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. CONCLUSION: 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.
RCT Entities:
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 humanpatient 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. CONCLUSION: 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: David P Seamans; Boshra F Louka; F David Fortuin; Bhavesh M Patel; John P Sweeney; Louis A Lanza; Patrick A DeValeria; Kim M Ezrre; Harish Ramakrishna Journal: Ann Card Anaesth Date: 2016-10
Authors: Melanie C Wright; Damian Borbolla; Rosalie G Waller; Guilherme Del Fiol; Thomas Reese; Paige Nesbitt; Noa Segall Journal: J Biomed Inform X Date: 2019-06-22
Authors: Plinio P Morita; Peter B Weinstein; Christopher J Flewwelling; Carleene A Bañez; Tabitha A Chiu; Mario Iannuzzi; Aastha H Patel; Ashleigh P Shier; Joseph A Cafazzo Journal: Crit Care Date: 2016-08-20 Impact factor: 9.097