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

National Taiwan University logo

National Taiwan University

Status

Completed

Conditions

Critical Care
Emergency Treatment
Readmission
Triage

Treatments

Procedure: Critical treatment
Other: AI-assisted models providing diagnosis and prognostic information

Study type

Interventional

Funder types

Other

Identifiers

NCT05272267
202108090RINC

Details and patient eligibility

About

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.

Enrollment

4,016 patients

Sex

All

Ages

20+ years old

Volunteers

No Healthy Volunteers

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.

Trial design

Primary purpose

Health Services Research

Allocation

Randomized

Interventional model

Crossover Assignment

Masking

None (Open label)

4,016 participants in 2 patient groups, including a placebo group

AI-assisted
Active Comparator group
Description:
AI-assisted models providing diagnosis and prognostic information
Treatment:
Other: AI-assisted models providing diagnosis and prognostic information
Usual care
Placebo Comparator group
Description:
usual care without AI-assisted models providing diagnosis and prognostic information
Treatment:
Procedure: Critical treatment

Trial contacts and locations

1

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Data sourced from clinicaltrials.gov

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