Utilizing buffer management to improve performance in a healthcare environment

For over 50 years, the United Kingdom’s National Health Service has aspired to provide universal access to quality health care. However, as evidenced by huge backlogs and lengthy waiting times for many services, the NHS is showing the strain of accommodating rising contemporary expectations within a constrained budget. This paper describes the concept of buffer management, its origins in manufacturing applications, and how it can be applied to generate improvements in health care systems. The successful implementation of buffer management is illustrated with recent applications in the Accident and Emergency departments and the hospital admissions process of three NHS facilities.     

Overcoming the barriers: a qualitative study of simulation adoption in the NHS

This paper addresses a key issue in the health OR literature, namely the apparent failure of OR modelling to become embedded and widely implemented within healthcare organisations. The research presented here is a case study to evaluate the adoption of one particular simulation modelling tool, Scenario Generator (S:G), which was developed by the SIMUL8 Corporation in a PPI partnership with the UK's National Health Service (NHS) Institute for Innovation and Improvement. The study involved semi-structured interviews with employees of 28 Primary Care Trusts who had all been engaged in some way with the initiative, with participants classified as ‘Not Started’, ‘Given Up’ and ‘Actively Using’. This paper presents a brief summary of barriers and facilitators to the successful use of the S:G software, but its main purpose is to focus more broadly on factors influencing the successful adoption of simulation tools in general within healthcare organisations. The insights gained in this study are relevant to improving the uptake of OR modelling in general within the NHS.     

Operational Research for informed innovation: <Emphasis Type="Italic">NHS Direct</Emphasis> as a case study in the design,implementation and evaluation of a new public service

Delivering modern and responsive public services requires informed innovation drawing on a combination of design, planning and evaluative skills. The development of the national 24-hour telephone helpline service NHS Direct provides a case study demonstrating how, through using these skills, OR has been instrumental in achieving public service innovation. Firstly, OR analysts led strategic design and scenario-planning work, assessing the evidence on the performance of ‘direct’ services here and around the world and developing scenarios of an NHS where much greater use was made of modern communication technology, such as telephone call centres, to provide services. Secondly, we did modelling work to help determine the size, distribution and staffing of the call centres required in England to meet the likely demand and satisfy service performance targets. Thirdly, we set out evaluation criteria and developed performance-monitoring systems. NHS Direct has been one of the best-received innovations in the history of the NHS and Operational Research has made a crucial contribution to its development.     

Improving health care through community OR

Two examples of Community OR (COR) applied to public and patient involvement in improving health services in the UK are described—the Hull and East Riding Impact project and Trailblazers. These projects are consumer controlled but professionally facilitated. The team members have or have had mental illnesses but are committed to improving mental health services by applying COR methods. These projects illustrate how COR can contribute to the improvement of health services by enabling service users to take the lead in facilitating multi-stakeholder planning and problem solving. The Impact team produced a tool based around Ulrich's critical heuristics and have used this in several successful consultations in the NHS. The Trailblazer project used an idealized planning approach in a novel way to produce a distance method of consultation aimed at enabling stakeholders to negotiate commitments to action. Longer-term evaluation of these approaches is planned.     

An analysis of p-median location problem: Effects of backup service level and demand assignment policy

Any solution to facility location problems will consider determining the best suitable locations with respect to certain criteria. Among different types of location problems, involving emergency service system (ESSs) are one of the most widely studied in the literature, and solutions to these problems will mostly aim to minimize the mean response time to demands. In practice, however, a demand may not be served from its nearest facility if that facility is engaged in serving other demands. This makes it a requirement to assign backup services so as to improve response time and service quality. The level of backup service is a key, strategic-level planning factor, and must be taken into consideration carefully. Moreover, in emergency service operations conducted in congested demand regions, demand assignment policy is another important factor that affects the system performance. Models failing to adopt sufficient levels of backup service and realistic demand assignment policies may significantly deteriorate the system performance.Considering the classic p-median problem (pMP) location model, this paper investigates the effects of backup service level, demand assignment policy, demand density, and number of facilities and their locations on the solution performance in terms of multiple metrics. For this purpose, we adopt a combined optimization and simulation approach. We will first modify the classic pMP to account for distances to backup services. Next, we employ a discrete event simulation to evaluate the performance of location schemes obtained from the deterministic mathematical model. Our results provide insights for decision-makers while planning ESS operations.     

