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Engineering Risk Analysis of a Hospital Oxygen Supply System

Department ofManagement Science and Engineering, Stanford University, Stanford, California

Gee Liek Yeo, PhD

206, John A. Blume Earthquake Engineering Center, Department of Civil and Environmental Engineering, Stanford, CA 94305-4020 geeliek{at}stanford.edu

Adam Seiver, MD, PhD

M. Elisabeth Paté-Cornell, PhD

Department ofManagement Science and Engineering, Stanford University, Stanford, California

Reports from the Food and Drug Administration (FDA) and the Joint Commission on Accreditation of Healthcare Organizations (JCAHO) have emphasized the potential for injury to patients caused by failures in oxygen supply systems. This article presents a model of patient risk related to the process of supplying oxygen at a single university hospital. One of the goals of the article is to illustrate how probabilistic risk analysis (PRA) can be used by hospitals to assess and mitigate risk and, therefore, to meet JCAHO requirements. PRA techniques are useful to 1) model the reliability of a complex system and 2) assess the cost-effectiveness of different risk mitigation measures. The authors focus on the risk estimation step, describing in detail their modeling of the oxygen supply system and analysis of the results. For the hospital that the authors study (20,000 admissions yearly), the total expected number of fatalities from oxygen system failure is 44 over a 30-year time horizon. The greatest contribution to the risk (94% of the expected number of fatalities) comes from problems that involve the supply network (e.g., damage to structure and poisoning) as opposed to incidents that occur inside patient rooms. Although the threat to patient safety is not dramatic, health care organizations should be concerned about potential failures of their oxygen system because improving this system could avoid low-probability, high-consequence failures at a low cost

Key Words: probabilistic risk analysis • hospital oxygen supply system • patient safety • decision support • engineering modeling

Medical Decision Making, Vol. 26, No. 2, 162-172 (2006)
DOI: 10.1177/0272989X06286477


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