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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| AI-assisted | Active Comparator | AI-assisted models providing diagnosis and prognostic information |
|
| Usual care | Placebo Comparator | usual care without AI-assisted models providing diagnosis and prognostic information |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| AI-assisted models providing diagnosis and prognostic information | Other | 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. |
| Measure | Description | 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. |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Dr. Huang | National Taiwan University Hospital | Study Chair |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| National Taiwan University Hospital | Taipei | Taiwan |
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| Critical treatment | Procedure | Critical treatment of the emergency room |
|