Being able to anticipate future needs for health services presents a challenge for health planners. Using existing population projections, two models are presented to estimate the demand for hospital beds in regions of Manitoba in 2020. The first, a current-use projection model, simply projects the average use for a recent three-year period into the future. The second, a 10-year trend analysis, uses Poisson regression to project future demand. The current-use projection suggests a substantial increase in the demand for hospital beds, while the trend analysis projects a decline. The last projections are consistent with ongoing increases in rates of day surgeries and declines in lengths of stay. The current-use projections need to be considered in the context of relatively low occupancy rates in rural hospitals and previous research on appropriateness of stays in acute care hospitals. If measures are taken to ensure more appropriate use of acute care hospital beds in the future, then the current-use projections of bed shortages are not a cause for concern.

A team of health researchers of the Manitoba Centre for Health Policy at the University of Manitoba was asked to forecast the number of acute care hospital beds that will be required to meet the needs of residents of the province of Manitoba by the year 2020. Methodological considerations for this request included identification of factors expected to affect bed use in the future, and how to account for these factors. The objective of this paper is to describe these methodological considerations, how decisions were made, and steps taken in our approach to this problem. The actual projections and their implications are the subject of another contribution in this issue of the journal (Finlayson, Stewart, Tate, MacWilliam & Roos, 2005).

Pour établir des plans de production et d'approvisionnements, une entreprise utilise un système de prévisions. Celui-ci est constitué d'une méthode de prévision de la demande et d'une méthode de lot-sizing permettant l'obtention de plans prévisionnels. Nous proposons une démarche permettant d'évaluer la fiabilité des prévisions fournies par un tel système. Cette analyse se base sur l'étude de l'impact des aléas de la demande sur les coûts induits par les plans prévisionnels. Nous avons appliqué ce protocole à un système de prévisions constitué du lissage exponentiel et de la méthode de Florian et Klein [5]. Dans les conditions expérimentales considérées, ce système de prévision fournit des informations fiables.