnesota USA., Driscoll CLW; Department of Otolaryngology-Head and Neck Surgery Mayo Clinic Rochester Minnesota USA., Shepard NT; Department of Otolaryngology-Head and Neck Surgery Mayo Clinic Rochester Minnesota USA., Totten DJ; Department of Otolaryngology-Head and Neck Surgery Indiana University School of Medicine Indianapolis Indiana USA., Albertson SM; Department of Quantitative Health Sciences Mayo Clinic Rochester Minnesota USA., Pasupathy KS; Department of Biomedical and Health information Sciences University of Illinois-Chicago Chicago Illinois USA., McCaslin DL; Department of Otolaryngology-Head and Neck Surgery University of Michigan Ann Arbor Michigan USA.
Jazyk: angličtina
Zdroj: OTO open [OTO Open] 2024 Sep 27; Vol. 8 (3), pp. e70006. Date of Electronic Publication: 2024 Sep 27 (Print Publication: 2024).
DOI: 10.1002/oto2.70006
Abstrakt: Objective: To report the first steps of a project to automate and optimize scheduling of multidisciplinary consultations for patients with longstanding dizziness utilizing artificial intelligence.
Study Design: Retrospective case review.
Setting: Quaternary referral center.
Methods: A previsit self-report questionnaire was developed to query patients about their complaints of longstanding dizziness. We convened an expert panel of clinicians to review diagnostic outcomes for 98 patients and used a consensus approach to retrospectively determine what would have been the ideal appointments based on the patient's final diagnoses. These results were then compared retrospectively to the actual patient schedules. From these data, a machine learning algorithm was trained and validated to automate the triage process.
Results: Compared with the ideal itineraries determined retrospectively with our expert panel, visits scheduled by the triage clinicians showed a mean concordance of 70%, and our machine learning algorithm triage showed a mean concordance of 79%.
Conclusion: Manual triage by clinicians for dizzy patients is a time-consuming and costly process. The formulated first-generation automated triage algorithm achieved similar results to clinicians when triaging dizzy patients using data obtained directly from an online previsit questionnaire.
Competing Interests: The model described in the manuscript was licensed by Mayo Clinic to a commercial third party, and J.P.S., S.D.Z.E., N.T.S., C.L.W.D., D.L.M., S.M.A., K.S.P., and S.R.‐B. are listed as inventors and received royalties as part of the license agreement. None of the co‐authors have received any income from the company in the last 24 months.
(© 2024 The Author(s). OTO Open published by Wiley Periodicals LLC on behalf of American Academy of Otolaryngology‐Head and Neck Surgery Foundation.)
Databáze: MEDLINE
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