Locating specialized service capacity in a multi-hospital network

Multi-hospital systems have become very common in today’s healthcare environment. However, there has been limited published research examining the opportunities and challenges of pooling specialized services to a subset of hospitals in the network. Therefore, this paper considers how hospital networks with multiple locations can leverage pooling benefits when deciding where to position specialized services, such as magnetic resonance imaging (MRI), transplants, or neonatal intensive care. Specifically, we develop an optimization model to determine how many and which of a hospital network’s hospitals should be set up to deliver a specialized service. Importantly, this model takes into account both financial considerations and patient service levels. Computational results illustrate the value of optimally pooling resources across a subset of hospitals in the network versus two alternate approaches: (1) delivering the service at all locations and requiring each site to handle its own demand, or (2) locating the service at one hospital that handles all network demand.     

Short-term and long-term competition between providers of shrink-wrap software and software as a service

Software as a service (SaaS) has moved quickly from a peripheral idea to a mainstream phenomenon. By bundling a software product with delivery and maintenance service, SaaS providers can effectively differentiate their products with traditional shrink-wrap software (SWS). This research uses a game theoretical approach to examine short- and long-term competition between SaaS and SWS providers. We analyze the factors that affect equilibrium outcomes, including user implementation costs, SaaS provider’s operation efficiency, and quality improvement over time. Bundling software with service lowers software implementation cost for users, and our results suggest that it increases equilibrium prices. In providing software services, SaaS providers have to incur significant operation cost. In the long run, service operation cost may significantly affect SaaS firm’s ability to improve its software quality.     

Market share in a post-entry game

This paper investigates the dynamics of market share within a contemporary, real life, entry deterrence situation in the provision of National Health Service (NHS) pathology services. The major conclusion is that the previous Government's initiatives did not succeed in promoting competition in markets where existing NHS providers were operating significantly more efficiently than their neighbours. Evidence suggests managers were influenced more by potential competition from a new private sector entrant than by actual competition among existing providers. The effect of a new entrant is modelled as a perturbation of a pre-existing stable equilibrium in a seeming oligopoly influenced by market forces subject to Government regulation. Economic analysis of the market share which a new entry might gain shows that, on the assumptions made and contrary to expectations, the likely impact of Government regulation of NHS prices would be an increase in price per test in most of the market. Nevertheless the policy objective of improving quality at value for money prices was achieved for the near-monopoly provider within our case study area.     

The Use of a Strategic Planning Model for Health and Personal Social Services

Experience in using a mathematical model in the strategic planning of Health and Personal Social Services (HPSS) suggests some lessons that may be relevant to strategic modelling in other public services. Two attributes of the HPSS are that its final outputs cannot be measured reliably and that it is organized hierarchically with field authorities having considerable autonomy from the central authority. A model that has proved useful in this situation simulates how the field authorities respond to decisions taken at the centre on aggregate resource availabilities and describes their responses in terms of intermediate or proxy measures of service output. The model is used interactively with planners to examine policy options. It takes account of ideal patterns of service delivery, not merely current practice, and of the priorities and preferences employed by service personnel in the field.     

The application of reliability constrained stochastic capacity planning models to the service sector

Emphasis on quality management has recently permeated not only the manufacturing sector, but the service sector as well. Consequently, quality service and consumer satisfaction have become realities for many monopolistic service oriented industries facing competition. In order to effectively implement timely service within these industries (i) capacity plans must be developed which provide adequate staffing during both peakload and offpeak hours, as well as optimal (ii) prices and (iii) reliability of service. This paper builds on the results of Boronico (1992) in illustrating how reliability constrained marginal cost, within which optimal price is embodied, and minimum cost capacity plans may be determined for a service provider facing stochastic demand. Excess demand is not lost, but is deferred: a characteristic that typifies the operation of many delivery systems, such as postal services. Results indicate that marginal costs are convex with respect to reliability of service, while changes in the demand distribution's variability may impact optimal capacity by either increasing or decreasing required capacity.     

