Real-time Evaluation of an Outlier-based Alerting System
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 发起方
- 入组人数
- 7,445
- 试验地点
- 4
- 主要终点
- Rate of Clinical Actions Following Revealed vs. Non-Revealed Alerts
研究概览
简要总结
Alerts related to outlier clinician behavior are generated in real-time by an intelligent system continuously scraping EHR (electronic health record) data. These alerts are passed to the bedside and their potential impact on bedside clinical behavior is evaluated.
详细描述
A clinician-informed AI model will generate outlier alerts from real-time review of the EHR (electronic health record) of UPMC Presbyterian/Montefiore ICU patients. These alerts will first be reviewed by an ICU clinician, along with the patients' EHR, for clinical relevance. For those alerts deemed potentially relevant, the ICU clinician will contact the treating ICU clinician (eg, an ICU pharmacist, physician, advanced practice provider) and discuss the alert. The treating ICU clinician will take whatever action, including no action, they deem best.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Sequential
- 主要目的
- Treatment
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 100 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •All patients in the Presbyterian and Montefiore ICUs
排除标准
- 未提供
研究组 & 干预措施
Revealed Alerts
干预措施: Revealed Alerts (Device)
Unrevealed Alerts
干预措施: Unrevealed Alerts (Device)
结局指标
主要结局
Rate of Clinical Actions Following Revealed vs. Non-Revealed Alerts
时间窗: From time of ICU admission until ICU discharge
The primary outcome compares the proportion of alerts that lead to documented clinical actions when revealed to treating ICU clinicians versus when not revealed. Alerts are generated by a decision support system and reviewed daily by ICU study clinicians. On average, 30 alerts are reviewed per ICU per day, with approximately 5 alerts revealed to the treating clinicians. The analysis uses a stepped wedge design with ICU beds as the unit of analysis, where each ICU acts as its own control. The outcome will assess whether revealing alerts increases the rate of appropriate clinical actions taken, as compared to when alerts are withheld.
次要结局
- Hospital Length of Stay(up to 90 days after hospital admission)
- In-Hospital Mortality(Up to 90 days after hospital admission)
- Rate of Any Clinically Responsive Action Following Alerts(Up to 90 days after ICU admission)
- True Positive Alert Rate (TPAR) by Study Group and Alert Type(Up to 90 days after ICU admission)
- Rate of Non-Responsive Actions Following Alerts(Up to 90 days after ICU admission)
- Overall Alert Rate(Through study completion, an average of 2 years)
- Alert Rate per Alerting Model(Daily, up to 90 days)
- Delay Between Alert Generation and First Responsive Action(Measured continuously through study completion, an average of 2 years)
- ICU Length of Stay(Up to 90 days after ICU admission)
- Time Trend of True Positive Alert Rate (TPAR)(Through study completion, an average of 2 years)
研究者
David T Huang
Professor
University of Pittsburgh