City Research Online

Abstract

The routine use of pulse oximeters, non-invasive blood pressure monitors and electrocardiogram monitors has considerably improved patient care in the post anaesthesia period. Using an automated data collection system (ARRES), the occurrence of several adverse events frequently revealed by these monitors has been investigated. The ARRES is an on-line Post Anaesthesia Care Unit clinical management system designed to minimise artifact, demonstrate the feasibility of collecting and processing data, and identify variables that predict adverse events. Results indicated that the overall incidence of hypoxia was 35%, hypertension 12%, hypotension 8%, tachycardia 25% and bradycardia 1%. Discriminant analysis was able to correctly predict classification of about 90% of patients into normal versus hypertensive or hypotensive groups. Use of the ARRES system, increased the yield of adverse physiological events to 50%, up from 20% in our retrospective study in which data were collected manually. The ARRES system minimised artifact through the use of data validation rules, collected continuous on-line data, and was able to identify variables that predict adverse events. It is anticipated the PACU clinical management system designed in this project would become a part of a larger process which consists of collecting information about the patient, identifying adverse events and suggesting a course of action.

Publication Type:	Thesis (Doctoral)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Departments:	School of Science & Technology > Computer Science School of Science & Technology > School of Science & Technology Doctoral Theses Doctoral Theses

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