• Bhupendra Gohil, Hamid Gholamhhosseini, Michael J Harrison, Andrew Lowe, and Ahmed Al-Jumaily.
• BioMEC, C-45, Auckland University of Technology, Auckland-1142, New Zealand. bhupeshgohil@gmail.com
• Conf Proc IEEE Eng Med Biol Soc. 2007 Jan 1; 2007: 4343-6.

AbstractExpert algorithms in the field of intelligent patient monitoring have rapidly revolutionized patient care thereby improving patient safety. Patient monitoring during anesthesia requires cautious attention by anesthetists who are monitoring many modalities, diagnosing clinically critical events and performing patient management tasks simultaneously. The mishaps that occur during day-to-day anesthesia causing disastrous errors in anesthesia administration were classified and studied by Reason [1]. Human errors in anesthesia account for 82% of the preventable mishaps [2]. The aim of this paper is to develop a clinically useful diagnostic alarm system for detecting critical events during anesthesia administration. The development of an expert diagnostic alarm system called ;RT-SAAM' for detecting critical pathological events in the operating theatre is presented. This system provides decision support to the anesthetist by presenting the diagnostic results on an integrative, ergonomic display and thus enhancing patient safety. The performance of the system was validated through a series of offline and real-time testing in the operation theatre. When detecting absolute hypovolaemia (AHV), moderate level of agreement was observed between RT-SAAM and the human expert (anesthetist) during surgical procedures. RT-SAAM is a clinically useful diagnostic tool which can be easily modified for diagnosing additional critical pathological events like relative hypovolaemia, fall in cardiac output, sympathetic response and malignant hyperpyrexia during surgical procedures. RT-SAAM is currently being tested at the Auckland City Hospital with ethical approval from the local ethics committees.

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