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Related Experiment Video

Updated: Jun 11, 2025

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
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Development of an Automated Triage System for Longstanding Dizzy Patients Using Artificial Intelligence.

Santiago Romero-Brufau1,2, Robert J Macielak1, Jeffrey P Staab1,3

  • 1Department of Otolaryngology-Head and Neck Surgery Mayo Clinic Rochester Minnesota USA.

OTO Open
|September 30, 2024
PubMed
Summary
This summary is machine-generated.

This study developed an artificial intelligence algorithm to automate scheduling for patients with dizziness. The AI achieved 79% concordance, improving upon manual clinician triage at 70%.

Keywords:
Dizziness Handicap Inventorydizzinessfunctional vestibular disorderpsychiatric disordervestibular dysfunction

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Medical Informatics
  • Patient Scheduling Optimization

Background:

  • Managing multidisciplinary consultations for patients with longstanding dizziness is complex and time-consuming.
  • Manual triage processes by clinicians can be inefficient and costly.
  • Optimizing appointment scheduling is crucial for timely patient care.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI) driven algorithm for automating and optimizing the scheduling of multidisciplinary consultations for patients experiencing longstanding dizziness.
  • To compare the performance of the AI triage system against manual clinician triage.

Main Methods:

  • A retrospective case review of 98 patients with longstanding dizziness was conducted at a quaternary referral center.
  • A previsit self-report questionnaire was used to gather patient-reported symptoms.
  • An expert panel retrospectively determined ideal appointment schedules; a machine learning algorithm was trained and validated using this data.

Main Results:

  • The AI-driven triage algorithm achieved a mean concordance of 79% in scheduling ideal patient appointments.
  • Manual triage by clinicians showed a mean concordance of 70% when compared to the expert panel's ideal schedules.
  • The AI algorithm demonstrated comparable, and slightly improved, performance to manual triage.

Conclusions:

  • Automating the triage process for dizzy patients using AI can streamline scheduling and potentially reduce costs associated with manual processes.
  • The first-generation automated triage algorithm shows promise in effectively managing complex patient scheduling.
  • AI-powered tools can aid in optimizing multidisciplinary consultations for specialized patient populations.