NHS hospitals contribute to medical education, training nurses and research, as well as to the care of patients. In the past they have been funded largely on the basis of resources employed, with additional funding for medical education and training nurses. The intellectual basis for the funding of medical education is a single econometric study of English hospitals in the financial year 1969-70. The methodology used has since been criticized, and it has been suggested that actual expenditure has been very much less than that earmarked by the health departments. New estimates are obtained using Scottish data for the financial year 1985-86. The method used is to proceed in a two-stage fashion, identifying via regression techniques variables measuring hospital activity and resources which contribute significantly to hospital costs. We then assess the significance of medical education, nurse training and hospitals' teaching status against this background. Our conclusions include: (1) actual expenditure on medical education was probably less than the funding formula allowed, but the error of margin is too large to suggest overfunding; (2) training nurses incurs significant financial costs, even after the explicit allowances made; and (3), major teaching hospitals tended to cost more, but not significantly more than their non-teaching counterparts. These financial implications for NHS hospitals should be borne in mind given the current NHS review.

This paper describes a case study in which a simulation model, specifically an attraction-constrained spatial interaction model, is used to provide information to assist in the exploration of different options for the future provision of in-patient hospital facilities in an English Regional Health Authority. The model is used to evaluate options on the basis of their effect on two different criteria: first the level of utilization of hospital in-patient facilities relative to need of populations resident in different parts of the region, and secondly the relative ease of geographical access of the facilities to the populations served.

An optimal solution is found to a real-world problem which has the characteristics of a multiple travelling-salesman problem. It deals specifically with methods of obtaining the 'best' routeing for four vehicles which provide a daily service from a depot to 38 locations. The solution technique uses both heuristic and exact algorithms to derive the order for visits to the 38 locations.

A screening programme is being introduced throughout the UK to reduce deaths from cancer of the cervix by early detection of precancerous lesions. A stochastic mathematical model has been developed in order to estimate the resource implications of this programme for hospital services in NW region. The model was developed over a period of 1 year, undergoing many changes in the process. The model has suggested operational policies for smear management and demonstrated the importance of wide coverage of the population.

A method is proposed for the allocation of units of blood from a regional blood transfusion centre to the hospitals of its area, taking into consideration the characteristics of the individual hospitals, such as the transfusion activity, the demand of each hospital and the regional blood transfusion service policy concerning the allocation of units of blood expressed through a utility function. The problem is formulated as a stochastic programming problem but reduces to a linear programming problem and therefore is easily applicable. The method is suitable for application in systems with a National Health Service system, such as the British. Finally the results are given of an application to the despatches of units of blood in the Regional Blood Transfusion Service of Glasgow and West of Scotland.

In this paper an integer, non-linear mathematical programming model is developed to allocate emergency medical service (EMS) ambulances to sectors within a county in order to meet a government-mandated response-time criterion. However, in addition to the response-time criterion, the model also reflects criteria for budget and work-load, and, since ambulance response is best described within the context of a queueing system, several of the model system constraints are based on queueing formulations adapted to a mathematical programming format. The model is developed and demonstrated within the context of an example of a county encompassing rural, urban and mixed sectors which exhibit different demand and geographic characteristics. The example model is solved using an integer, non-linear goal-programming technique. The solution results provide ambulance allocations to sectors within the county, the probability of an ambulance exceeding a prespecified response time, and the utilization factor for ambulances per sector.

This paper presents a simple method of determining optimal screening policies for detecting immunity to Hepatitis A virus in travellers to areas of high endemicity. The method is constrained by the requirement that screening policies be kept simple in order to ensure their wide implementation.

The problem of locating emergency-service facilities involves the assignment of a set of demand points to a set of facilities. One way to formulate the problem is to minimize the number of required facilities, given that the maximum distance between the demand points and their nearest facility does not exceed some specified value. We present a procedure for determining the numbers of such facilities for all possible values of the maximum distance. Computational results are presented for a microcomputer implementation.

