Transforming ED Throughput With AI-Driven Clinical Decision Support System (TEDAI)

Transforming ED Throughput With AI-Driven Clinical Decision Support System (TEDAI): The Impact on the Delivery of Care and Patient Experience

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.

Study Overview

• Status: Completed
• Conditions: Critical Care; Emergency Treatment; Triage; Readmission
• Intervention / Treatment: Other: AI-assisted models providing diagnosis and prognostic information; Procedure: Critical treatment

• Study Type: Interventional
• Enrollment (Actual): 4016
• Phase: Not Applicable

Contacts and Locations

Study Locations

• Taiwan
  • Taipei, Taiwan
    • National Taiwan University Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 20 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

• ED patients aged 20 years or older
• Patients were treated by the recruited 16 ED attendings.

Exclusion Criteria:

• Patients aged less than 20 years.
• Patients were not treated by the recruited 16 ED attendings.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Health Services Research
• Allocation: Randomized
• Interventional Model: Crossover Assignment
• Masking: None (Open Label)

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Active Comparator: AI-assisted; AI-assisted models providing diagnosis and prognostic information	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.
Placebo Comparator: Usual care; usual care without AI-assisted models providing diagnosis and prognostic information	Procedure: Critical treatment; Critical treatment of the emergency room

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Time Frame
ED length of stay	From ED arrival to 3 days after ED discharge. For hospitalized patients with cardiac arrest, the outcome ascertainment continues until hospital discharge.

Collaborators and Investigators

• Sponsor: National Taiwan University Hospital
• Investigators: Study Chair: Dr. Huang, National Taiwan University Hospital

Study record dates

Study Major Dates

• Study Start (Actual): August 30, 2022
• Primary Completion (Actual): December 31, 2022
• Study Completion (Actual): April 27, 2023

Study Registration Dates

• First Submitted: February 28, 2022
• First Submitted That Met QC Criteria: February 28, 2022
• First Posted (Actual): March 9, 2022

Study Record Updates

• Last Update Posted (Actual): August 1, 2023
• Last Update Submitted That Met QC Criteria: July 28, 2023
• Last Verified: July 1, 2022

More Information

Terms related to this study

• Keywords: Triage; Critical Care; Readmission; Emergency Treatment
• Additional Relevant MeSH Terms: Pathologic Processes; Disease Attributes; Emergencies
• Other Study ID Numbers: 202108090RINC

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No