Evaluation of Emergency Department AI Prediction Algorithm
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- Mayo Clinic
- 入组人数
- 80
- 试验地点
- 1
- 主要终点
- Hospital Admissions
研究概览
简要总结
The purpose of this study is to evaluate the impact of an AI admission prediction tool on the number of preventable hospital admissions, emergency department (ED) length of stay, when the predictions are displayed only to a dedicated ED triage team. Also, to evaluate user perceptions of the AI tool among the triage team users and medical officer of the day users. Additionally, to evaluate any impact of the AI tool on the number of interventions performed by the triage team, and to evaluate the impact of the tool on time-to-admission after an admission order is placed.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •For the survey component, any HIM clinician that works a shift in the triage area, ED physicians, and the medical officer of the day will be included.
- •For length of stay data, adult patients registered in the Mayo Clinic-Rochester St. Mary's Emergency Department will be included.
排除标准
- •For the survey, clinicians not working a triage shift during the study period will be excluded.
- •For the length of stay analysis, only adult ED patients will be included, who do not triaged to the behavioral health/psychiatry pathway, nor patients who are triaged to the Emergency Department observation pathway.
结局指标
主要结局
Hospital Admissions
时间窗: 282 days
Number of avoidable admissions prevented as a fraction of all ED patients in a day, specifically, the number of patients who were seen by the SAPPHIRE triage team and discharged home
次要结局
未报告次要终点
研究者
Alexander J. Ryu
Principal Investigator
Mayo Clinic