Improving Acceptance of Computerized Prescribing Alerts in Ambulatory Care

Computerized drug prescribing alerts can improve patient safety, but are often overridden because of poor specificity and alert overload. Our objective was to improve clinician acceptance of drug alerts by designing a selective set of drug alerts for the ambulatory care setting and minimizing workfl...

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Published inJournal of the American Medical Informatics Association : JAMIA Vol. 13; no. 1; pp. 5 - 11
Main Authors Shah, Nidhi R., Seger, Andrew C., Seger, Diane L., Fiskio, Julie M., Kuperman, Gilad J., Blumenfeld, Barry, Recklet, Elaine G., Bates, David W., Gandhi, Tejal K.
Format Journal Article
LanguageEnglish
Published England Elsevier Inc 01.01.2006
Oxford University Press
American Medical Informatics Association
Subjects
Adult
Ambulatory Care Information Systems
Attitude of Health Personnel
Attitude to Computers
Contraindications
Drug Prescriptions
Drug Therapy, Computer-Assisted
Female
Humans
Medical Order Entry Systems
Medical Records Systems, Computerized
Medication Errors - prevention & control
Pharmaceutical Preparations
Pregnancy
Primary Health Care
Reminder Systems
The Practice of Informatics
User-Computer Interface
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Summary:Computerized drug prescribing alerts can improve patient safety, but are often overridden because of poor specificity and alert overload. Our objective was to improve clinician acceptance of drug alerts by designing a selective set of drug alerts for the ambulatory care setting and minimizing workflow disruptions by designating only critical to high-severity alerts to be interruptive to clinician workflow. The alerts were presented to clinicians using computerized prescribing within an electronic medical record in 31 Boston-area practices. There were 18,115 drug alerts generated during our six-month study period. Of these, 12,933 (71%) were noninterruptive and 5,182 (29%) interruptive. Of the 5,182 interruptive alerts, 67% were accepted. Reasons for overrides varied for each drug alert category and provided potentially useful information for future alert improvement. These data suggest that it is possible to design computerized prescribing decision support with high rates of alert recommendation acceptance by clinicians.
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Supported by grants from the Agency for Health Care Research and Quality (RO1-HS1169) and (2-T32-HS000020-18). The authors are grateful to Mike Sperling, Saverio Maviglia, MD, Irene Galperin, Lynn Volk, and Josh Peterson, MD, for their help in designing and implementing the drug alerts. For further information about our final knowledge base, please contact Diane Seger at dseger@partners.org.
ISSN:1067-5027
1527-974X
DOI:10.1197/jamia.M1868