Critical CareTEDAINCT05272267
Transforming ED Throughput With AI-Driven Clinical Decision Support System
A study testing AI-assisted models providing diagnosis and prognostic information and Critical treatment in people with Critical Care and Emergency Treatment, run by National Taiwan University Hospital at 1 site in Taiwan.
Trial 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. Read more about this study
- Who is it for?
- Ages 20 years and older
- Conditions the study is for
- Critical Care, Emergency Treatment, Triage
- What is being tested?
- AI-assisted models providing diagnosis and prognostic information and Critical treatment
- Type of treatment
- Surgery or procedure
- Could I receive a placebo?
- Yes, some participants may receive a placebo
- How many people
- 4,016 participants
- Where
- 1 siteTaiwan
- Study dates
- 30 Aug 2022to 27 Apr 2023
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 20 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- ED patients aged 20 years or older
- Patients were treated by the recruited 16 ED attendings.
Who cannot join
- Patients aged less than 20 years.
- Patients were not treated by the recruited 16 ED attendings.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: 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.
- Conditions
- Critical Care, Emergency Treatment, Triage, Readmission
- Being tested
- AI-assisted models providing diagnosis and prognostic information, Critical treatment
- Keywords
- Critical Care, Emergency Treatment, Triage, Readmission
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-assisted models providing diagnosis and prognostic informationOther
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.
Critical treatmentProcedure or surgery
Critical treatment of the emergency room
| Study group | Role | What they receive |
|---|---|---|
| AI-assisted | Active comparator | AI-assisted models providing diagnosis and prognostic information Receives: Other: AI-assisted models providing diagnosis and prognostic information |
| Usual care | Placebo comparator | usual care without AI-assisted models providing diagnosis and prognostic information Receives: Procedure: Critical treatment |
What is being measured
The primary outcome is the main question the study is designed to answer.
ED length of stay
Measured: From ED arrival to 3 days after ED discharge. For hospitalized patients with cardiac arrest, the outcome ascertainment continues until hospital discharge.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Mar 2022
- Study start30 Aug 2022
- Primary completion31 Dec 2022
- Study completion27 Apr 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Critical CareStudies of the same condition, open and under way.
- More trials run by National Taiwan University HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- None (open label)
- Enrolment
- 4,016 participants (actual)
Sponsor and investigators
- Lead sponsor
- National Taiwan University Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Dr. Huang, Study chair, National Taiwan University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05272267
- Sponsor's protocol ID
- 202108090RINC
- Acronym
- TEDAI
- Status last verified
- Jul 2022
- First submitted
- 28 Feb 2022
- Last updated
- 1 Aug 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 1 Aug 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Transforming ED Throughput With AI-Driven Clinical Decision Support System (TEDAI): The Impact on the Delivery of Care and Patient Experience
- ClinicalTrials.gov ID
- NCT05272267
- Acronym / sponsor ID
- TEDAI / 202108090RINC
- Conditions
- Critical Care, Emergency Treatment, Triage, Readmission
- Keywords
- Critical Care, Emergency Treatment, Triage, Readmission
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 4,016 participants (actual)
- Lead sponsor
- National Taiwan University HospitalUniversity, hospital or other
- Study start
- 30 Aug 2022 (actual)
- Primary completion
- 31 Dec 2022 (actual)
- Study completion
- 27 Apr 2023 (actual)
- First posted
- 9 Mar 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AI-assisted | Active comparator | Other: AI-assisted models providing diagnosis and prognostic informationAI-assisted models providing diagnosis and prognostic information |
| Usual care | Placebo comparator | Procedure: Critical treatmentusual care without AI-assisted models providing diagnosis and prognostic information |
Who may qualify
- Ages
- 20 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (2 listed)
- ED patients aged 20 years or older
- Patients were treated by the recruited 16 ED attendings.
Key exclusion criteria (2 listed)
- Patients aged less than 20 years.
- Patients were not treated by the recruited 16 ED attendings.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-assisted models providing diagnosis and prognostic informationAI-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. | Other | AI-assisted |
| Critical treatmentCritical treatment of the emergency room | Procedure or surgery | Usual care |
Primary outcome
ED length of stay
Time frame: From ED arrival to 3 days after ED discharge. For hospitalized patients with cardiac arrest, the outcome ascertainment continues until hospital discharge.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 1 Aug 2023.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Dr. Huang study chair
National Taiwan University Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI-assisted models providing diagnosis and prognostic information and Critical treatment in people with Critical Care and Emergency Treatment, run by National Taiwan University Hospital at 1 site in Taiwan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 1 Aug 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Taiwan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. Some participants in this study may receive a placebo. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
ED length of stay (time frame: From ED arrival to 3 days after ED discharge. For hospitalized patients with cardiac arrest, the outcome ascertainment continues until hospital discharge.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 1 Aug 2023.