Objective: To determine the incidence of errors anonymously reported by general practitioners in NSW. Design: The Threats to Australian Patient Safety (TAPS) study used anonymous reporting of errors by GPs via a secure web‐based questionnaire for 12 months from October 2003. Setting: General practices in NSW from three groupings: major urban centres (RRMA 1), large regional areas (RRMA 2–3), and rural and remote areas (RRMA 4–7). Participants: 84 GPs from a stratified random sample of the population of 4666 NSW GPs — 41 (49%) from RRMA 1, 22 (26%) from RRMA 2–3, and 21 (25%) from RRMA 4–7. Participants were representative of the GP source population of 4666 doctors in NSW (Medicare items billed, participant age and sex). Main outcome measures: Total number of error reports and incidence of reported errors per Medicare patient encounter item and per patient seen per year. Results: 84 GPs submitted 418 error reports, claimed 490 864 Medicare patient encounter items, and saw 166 569 individual patients over 12 months. The incidence of reported error per Medicare patient encounter item per year was 0.078% (95% CI, 0.076%–0.080%). The incidence of reported errors per patient seen per year was 0.240% (95% CI, 0.235%–0.245%). No significant difference was seen in error reporting frequency between RRMA groupings. Conclusions: This is the first study describing the incidence of GP‐reported errors in a representative sample. When an anonymous reporting system is provided, about one error is reported for every 1000 Medicare items related to patient encounters billed, and about two errors are reported for every 1000 individual patients seen by a GP.

Patients give many reasons for why they have not kept up with their resolutions; research shows that many of these causal attributions are wrong. This article provides a tool to help patients sort out causes of and constraints on their behavior, in general, and exercise, in particular. Patient's diary data can be analyzed to flag erroneous causal attributions, and thus assist patients to understand their behavior. To start the diary, the clinician works with the patient to assemble a list of possible causes. Using the list, a diary is organized that tracks the occurrences of various causes and the target behavior. At the end of 2 to 3 weeks, the diary data is analyzed using conditional probability models, causal Bayesian networks or logistic regression. A key issue in the analysis of diary data is to separate out the effect of various causes. Typically, causes co-occur, making it difficult to understand their independent effects. Another problem with analysis of diary data is the small size of the data. This article shows how small longitudinal data from patient diaries can be analyzed. The analysis may refute or support causes hypothesized by the client. The patient uses the insights gained from the diary analysis to prevent relapse to unhealthy behaviors. The process is continued for several cycles of organizing, keeping, and analyzing the diary data. In each cycle, the patient gains new insights and makes additional attempts to create a positive environment that allows him or her to succeed even if his or her motivation waivers. This article provides details of how diary data can be analyzed to help patients make correct causal attributions.