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Updated: Jun 6, 2025

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
Published on: December 11, 2016
Defining key deprescribing measures from electronic health data: A multisite data harmonization project
Sascha Dublin1,2,3, Ladia Albertson-Junkans1, Thanh Phuong Pham Nguyen4
1Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA.
Background:
Stopping or reducing risky or unneeded medications ("deprescribing") could improve older adults' health. Electronic health data can support observational and intervention studies of deprescribing, but there are no standardized measures for key variables, and healthcare systems have differing data types and availability. We developed definitions for chronic medication use and discontinuation based on electronic health data and applied them in a case study of benzodiazepines and Z-drugs in five diverse US healthcare systems.
Methods:
We conducted a retrospective cohort study of adults age 65+ from 2017 to 2019 with chronic benzodiazepine or Z-drug use. We determined whether sites had access to medication orders and/or dispensings. We developed definitions for chronic use and discontinuation using both data types. Discontinuation definitions were based on (1) gaps in medication availability during follow-up or (2) not having medication available at a fixed time point. We examined the impact of varying the gap length and requiring a 30-day period without orders/dispensings ("halo") around the fixed time point. We compared results derived from orders versus dispensings at one site.
Results:
Approximately 1.6%-2.6% of older adults had chronic benzodiazepine/Z-drug use (total N = 6775, ranging from 431 to 2122 across sites). Depending on the definition and site, the proportion discontinuing use during 12 months ranged from 6% to 49%. Requiring a longer gap or a 30-day "halo" resulted in lower estimates. At one site, only 56% of those with chronic use defined from orders also qualified based on dispensings, and the discontinuation rate at 180 days was 20% from orders versus 32% from dispensings.
Conclusions:
Requiring a gap of ≥90 days or a "halo" around a time point may more accurately capture discontinuation than using a shorter gap or no halo. Orders data underestimate discontinuation compared to dispensings. Work is needed to adapt these definitions for other drug classes and settings.
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