Automated detection of physiologic deterioration in hospitalized patients. (27th August 2014)

• Record Type: Journal Article
• Authors: Evans, R Scott; Kuttler, Kathryn G; Simpson, Kathy J; Howe, Stephen; Crossno, Peter F; Johnson, Kyle V; Schreiner, Misty N; Lloyd, James F; Tettelbach, William H; Keddington, Roger K; Tanner, Alden; Wilde, Chelbi; Clemmer, Terry P
• Is Part Of: Journal of the American Medical Informatics Association. Volume 22:Number 2(2015:Mar.)
• Page Start: 350
• Page End: 360
• Publication Date: 2014-08-27
• Subjects: Clinical Decision Support -- physiologic deterioration -- medical emergency team -- patient specific alerts; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1136/amiajnl-2014-002816
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective Develop and evaluate an automated case detection and response triggering system to monitor patients every 5 min and identify early signs of physiologic deterioration. Materials and methods A 2-year prospective, observational study at a large level 1 trauma center. All patients admitted to a 33-bed medical and oncology floor (A) and a 33-bed non-intensive care unit (ICU) surgical trauma floor (B) were monitored. During the intervention year, pager alerts of early physiologic deterioration were automatically sent to charge nurses along with access to a graphical point-of-care web page to facilitate patient evaluation. Results Nurses reported the positive predictive value of alerts was 91–100% depending on erroneous data presence. Unit A patients were significantly older and had significantly more comorbidities than unit B patients. During the intervention year, unit A patients had a significant increase in length of stay, more transfers to ICU (p = 0.23), and significantly more medical emergency team (MET) calls (p = 0.0008), and significantly fewer died (p = 0.044) compared to the pre-intervention year. No significant differences were found on unit B. Conclusions We monitored patients every 5 min and provided automated pages of early physiologic deterioration. This before–after study found a significant increase in MET calls and a significant decrease in mortality only in the unit with older patients with multiple comorbidities, and thus further study is warranted to detect potential confounding. Moreover, nurses reported the graphical alerts provided information needed to quickly evaluate patients, and they felt more confident about their assessment and more comfortable requesting help.