A Padilla1, R Ferriols, M Alós. 1. Servicio de Farmacia, Hospital General de Castellón, Avda. Benicassim, s/n. 12004 Castellón. anucapl@hotmail.com
Abstract
OBJECTIVE: To determine the evolution of the most common drug treatment groups in an intensive care unit over a period of seven years, characterise the time-cost relationship and establish an ARIMA prediction model using Box-Jenkins methodology. METHOD: A retrospective analysis of the costs of thirteen drug treatment groups was carried out in an intensive care unit with 19 beds between the period of 1998 to 2004. The monthly cost of these treatment groups constituted the time series. The descriptive analysis was carried out by means of descriptive statistics and graphs. The trend was analysed by means of smoothing by weighted local regression, and seasonality was analysed by multiple linear regression. Stochastic models for time series were developed using Box-Jenkins methodology for descriptive and forecasting purposes. RESULTS: 70% of drug costs are generated by thirteen groups. Three of these groups display a downward trend, four have an upward trend and the remaining groups do not display any significant trend. Seasonality is only relevant in series with upward trends. The ARIMA model allows models to be obtained for seven series. CONCLUSIONS: The descriptive cost analysis, the determination of trends and the analysis of seasonality provide information about the dynamics in drug use in an intensive care unit. However, the use of ARIMA models to optimise the planning of treatment resources in these types of hospital units is still extremely limited.
OBJECTIVE: To determine the evolution of the most common drug treatment groups in an intensive care unit over a period of seven years, characterise the time-cost relationship and establish an ARIMA prediction model using Box-Jenkins methodology. METHOD: A retrospective analysis of the costs of thirteen drug treatment groups was carried out in an intensive care unit with 19 beds between the period of 1998 to 2004. The monthly cost of these treatment groups constituted the time series. The descriptive analysis was carried out by means of descriptive statistics and graphs. The trend was analysed by means of smoothing by weighted local regression, and seasonality was analysed by multiple linear regression. Stochastic models for time series were developed using Box-Jenkins methodology for descriptive and forecasting purposes. RESULTS: 70% of drug costs are generated by thirteen groups. Three of these groups display a downward trend, four have an upward trend and the remaining groups do not display any significant trend. Seasonality is only relevant in series with upward trends. The ARIMA model allows models to be obtained for seven series. CONCLUSIONS: The descriptive cost analysis, the determination of trends and the analysis of seasonality provide information about the dynamics in drug use in an intensive care unit. However, the use of ARIMA models to optimise the planning of treatment resources in these types of hospital units is still extremely limited.