Objective: To assess the impact of the opening of an after‐hours general practice clinic on the number of daily low‐urgency presentations to the nearby emergency department. Design, participants and setting: Retrospective time series analysis of emergency presentation data, from the New South Wales Health Emergency Department Information System, for all patients presenting to the emergency department of Wagga Wagga Base Hospital between January 1998 and October 2008. Main outcome measures: Daily emergency department presentations, before and after the March 2003 opening of the after‐hours clinic, of patients triaged as Australasian Triage Scale (ATS) category 4 or 5 (at any time of day, and during the hours of operation of the clinic), and of patients triaged as ATS category 1, 2 or 3 (at any time of day). Results: After adjusting for long‐term trends and weekly and annual cycles, the opening of the after‐hours clinic was associated with a daily reduction of 7.04 patients (95% CI, 5.39–8.70) in emergency department presentations with an ATS category of 4 or 5. This represented an 8.2% reduction in total presentations (95% CI, 6.2%–10.2%). Presentations of ATS category 1, 2 or 3 patients rose by 1.36 patients a day (95% CI, 0.36–2.35), representing 1.6% of total presentations (95% CI, 0.4%–2.7%). The impact of the after‐hours clinic was best modelled by a gradual permanent change. Conclusion: An after‐hours general practice clinic was associated with a reduction in low‐urgency presentations to the emergency department in Wagga Wagga.

Objective : To determine if the addition of a video link to the existing phone connection, enabling patients admitted for mental and behavioural disorders to be seen by a centrally located psychiatrist or mental health clinician, would change the probability of these patients being transferred to the central mental health unit. Methods : Data analysed were patients admitted (n=1,943) to a health services regional hospital with a primary diagnosis of mental and behavioural disorders (ICD10‐AM code F00‐F99) between January 2002 and December 2010. The probability of being transferred was modelled using multilevel random intercept logistic regression. The introduction of videoconferencing in January 2008 was examined by testing if the inclusion of a binary intervention variable was significant when added to the best fitting risk adjustment model. Results : After the introduction of videoconferencing the percentage of patients transferred fell from 66.8% (95%CI 64.0 to 69.5) to 59.6% (95%CI 56.1 to 63.1) (χ2=10.42, p=0.001). After adjusting for age, sex, clustering in hospitals and repeat visits the odds of transfer were 0.69 (95%CI 0.49 to 0.97) of previous. Aboriginality, being non‐Australian, long‐term linear trend, admitted on the weekends or after hours were not significant predictors of the probability of transfer. Conclusions and Implications : The ability for the psychiatrist or senior mental health clinician to see mental health patients via videoconferencing was associated with a reduced probability of the patient being transferred. This satisfies the preference of patients to remain in their community and access mental health services.

Children retrieved to the national PICU in New Zealand have greater predicted mortality risk and PICU-specific resource use than nontransported patients. There is no significant difference in risk-adjusted mortality between retrieved and the same institution admissions. Critically ill pediatric patients can be transported long distances by specially trained and equipped transport teams, without an increase in risk-adjusted PICU mortality.

Studies on the rate of adverse events in hospitalized patients seldom examine temporal patterns. This study presents evidence of both weekly and annual cycles. The study is based on a large and diverse data set, with nearly 5 yrs of data from a voluntary staff-incident reporting system of a large public health care provider in rural southeastern Australia. The data of 63 health care facilities were included, ranging from large non-metropolitan hospitals to small community and aged health care facilities. Poisson regression incorporating an observation-driven autoregressive effect using the GLARMA framework was used to explain daily error counts with respect to long-term trend and weekly and annual effects, with procedural volume as an offset. The annual pattern was modeled using a first-order sinusoidal effect. The rate of errors reported demonstrated an increasing annual trend of 13.4% (95% confidence interval [CI] 10.6% to 16.3%); however, this trend was only significant for errors of minor or no harm to the patient. A strong "weekend effect" was observed. The incident rate ratio for the weekend versus weekdays was 2.74 (95% CI 2.55 to 2.93). The weekly pattern was consistent for incidents of all levels of severity, but it was more pronounced for less severe incidents. There was an annual cycle in the rate of incidents, the number of incidents peaking in October, on the 282 nd day of the year (spring in Australia), with an incident rate ratio 1.09 (95% CI 1.05 to 1.14) compared to the annual mean. There was no so-called "killing season" or "July effect," as the peak in incident rate was not related to the commencement of work by new medical school graduates. The major finding of this study is the rate of adverse events is greater on weekends and during spring. The annual pattern appears to be unrelated to the commencement of new graduates and potentially results from seasonal variation in the case mix of patients or the health of the medical workforce that alters health care performance. These mechanisms will need to be elucidated with further research.