Prescription reporting with immediate medication utilization mapping (PRIMUM): development of an alert to improve narcotic prescribing. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Seymour, Rachel; Leas, Daniel; Wally, Meghan; Hsu, Joseph
• Is Part Of: BMC medical informatics and decision making. Volume 16:Issue 1(2016)
• Page Start: 1
• Page End: 10
• Publication Date: 2016-12
• Subjects: Opioids -- Electronic medical record -- Prescription drug abuse -- Clinical decision support; Medical informatics -- Periodicals; Clinical medicine -- Decision making -- Periodicals; 610.285
• DOI: 10.1186/s12911-016-0352-x
• Languages: English
• ISSNs: 1472-6947

Abstract

Abstract Background Prescription narcotic overdoses and abuse have reached alarming numbers. To address this epidemic, integrated clinical decision support within the electronic medical record (EMR) to impact prescribing behavior was developed and tested. Methods A multidisciplinary Expert Panel identified risk factors for misuse, abuse, or diversion of opioids or benzodiazepines through literature reviews and consensus building for inclusion in a rule within the EMR. We ran the rule "silently" to test the rule and collect baseline data. Results Five criteria were programmed to trigger the alert; based on data collected during a "silent" phase, thresholds for triggers were modified. The alert would have fired in 21.75 % of prescribing encounters (1.30 % of all encounters;n = 9998), suggesting the alert will have a low prescriber burden yet capture a significant number of at-risk patients. Conclusions While the use of the EMR to provide clinical decision support is not new, utilizing it to develop and test an intervention is novel. We successfully built an alert system to address narcotic prescribing by providing critical, objective information at the point of care. The silent phase data were useful to appropriately tune the alert and obtain support for widespread implementation. Future healthcare initiatives can utilize similar methodology to collect data prospectively via the electronic medical record to inform the development, delivery, and evaluation of interventions.