Food intake was weighed and recorded daily during one complete menstrual cycle in 18 healthy normally menstruating women. Urinary luteinizing hormone indicated the time of ovulation. Mean daily intakes of energy, macronutrients, and alcohol were calculated for five phases during the menstrual cycle: menses, postmenses, ovulatory, postovulatory, and premenses. Weekly variations were also measured. Energy intake was lowest during the ovulatory phase compared with postovulatory, premenses, and menses phases (p less than 0.05). The maximum difference, 1.36 MJ (324 kcal)/d, occurred between ovulatory and postovulatory phases and was twofold higher than the increase of 0.64 MJ (152 kcal)/d observed at weekends. This reduction of food intake at ovulation has not been previously described in humans. It coincides with the expected peak in circulating estrogen levels and is consistent with the hypothesis in animal models that estrogen is an appetite suppressant.

Although GP pre-discharge visits did not alter the likelihood of 'hard outcomes such as risk of readmission', the results suggest that quality of care is enhanced amongst patients receiving a pre-discharge visit and that GPs can perform a key role in planning post-discharge care with other services.

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.

The objective of this study was to assess the quality of communications between hospitals and general practitioners (GPs). The proportion of medical records in which the patient's general practitioner (GP) was identified, the accuracy of medications recorded in the discharge summary, the proportion of GPs who received discharge summaries, and the timeliness of receipt of discharge summaries were all evaluated. Discussions were held with all stakeholders, the literature was reviewed and GPs were surveyed to identify potential measures of quality. These were then trialled to assess their utility and practicability. Timeliness, issues that required follow-up and treatment provided in hospital were of greatest importance to general practitioners. The GP's name was recorded in 88% of audited records. Few inaccuracies were detected in the medications recorded in the discharge summaries, and GPs received 77% of discharge summaries. Methods similar to those used in this study might be broadly applied to improve the quality of discharge communication throughout Australia.