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First published June 25, 2008 as JAMIA PrePrint; doi:10.1197/jamia.M2634
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J Am Med Inform Assoc. 2008;15:647-653. DOI 10.1197/jamia.M2634.
© 2008 American Medical Informatics Association


Research Paper

Can Surveillance Systems Identify and Avert Adverse Drug Events? A Prospective Evaluation of a Commercial Application

Ashish K. Jha, MD, MPHa,b,d,*, Julia Laguette, MDb, Andrew Seger, PharmDb and David W. Bates, MD, MSca,b,c

a Department of Health Policy and Management, Harvard School of Public Health, Boston, MA
b Division of General Medicine, Brigham and Women's Hospital, Boston, MA
c Harvard Medical School, Boston, MA
d VA Boston Healthcare System, Boston, MA

* Correspondence: Dr. Ashish Jha, Department of Health Policy and Management, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115 (Email: ajha{at}hsph.harvard.edu).

Received for publication: 09/28/07; accepted for publication: 05/21/08.

Objective: Computerized monitors can effectively detect and potentially prevent adverse drug events (ADEs). Most monitors have been developed in large academic hospitals and are not readily usable in other settings. We assessed the ability of a commercial program to identify and prevent ADEs in a community hospital.

Design and Measurement: We prospectively evaluated the commercial application in a community-based hospital. We examined the frequency and types of alerts produced, how often they were associated with ADEs and potential ADEs, and the potential financial impact of monitoring for ADEs.

Results: Among 2,407 patients screened, the application generated 516 high priority alerts. We were able to review 266 alerts at the time they were generated and among these, 30 (11.3%) were considered substantially important to warrant contacting the physician caring for the patient. These 30 alerts were associated with 4 ADEs and 11 potential ADEs. In all 15 cases, the responsible physician was unaware of the event, leading to a change in clinical care in 14 cases. Overall, 23% of high priority alerts were associated with an ADE (95% confidence interval [CI] 12% to 34%) and another 15% were associated with a potential ADE (95% CI 6% to 24%). Active surveillance used approximately 1.5 hours of pharmacist time daily.

Conclusions: A commercially available, computer-based ADE detection tool was effective at identifying ADEs. When used as part of an active surveillance program, it can have an impact on preventing or ameliorating ADEs.







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Copyright © 2008 by the American Medical Informatics Association.