Real-time Evaluation of an Outlier-based Alerting System
David T Huang
Summary
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.
Description
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.
Eligibility
- Age range
- 18–100 years
- Sex
- All
- Healthy volunteers
- No
Inclusion Criteria: * All patients in the Presbyterian and Montefiore ICUs Exclusion Criteria: * None
Interventions
- DeviceRevealed Alerts
Bedside reveal of alerts generated by the alerting system
- DeviceUnrevealed Alerts
Alerts will be generated but not revealed.
Locations (2)
- UPMC MontefiorePittsburgh, Pennsylvania
- UPMC PresbyterianPittsburgh, Pennsylvania