Detecting Adverse Events Using Information Technology
- Affiliations of the authors: Division of General Medicine, Department of Medicine, Brigham and Women's Hospital; Center for Applied Medical Information Systems, Partners Healthcare System; and Harvard Medical School, Boston, Massachusetts (DWB, HM, LP); LDS Hospital/Intermountain Health Care and University of Utah, Salt Lake City, Utah (RSE); Department of Medical Informatics, Columbia University, New York (PDS, GH)
- Correspondence and reprints to: David W. Bates, MD, MSc, Division of General Medicine and Primary Care, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115; e-mail: <dbates{at}partners.org>
- Received 7 January 2002
- Accepted 29 October 2002
Abstract
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. As a result, 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 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 study results for specific types of adverse events.
Design Structured review.
Methodology English-language studies that 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 such as 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 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.
