Identifying and quantifying general practice-type emergency department presentations.

OBJECTIVE: This research aims to (i) identify general practice-type (GP-type) presentations to EDs in South-East Queensland, Australia and (ii) compare and quantify the clinical, socio-demographic and time-varying characteristics between GP-type and non-GP-type presentations. METHODS: Data were collected from presentations to four EDs in Queensland from 2009 to 2014. A modified version of the Australasian College for Emergency Medicine (ACEM) method for identifying GP-type ED presentations was used. RESULTS: The four EDs have different proportions of GP-type presentations, between 7% and 33%. Between 2009 and 2014, the amount of GP-type presentations increased in three EDs, by between 5% and 16%, and decreased by 30% in the other ED. Different holidays, for example, the public holidays over the Christmas to New Year period, impact GP-type presentations. Over 50% of GP-type presentations occurred in those aged 0-34 years, and <1% were aged 85+ years. Injury-related diagnoses made up around 37% of the GP-type presentations, and around 13% did not wait for a diagnosis, averaged over the EDs. GP-type presentations are more likely to present to EDs outside standard general practitioner hours. CONCLUSIONS: Existing methods for identifying GP-type presentations have drawbacks, and modified methods are required to better identify these types of presentations. Temporal effects not previously investigated in Australian studies, such as holidays, are significantly associated with GP-type presentations. These findings aid strategic planning and interventions to support review of GP-type presentations, instead, in primary-care facilities, and such interventions may be assistive in some EDs more than others.

Forecasting daily counts of patient presentations in Australian emergency departments using statistical models with time-varying predictors.

OBJECTIVE: This research aimed to (i) assess the effects of time-varying predictors (day of the week, month, year, holiday, temperature) on daily ED presentations and (ii) compare the accuracy of five methods for forecasting ED presentations, including four statistical methods and a machine learning approach. METHODS: Predictors of ED presentations were assessed using generalised additive models (GAMs), generalised linear models, multiple linear regression models, seasonal autoregressive integrated moving average models and random forest. The accuracy of short-term (14 days), mid-term (30 days) and long-term (365 days) forecasts were compared using two measures of forecasting error. RESULTS: The data are the numbers of presentations to public hospital EDs in South-East Queensland, Australia, from 2009 to 2015. ED presentations are largely affected by year of presentation, and to a lesser extent by month, day of the week and holidays. Maximum daily temperature is also a significant predictor of ED presentations. Of the four statistical models considered, the GAM had the greatest forecasting accuracy, and produced consistent and coherent forecasts, likely due to its flexibility in modelling complex time-varying effects. The random forest machine learning approach had the lowest forecasting accuracy, likely due to overfitting the data. CONCLUSIONS: Calendar and temperature variables, not previously considered in the Australian literature, were found to significantly impact ED presentations. This study also demonstrates the potential of GAMs as a dual explanatory and forecasting method for the modelling, and more accurate prediction, of ED presentations.

Incorporating adverse event relatedness into dose-finding clinical trial designs.

Dose-finding designs estimate the dose level of a drug based on observed adverse events. Relatedness of the adverse event to the drug has been generally ignored in all proposed design methodologies. These designs assume that the adverse events observed during a trial are definitely related to the drug, which can lead to flawed dose-level estimation. We incorporate adverse event relatedness into the so-called continual reassessment method. Adverse events that have 'doubtful' or 'possible' relationships to the drug are modelled using a two-parameter logistic model with an additive probability mass. Adverse events 'probably' or 'definitely' related to the drug are modelled using a cumulative logistic model. To search for the maximum tolerated dose, we use the maximum estimated toxicity probability of these two adverse event relatedness categories. We conduct a simulation study that illustrates the characteristics of the design under various scenarios. This article demonstrates that adverse event relatedness is important for improved dose estimation. It opens up further research pathways into continual reassessment design methodologies.