Root Cause Analysis
Farrokh Alemi, Ph.D.
Jee Vang
Definitions
Root cause analysis is a process for
identifying the causes that underlie
variation in performance, including
the occurrence or possible
occurrence of a sentinel event.
Sentinel event is a major adverse
event that could have prevented (e.g.
wrong side surgery)
Conducting Root Cause Analysis
Before a sentinel event occurs, an
investigative team is organized.
When a sentinel event is reported, the people
closest to the incidence are asked to record
facts (not accusations) about the event.
The investigative team meets and
brainstorms:
–
potential causes for the incidence
–
key constraints that if they were in place would
have prevented the incidence.
Causes are organized into direct and root
causes.
A flow chart is organized showing the direct
causes linked to their effects
Analysis validated by checking assumptions
and accuracy of predictions
Examples
Investigation of eye splash and
needle

stick incidents from an HIV

positive donor on an intensive care
unit
using root cause analysis
The Veterans Affairs
root cause
analysis system in action.
Root cause analysis
in perinatal care
.
Root

cause analysis of an
airway
filter occlusion.
Definitions Continued
Bayesian networks transfer probability
calculus into a Directed Acyclical Graph
and vice versa.
A Directed Acyclical Graph is directed
because each arc has a direction
The node at the end of the arrow is
understood as the cause of the node at
the head of the arrow.
It is acyclic because there is no path
starting with any node and leading back to
itself.
Links Between Graphs &
Probabilities
Conditional independence implies a
specific root cause graph & vice
versa
Probability calculations are based on
assumptions of conditional
independence and vice versa
Conditional
Dependence
Root Cause
Graph
Probability
Calculus
Conditional Independence in
Serial Graph
Root
cause
Sentinel
event
Direct
cause
Conditional Independence in
Diverging Graph
Cause
Effect
Effect
Weight
gain
Diabetes
High
blood
pressure
Conditional Independence in
Complex Graphs
Any two nodes with a direct connection
are dependent
Any two nodes without a direct connection
are independent if and only if:
–
Either serial or diverging
Not converging
–
If condition is removed, the directed link
between root cause and sentinel event is lost
Assumptions of conditional independence
can be verified by asking the expert or
checking against objective data
Identify Conditional
Independencies in the Graph
Prediction from Root Causes
Use Bayes formula and Total Probability
formula:
Use software:
http://www.norsys.com/download.html
download free version at the bottom of the
page
–
Download
–
Double click to self extract to directory Netica
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Netica
Create a New Network
Add nodes
Click on this &
click into white
space
Add arcs
Click on
this, click
on start,
click on
end
Add Descriptions
Double click on a node
Enter description with no spaces
Add Marginal Probabilities
Double click on node
Select Table
Enter 100 times marginal probability,
click for the “Missing probabilities”
button for the system to calculate 1
minus marginal probability
Enter marginal probability of poor
training as 12 standing for 12%
Recalculates
Remaining
probabilities
Adding Conditional Probabilities
Double click on the node
Select table
Enter 100 times probability of effect
given the cause
–
Enter data for each condition. When
conditions change, probabilities cannot
be calculated from previous data
Select the button for calculating
remaining probabilities
Entering Probability of Not
Following Markings Given Poor or
Good Training
Calculates
remaining
probabilities
Enter Conditional Probabilities for
All Combined Direct Causes
Conditions
Probability of
wrong side
surgery given
conditions
Patient
provided
wrong
information
Surgeon did
not follow
markings
Nurse marked
patient wrong
True
True
True
0.75
True
True
False
0.75
True
False
True
0.70
True
False
False
0.60
False
True
True
0.75
False
True
False
0.70
False
False
True
0.30
False
False
False
0.01
Compile the Graph
Making Predictions
Select a node
Select the condition that is true
Read off probability of other nodes
–
Predict sentinel event from combination
of root causes
–
Predict most likely cause from
observed sentinel event
–
Estimate prevalence of root causes
from observed direct causes
Predicting Sentinel Event With No
Information on Causes
Predicting Sentinel Event with
Three Observed Causes
Predicting Prevalence of Fatigued
Nurse if Patient is Marked Wrong
Selecting Most Likely Cause of
Sentinel Event
Discussion
Estimating the probabilities can verify
if assumptions are reasonable,
conclusions fit observed frequencies,
and help select most likely cause.
JCAHO reports some conditional
probabilities
Experts estimates are accurate if
–
brief training in conditional probabilities
–
Provided with available objective data
–
Allowed to discuss their different
estimates
Take Home Lesson
Question the obvious.
Examine your root cause
assumptions & predictions
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