Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

OBJECTIVE: The aim of the study was to evaluate the ability of a data-fusion patient status index (PSI) to detect patient deterioration in the emergency department (ED) in comparison with track-and-trigger (T&T). MATERIALS AND METHODS: A single-centre observational cohort study was conducted in a medium-sized teaching hospital ED. Vital sign data and any documented T&T scores (paper T&T) were collected from adults attending the resuscitation room, majors or observation ward. For each set of vital signs, we retrospectively calculated T&T (eT&T). PSI was calculated retrospectively from the continuous vital sign data using a statistical model of normality. Clinical notes were examined to identify 'escalation' events, and the numbers of these escalations identified by paper T&T, eT&T and PSI were retrospectively calculated. RESULTS: Data from 472 patient episodes were examined. A total of 20 patients had PSI data at the time of an escalation related to vital sign abnormalities that occurred during their ED stay (vs. on arrival). Only four patient events were detected at the time by paper T&T. In all, 17 were detected retrospectively by eT&T and 15 by PSI. PSI had a calculated false-alert rate of 1.13 alerts/bed-day. CONCLUSION: Electronic data capture offers opportunities for increased detection of deteriorating patients in a busy clinical environment compared with paper charts. Sample size in this study is insufficient to determine which electronic method (eT&T or PSI) offers superior detection of the need for escalation.

Original publication




Journal article


Eur J Emerg Med

Publication Date





28 - 32


Adult, Cohort Studies, Data Interpretation, Statistical, Emergency Service, Hospital, Female, Hospitals, Teaching, Humans, Male, Middle Aged, Monitoring, Physiologic, Outcome Assessment (Health Care), Systems Integration, United Kingdom, Vital Signs