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.
Data availability
The datasets generated during the current study are not publicly available because we are still analyzing secondary outcomes, but are available for replication from the corresponding author at reasonable request.
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Funding
This study was funded by the Frederick Thomas Advanced Medical Analytics Fund, University of Virginia and AHRQ R01HS028803 (Keim-Malpass/Bourque MPI). The investigators received in-kind support from Nihon Kohden Digital Health Solutions for use and support of the CoMET system. The funders had no direct role/oversight of this published work.
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JKM and JMB secured funding and maintained oversight of the studyJKM, SJR, MTC, KNK, LPM, JRM, and JMB conceptualized the study KNK, SH, GY, and AN collected primary data for the studySJR and MKJ conducted analyses for the studyJKM drafted the initial manuscriptJKM, SJR, MTC, KNK, OJM, SH, GY, MKJ, AN, LPM, JRM, JMB provided edits and approved the final manuscript.
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MC is an employee of Nihon Kohden Digital Health Solutions. LPM and JRM are consultants for Nihon Kohden Digital Health Solutions. JKM owns equity shares of ArteraAI and JRM owns equity shares of Medical Predictive Science Corporation, whose products are not discussed in this unrelated work. The other authors decalre no competing interests.
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Keim-Malpass, J., Ratcliffe, S.J., Clark, M.T. et al. A randomized controlled trial of artificial intelligence-based analytics for clinical deterioration. Sci Rep (2026). https://doi.org/10.1038/s41598-026-39051-z
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DOI: https://doi.org/10.1038/s41598-026-39051-z
