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Transforming ED Throughput With AI-Driven Clinical Decision Support System

NCT05272267 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 4016

Last updated 2023-08-01

No results posted yet for this study

Summary

The aims of this study is to integrate real-time data flow infrastructure between hospital information system and AI models and to conduct a cluster randomized crossover trial to evaluate the efficacy of the AI models in improving patient flow and relieving ED crowding.

Conditions

  • Critical Care
  • Emergency Treatment
  • Triage
  • Readmission

Interventions

OTHER

AI-assisted models providing diagnosis and prognostic information

AI-assisted models providing diagnosis and prognostic information in the ED, including triage, ICD coding, chest x ray alerts, critical event alerts, readmission prediction, and post-cardiac arrest prognostication.

PROCEDURE

Critical treatment

Critical treatment of the emergency room

Sponsors & Collaborators

  • National Taiwan University Hospital

    lead OTHER

Principal Investigators

  • Dr. Huang · National Taiwan University Hospital

Study Design

Allocation
RANDOMIZED
Purpose
HEALTH_SERVICES_RESEARCH
Masking
NONE
Model
CROSSOVER

Eligibility

Min Age
20 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2022-08-30
Primary Completion
2022-12-31
Completion
2023-04-27

Countries

  • Taiwan

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05272267 on ClinicalTrials.gov