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.

Agency
National Institute of Health (NIH)
Institute
Agency for Healthcare Research and Quality (AHRQ)
Type
Research Demonstration and Dissemination Projects (R18)
Project #
5R18HS011806-02
Application #
6528316
Study Section
Special Emphasis Panel (ZHS1-HSR-S (01))
Program Officer
Edinger, Stanley
Project Start
2001-09-27
Project End
2004-08-31
Budget Start
2002-09-11
Budget End
2003-08-31
Support Year
2
Fiscal Year
2002
Total Cost
Indirect Cost
Name
Columbia University (N.Y.)
Department
Miscellaneous
Type
Schools of Medicine
DUNS #
167204994
City
New York
State
NY
Country
United States
Zip Code
10032
Melton, Genevieve B; Hripcsak, George (2005) Automated detection of adverse events using natural language processing of discharge summaries. J Am Med Inform Assoc 12:448-57
Hripcsak, George; Stetson, Peter D; Gordon, Peter G (2004) Using the Federated Council for Internal Medicine curricular guide and administrative codes to assess IM residents' breadth of experience. Acad Med 79:557-63
Bakken, Suzanne; Cimino, James J; Hripcsak, George (2004) Promoting patient safety and enabling evidence-based practice through informatics. Med Care 42:II49-56
Cao, Hui; Stetson, Peter; Hripcsak, George (2003) Assessing explicit error reporting in the narrative electronic medical record using keyword searching. AMIA Annu Symp Proc :803
Hripcsak, George; Bakken, Suzanne; Stetson, Peter D et al. (2003) Mining complex clinical data for patient safety research: a framework for event discovery. J Biomed Inform 36:120-30
Cao, Hui; Stetson, Peter; Hripcsak, George (2003) Assessing explicit error reporting in the narrative electronic medical record using keyword searching. J Biomed Inform 36:99-105
McKnight, Lawrence K; Wilcox, Adam; Hripcsak, George (2002) The effect of sample size and disease prevalence on supervised machine learning of narrative data. Proc AMIA Symp :519-22
Chuang, Jen-Hsiang; Friedman, Carol; Hripcsak, George (2002) A comparison of the Charlson comorbidities derived from medical language processing and administrative data. Proc AMIA Symp :160-4
Stetson, Peter D; Johnson, Stephen B; Scotch, Matthew et al. (2002) The sublanguage of cross-coverage. Proc AMIA Symp :742-6