Evidence map›Paper›PMID 40983365›Full record

ArticleBMJ health & care informatics2025

Enhancing medication safety with System Approach to Verifying Electronic Prescriptions (SAV E-Rx): pharmacists' review of product selection outcomes between prescribed and dispensed medications.

Jun Gong, Vincent D Marshall, Megan Whitaker, Brigid Rowell, Michael P Dorsch, James P Bagian, Corey A Lester

Abstract read
In one paragraph

Article in BMJ health & care informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Observational
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jun GongDepartment of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, Michigan, USA.ORCID http://orcid.org/0000-0002-5306-3895
Vincent D MarshallDepartment of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, Michigan, USA.
Megan WhitakerDepartment of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, Michigan, USA.
Brigid RowellDepartment of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, Michigan, USA.
Michael P DorschDepartment of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, Michigan, USA.ORCID http://orcid.org/0000-0003-2910-1879
James P BagianDepartment of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan, USA.
Corey A LesterDepartment of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, Michigan, USA lesterca@umich.edu.

Funding

AHRQ HHS R18 HS028786
6 · The paper itself

Abstract

objectivesElectronic prescriptions (e-prescriptions) introduce drug product selection mismatches during pharmacy data entry. System Approach to Verifying Electronic Prescriptions (SAV E-Rx) detects and alerts pharmacy staff to clinically significant occurrences. This study evaluates outcomes of the identified mismatches.

methodsA retrospective analysis was conducted using 1 year of e-prescriptions and dispensing data from 14 community pharmacies across 9 US states. SAV E-Rx screened the data, and flagged mismatches were reviewed by pharmacists using the Common Formats for Event Reporting. Data were analysed using descriptive statistics, the Mann-Whitney U test and χ

resultsOf 1 250 804 records processed, 699 662 included sufficient data for comparison. Pharmacists classified 587 (88.7%) flagged records as intended mismatches and 75 (11.3%) as unintended. Intended mismatches involved ingredients (26.2%), strengths (53.7%) and dosage forms (47.4%), mainly due to prescriber-approved substitutions (62.4%). Unintended mismatches stemmed from ingredients (42.7%), strengths (36.0%) and dosage forms (54.7%) discrepancies, primarily reported as human error (82.7%) and labelling issues (76.0%). Future alerts were favoured for unintended mismatches (96.0%) compared with intended mismatches (56.7%) (p<0.001). DISCUSSION: While routine substitutions are a normal part of quality and timely care, unintended mismatches may pose clinical risks. These errors can arise from human factors and workflow challenges, including high prescription volumes and manual overrides. SAV E-Rx serves as an independent, automated safety net that flags mismatches, catching postdispensing errors that would otherwise go unnoticed.

conclusionsE-prescription errors remain a safety concern. Routine implementation of SAV E-Rx could enhance error detection and enable timely interventions.

Indexed as

Electronic PrescribingMedication ErrorsPharmacistsHumansMedical Order Entry SystemsRetrospective StudiesUnited StatesClinical Pharmacy Information SystemsDecision Support Systems, ClinicalMedical Informatics ApplicationsPharmacy ResearchSafety Management

Identifiers

PMID40983365
PMCPMC12458867

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.