Forecasting hospital bed needs.

Abstract

The historical trends for each category were compared by age group and sex and with the corresponding data from series of mortality statistics'2 whenever these were an acceptable substitute for morbidity. The direction of the trends in the mortality series, tested for statistical significance, was compared with the direction of the curves used to prepare extrapolations. A commentary was written and questions framed about discharge rates and durations of stay in terms which drew attention to compatibility or any clear inconsistency between the discharge rates and mortality data-for example, the projection of historical trends of discharge rates for strokes shows a rise in most age groups contrary to the current falling mortality rates.A similar procedure was followed using statistics from notifications in the case of tuberculosis. When mortality was not an acceptable proxy for morbidity and when it was thought helpful to do so, attention was drawn to the relation between the trend in hospital discharge rates and the relative levels of general practitioners' consultation rates in 1958 and 1970-1, taken from the two national studies of morbidity in general practice.13 14 The projections and questionnaires were sent for comment to selected individual clinicians and national professional organisations whose members had specific interest in the particular disease. The same procedure was followed with epidemiologists, chosen on similar grounds. They could also be expected in making their comments to draw on other available evidence of the incidence of these diseases for which it is not possible validly to compare hospital discharge rates with mortality statistics or general practitioners' consultation rates.In nearly all instances the comments that were received were interpreted in line with the following predetermined ground rules:(1) When those consulted agreed with the projected values, that was accepted as the forecast.(2) Alternative figures supplied were accepted as the forecast if they fell within the 95% confidence limits.(3) When advised that discharge rates would rise higher than projected the upper confidence limit was chosen likewise; the lower

Would the United States Have Had Too Few Beds for Universal Emergency Care in the Event of a More Widespread Covid-19 Epidemic?

“…Unfortunately, these relationships have never been incorporated into methods for forecasting healthcare demand and capacity planning. Such methods almost exclusively rely on time series analysis or variants of demographic or age-based forecasting [ 14 , 15 , 16 , 17 , 18 , 19 ]. The limitations of these methods are discussed elsewhere [ 15 , 18 , 19 , 20 , 21 , 22 , 23 ].…”

“…This method avoids the limitations of simplistic international comparisons based on beds per 1000 population [ 20 ], which contains no adjustment for age structure or nearness to death. Regarding the use of deaths per thousand population it should be noted that as far back as 1981 a relationship between bronchitis and emphysema (men aged 65–74) admissions per 1000 population and deaths per 1000 population had been demonstrated [ 19 ].…”

The Efficient Management of Medical Beds

“…The problem appears to be that no one can confidently predict the ideal number of acute beds. Previous emphasis upon bed requirements per unit population 8,9,10 has given way to a more pragmatic approach, often based on the assumed level of finances available. Yet these external constraints appear to facilitate, in ways which are by no means clear, complex changes in professional behaviour which result in a still higher turnover of patients, and more than offset the apparent reductions in facilities.…”

Choice of Models for the Analysis and Forecasting of Hospital Beds

“…Yet the modelling of issues relating to hospital beds for improved decision-making is not new. For example, work using autoregressive integrated moving average (ARIMA) methodology [12,13], population based approaches [14,15] and other methods to forecast bed requirements [16][17][18][19] has been undertaken. Some of this work, such as that by Sorensen [19], and Farmer and Emami [12] has relied upon the average length of stay (ALOS) and is only a slightly more sophisticated approach than the rule of thumb methods often used by health care managers and clinicians.…”