RT Journal Article SR Electronic T1 Patient safety events reported in general practice: a taxonomy JF Quality and Safety in Health Care JO Qual Saf Health Care FD BMJ Publishing Group Ltd SP 53 OP 57 DO 10.1136/qshc.2007.022491 VO 17 IS 1 A1 M A B Makeham A1 S Stromer A1 C Bridges-Webb A1 M Mira A1 D C Saltman A1 C Cooper A1 M R Kidd YR 2008 UL http://qualitysafety.bmj.com/content/17/1/53.abstract AB Objective: To develop a taxonomy describing patient safety events in general practice from reports submitted by a random representative sample of general practitioners (GPs), and to determine proportions of reported event types.Design: 433 reports received by the Threats to Australian Patient Safety (TAPS) study were analysed by three investigating GPs, classifying event types contained. Agreement between investigators was recorded as the taxonomy developed.Setting and participants: 84 volunteers from a random sample of 320 GPs, previously shown to be representative of 4666 GPs in New South Wales, Australia.Main outcome measures: Taxonomy, agreement of investigators coding, proportions of error types.Results: A three-level taxonomy resulted. At the first level, errors relating to the processes of healthcare (type 1; n = 365 (69.5%)) were more common than those relating to deficiencies in the knowledge and skills of health professionals (type 2; n = 160 (30.5%)). At the second level, five type 1 themes were identified: healthcare systems (n = 112 (21.3%)); investigations (n = 65 (12.4%)); medications (n = 107 (20.4%)); other treatments (n = 13 (2.5%)); and communication (n = 68 (12.9%)). Two type 2 themes were identified: diagnosis (n = 62 (11.8%)) and management (n = 98 (18.7%)). The third level comprised 35 descriptors of the themes. Good inter-coder agreement was demonstrated with an overall κ score of 0.66. A least two out of three investigators independently agreed on event classification in 92% of cases.Conclusions: The proposed taxonomy for reported events in general practice provides a comprehensible tool for clinicians describing threats to patient safety, and could be built into reporting systems to remove difficulties arising from coder interpretation of events.