ArticleIntensive care medicine2010
Impact of simulation-based learning on medication error rates in critically ill patients.
Article in Intensive care medicine, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 4 of them syntheses 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
29 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Assessing the Effectiveness of Interventions Implemented by Nurses to Reduce Medication Administration Errors in Hospitalised Acute Adult Patient Settings: Systematic Review and Meta-Analysis.Journal of clinical nursing · 2026Pooled it
- Impact of interventions designed to reduce medication administration errors in hospitals: a systematic review.Drug safety · 2014Pooled it
- Pooled it
- Interventions to reduce medication errors in adult intensive care: a systematic review.British journal of clinical pharmacology · 2012Pooled it
- In-line filtration reduces severe complications and length of stay on pediatric intensive care unit: a prospective, randomized, controlled trial.Intensive care medicine · 2012Trial
- Article
- Development of a Competency Assessment Framework for Pharmacy Residents Participating in High-Fidelity Simulation and Creation of the SIMPHARM Assessment Tool.The Canadian journal of hospital pharmacy · 2024Article
- Medication Errors in Intensive Care Units: An Umbrella Review of Control Measures.Healthcare (Basel, Switzerland) · 2022Review
- Lessons from the design, development and implementation of a three-dimensional (3D) neonatal resuscitation training smartphone application: Life-saving Instruction for Emergencies (LIFE app).Advances in simulation (London, England) · 2022Article
- Theory-Based Failure Modes and Effect Analysis for Medication Errors.Journal of healthcare engineering · 2021Article
- Simulation Training in Hemodynamic Monitoring and Mechanical Ventilation: An Assessment of Physician's Performance.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2020Article
- Article
- Simulation-based Education and Human Factors Training in Postgraduate Medical Education: A Northern Ireland Perspective.The Ulster medical journal · 2018Review
- Qualitative exploration of practices to prevent medication errors in neonatal intensive care units: a focus group study.Therapeutic advances in drug safety · 2018Article
- ICU director data: using data to assess value, inform local change, and relate to the external world.Chest · 2015Review
- Analysis of medication errors in simulated pediatric resuscitation by residents.The western journal of emergency medicine · 2014Observational
- High-fidelity simulation training in advanced resuscitation for pharmacy residents.American journal of pharmaceutical education · 2014Article
- Reducing medication errors in critical care: a multimodal approach.Clinical pharmacology : advances and applications · 2014Review
- Teaching medical students a clinical approach to altered mental status: simulation enhances traditional curriculum.Medical education online · 2013Article
- Pharmacy students' learning and satisfaction with high-fidelity simulation to teach drug-induced dyspepsia.American journal of pharmaceutical education · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo compare medication administration error rates before and after the provision of educational sessions using either traditional didactic lecture or simulation-based training.
methodsA single-center, parallel, controlled, prospective study conducted in adult coronary critical care (CCU) and medical intensive care units (MICU). Twenty-four nurses were observed administering medications. Documentation included drug name, dose, route, time and technique during observation and active medication orders in the patient's chart. A direct observation method was completed at baseline and repeated twice after the interventions. Data obtained during observation were analyzed for medication administration error rates. Interventions were two types of educational sessions with content developed from baseline medication administration error data: simulation-based training for CCU nurses and a didactic lecture for MICU nurses. Quizzes completed before and after the interventions were used to assess knowledge.
resultsA total of 880 doses (402 CCU, 478 MICU) were observed. After the simulation-based educational intervention in the CCU, medication administration error rates decreased from 30.8 to 4.0% (p < 0.001) in the initial post-intervention observation and were sustained in the final post-intervention observation (30.8 to 6.2%; p < 0.001). The error rate in the MICU after the didactic lecture intervention was not significantly different from the baseline and increased in the final post-intervention observation from 20.8 to 36.7% (p = 0.002). Mean quiz scores were significantly improved after education sessions in both ICUs.
conclusionsSimulation-based learning provides a significant advantage to patient care through the reduction of medication administration errors compared to lecture style education.
Indexed as
Identifiers
20300731What OpenQuestion holds
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.