A randomized controlled trial of artificial intelligence-based analytics for clinical deterioration

This pragmatic randomized controlled trial aimed to assess the effect of a passive display of artificial intelligence (AI)-based predictive analytics on hours free of clinical deterioration events among medical and surgical patients in an acute care cardiology medical-surgical ward. 10,422 inpatient visits were randomly assigned by cluster to the intervention group of a display of risk trajectories or to a control group of usual medical care. The trial was undertaken on an 85-bed inpatient cardiology and cardiac surgery ward of an academic hospital with a substantial implementation and education plan. This was a passive display with no specific response mandated. The primary analysis compared events of clinical deterioration (death, emergent ICU transfer, emergent endotracheal intubation, cardiac arrest, or emergent surgery) and compared mortality 21 days after admission. Patients with a large spike in risk score had, on average, twice the length of hospital stay (6.8 compared to 3.4 days). There was no change in the primary outcome between groups. Among those who had a clinical event, there were more event-free hours in the intervention/display-on group compared to the standard-of-care/display-off, but this did not reach statistical significance. Clinicians chose to transfer 11% of patients into or out of display beds, a censoring event removing them from the analysis, thereby undermining aspects of the randomized nature of the study. Predictive analytics monitoring incorporating continuous cardiorespiratory monitoring and displays of risk trajectories coupled with an education plan did not improve patient outcomes. While necessary to conduct the study, the pragmatic design allowed for significant movement towards intervention/displayed beds for sicker patients. Design considerations in the future must focus on understanding clinicians' interpretation, care processes, and communication practices.Clinical trial registration number: NCT04359641 Registered 4/24/20.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2026-02-05
DOI
https://doi.org/10.1038/s41598-026-39051-z
Primary Topic
Sepsis Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A randomized controlled trial of artificial intelligence-based analytics for clinical deterioration

Susan Hamil, Jamieson M. Bourque, Ashley M. Nelson, Jessica Keim‐Malpass et al.
Scientific Reports
Sepsis Diagnosis and Treatment
article

A randomized controlled trial of artificial intelligence-based analytics for clinical deterioration

Susan Hamil, Jamieson M. Bourque, Ashley M. Nelson, Jessica Keim‐Malpass, Katy Nash Krahn, Liza P Moorman, Oliver Monfredi, Gholamreza Yousefvand, M. Jones, J. Randall Moorman, Sarah J. Ratcliffe, Matthew T. Clark
article en

Abstract

This pragmatic randomized controlled trial aimed to assess the effect of a passive display of artificial intelligence (AI)-based predictive analytics on hours free of clinical deterioration events among medical and surgical patients in an acute care cardiology medical-surgical ward. 10,422 inpatient visits were randomly assigned by cluster to the intervention group of a display of risk trajectories or to a control group of usual medical care. The trial was undertaken on an 85-bed inpatient cardiology and cardiac surgery ward of an academic hospital with a substantial implementation and education plan. This was a passive display with no specific response mandated. The primary analysis compared events of clinical deterioration (death, emergent ICU transfer, emergent endotracheal intubation, cardiac arrest, or emergent surgery) and compared mortality 21 days after admission. Patients with a large spike in risk score had, on average, twice the length of hospital stay (6.8 compared to 3.4 days). There was no change in the primary outcome between groups. Among those who had a clinical event, there were more event-free hours in the intervention/display-on group compared to the standard-of-care/display-off, but this did not reach statistical significance. Clinicians chose to transfer 11% of patients into or out of display beds, a censoring event removing them from the analysis, thereby undermining aspects of the randomized nature of the study. Predictive analytics monitoring incorporating continuous cardiorespiratory monitoring and displays of risk trajectories coupled with an education plan did not improve patient outcomes. While necessary to conduct the study, the pragmatic design allowed for significant movement towards intervention/displayed beds for sicker patients. Design considerations in the future must focus on understanding clinicians' interpretation, care processes, and communication practices.Clinical trial registration number: NCT04359641 Registered 4/24/20.

Scientific ReportsVol. 16(1)
University of Virginia Health System (US), University of Virginia Medical Center (US), University of Virginia (US)
Agency for Healthcare Research and Quality
Quality Education
Openalex Percentile: Top 64%
Sepsis Diagnosis and Treatment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.