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First published November 26, 2002 as JAMIA PrePrint; doi:10.1197/jamia.M1074
Journal of the American Medical Informatics Association 2003;10(2):115-128
© 2003 American Medical Informatics Association

A more recent version of this article appeared on March 1, 2003
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Submitted on January 7, 2002
Accepted on October 29, 2002

Detecting Adverse Events Using Information Technology

David W. Bates MD, MSc1*, R. Scott Evans MS, PhD2, Harvey Murff MD1, Peter D. Stetson MD3, Lisa Pizziferri1, and George Hripcsak MD3

Affiliation of the authors: 1 Division of General Medicine, Department of Medicine, Brigham and Women's Hospital, the Center for Applied Medical Information Systems, Partners Healthcare System, Harvard Medical School, Boston, MA; 2 LDS Hospital/Intermountain Health Care and University of Utah, Salt Lake City, UT; 3 Department of Medical Informatics, Columbia University, New York, NY

* To whom correspondence should be addressed.

Context Although patient safety is a major problem, most health care organizations rely on spontaneous reporting which detects only a small minority of adverse events. The result has been that problems with safety have remained hidden. Chart review can detect adverse events in research settings, but it is too expensive for routine use. Information technology techniques have been developed that can detect some adverse events in a timely and cost-effective way, in some cases early enough to prevent patient harm.

Objective To review methodologies of detecting adverse events using information technology, reports of studies that used these techniques to detect adverse events, and their results for specific types of adverse events.

Design Structured Review

Methodology English-language studies which reported using information technology to detect adverse events were identified using standard techniques. Only studies that contained original data were included.

Main Outcome Measures Adverse events, with specific focus on nosocomial infections, adverse drug events, and injurious falls.

Results Tools like event monitoring and natural language processing can inexpensively detect certain types of adverse events in clinical databases. These approaches already work well for some types of adverse events, including adverse drug events and nosocomial infections, and are in routine use in a few hospitals. In addition, it appears likely that these techniques will be adaptable in ways that will allow detection of a broad array of adverse events, especially as more medical information becomes computerized.

Conclusion Computerized detection of adverse events will soon be practical on a widespread basis.




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