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Detecting Potential Medication Selection Errors During Outpatient Pharmacy Processing of Electronic Prescriptions
Corey A Lester1, Liyun Tu2, Yuting Ding1
1Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, MI, United States.
An automated double-check using the RxNorm application programming interface (API) effectively identified medication selection errors in e-prescriptions. This technology can improve patient safety by verifying correct medication selection during pharmacy dispensing.
Area of Science:
- Health Informatics
- Clinical Pharmacy
- Health Information Technology
Background:
- Medication errors are a significant patient safety concern in outpatient pharmacies.
- Electronic prescriptions (e-prescriptions) require manual transcription and pharmacist double-checks, processes prone to human error due to workload and fatigue.
- Health information technology offers potential solutions for identifying and mitigating medication selection errors.
Purpose of the Study:
- To evaluate the performance of an automated double-check system utilizing the RxNorm application programming interface (API) for identifying medication selection errors in e-prescription processing.
- To assess the accuracy of RxNorm API in matching e-prescription National Drug Codes (NDCs) with dispensing records.
Main Methods:
- A retrospective analysis of 537,710 e-prescription and dispensing record pairs from January 2017 to October 2018 was conducted.
- National Drug Codes (NDCs) were mapped to RxNorm concept unique identifiers (RxCUIs) using the National Library of Medicine's RxNorm API.
- Performance metrics, including sensitivity, specificity, and precision, were calculated by comparing RxCUIs from e-prescriptions and dispensing records.
Main Results:
- Analysis of 527,881 record pairs revealed near-complete matching of RxCUIs (99.67%-99.90%).
- Four clinically significant medication selection errors (3 ingredient, 1 strength) and 546 less significant mismatches were identified.
- The RxNorm API demonstrated high sensitivity (1) and specificity (0.99896–0.99688) in detecting discrepancies.
Conclusions:
- The National Library of Medicine's RxNorm API can effectively perform automated double-checks for medication selection accuracy in outpatient pharmacies.
- RxNorm provides comprehensive drug coverage and can be utilized to detect and correct medication selection errors, enhancing patient safety.
- Automated verification using RxNorm may eventually reduce the need for manual pharmacist double-checks, optimizing workflow and improving efficiency.
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