Original Articles: PDF OnlyMedical Application of Engineering Risk Analysis and Anesthesia Patient Risk IllustrationPaté-Cornell, ElisabethAuthor Information Department of Industrial Engineering and Engineering Management, Stanford University, Stanford, CA, USA. The study of human and management factors in risk analysis was supported by the National Science Foundation under grant # SBR 9422946. The application of that method to the case of anesthesia patient risk was funded by the Anesthesia Patient Safety Foundation. American Journal Of Therapeutics: September 1999 - Volume 6 - Issue 5 - p 245-256 Buy Abstract The engineering risk analysis method can be extended to include some human and organizational factors and can be used in the medical domain; this transfer is illustrated by a description of a study of anesthesia patient risk. This study involves first a dynamic analysis of accident risks. The model is then extended by relating the basic events of accident scenarios to the state of the practitioner described by the probability of personal problems that may affect his or her level of competence and alertness. These potential problems, in turn, are linked (by probabilistic relations) to the way the system is managed. This extension of the analytical framework allows assessment of the effect of particular types of practitioner problems and therefore of corresponding risk mitigation measures on the probability of the different accident scenarios. The risk analysis model can then be used as a management tool that permits setting priorities among patient safety measures, based either on the sole benefits of the corresponding decrease of patient risk or on a cost-to-benefit ratio. This probabilistic approach constitutes a departure from the classic risk studies exclusively based on statistical frequencies because it involves both available statistics and expert opinions. It is commonly used in engineering for systems for which there is not enough information at the time when decisions need to be made. I show here how the probabilistic model can be used in the medical field to support patient safety decisions before complete data sets can be gathered or in cases in which some key factors are not directly observable. © 1999 Lippincott Williams & Wilkins, Inc.