ArticleMedical care2017
Measuring Harm in Health Care: Optimizing Adverse Event Review.
Article in Medical care, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prevalence of Medication Errors Among Paediatric Inpatients: Systematic Review and Meta-Analysis.Drug safety · 2019Pooled it
- Incidence and Characteristics of Adverse Events: The 2024 Patient Safety Incidents Inquiry in Korea.Journal of Korean medical science · 2026Article
- Comparison of AI-generated radiology impressions: a multi-stakeholder evaluation.NPJ digital medicine · 2026Article
- Adapted large language models can outperform medical experts in clinical text summarization.Nature medicine · 2024Article
- Assessing the severity of medication administration errors identified in an observational study using a valid and reliable method.Journal of pharmaceutical policy and practice · 2023Article
- Large language models encode clinical knowledge.Nature · 2023Article
- Standardising the Classification of Harm Associated with Medication Errors: The Harm Associated with Medication Error Classification (HAMEC).Drug safety · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
objectiveThe objective of this study was to identify modifiable factors that improve the reliability of ratings of severity of health care-associated harm in clinical practice improvement and research.
methodsA diverse group of clinicians rated 8 types of adverse events: blood product, device or medical/surgical supply, fall, health care-associated infection, medication, perinatal, pressure ulcer, surgery. We used a generalizability theory framework to estimate the impact of number of raters, rater experience, and rater provider type on reliability.
resultsPharmacists were slightly more precise and consistent in their ratings than either physicians or nurses. For example, to achieve high reliability of 0.83, 3 physicians could be replaced by 2 pharmacists without loss in precision of measurement. If only 1 rater was available for rating, ∼5% of the reviews for severe harm would have been incorrectly categorized. Reliability was greatly improved with 2 reviewers.
conclusionsWe identified factors that influence the reliability of clinician reviews of health care-associated harm. Our novel use of generalizability analyses improved our understanding of how differences affect reliability. This approach was useful in optimizing resource utilization when selecting raters to assess harm and may have similar applications in other settings in health care.
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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.