ArticleApplied clinical informatics2023
A Novel Use of Bar Code Medication Administration Data to Assess Nurse Staffing and Workload.
Article in Applied clinical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
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.
Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- The Influence of Nurse Staffing and Workload on Medication Administration Errors: A Scoping Review.Journal of advanced nursing · 2026Article
- Nurses' innovative use of information systems: a narrative review.Frontiers in public health · 2026Review
- Examining the Influence of Nurse Staffing on Delayed Insulin Administration in Acute Care Settings Using EHR and BCMA Data.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveThe aim of the study is to introduce an innovative use of bar code medication administration (BCMA) data, medication pass analysis, that allows for the examination of nurse staffing and workload using data generated during regular nursing workflow.
methodsUsing 1 year (October 1, 2014-September 30, 2015) of BCMA data for 11 acute care units in one Veterans Affairs Medical Center, we determined the peak time for scheduled medications and included medications scheduled for and administered within 2 hours of that time in analyses. We established for each staff member their daily peak-time medication pass characteristics (number of patients, number of peak-time scheduled medications, duration, start time), generated unit-level descriptive statistics, examined staffing trends, and estimated linear mixed-effects models of duration and start time.
resultsAs the most frequent (39.7%) scheduled medication time, 9:00 was the peak-time medication pass; 98.3% of patients (87.3% of patient-days) had a 9:00 medication. Use of nursing roles and number of patients per staff varied across units and over time. Number of patients, number of medications, and unit-level factors explained significant variability in registered nurse (RN) medication pass duration (conditional
conclusionMedication pass analysis of BCMA data can provide health systems a means for assessing variations in staffing, workload, and nursing practice using data generated during routine patient care activities.
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Registered trials
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.