FOURTH ANNUAL CONFERENCE OF THE SCHOOL OF EMERGENCY MEDICINE

OXFORD Tuesday 14th December

ABSTRACT FORM

Title of Paper: Identifying Patient Deterioration in the using Data Fusion Systems

Author(s) Title (Dr/Mr/Miss/Prof), Professional Grade and (Underline name of presenter. If abstract is selected this information will appear in the Programme)

Dr. R. Pullinger, Consultant, John Radcliffe Hospital, Oxford Dr. Sarah Wilson, Consultant, Wexham Park Hospital, Slough Rob Way, Head Nurse, John Radcliffe Hospital, Oxford Dr. D.A Clifton, Biomedical Engineering, Mr. D. Wong, Biomedical Engineering, University of Oxford Ms. S. Fleming, Biomedical Engineering, University of Oxford Prof. L. Tarassenko, Biomedical Engineering, University of Oxford

Mailing Address:

Dr. Rick Pullinger, Emergency Department, John Radcliffe Hospital, Oxford, UK.

E-Mail: [email protected] Telephone: 01865 221173 [email protected]

Date: 13 October, 2010

AUTHOR(S): Dr. R. Pullinger, Consultant, John Radcliffe Hospital Dr. Sarah Wilson, Consultant, Wexham Park Hospital Rob Way, Head Nurse, John Radcliffe Hospital Dr. D.A Clifton, Biomedical Engineering, University of Oxford Mr. D. Wong, Biomedical Engineering, University of Oxford Ms. S. Fleming, Biomedical Engineering, University of Oxford Prof. L. Tarassenko, Biomedical Engineering, University of Oxford

TITLE: Identifying Patient Deterioration in the Emergency Department using Data Fusion Systems

This study was a collaboration between the Emergency Department (ED) of the John Radcliffe Hospital, Oxford, UK, and the Institute of Biomedical Engineering, University of Oxford, UK.

UK NHS are required to use “track-and-trigger” systems in which vital-sign data collected periodically from patients are scored, so that if the scores exceed a pre-defined threshold, care of the patient is escalated. Such systems are typically used in wards where vital-signs are observed every 4-8 hours. The problem being addressed is that vital-sign observations are made much more frequently in the ED, with some patients having vital-signs observed every 5 minutes. This resulted in incomplete scoring of vital-signs, which introduced the possibility of failing to detect patient deterioration.

To determine if the number of incomplete and incorrectly-scored track-and-trigger observations could be reduced, bed-side vital-sign monitors to which the more unwell patients are connected were linked to an intelligent data-fusion system. This system compares vital-sign data acquired by the bed-side monitors against a probabilistic model of “stable” patient behaviour, trained using 3,500 hours of previously acquired vital-sign data. If vital-signs acquired by the bed-side monitors were deemed to be “abnormal” with respect to this probabilistic model, an alert was registered.

A data-fusion system was connected to each bed-side monitor, and 476 patients were consented such that their data could be used for analysis. 207 patients had at least one escalation of care recorded. There were 64 escalations due to physiological abnormalities which occurred after ED admission, while the patient was connected to a bedside monitor. The track-and-trigger and the data-fusion systems correctly identified 22% and 45% of these, respectively. Many of the escalations not identified by the data-fusion system were due to temporary data loss at those times.

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