Mining Complex Clinical Data for Patient Safety Research

点击翻译按钮获取中文摘要

Medical errors hurt patients, cost money, and undermine the health care system. The first step to reducing errors is detecting them, for what cannot be detected cannot be managed. A number of approaches have been applied to medical error detection, including mandatory event reporting, voluntary near-miss reporting, chart review, and automated surveillance using information systems. Automated surveillance promises large-scale detection, minimal labor, and, potentially, detection in real time to prevent or recover from errors. Unfortunately, large amounts of important clinical information lie locked in narrative reports, unavailable to automated decision support systems. A number of tools have emerged from medical informatics and computer science- natural language processing, visualization tools, and machine learning- as well as methods for understanding cognitive processes. We hypothesize that the electronic medical record contains information useful for detecting errors and that natural language processing and other tools will allow us to retrieve the information. We will assemble a team skilled in natural language processing, data mining, terminology, patient safety research, and health care. We will use a clinical repository with ten years of data on two million patients. It includes administrative, laboratory, and pharmacy coded information as well as a wide range of narrative reports including discharge summaries, operative reports, outpatient notes, autopsy reports, resident signout notes, nursing notes, and reports from numerous ancillary services (radiology, pathology, etc.). We will apply a proven natural language processor called MedLEE to code the information and measure the accuracy of automated queries to detect and characterize errors. We will target several areas: explicit error reporting in the medical record, NYPORTS mandatory event reporting, clinical conflicts in record, and other sources of error information. We will use a systems approach to errors and cognitive analysis to uncover cues to improve error detection. We will incorporate the system into the hospital's current event surveillance program and assess the impact on error detection. We will adhere to strict privacy policies and security procedures. This project represents a unique opportunity to apply the most advanced medical language processing system to a large, comprehensive clinical repository to advance patient safety research.