ArticleJournal of the American Medical Informatics Association : JAMIA2015
Temporal and other factors that influence the time doctors take to prescribe using an electronic prescribing system.
Article in Journal of the American Medical Informatics Association : JAMIA, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Using electronic health record audit logs to study clinical activity: a systematic review of aims, measures, and methods.Journal of the American Medical Informatics Association : JAMIA · 2020Pooled it
- Evaluation of Multimedia Medication Reconciliation Software: A Randomized Controlled, Single-Blind Trial to Measure Diagnostic Accuracy for Discrepancy Detection.Applied clinical informatics · 2018Trial
- The impact of patient information on prescribing errors: Insights from pharmaceutical interventions.Exploratory research in clinical and social pharmacy · 2025Article
- Biases in electronic health record data due to processes within the healthcare system: retrospective observational study.BMJ (Clinical research ed.) · 2018Observational
- Pharmacist-Physician Communications in a Highly Computerised Hospital: Sign-Off and Action of Electronic Review Messages.PloS one · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundA computerized physician order entry (CPOE) system with embedded clinical decision support can reduce medication errors in hospitals, but might increase the time taken to generate orders.
aimsWe aimed to quantify the effects of temporal (month, day of week, hour of shift) and other factors (grade of doctor, prior experience with the system, alert characteristics, and shift type) on the time taken to generate a prescription order.
settingA large university teaching hospital using a locally developed CPOE system with an extensive audit database.
designWe retrospectively analyzed prescription orders from the audit database between August 2011 and July 2012.
resultsThe geometric mean time taken to generate a prescription order within the CPOE system was 11.75 s (95% CI 11.72 to 11.78). Time to prescribe was most affected by the display of high-level (24.59 s (24.43 to 24.76); p<0.001) or previously unseen (18.87 s (18.78 to 18.96); p<0.001) alerts. Prescribers took significantly less time at weekends (11.29 s (11.23 to 11.35)) than on weekdays (11.88 s (11.84 to 11.91); p<0.001), in the first (11.25 s (11.16 to 11.34); p<0.001) and final (11.56 s (11.47 to 11.66); p<0.001) hour of their shifts, and after the first month of using the system.
conclusionsThe display of alerts, prescribing experience, system familiarity, and environment all affect the time taken to generate a prescription order. Our study reinforces the need for appropriate alerts to be presented to individuals at an appropriate place in the workflow, in order to improve prescribing efficiency.
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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.