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Farrokh Alemi, Ph.D. Jee Vang

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1 Farrokh Alemi, Ph.D. Jee Vang
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 Conditional Dependence Root Cause Graph Probability Calculus

7 Conditional Independence in Serial Graph
Root cause Sentinel event Direct cause

8 Conditional Independence in Diverging Graph
Cause Effect Weight gain Diabetes High blood pressure

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: 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 Click on this & click into white space
Add nodes Click on this & click into white space

15 Add arcs Click on this, click on start, click on end

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 Calculates remaining probabilities
Entering Probability of Not Following Markings Given Poor or Good Training Calculates remaining probabilities

21 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 0.75 False 0.70 0.60 0.30 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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