Online Program

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Monday, January 6
Mon, Jan 6, 5:30 PM - 6:30 PM
Pacific D
Welcome Reception & Poster Session I

A Probability Digraph of Adverse Events Based on Medical Malpractice Litigation Data (307859)

*Shengjie Dong, School of Public Health, Shanghai Jiao Tong University,China 

Keywords: adverse events,Bayesian belief networks,healthcare,patient safety,risk analysis

Background Adverse events (AEs) are frequent in clinical practice but can be preventable. However, efforts to comprehensively investigate the joint probability of relationship between the factors affecting risk of AEs have been limited. Objective This study aimed to propose a relationship framework embedded probability to identify temporal precursor and causal factors for AEs in healthcare settings for patient safety. Methods A sample of 5 844 medical malpractice litigation cases were reviewed. Temporal sequences of incidents contributing to AEs were identified from each case, using the taxonomy from the International Classification of Patient Safety (ICPS). We applied Bayesian belief network (BBN) framework to analysis the sequences that represented risky factors in medical service and constructed probabilistic relations between incidents. Furthermore, mediating effects were explored between those which appeared on high-risk paths. Results The methodology has been validated with ten years of medical malpractice litigation data and can dynamically capture the probability of medical risks. It could be used as a tool to predict the consequences of decisions and policies on safety, and thus provide reference for optimizing decision making.