Best Practice Alert for Post-Surgical Opioid Prescribing At Discharge
Phase: NA
Conditions: Opioid Use
Sponsor: University of Colorado, Denver

Study Title: “Best Practice Alert for Opioid Prescribing” NCT04446975  
 
09/01/2022 
 
Study Protocol and Statistical Analysis Plan 
 
The study is a randomized multiple crossover cluster trial to evaluate a real-time best 
practice alert embedded into the electronic health record. Our clusters are an academic 
medical center and three community hospitals, all part of the University of Colorado 
Health system, which serves both inner-city and rural populations. Primary study 
subjects are credentialed prescribing providers at each site. Eligible secondary study 
subjects are postsurgical hospital inpatients who are hospitalized at least one night prior 
to discharge. There are no exclusion criteria for the prescribing providers. The exclusion 
criteria for eligible patients is being less than 18 years of age. 
 
During the eight-week periods when prescriber alerts are active, providers that attempt 
to prescribe higher-than-optimal doses are automatically notified on the order screen 
with a suggestion for a recommended dose. In the subsequent eight-week period, there 
is no best practice alert available to trigger. The four hospitals are randomized to the 
alert starting configuration (ON vs. OFF) in alternating clusters between best practice 
alert ON and OFF conditions for four eight-week periods, each separated by a four-
week washout interval to minimize learning and augment masking. Randomization is 
implemented, so investigators and data analysts remain masked to the condition 
designation. While providers are exposed to the alerts during active periods if their 
prescription triggers a best practice alert, the schedule was not shared with providers or 
patients. 
 
Baseline characteristics balance is assessed using absolute standardized difference 
calculated as the difference in means or proportions divided by the pooled standard 
deviation. An absolute standardized difference > 0.1 was considered to indicate an 
imbalance. The analysis is conducted at the individual patient level using a modified 
intention-to-treat strategy. The effect of the best practice alert on opioid prescription at 
discharge is evaluated using a linear regression model. 
 
We determined that it would be feasible to recruit 1,500 patients per cluster period 
across four hospitals for four time periods. Although we considered a 33.3% reduction 
of prescribed opioids a clinically meaningful reduction for prescriptions “exposed” to the 
best practice alert, we utilized a smaller effect size (11.1% reduction) for the power 
analysis to incorporate the average effect across all patients. We estimated that the trial 
would have >99% power to detect a ratio of geometric means of 0.89 (ON/OFF) for the 
amount of prescribed opioids or a difference of -0.12 (ON – OFF) on the log-scale, at 
the 0.05 significance level.