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**Farrokh Alemi, Ph.D. Jee Vang**

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 Conditional Dependence Root Cause Graph Probability Calculus

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**Conditional Independence in Serial Graph**

Root cause Sentinel event Direct cause

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**Conditional Independence in Diverging Graph**

Cause Effect Weight gain Diabetes High blood pressure

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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: 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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**Click on this & click into white space**

Add nodes Click on this & click into white space

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

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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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**Calculates remaining probabilities**

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 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

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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

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