Abstract

Morbidity and mortality due to “medical errors” compel better understanding of health care as a system. Probabilistic risk assessment (PRA) has been used to assess the designs of high hazard, low risk systems such as commercial nuclear power plants and chemical manufacturing plants and is now being studied for its potential in the improvement of patient safety. PRA examines events that contribute to adverse outcomes through the use of event tree analysis and determines the likelihood of event occurrence through fault tree analysis. It complements tools already in use in patient safety such as failure modes and effects analyses (FMEAs) and root cause analyses (RCAs). PRA improves on RCA by taking account of the more complex causal interrelationships that are typical in health care. It also enables the analyst to examine potential solution effectiveness by direct graphical representations. However, PRA simplifies real world complexity by forcing binary conditions on events, and it lacks adequate probability data (although recent developments help to overcome these limitations). Its reliance on expert assessment calls for deep domain knowledge which has to come from research performed at the “sharp end” of acute care.