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A Randomized Trial of the Effectiveness of On-demand versus Computer-triggered Drug Decision Support in Primary Care

^a Department of Epidemiology and Biostatistics, McGill University, Montreal, Quebec, Canada
^b Department of Medicine, McGill University, Montreal, Quebec, Canada
^c Faculty of Management, McGill University, Montreal, Quebec, Canada
^d Department of Family Medicine, McGill University, Montreal, Quebec, Canada
^e College of Physicians of Quebec, Montreal, Quebec, Canada
^f Department of Public Health, Régie Régionale de Montréal, Montreal, Quebec, Canada.

^_* Correspondence: Dr. Robyn Tamblyn, McGill University, Morrice House, 1140 Pine Avenue West, Montreal Quebec, Canada, H3A 1A3 (Email: robyn.tamblyn{at}mcgill.ca).

Received for publication: 08/23/07; accepted for publication: 04/14/08.

Objectives: 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 because 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 preset levels that triggered alerts.

Design: This was a 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 CDDS 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 the interaction was 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 (odds ratio 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.