Real-time NOMA Evaluation

David T Huang

Status: Enrolling

Conditions: Critical Illness

Treatments: Device: Unrevealed Alerts; Device: Revealed Alerts

Study type: Interventional

Funder types: Other; NIH

Identifiers

NCT06996626

5R01EB032752-10 (U.S. NIH Grant/Contract)

STUDY25020040

Details and patient eligibility

About

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.

Full 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.

Enrollment: 3,000 estimated patients

Sex: All

Ages: 18 to 100 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• All patients in the Presbyterian and Montefiore ICUs

Exclusion criteria

• None

Trial design

Primary purpose: Treatment

Allocation: Randomized

Interventional model: Sequential Assignment

Masking: None (Open label)

---

3,000 participants in 2 patient groups

Unrevealed Alerts

Active Comparator group

Treatment:

Device: Unrevealed Alerts

Revealed Alerts

Experimental group

Treatment:

Device: Revealed Alerts