ReviewClinical pharmacology : advances and applications2014
Reducing medication errors in critical care: a multimodal approach.
Review in Clinical pharmacology : advances and applications, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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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
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- Automation of in-hospital pharmacy dispensing: a systematic review.European journal of hospital pharmacy : science and practice · 2021Pooled it
- Steps to Adapt the Medication Administration Error Survey in Highly Specialised Units-Polish Perspectives.Nursing reports (Pavia, Italy) · 2025Article
- A Complex Intervention to Minimize Medication Error by Nurses in Intensive Care: A Case Study.Healthcare (Basel, Switzerland) · 2025Article
- Cost-effectiveness analysis of pharmaceutical care in adult critically ill patients: based on a prospective cohort study.Frontiers in pharmacology · 2024Article
- Interventions to Minimize Medication Error by Nurses in Intensive Care: A Scoping Review Protocol.Nursing reports (Pavia, Italy) · 2023Article
- Evaluation of medication risk at the transition of care: a cross-sectional study of patients from the ICU to the non-ICU setting.BMJ open · 2022Article
- Review
- Where is my infusion pump? Harnessing network dynamics for improved hospital equipment fleet management.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Medication errors and drug knowledge gaps among critical-care nurses: a mixed multi-method study.BMC health services research · 2019Article
- Pharmacists in Critical Care.Innovations in pharmacy · 2019Article
- INTRAVENOUS MEDICATION ADMINISTRATION ERRORS AND THEIR CAUSES IN CARDIAC CRITICAL CARE UNITS IN IRAN.Materia socio-medica · 2015Article
- Prescribing pattern of interns: Time for new interventions.Journal of basic and clinical pharmacy · 2014Article
- Prevalence and Risk Factors of Medication Errors in Enteral Tube Feeding Among ICU Patients: A Comprehensive Cross-Sectional Analysis.Journal of research in pharmacy practiceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Institute of Medicine has reported that medication errors are the single most common type of error in health care, representing 19% of all adverse events, while accounting for over 7,000 deaths annually. The frequency of medication errors in adult intensive care units can be as high as 947 per 1,000 patient-days, with a median of 105.9 per 1,000 patient-days. The formulation of drugs is a potential contributor to medication errors. Challenges related to drug formulation are specific to the various routes of medication administration, though errors associated with medication appearance and labeling occur among all drug formulations and routes of administration. Addressing these multifaceted challenges requires a multimodal approach. Changes in technology, training, systems, and safety culture are all strategies to potentially reduce medication errors related to drug formulation in the intensive care unit.
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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.