This text illustrates one way of using Analytic Graphs for Root Cause Analysis, with an example from healthcare.
What is Root Cause Analysis?
Root Cause Analysis is a method for identifying causes of errors, or more generally, of variations in performance.
It was originally developed in psychology and systems engineering.
It is used in such diverse domains as healthcare, manufacturing, and information technology.
There is no conclusive empirical evidence to confirm that doing Root Cause Analysis in fact reduces risk and improves safety. Nevertheless, it remains widely used.[1,2]
Why perform Root Cause Analysis?
The following passage gives an example of when and why Root Cause Analysis is used in in healthcare.
“Preventable mistakes are common in medicine. For example, at 1 hospital, a patient received patient-controlled analgesia (PCA), a combination of local anesthetic and narcotic. The medication was intended to be infused into the epidural space. Instead, a nurse inadvertently connected the tubing to an intravenous catheter, delivering potentially lethal anesthetic into the patient’s bloodstream. What followed were the nurse’s anguish and guilt and, almost as inevitably, the hospital’s root cause analysis (RCA). In the last decade, this process has become the main way medicine investigates mistakes and tries to prevent future mistakes.”
Why use Analytic Graphs for Root Cause Analysis?
In healthcare, experts estimate that one Root Cause Analysis requires between 20 to 90 person-hours to complete.
It requires communication between different people, representation of information about causes and links between causes, explanations for links and causes, and discussion to reach agreement. The following passage illustrates this.
“A root cause analysis should be performed as soon as possible after the error or variance occurs. Otherwise, important details may be missed. All of the personnel involved in the error must be involved in the analysis. Without all parties present, the dis- cussion may lead to fictionalization or speculation that will di- lute the facts.” 
Participants in Root Cause Analysis can make Analytic Graphs to record causes and causal links, to use the resulting graphs as documentation, to query these graphs to get answers to questions they have while doing analysis, and so on.
The following passage summarises a safety failure, which led to the application of Root Cause Analysis.
“A laboratory aide was cleaning one of the gross dissection rooms where the residents work. This aide was a relatively new employee who had transferred to the department just a few days prior to the event. When she was cleaning the sink in the dis- section room, she accidentally ran her thumb along the length of a dissecting knife—an injury that required 10 to 15 stitches. Since there had been other less serious accidents in this room and several previous attempts to address the safety issues had not been effective, the department completed a root cause analysis.” 
In all graphs below, you can zoom in and out and move the graph around.
First graph: Events and causality
The first graph represents some of the information accumulated in the case.
All links, shown as circles marked with “L”, are labelled “cause” to indicate that each is an instance of the relationship that designates causation. The relationship is binary, irreflexive, antisymmetric, and transitive, that is, the cause relation is a partial order.
Each node is shown as a square, is marked “N” and has two labels:
– One label is “Event” to indicate that the node is an event, that is, an instance of an event class.
– The other label is text describing the event instance.
Second graph: Classification of events
The second graph below was made by adding information to the first graph. The additional information is about the source events in the graph, or the events which are the root causes. Each root cause event now has an additional label, used to indicate the category of that event, following the Eindhoven Classification model of system failure . Each label is an abbreviation, as follows:
– OP: Events due to quality and availability of processes, protocols;
– OC: Events due to organizational culture, that is, collective promotion or suppression of specific behaviors;
– HSS: Failures in applying or performing fine motor skills;
– TEX: Technical failures which are beyond control and responsibility of the relevant organization;
– OEX: Organizational failures which are beyond control and responsibility of the relevant organization.
 Wu, Albert W., Angela KM Lipshutz, and Peter J. Pronovost. “Effectiveness and efficiency of root cause analysis in medicine.” Jama 299.6 (2008) 685-687. Link to the paper.
 Williams, Patricia M. “Techniques for root cause analysis.” Proceedings (Baylor University. Medical Center) 14.2 (2001)- 154. Link to the paper.
 Battles, James B., et al. “The attributes of medical event-reporting systems.” Arch Pathol Lab Med 122.3 (1998): 132-8. Link to the paper.