P-WIN deployment: real-time personalized machine learning to predict critical deterioration in a pediatric intensive care unit
In brief
Machine-learning alerts detected 73% of pediatric ICU deterioration events up to 24 hours early
At one children's hospital, the personalized system detected 73 of 99 critical deterioration events among 1,005 patients, compared with 39% for the automated warning tool and 23% for clinician huddles. It issued about one alert every five patient-days, with a similar positive predictive value to the existing workflows. Whether earlier alerts improve patient outcomes remains unknown.
- Journal
- JAMIA open (Q1)
- Published
- 19 October 2026
- Study design
- Prospective / inception cohort
- Evidence level
- Level 2, Moderate (CEBM 2b)
- Authors
- Eamonn Tweedy, Sanjiv Mehta, Sachin Grover, Hannah R Stinson, Victor M Ruiz, Erika Pleskunas, et al.
- PMID
- 42899309
- DOI
- 10.1093/jamiaopen/ooag202
Why clinicians should know about it
- Picked for Health Informatics (paper of the day, 21 October 2026): Prospective real-world deployment of EHR-linked deterioration alerts
Abstract
OBJECTIVES: Early recognition of critical deterioration events (CDEs) in pediatric intensive care units (PICUs) is crucial to improving outcomes, but the real-time performance of machine-learning (ML)-based alert systems is rarely assessed. We prospectively evaluated the performance, operational utility, and deployment feasibility of the PICU Warning Index (P-WIN), a personalized ML alert system predicting CDE in the PICU. MATERIALS AND METHODS: In this prospective cohort study at a quaternary-care children's hospital, P-WIN estimated deterioration risk for PICU patients aged 0-24 years at 30-minute intervals between January 7 and April 7, 2026 using electronic health record data collected via Fast Healthcare Interoperability Resources and proprietary EPIC API endpoints. CDEs included cardiopulmonary resuscitation, initiation of extracorporeal membrane oxygenation, dilute (10 μg/mL) epinephrine bolus, or unplanned endotracheal intubation. P-WIN was compared to baseline workflows: an automated PICU Warning Tool and a clinician-huddle-based assessment. Primary measures were CDE-level sensitivity, admission-level positive predictive value (PPV), and alerts per patient-day. RESULTS: Among 1005 PICU patients, 99 CDEs were experienced by 62 patients. P-WIN detected 73% of these CDE 1-24 hours in advance, outperforming the PICU Warning Tool (39%, P < .05) and huddle workflow (23%, P < .05). P-WIN generated 0.21 (95% CI, 0.18-0.23) alerts per patient-day. The PICU Warning Tool and huddle workflow had similar PPV to P-WIN. CONCLUSION: P-WIN detected significantly more CDEs than existing PICU Warning tool and huddle workflows while maintaining a similar PPV. Our findings underscore the feasibility of ML-based personalized warning systems to enhance shared situational awareness of PICU deterioration.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.