Evaluating the introduction of a computerized prior‐authorization system on the completeness of drug exposure data. (8th March 2013)

• Record Type: Journal Article
• Authors: Gamble, John‐Michael; Johnson, Jeffrey A.; Majumdar, Sumit R.; McAlister, Finlay A.; Simpson, Scot H.; Eurich, Dean T.
• Is Part Of: Pharmacoepidemiology and drug safety. Volume 22:Number 5(2013:May)
• Page Start: 551
• Page End: 555
• Publication Date: 2013-03-08
• Subjects: prior authorization -- pharmaceutical policy -- time‐series analysis -- exposure misclassification -- pharmacoepidemiology; Pharmacoepidemiology -- Periodicals; Chemotherapy -- Periodicals; Epidemiology -- Periodicals; 615.705
• DOI: 10.1002/pds.3427
• Languages: English
• ISSNs: 1053-8569

Abstract

ABSTRACT: Purpose: Administrative databases that only capture records for benefit‐approved prescriptions may underestimate exposure because they do not capture non‐benefit prescriptions. Using a natural experiment, we illustrate the impact of automating a prior‐authorization policy on the completeness of drug exposure. Methods: Using Saskatchewan (Canada) databases, weekly counts of benefit‐approved and total prescription records in 2006 for new users of antidiabetic agents were examined across four categories: thiazolidinediones (TZDs), metformin, glyburide, and insulin. On July 1, 2006, Saskatchewan's public drug plan implemented an automated, online‐adjudicated, prior‐authorization process for TZDs; previously, prior approval was paper based. No such policy changes occurred for other drugs. We estimated the effect of this policy change on drug exposure using interrupted time‐series analyses. Results: We examined 223 552 prescription records: 19% were for TZDs, 48% for metformin, 20% for glyburide, and 13% for insulin. Prior to automation, there were, on average, 571 benefit‐approved TZD records per week; however, the number of benefit‐approved TZD records increased immediately after the automated process was introduced by 240 prescriptions per week (95% CI 200–280, p < 0.001). The average proportion of TZD benefit‐approved records was 73% before and increased to 93% immediately following policy change (20% absolute change, 95% CI 18.7–20.4%). No changes were observed for metformin, glyburide, or insulin ( p > 0.1 for all). Conclusions: Automating prior authorization for TZDs immediately increased the proportion of captured TZD records, suggesting in our study that one‐fifth of TZD exposure was previously misclassified. If replicable, this indicates that even subtle changes in reimbursement policy may affect the validity of drug exposure data. Copyright © 2013 John Wiley & Sons, Ltd.