help button home button JAMIA Bigger figures
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH
First published April 24, 2008 as JAMIA PrePrint; doi:10.1197/jamia.M2606
Journal of the American Medical Informatics Association 2008;15(4):430-438
© 2008 American Medical Informatics Association

A more recent version of this article appeared on July 1, 2008
This Article
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
M2606v1
15/4/430    most recent
Right arrow Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Tamblyn, R.
Right arrow Articles by Pinsonneault, A.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Tamblyn, R.
Right arrow Articles by Pinsonneault, A.

Submitted on August 23, 2007
Accepted on April 14, 2008

A Randomized Trial of the Effectiveness of On-Demand vs. Computer-Triggered Drug Decision-Support in Primary Care

Robyn Tamblyn PhD1*, Allen Huang MDCM2, Laurel Taylor PhD3, Yuko Kawasumi MSc4, Gillian Bartlett PhD2, Roland Grad MDCM5, André Jacques MD6, Martin Dawes MD3, Michal Abrahamowicz PhD4, Robert Perreault MD7, Nancy Winslade PharmD8, Lise Poissant PhD8, and Alain Pinsonneault PhD3

Affiliation of the authors: 1 Department of Epidemiology and Biostatistics, McGill University, Montreal Quebec, Canada; Department of Medicine, McGill University, Montreal Quebec, Canada ; 2 Department of Medicine, McGill University, Montreal Quebec, Canada; 3 Faculty of Management, McGill University, Montreal Quebec, Canada; 4 Department of Epidemiology and Biostatistics, McGill University, Montreal Quebec, Canada ; 5 Department of Family Medicine, McGill University, Montreal Quebec, Canada ; 6 College of Physicians of Quebec, Montreal Quebec, Canada; 7 Department of Public Health, Régie Régionale de Montréal, Montreal Quebec, Canada ; 8 Department of Medicine, McGill University, Montreal Quebec, Canada

* To whom correspondence should be addressed.

Objective Prescribing alerts generated by computerized drug decision support (CDDS) may prevent drug-related morbidity. However, the vast majority of alerts are ignored because of clinical irrelevance. The ability to customize commercial alert systems should improve physician acceptance as the physician can select the circumstances and types of drug alerts that are viewed. We tested the effectiveness of two approaches to medication alert customization to reduce prevalence of prescribing problems: on-physician-demand versus computer-triggered decision-support. Physicians in each study condition were able to pre-set levels that triggered alerts.

Design Cluster trial with 28 primary care physicians randomized to either automated or on-demand CDDS in the MOXXI drug management system for 3,449 of their patients seen over the next 6-months.

Measurements The CDSS generated alerts for prescribing problems that could be customized by severity level. Prescribing problems included dosing errors, drug-drug, age, allergy, and disease interactions. Physicians randomized to on-demand activated the drug review when they considered it clinically relevant whereas physicians randomized to computer-triggered decision-support viewed all alerts for electronic prescriptions in accordance with the severity level they selected for both prevalent and incident problems. Data from administrative claims and MOXXI were used to measure the difference in the prevalence of prescribing problems at the end of follow-up.

Results During follow-up, 50% of the physicians receiving computer-triggered alerts modified the alert threshold (n=7), and 21% of the physicians in the "alert on-demand" group modified the alert level (n=3). In the on-demand group 4,445 prescribing problems were identified, 41 (0.9%) were seen by requested drug review, and in 31 problems (75.6%), the prescription was revised. In comparison, 668 (10.3%) of the 6,505 prescribing problems in the computer-triggered group were seen, and 81 (12.1%) were revised. The majority of alerts were ignored because the benefit was judged greater than the risk, the interaction was known, or considered clinically not important (computer-triggered: 75.8% of 585 ignored alerts; on-demand 90% of 10 ignored alerts). At the end of follow-up, there was a significant reduction in therapeutic duplication problems in the computer-triggered group (OR: 0.55; p=0.02) but no difference in the overall prevalence of prescribing problems.

Conclusion Customization of computer-triggered alert systems is more useful in detecting and resolving prescribing problems than on-demand review but neither approach was effective in reducing prescribing problems. New strategies are needed to maximize the use of drug decision-support systems to reduce drug-related morbidity.






HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH
Copyright © 1994 by the American Medical Informatics Association.