A mathematical programming model is proposed to select an optimal cooperation policy between autonomous service networks for dispatching purposes. In addition to traditional characteristics such as network topology and station location, this model takes into account 'political' constraints on minimum response-time in certain subzones. Such constraints are translated into performance requirements, which are imposed on the cooperation policy. Testing the model under different assumptions can be useful for analysing various cooperation policies. The paper formulates a mathematical programming model, derives example policies for various circumstances, and tests the sensitivity of the resultant policies to some parameters, such as the penalty for not providing service, and distances between adjacent networks. The paper suggests also a less constrained approach, which entails a linear programming model. A comparison between the two approaches is presented.

A microcomputer-based expert system to interpret hospital and community health service data is described. The system analyses performance indicators, which are key statistics describing levels of achievement in terms both of policy objectives and efficiency. The system is being used to support annual performance reviews of English health authorities. The potential for application of similar systems is discussed.

A discrete-event simulation model is being developed to provide information for national and regional health planners about the effect of different treatment policies and practices on the increasing numbers of patients with irreversible kidney failure. The simulation program, written in Pascal using Pascal-SIM, describes the activities of individual patients whose progress through treatment depends on their characteristics and treatment history, as well as on treatments availability. Programming techniques have been developed to cope with the multiple scheduling of patient entities and the resultant queue-management problems. In order to provide simulation runs on request, for any population group, appropriate data is selected from the European Dialysis and Transplant Association Registry data base for use in the simulation program. Results are shown on a computer screen while the simulation is running, and are saved for further analysis to provide feedback to the requesting organization.

A multistep approach to determining the optimal parameters of an exponential smoothing model was used to forecast emergency medical service (E.M.S.) demand for four counties of South Carolina. Daily emergency and routine (non-emergency) demand data were obtained and forecast statistics generated for each county sampled, using Winters' exponential smoothing model. A goal programme was formulated to combine forecast results for emergency calls with routine call forecasts. The goal programme gave a higher priority to accurate forecasting of emergency demand. The forecast model generated implicitly weights demand by severity and provides a reliable estimate of demand overall. The optimal parameter values for the smoothing model were obtained by minimizing the objective function value of the goal programming problem. The parameter values obtained were used to forecast demand for E.M.S. in the selected counties. The results of the model were compared to those using a multiple linear regression model and a single-objective-based exponential smoothing model for 2 months of data. When compared with two single-objective forecast models, the multiple-objective approach yielded more accurate forecasts and, therefore, was more cost-effective for the planner. The model presents and demonstrates a theoretical approach to improving the accuracy of ambulance demand forecasts. The possible impact of this approach on planning efficiency is discussed.

Community nurses spend a relatively large proportion of their time travelling. If this could be reduced, benefits would include increased time for direct patient care and reduced costs. This paper describes a pilot study in the Dumfries and Galloway Health Board to see if computerized vehicle routeing could reduce the distance and time of travelling. It is concluded that problems with implementation and characteristics of nurses' journeys (particularly the short distances travelled between long stops) make it unlikely that net savings could be achieved using computers. Nonetheless, some benefits might be obtained by taking a rational look at aspects of nurses' travelling.

Earlier work has criticized the dominant tendencies in operational research contributions to health services planning as characterized by optimization, implausible demands for data, depoliticization, hierarchy and inflexibility. This paper describes an effort which avoids at least some of these pitfalls. The project was to construct a planning system for a regional health council in Ontario, Canada, which would take account of the possible alternative future states of the health-care system's environment and would aim to keep options for future development open. The planning system devised is described in the paper. It is based on robustness analysis, which evaluates alternative initial action sets in terms of the useful flexibility they preserve. Other features include the explicit incorporation of pressures for change generated outside the health-care system, and a satisficing approach to the identification of both initial action sets and alternative future configurations of the health-care system. It was found possible to borrow and radically 're-use' techniques or formulations from the mainstream of O.R. contributions. Thus the 'reference projection' method was used to identify inadequacies in performance which future health-care system configurations must repair. And Delphi analysis, normally a method for generating consensus, was used in conjunction with cluster analysis of responses to generate meaningfully different alternative futures.

