Application of Decision Analysis to Root Cause Analysis

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Probabilistic Root Cause Analysis Page

Application of Decision Analysis to Root Cause Analysis

By Farrokh Alemi, Ph.D.
Jee Vang
Laskey, Kathryn B.

Last revised on Thursday, January 05, 2006


Listen to lecture on root cause analysis at

Root Cause and Failure Mode Analyses are commonly performed in hospitals to understand factors that contribute to errors and mistakes. Despite the effort that healthcare professionals are putting into creating these analyses, few models of root causes are validated or used to predict future occurrences of adverse events. This chapter shows how assumptions and conclusions of Root Cause Analysis can be verified against observed data. This chapter builds on the Chapter on Modeling Uncertainties and the Chapter on Measuring Uncertainties.

Root Cause Analysis, according to the Joint Commission on Accreditation of Health Care Organizations is a "process for identifying the basic or causal factors that underlie variation in performance, including the occurrence or possible occurrence of a sentinel event." Sentinel events include medication errors, patients' suicide, procedure complications, wrong site surgery, treatment delay, restraint death, elopement death, assault or rape, transfusion death, and infant abduction.  Direct causes bring about the sentinel event without any other intervening event.  Most direct causes are physically proximate to the sentinel event.  The effect of root causes on sentinel events are always through some direct cause.  Because of accreditation requirements and due to renewed interest in patient safety, many hospitals and clinics are actively conducting Root Cause Analyses.

When a sentinel event occurs, most employees are focused on the direct causes that have led to the event.  For example, many will claim that the cause of medication error is a failure to check label against the patient's armband.  But this is just the direct cause.  To get to the real reasons, one should ask why did the clinician not check the label against the armband.  The purpose of Root Cause analysis is to go beyond direct and somewhat apparent causes and figure out the underlying reasons for the event.  The objective is to force one to think harder about the source of the problem.  It is possible that the label was not checked against the armband because the label was missing.  Furthermore it is also possible that the label was missing because the computer was not printing.  Then, the root cause is computer malfunction and the direct cause is the failure to check the label against the armband.  Exhorting employees to check the armband against the label is a waste of time, if there is no label to check in the first place.  A focus on direct causes may prevent the sentinel event for a while, but sooner or later the root cause will lead to a sentinel event.  Inattention to root causes promotes palliative solutions that do not work in the long run.  The value of root cause analysis lies in identifying the true, underlying causes.  An investigation that dos not do this is at best a waste of time and resources, and at worst can exacerbate the problems it was intended to fix.  But how do we know if our speculations about the causes of an event are correct?

To make the situation worse, almost all who conduct Root Cause analyses become overconfident about the accuracy of their own insights. No matter how poorly an analysis is carried out, since there is no way of proving a person wrong, people persist in their own fallacies. Some are even incredulous about the possibility that their imagined causal influences could be wrong. They insist on the correctness of their insights because "it is obvious." Unfortunately, it is not clear why a complex problem, which has led to a sentinel event, which has not been corrected for years, which has been left unaddressed by hundreds of smart people should have such an obvious solution. After all, if the solution was so obvious why was it not adopted earlier? Search for obvious solutions contradicts the elusiveness of correcting for sentinel events. If a sound and reliable method existed for checking the accuracy and consistency of Root Cause Analysis, then employees might correct their misperceptions and not be so overconfident.  

One way to check on accuracy of Root Cause Analysis is to examine time to next sentinel event. Unfortunately, because sentinel events are rare, one has to wait a long time to see rare events occur again, even if no changes were made. Thus, the organization may have little solace by marking time as long periods of time are no sign of success and the event may reoccur any day. An alternative needs to be found to check the accuracy and consistency of Root Cause Analysis without having to wait for the next sentinel incidence.

Simple methods for checking the accuracy of a Root Cause analysis have not been available to date.  This paper suggests a method for doing so.  As before, clinicians propose a set of causes.  But now several additional steps are taken.  First, probabilities are used to quantify the relationship between causes and effect.  Then, the laws of probability and causal diagrams are examined to see if the suggested causes are consistent with the clinician's other beliefs and with existing objective data.  Through a cycle of testing model assumptions and conclusions against observed data, one improves the accuracy of the analysis and gains new insights into the causes of the sentinel event.
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