Root Cause Analysis

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Root Cause Analysis

• Farrokh Alemi, Ph.D.
• Jee Vang

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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)

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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

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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.

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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.

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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

ConditionalDependence
Root CauseGraph
Probability Calculus
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Conditional Independence in Serial Graph
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Conditional Independence in Diverging Graph
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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

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Identify Conditional Independencies in the Graph
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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
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Create a New Network
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Add nodes
Click on this click into white space
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Add arcs
Click onthis, click on start, click onend
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Add Descriptions

• Double click on a node
• Enter description with no spaces

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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

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Enter marginal probability of poor training as 12
standing for 12
Recalculates Remaining probabilities
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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

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Entering Probability of Not Following Markings
Given Poor or Good Training
Calculates remaining probabilities
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Enter Conditional Probabilities for All Combined
Direct Causes
Conditions Conditions Conditions Probability of wrong side surgery given conditions
Patient provided wrong information Surgeon did not follow markings Nurse marked patient wrong Probability of wrong side surgery given conditions
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
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Compile the Graph
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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

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Predicting Sentinel Event With No Information on
Causes
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Predicting Sentinel Event with Three Observed
Causes
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Predicting Prevalence of Fatigued Nurse if
Patient is Marked Wrong
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Selecting Most Likely Cause of Sentinel Event
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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

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Take Home Lesson

• Question the obvious. Examine your root cause
  assumptions predictions