Diagnostic error research has mostly focused on methods to detect, characterise and analyse lapses in the diagnostic process by using incident reports, malpractice claims, autopsies and electronic trigger tools. The associated literature shows how frequently important diagnostic errors occur1 and examines cognitive2 and system-based3 causes of these errors. Relatively absent from this portfolio of research have been large-scale approaches for measuring institutional diagnostic performance, either for benchmarking purposes or for driving improvement efforts.
In this issue of BMJQS, Liberman and Newman-Toker introduce Symptom–Disease Pair Analysis of Diagnostic Error (SPADE) as a new approach to identify diagnostic errors by analysing large patient data sets (tens of thousands of patient encounters housed in electronic medical records or administrative databases).4 The SPADE methodology starts with a symptom that is misdiagnosed at an appreciable rate such as chest pain or dizziness. It then looks for...
Adverse events (AEs) and no-harm incidents are common and of great concern in healthcare. A common method for identification of AEs is retrospective record review (RRR) using predefined triggers. This method has been used frequently in inpatient care, but AEs in home healthcare have not been explored to the same extent. The aim of this study was to develop a trigger tool (TT) for the identification of both AEs and no-harm incidents affecting adult patients admitted to home healthcare in Sweden, and to describe the methodology used for this development.Methods
The TT was developed and validated in a stepwise manner, in collaboration with experts with different skills, using (1) literature review and interviews, (2) a five-round modified Delphi process, and (3) two-stage RRRs. Ten trained teams from different sites in Sweden reviewed 600 randomly selected records.Results
In all, triggers were found 4031 times in 518 (86.3%) records, with a mean of 6.7 (median 4, range 1–54) triggers per record with triggers. The positive predictive values (PPVs) for AEs and no-harm incidents were 25.4% and 16.3%, respectively, resulting in a PPV of 41.7% (range 0.0%–96.1% per trigger) for the total TT when using 38 triggers. The most common triggers were unplanned contact with physician and/or registered nurse, moderate/severe pain, moderate/severe worry, anxiety, suffering, existential pain and/or psychological pain. AEs were identified in 37.7% of the patients and no-harm incidents in 29.5%.Conclusion
This study shows that adapted triggers with definitions and decision support, developed to identify AEs and no-harm incidents that affect patients admitted to home healthcare, may be a valid method for safety and quality improvement work in home healthcare.