Control problems and management policies in health systems: application to intensive care units

The stochastic nature of both patient arrivals and lengths of stay leads inevitably to periodic bed shortages in healthcare units. Physicians are challenged to fit demand to service capacity. If all beds are occupied eligible patients are usually referred to another ward or hospital and scheduled surgeries may be cancelled. Lack of beds may also have consequences for patients, who may be discharged in advance when the number of occupied beds is so high as to compromise the medical care of new incoming patients. In this paper we deal with the problem of obtaining efficient bed-management policies. We introduce a queuing control problem in which neither the arrival rates nor the number of servers can be modified. Bed occupancy control is addressed by modifying the service time rates, to make them dependent on the state of the system. The objective functions are two quality-of-service components: to minimize patient rejections and to minimize the length of stay shortening. The first objective has a clear mathematical formulation: minimize the probability of rejecting a patient. The second objective admits several formulations. Four different expressions, all leading to nonlinear optimization problems, are proposed. The solutions of these optimization problems define different control policies. We obtain the analytical solutions by adopting Markov-type assumptions and comparing them in terms of the two quality-of-service components. We extend these results to the general case using optimization with simulation, and propose a way to simulate general length of stay distributions enabling the inclusion of state-dependent service rates.     

Some operations research applications to problems of health care systems (a survey)

In this paper, a brief survey of opérations research applications to problems of health care systems is made. It shows the possibilities which exist for improving the design and operations of hospitals and other health care systems through the use of operations research approaches. The different applications have been divided into groups, but it should be noted that there is a great deal of overlapping among them.     

DNA computing of solutions to knapsack problems

One line of DNA computing research focuses on parallel search algorithms, which can be used to solve many optimization problems. DNA in solution can provide an enormous molecular library, which can be searched by molecular biological techniques. We have implemented such a parallel search for solutions to knapsack problems, which ask for the best way to pack a knapsack of limited volume. Several instances of knapsack problems were solved using DNA. We demonstrate how the computations can be extended by in vivo translation of the DNA library into protein. This combination of DNA and protein allows for multi-criterion optimization. The knapsack computations performed can then be seen as protein optimizations, one of the most complex computations performed by natural systems.     

Public perceptions of health care problems. An analysis from Arizona.

Public perception of 17 health problems was assessed by telephone and in-person interviews in Arizona. Drug abuse (64.7%), the costs of health care (62.8%), and drunk driving (60.6%) were considered the most serious health care problems. Elderly and rural residents tended to view drug abuse, drunk driving, teenage pregnancy, and economic aspects of health care as less serious than did the younger and urban respondents, while the poor thought these problems were more serious. Respondents in this survey were less concerned with the lack of specific clinical services for high-risk groups--the old and frail, pregnant women, people with the acquired immunodeficiency syndrome, suicidal teenagers, and abused children.     

Immunity in society: diverse solutions to common problems

Understanding how organisms fight infection has been a central focus of scientific research and medicine for the past couple of centuries, and a perennial object of trial and error by humans trying to mitigate the burden of disease. Vaccination success relies upon the exposure of susceptible individuals to pathogen constituents that do not cause (excessive) pathology and that elicit specific immune memory. Mass vaccination allows us to study how immunity operates at the group level; denser populations are more prone to transmitting disease between individuals, but once a critical proportion of the population becomes immune, "herd immunity" emerges. In social species, the combination of behavioural control of infection--e.g., segregation of sick individuals, disposal of the dead, quality assessment of food and water--and aggregation of immune individuals can protect non-immune members from disease. While immune specificity and memory are well understood to underpin immunisation in vertebrates, it has been somewhat surprising to find similar phenomena in invertebrates, which lack the vertebrate molecular mechanisms deemed necessary for immunisation. Indeed, reports showing alternative forms of immune memory are accumulating in invertebrates. In this issue of PLoS Biology, Konrad et al. present an example of fungus-specific immune responses in social ants that lead to the active immunisation of nestmates by infected individuals. These findings join others in showing how organisms evolved diverse mechanisms that fulfil common functions, namely the discrimination between pathogens, the transfer of immunity between related individuals, and the group-level benefits of immunisation.