Study objective-The relation between a history of disorders suggestive of acute otitis media, symptoms, and findings of an examination of the tympanic membrane and doctors0 certainty of diagnosis. Also, to examine differences in prescribing habits for acute otitis media among doctors from different countries.Design-Questionnaires were completed by participating doctors for a maximum of 15 consecutive patients presenting with presumed acute otitis media.

Objective: To describe the steps taken by health professionals to diagnose dementia and the timeframes for these steps, as reported by carers. Design, setting and participants: A cross‐sectional, anonymous survey was mailed or distributed by Alzheimer's Australia New South Wales, six Sydney residential aged care facilities and 13 Sydney general practitioners to 415 carers or family members of patients with dementia between May and August 2007. Main outcome measures: First symptoms noticed and actions taken; time to first health professional consultation and diagnosis; reported actions of first health professional; satisfaction with first consultation; and use of dementia and chronic illness resources. Results: 209 surveys were returned. Family members noticed the first symptoms of dementia at a mean of 1.9 years before the first health professional consultation about dementia, and 3.1 years before a firm diagnosis. Resource use first occurred 2.8 years after the first symptoms. Most carers (72%) were satisfied with the first consultation, which was usually with a GP (84%). Two‐thirds of carers (64%) reported that the first health professional had performed a memory test. Conclusions: Delays in presentation, diagnosis and resource use may have clinical and social implications for people with dementia and their families, in addition to the challenges of the process of obtaining a firm diagnosis.

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.

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From 1984 to 1986 a prospective study was conducted of 104 general practice patients who started treatment with a benzodiazepine or an antidepressant drug. The duration of reported use of the drugs was two months for 45% of patients, four months for 17% of patients, and six months for 15%. Type of drug, age, and level of education were found to be predictive of continuing use.

A randomised controlled trial studied the effect of an educational visit on benzodiazepine prescribing. An approximately representative sample of 286 general practitioners was allocated to an intervention or a control group. Rates of benzodiazepine prescriptions were derived from two comprehensive self-report surveys seven months apart. Two months after the first survey the intervention group received an educational visit and supporting material from a doctor or pharmacist, ostensibly unconnected with the surveys. The overall benzodiazepine prescribing rate fell by 23.7 per cent from the first to the second surveys, from 4.93 to 3.76 prescriptions per 100 encounters (P < 0.001). Anxiety and insomnia diagnosis rates also declined from 4.68 to 3.76 per 100 encounters (19.7 per cent). After adjusting for confounders, there was a differential downward trend in prescriptions per diagnosis of insomnia but not to a swtistical level. The same was true of initial prescriptions per insomnia diagnosis. In a subsidiary analysis selecting only new insomnia diagnoses, the intervention had a strong effect in reducing initial prescriptions (odds ratio 0.18, 95 per cent confidence interval 0.04 to 0.73). N o effect was seen on prescribing for anxiety diagnoses. Educational practice visiting for benzodiazepine prescribing in anxiety, as we conducted it, is notjustified in an unselected population of general practitioners. Specific education on prescribing for insomnia is probably useful. Our interpretation of the reduction in benzodiazepine prescribing is that probably there was an effect from self-monitoring alone which overwhelmed a main-analysis intervention effect. Retrospective diagnosis may also have obscured a real intervention effect.