Myths of Ideal Hospital Occupancy

¶ … Myths of ideal hospital occupancy

Christopher a Bain, Peter G. Taylor, Geoff McDonnell and Andrew Georgiou

Hospital overcrowding is one of the most frequently-cited reasons for poor patient care. However, according to the article "Myths of ideal hospital occupancy" by Christopher a Bain, Peter G. Taylor, Geoff McDonnell and Andrew Georgiou, the idea that there is a specific, optimal percentage of hospital occupancy for all institutions is not supported by the currently-existing medical research. The title of the article sums up its thesis in a succinct fashion: 85% occupancy has often been cited as the ideal patient occupancy percentile to ensure rapid turnover between incoming and outgoing patients and to optimize patient care. However, this is based in statistical analysis nearly 100 years old, according to the authors.

The article's authors concede that "there is a trade-off between mean occupancy of a system and its availability for new arrivals" but believe that "the precise nature of this trade-off depends on the characteristics of the system" and cannot be subsumed under an 85% ratio (Taylor, McDonnell & Georgiou 2010:42). The authors imply that there is too much emphasis placed upon keeping hospitals slightly under-capacity in their occupancy figures, resulting in inefficient resource utilization. The central focus of the article is questioning the usefulness of 'queuing theory', a mode of statistical analysis used to determine how much patient care may be impacted based upon bed turnover. The authors state that this mode of analysis is somewhat limited by the variables it can take into consideration.

Queuing theory attempts to predict capacity overload and optimization in a predetermined fashion that is universally applicable, but this is not possible, state the authors, in the field of healthcare. To demonstrate how queuing analysis can be faulty if not applied in a situation-specific manner, the authors cite a queuing study conducted at an acute hospital that found that regular bed shortages and periodic bed crises occurred when occupancy rises to 90% or more, and that crises experienced a substantial increase even at 85%. While this might seem to support the 85% occupational thesis on one hand, the authors note that acute facilities often have unique crises which could create a more extreme tipping point than other types of healthcare venues. To them this suggests that non-acute facilities might not be similarly impacted, and that a wider variety of situational contexts must be examined to make such a sweeping generalization, based upon what might be a unique ratio particular to acute settings. The need for further study questioning the 85% statistic is further underlined in the abstract and conclusion of the article.

One problem with the assertions made in the article is that the authors, in their literature review, do not cite any statistical information in support of their own thesis. Only one alternative study is suggested to replace previous generalized 'queuing' analysis which "showed that the optimal number of beds depends on the relative cost that is incurred when a patient is blocked compared with that of maintaining an empty bed. The optimal utilization at which the unit should be maintained also depends on this relative cost" (Taylor, McDonnell & Georgiou 2010:42). Although the authors criticize queuing theory as being overly generalized and unsupported by specific data, the fact that only one cited study supports their own thesis seems to suggest that their n conclusion is generalized in its approach. True, part of their argument is that insufficient attention has been directed to question the 85% statistic, hence their lack of existing supporting data in defense of their own thesis. On the other hand, they do not show any evidence that hospitals or other healthcare facilities, even non-acute facilities, can provide high-quality care with higher levels of occupancy or that these institutions experience different patterns of turnover to reduce the need for allowing for appropriate bed turnover rates.

The method of the article is a literature review. The authors critique a variety of studies supporting the 85% thesis as being insufficiently thorough, or focusing on too narrow a segment of the healthcare market. While these critiques are certainly valid, and the authors clearly demonstrate, as stated in the abstract, that more research may be required, their approach also underlines the weaknesses of a literature review-based article to make a healthcare proposal. Another problem with the use of literature reviews is that it can be difficult to present the results in a meaningful fashion in a chart or graph, to demonstrate the researcher's findings. The format of a literature review -- drawing upon the statistical evidence of a wide range of studies and examining general trends, patterns, and weaknesses -- often means it is comparing apples to oranges, as every study has a slightly different format. The article has a deconstructive purpose, but offers nothing constructive to suggest in the policy's place.

But more disturbingly, there are serious repercussions if the author's ideas are taken seriously, given the problems that could ensue with overcrowding and overburdened healthcare professionals. While the idea that the uniqueness of different healthcare facilities may seem self-evident, it also seems evident that even a non-acute, well-staffed setting at 95% capacity might experience quality-of-care deficits. Additionally, in the event of an emergency or the need to operate under extraordinary circumstances, an already overburdened, nearly fully 'booked' hospital might experience extreme resource strain upon staff and medical supplies. The focus of the analysis is upon the crises that result in turnover or 'bed blockage' or when not enough patients are leaving the hospital to allow for incoming patients. But even if a higher rate of capacity than 85% is possible without 'bed blockage,' a single-minded focus upon this issue ignores other, serious concerns related to capacity beyond that of merely finding enough space for patients.

The article's conclusion seems intuitive -- that 85% is too arbitrary a figure to be merely asserted, without question, as straining the resources of all facilities and resulting in bed blockage. On the other hand, there is the question of what the consequences might be if this requirement was waived, and hospitals attempted to maximize their capacity to a greater degree. The authors assert: "new tools that continually monitor service utilization and estimate future mismatches between patient demand and hospital capacity obviate the need for an arbitrary figure such as 85%. The mathematical modeling that lies behind these tools is reasonably well understood, but the models still need to be tailored to fit different hospital environments" (Taylor, McDonnell & Georgiou 2010:42). But estimating overcapacity to such a fine degree and trying to press the limits of the facility seems dangerous, given that such a site-specific model would presumably have to take into consideration the experience levels of nurses, fatigue of residents, and other factors that might affect patient care. Even if a statistical analysis suggested that the facility could operate at a higher level of capacity without problems of bed blockage and patient bed turnover rates, the limits of looking at bed blockage rates alone seem dubious.