RESUMO
Loddon Mallee Integrated Cancer Service plays a key role in planning the delivery of cancer services in the Loddon Mallee Region of Victoria, Australia. Such planning relies on the accuracy of forecasting the incidence of cancer. Perhaps more importantly is the need to reflect the uncertainty of these forecasts, which is usually carried out through prediction intervals. Standard confidence levels (e.g., 90% or 95%) are typically employed when forecasting the incidence of cancer, but decision-theoretic approaches are available to help choose an optimal coverage probability by minimizing the combined risk of the interval width and noncoverage of the interval. We proceed with the decision-theoretic framework and discuss some general strategies for defining candidate loss functions for forecasting the incidence of cancer, such as the data we analyze for the Loddon Mallee Region.
Assuntos
Projetos de Pesquisa Epidemiológica , Neoplasias/epidemiologia , Regionalização da Saúde/métodos , Feminino , Previsões/métodos , Humanos , Incidência , Masculino , Avaliação das Necessidades , Probabilidade , Vitória/epidemiologiaRESUMO
The Australian Government introduced the National Transition Care Program in the 2004-2005 Federal Budget. This program is designed to assist elderly patients who have completed a stay in hospital to move from the hospital to their homes or other suitable accommodation. In planning for transition care services, managers are faced with the question, "How many places should be allocated to transition care in our facility?" This case study offers an approach to this question based on queueing theory.
Assuntos
Unidades Hospitalares , Modelos Estatísticos , Alta do Paciente/estatística & dados numéricos , Assistência Centrada no Paciente/estatística & dados numéricos , Cuidados Semi-Intensivos/estatística & dados numéricos , Idoso , Idoso de 80 Anos ou mais , Austrália , Definição da Elegibilidade , Serviços de Assistência Domiciliar/estatística & dados numéricos , Instituição de Longa Permanência para Idosos/estatística & dados numéricos , Humanos , Pesquisa Operacional , Estudos de Casos Organizacionais , Distribuição de PoissonRESUMO
OBJECTIVE: To forecast the number of patients who will present each month at the emergency department of a hospital in regional Victoria. METHODS: The data on which the forecasts are based are the number of presentations in the emergency department for each month from 2000 to 2005. The statistical forecasting methods used are exponential smoothing and Box-Jenkins methods as implemented in the software package SPSS version 14.0 (SPSS Inc, Chicago, Ill, USA). RESULTS: For the particular time series, of the available models, a simple seasonal exponential smoothing model provides optimal forecasting performance. Forecasts for the first five months in 2006 compare well with the observed attendance data. CONCLUSIONS: Time series analysis is shown to provide a useful, readily available tool for predicting emergency department demand. The approach and lessons from this experience may assist other hospitals and emergency departments to conduct their own analysis to aid planning.
