The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Adverse drug events and medication errors: detection and classification methods
Open accessAbstract
Investigating the incidence, type, and preventability of adverse drug events (ADEs) and medication errors is crucial to improving the quality of health care delivery. ADEs, potential ADEs, and medication errors can be collected by extraction from practice data, solicitation of incidents from health professionals, and patient surveys. Practice data include charts, laboratory, prescription data, and administrative databases, and can be reviewed manually or screened by computer systems to identify signals. Research nurses, pharmacists, or research assistants review these signals, and those that are likely to represent an ADE or medication error are presented to reviewers who independently categorize them into ADEs, potential ADEs, medication errors, or exclusions. These incidents are also classified according to preventability, ameliorability, disability, severity, stage, and responsible person. These classifications, as well as the initial selection of incidents, have been evaluated for agreement between reviewers and the level of agreement found ranged from satisfactory to excellent (kappa = 0.32-0.98). The method of ADE and medication error detection and classification described is feasible and has good reliability. It can be used in various clinical settings to measure and improve medication safety.
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Cites
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Cited by
National Surveillance of Emergency Department Visits for Outpatient Adverse Drug Events
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A roadmap for national action on clinical decision support
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Epidemiology and risk factors for drug allergy
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Clinical decision support capabilities of commercially-available clinical information systems
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Metadata
- Authors
- Morimoto, T.
- Venue
- Quality and Safety in Health Care
- Publisher
- BMJ
- Published
- 2004-08-01
- Volume
- 13
- Issue
- 4
- Pages
- 306-314
- ISSN
- 1475-3898 (ISSN-L)
- 1475-3901
- OpenAlex
- W2113274285
- Cited by
- 500
- References
- 6
- FWCI
- 15.08
- Type
- journal article
- Language
- en
- Open access
- bronze
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
