Developing an autoverification framework for medication orders at UNC Health.

Abstract

Purpose: Autoverification (AV) is the process in which a medication is automatically verified in the electronic health record (EHR), bypassing a pharmacist's approval. If concerns of safety and efficacy for AV are addressed, broad implementation can allow AV to be a powerful tool within a hospital system to verify high-volume, low-risk medication orders. This study aims to identify parameters for risk stratification of medications and develop a replicable framework model for identifying medications appropriate for AV at UNC Health.

Methods: The modified Delphi methodology was utilized to reach consensus on parameters used in a risk stratification tool for medication orders. This tool was applied retroactively to a sample of medication orders at UNC Health during a 1-month period (October 2023) to determine risk of adverse event for potentially autoverified orders.

Results: Fifty-five criteria met consensus for consideration for use for an AV risk appraisal tool. Results from a consensus meeting for criteria that would be used in the autoverification risk appraisal tool (AVRAT) to flag medication orders as "high-risk for AV" were age, estimated glomerular filtration rate, hemoglobin level, platelet count, body weight, and EHR documentation of continuous renal replacement therapy. Twenty medications were selected for an initial proof-of-concept evaluation of the AVRAT. Using AVRAT criteria, it was determined that a total of 6.89% of all October medication orders at UNC Health posed a low risk of a potential adverse event with AV.

Conclusion: A proof-of-concept study for the utilization of AV was effectively developed. The study results indicated that AV can possibly reduce time for medication order review across a hospital system, with a relatively small number of orders being potentially eligible for AV.