Modelling the operation of outpatient departments, with the aim of devising an appointment system that would reduce patient waiting time, was an early application area for O.R. Yet outpatients still spend an excessive amount of time waiting. This paper reports an investigation into the operation of outpatient departments. The attempt to arrive at a sensible policy is considered at length. It is concluded that the determination of an implementable policy, the education of participants into acceptance of that policy, and monitoring any policy once implemented are of paramount importance. The approach taken is briefly compared with qualitative and soft approaches.

A variety of stochastic and deterministic non-optimizing techniques have been used both predictively and for comparing policy options for patient treatment. Models of the system of the treatment of kidney patients are reviewed and are shown to be based on too small a subsystem to be useful for planning and budgeting. Other drawbacks include poor user-credibility and lack of robustness. Discrete-event simulation is shown to be the most appropriate technique which does not limit the type of distribution functions that may be used and can model patient attributes, resource use and constraints.

There is an unmet demand for the treatment of irreversible kidney failure, particularly in the older age groups. A model of the treatment system was built to explore the implications of meeting the demand, giving different priorities to the available treatments and changing the balance between home and hospital. This discrete event simulation, developed in the Wessex Region, describes the system realistically, including resource use and constraints, the arrival of kidneys for transplantation and the matching of donors with recipients. It is written in Pascal on an Apple II computer and uses shadow entities to describe the survival of patients on each type of treatment. The model was validated with techniques which included the use of a tabular display while the simulation was running. The model has proved to be easy to use and robust both to different data requirements and extreme policy changes. The techniques developed have more general application in the Health Service context.

The consumer electronics industry is a $240 billion global industry with a small number of highly competitive global players. We describe many of the risks associated with any global supply chain in this industry. As illustration, we also list steps that Samsung Electronics and its subsidiary, Samsung Electronics UK, have taken to mitigate these risks. Our description of the risks and illustration of mitigation efforts provides the backdrop to identify areas of future research.

A central problem in managing risk is dealing with social processes that either exaggerate or understate it. A longstanding approach to understanding such processes has been the social amplification of risk framework. But this implies that some true level of risk becomes distorted in social actors' perceptions. Many risk events are characterised by such uncertainties, disagreements and changes in scientific knowledge that it becomes unreasonable to speak of a true level of risk. The most we can often say in such cases is that different groups believe each other to be either amplifying or attenuating a risk. This inherent subjectivity raises the question as to whether risk managers can expect any particular kinds of outcome to emerge. This question is the basis for a case study of zoonotic disease outbreaks using systems dynamics as a modelling medium. The model shows that processes suggested in the social amplification of risk framework produce polarised risk responses among different actors, but that the subjectivity magnifies this polarisation considerably. As this subjectivity takes more complex forms it leaves problematic residues at the end of a disease outbreak, such as an indefinite drop in economic activity and an indefinite increase in anxiety.

A large number of studies have applied simulation to a multitude of issues relating to healthcare. These studies have been published in a number of unrelated publishing outlets, which may hamper the widespread reference and use of such resources. In this paper, we analyse existing research in healthcare simulation in order to categorise and synthesise it in a meaningful manner. Hence, the aim of this paper is to conduct a review of the literature pertaining to simulation research within healthcare in order to ascertain its current development. A review of approximately 250 high-quality journal papers published between 1970 and 2007 on healthcare-related simulation research was conducted. The results present a classification of the healthcare publications according to the simulation techniques they employ; the impact of published literature in healthcare simulation; a report on demonstration and implementation of the studies' results; the sources of funding; and the software used. Healthcare planners and researchers will benefit from this study by having ready access to an indicative article collection of simulation techniques applied to healthcare problems that are clustered under meaningful headings. This study facilitates the understanding of the potential of different simulation techniques in solving diverse healthcare problems.