Adverse drug events and medication errors: detection and classification methods
- 1Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA, USA
- 2Division of General Internal Medicine, Kyoto University Hospital, Kyoto, Japan
- 3Harvard Medical School, Boston, MA, USA
- 4Massachusetts College of Pharmacy and Health Science, Boston, MA, USA
- Correspondence to: Professor D W Bates Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, 1620 Tremont Street, Boston, MA 02120-1613, USA;
- Accepted 22 May 2004
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 (κ = 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.
Supported in part by grants from the Pfizer Health Research Foundation, the Health Care Science Institute, grant-in-aid from the Ministry of Health, Labour and Welfare of Japan, and grant R01-HS11169 from the Agency for Healthcare Research and Quality.