title: An evaluation of an expert system for detecting critical events during anesthesia in a human patient simulator: a prospective randomized controlled study.
abstract: BACKGROUND
  Perioperative monitoring systems produce a large amount of uninterpreted data, use threshold alarms prone to artifacts, and rely on the clinician to continuously visually track changes in physiological data. To address these deficiencies, we developed an expert system that provides real-time clinical decisions for the identification of critical events. We evaluated the efficacy of the expert system for enhancing critical event detection in a simulated environment. We hypothesized that anesthesiologists would identify critical ventilatory events more rapidly and accurately with the expert system.
  
  
  METHODS
  We used a high-fidelity human patient simulator to simulate an operating room environment. Participants managed 4 scenarios (anesthetic vapor overdose, tension pneumothorax, anaphylaxis, and endotracheal tube cuff leak) in random order. In 2 of their 4 scenarios, participants were randomly assigned to the expert system, which provided trend-based alerts and potential differential diagnoses. Time to detection and time to treatment were measured. Workload questionnaires and structured debriefings were completed after each scenario, and a usability questionnaire at the conclusion of the session. Data were analyzed using a mixed-effects linear regression model; Fisher exact test was used for workload scores.
  
  
  RESULTS
  Twenty anesthesiology trainees and 15 staff anesthesiologists with a combined median (range) of 36 (29-66) years of age and 6 (1-38) years of anesthesia experience participated. For the endotracheal tube cuff leak, the expert system caused mean reductions of 128 (99% confidence interval [CI], 54-202) seconds in time to detection and 140 (99% CI, 79-200) seconds in time to treatment. In the other 3 scenarios, a best-case decrease of 97 seconds (lower 99% CI) in time to diagnosis for anaphylaxis and a worst-case increase of 63 seconds (upper 99% CI) in time to treatment for anesthetic vapor overdose were found. Participants were highly satisfied with the expert system (median score, 2 on a scale of 1-7). Based on participant debriefings, we identified avoidance of task fixation, reassurance to initiate invasive treatment, and confirmation of a suspected diagnosis as 3 safety-critical areas.
  
  
  CONCLUSIONS
  When using the expert system, clinically important and statistically significant decreases in time to detection and time to treatment were observed for the endotracheal tube cuff Leak scenario. The observed differences in the other 3 scenarios were much smaller and not statistically significant. Further evaluation is required to confirm the clinical utility of real-time expert systems for anesthesia.
authors.affiliation: Department of Electrical and Computer Engineering in Medicine Pediatric Anesthesia Research Team, BC Children's Hospital, 1L7-4480 Oak St., Vancouver, BC V6H 3V4, Canada. mgorges@cw.bc.ca
authors.forename: Matthias
authors.initials: M
authors.lastname: Görges
authors.forename: Pamela
authors.initials: P
authors.lastname: Winton
authors.forename: Valentyna
authors.initials: V
authors.lastname: Koval
authors.forename: Joanne
authors.initials: J
authors.lastname: Lim
authors.forename: Jonathan
authors.initials: J
authors.lastname: Stinson
authors.forename: Peter T
authors.initials: PT
authors.lastname: Choi
authors.forename: Stephan K W
authors.initials: SK
authors.lastname: Schwarz
authors.forename: Guy A
authors.initials: GA
authors.lastname: Dumont
authors.forename: J Mark
authors.initials: JM
authors.lastname: Ansermino
mesh_terms: D000328:Adult; D000368:Aged; D000707:Anaphylaxis / Q000209:etiology / Q000628:therapy; D000768:Anesthesia, General / Q000009:adverse effects* / Q000295:instrumentation; D018685:Anesthetics, Inhalation / Q000009:adverse effects; D001955:British Columbia; D056902:Clinical Alarms; D002983:Clinical Competence; D003198:Computer Simulation; D062787:Drug Overdose / Q000628:therapy; D004867:Equipment Design; D004868:Equipment Failure; D005103:Expert Systems; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D008348:Manikins; D008875:Middle Aged; D015999:Multivariate Analysis; D009873:Operating Rooms; D011030:Pneumothorax / Q000209:etiology / Q000628:therapy; D011446:Prospective Studies; D012815:Signal Processing, Computer-Assisted; D011795:Surveys and Questionnaires; D013647:Task Performance and Analysis; D013997:Time Factors; D061665:Time-to-Treatment; D016526:Workload; D055815:Young Adult
chemical_list: D018685:Anesthetics, Inhalation