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## Root Cause Analysis

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**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 ConditionalDependence Root CauseGraph Probability Calculus**Rootcause**Directcause Sentinel event Conditional Independence in Serial Graph**Cause**Weightgain Effect Effect High bloodpressure Diabetes Conditional Independence in Diverging Graph**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**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**Add nodes**Click on this & click into white space**Add arcs**Click onthis, click on start, click onend**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**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 Prevalence of Fatigued Nurse if Patient is Marked**Wrong**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