Utilization of an Innovative Decision Support Software System to Enhance Detection of Decompensating Ward Patients and Reduce Mortality for Patients Requiring Transfer to Critical Care Units

Authors: Christopher Morgan, JP Herlihy, Christopher Howard, Seanna Davignon



Patients who require transfer from the medical wards to an intensive care unit (ICU) have increased morbidity and mortality compared to patients who are admitted to the ICU directly from the emergency department (ED).  More specifically, patients who require transfer from the hospital ward to the ICU within 24 hours of admission from the ED may have higher rates of adverse events and death.  Delayed recognition of early decompensation and intervention can lead to increased morbidity and mortality; some reports indicate an increase in mortality of up to 30%.  Rapid response teams and systems were developed to identify and respond to these decompensation events and decrease hospital mortality and non-ICU cardiac arrests.


Historically at our institution (Baylor St. Luke’s Medical Center; Houston, TX, USA), rapid response teams (RRT) respond to patient emergencies on the wards.  Recently, in order to recognize decompensation events earlier, an innovative decision support software system was installed to alert RRTs to patient decompensation.  In real-time, this software system continuously calculated a vital sign-based early warning score; once the score reached an abnormal threshold, the system alerted the RRTs to conduct a bedside evaluation. We analyzed data from patients who had an ICU transfer within 24 hours of hospital ward admission from the ED.  We compared data for four months before and after installation of the early warning, decision support software system.


During the study period, 2.1% of ward admissions from the ED (n=46) in the pre-intervention period versus 2.8% (n=78) in the post-intervention period required transfer to the ICU within 24 hours (p=0.141).  Hospital mortality rates for these patients decreased from 21.7% (n=10) in the pre-intervention period to 6.4% (n=5) in the post-intervention period (p=0.012).


These results demonstrate that utilization of this real-time surveillance and decision support system resulted in a significant reduction in mortality rates for this specific high-risk group of ICU transfers.


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