ArticleBMJ open quality2020
Lessons learned: using adverse incident reports to investigate the characteristics and causes of prescribing errors.
Article in BMJ open quality, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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
1 citing paper in PubMed.
- Implementation of an Automated Dispensing Cabinet System and Its Impact on Drug Administration: Longitudinal Study.JMIR formative research · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionPrescribing errors are a principal cause of preventable harm in healthcare. This study aims to establish a systematic approach to analysing prescribing-related adverse incident reports, in order to elucidate the characteristics and contributing factors of common prescribing errors and target multifaceted quality improvement initiatives.
methodsAll prescribing-related adverse incident reports submitted across one NHS board over 12 months were selected. Incidents involving commonly implicated drugs (involved in ≥10 incidents) underwent analysis to establish likely underlying causes using Reason's Model of Accident Causation.
results330 prescribing-related adverse incident reports were identified. Commonly implicated drugs were insulin (10% of incidents), gentamicin (7%), co-amoxiclav (5%) and amoxicillin (5%). The most prevalent error types were prescribing amoxicillin when contraindicated due to allergy (5%); prescribing co-amoxiclav when contraindicated due to allergy (5%); prescribing the incorrect type of insulin (3%); and omitting to prescribe insulin (3%). Error-producing factors were identified in 86% of incidents involving commonly implicated drugs. 53% of incidents involved error-producing factors related to the working environment; 38% involved factors related to the healthcare team; and 37% involved factors related to the prescriber. DISCUSSION: This study establishes that systematic analysis of adverse incident reports can efficiently identify the characteristics and contributing factors of common prescribing errors, in a manner useful for targeting quality improvement. Furthermore, this study produced a number of salient findings. First, a narrow range of drugs were implicated in the majority of incidents. Second, a small number of error types were highly recurrent. Lastly, a range of contributing factors were evident, with those related to the working environment contributing to the majority of prescribing errors analysed.
Indexed as
Identifiers
What 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.