Root%20Cause%20Analysis - PowerPoint PPT Presentation

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Root%20Cause%20Analysis

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Root Cause Analysis Farrokh Alemi, Ph.D. Jee Vang – PowerPoint PPT presentation

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Title: Root%20Cause%20Analysis


1
Root Cause Analysis
  • Farrokh Alemi, Ph.D.
  • Jee Vang

2
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)

3
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

4
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.

5
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.

6
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
7
Conditional Independence in Serial Graph
8
Conditional Independence in Diverging Graph
9
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

10
Identify Conditional Independencies in the Graph
11
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

12
Netica
13
Create a New Network
14
Add nodes
Click on this click into white space
15
Add arcs
Click onthis, click on start, click onend
16
Add Descriptions
  • Double click on a node
  • Enter description with no spaces

17
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

18
Enter marginal probability of poor training as 12
standing for 12
Recalculates Remaining probabilities
19
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

20
Entering Probability of Not Following Markings
Given Poor or Good Training
Calculates remaining probabilities
21
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
22
Compile the Graph
23
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

24
Predicting Sentinel Event With No Information on
Causes
25
Predicting Sentinel Event with Three Observed
Causes
26
Predicting Prevalence of Fatigued Nurse if
Patient is Marked Wrong
27
Selecting Most Likely Cause of Sentinel Event
28
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

29
Take Home Lesson
  • Question the obvious. Examine your root cause
    assumptions predictions
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