Development and implementation of a real time statistical control method to identify the start and end of the winter surge in demand for paediatric intensive care

Winter surge management in intensive care is hampered by the annual variability in the winter surge. We aimed to develop a real-time monitoring system that could promptly identify the start, and accurately predict the end, of the winter surge in a paediatric intensive care (PIC) setting. We adapted a statistical process control method from the stock market called “Bollinger bands” that compares current levels of demand for PIC services to thresholds based on the medium term average demand. Algorithms to identify the start and end of the surge were developed for a specific PIC service: the North Thames Children's Acute Transport Service (CATS) using eight winters of data (2005–12) to tune the algorithms and one winter to test the final method (2013/14). The optimal Bollinger band thresholds were 1.2 and 1 standard deviations above and below a 41-day moving average of demand respectively. A simple linear model was found to predict the end of the surge and overall demand volume as soon as the start had been identified. Applying the method to the validation winter of 2013/14 showed excellent performance, with the surge identified from 18th November 2013 to 4th January 2014.An Excel tool running the algorithms has been in use within CATS since September 2014. There were three factors which facilitated the successful implementation of this tool: the perceived problem was pressing and identified by the clinical team; there was close clinical engagement throughout and substantial effort was made to develop an easy-to-use Excel tool for sustainable use.     

The implementation and utilisation of operational research in the reorganised National Health Service Part II

The paper outlines some effects of the reorganisation of the British National Health Service with its increasing emphasis on service planning. Current policies will involve substantial change in the configuration of the Health Service. Various aspects of the management of change, the involvement with decision makers and the implementation of results are discussed. It is expected that Operational Research in the health services will become more closely concerned with the medical and nursing aspects of the provision of care and the measured benefits to patients from this care. This involvement will assist in the production of more realistic models for decision making within districts, areas, regions and at the national level.     

The Development of a Decision-support System for Planning Services within Hospitals

The planning of services within district health authorities is a complex task, but the process of planning is often crude: few options for addressing issues are considered, and these are inadequately assessed. These deficiencies led to research of which the aim was to identify the types of information and systems needed for improved planning. This paper describes the product of that research, the Hospital Planning Model. It is a decision-support system which gives planners fast access to relevant information for planning hospital services. This allows them to consider quickly a range of options for addressing a particular planning issue, and to test how sensitive the implications of proposals are to differing planning assumptions. The system has been shown to be generalizable, and is currently used in 28 health districts and other health organizations throughout the United Kingdom. The proposed reforms of the NHS are likely to increase the relevance of the system: the purchaser/provider market will result in a greater awareness by hospitals of the importance of identifying measures to improve the cost-effectiveness of care. Purchasers too may want to scrutinize hospitals in terms of their efficiency.     

OR in healthcare: A European perspective

The European Working Group “Operational Research Applied to Health Services” (ORAHS) is one of the domain specific EURO Working Groups organized by EURO - the European Association of Operational Research Societies. In this paper we report on the development of ORAHS as a platform for OR in health, and analyse the papers presented at meetings over the 35 years of its existence. We propose a two-way framework for analysis, where one dimension is the nine stages of the product life cycle: identifying consumer requirements, designing a new service to meet these requirements, forecasting demand for such a service, securing resources for it, allocating these resources, developing Programs & Plans to use these resources for delivering the service, establishing criteria for service delivery, managing the performance of the service, and finally, evaluating its performance. The other dimension is a three-level classification into broad application areas referring to processes at different levels in healthcare: Patients & Providers, Units & Hospitals, and Regional & National. We use this framework to carry out a quantitative analysis of all the papers presented during the meetings of ORAHS since its inception in 1975. We then describe developments over this period in applying OR approaches and techniques to healthcare, and present an overview of the main application areas and challenges.     

A Cournot–Nash–Bertrand game theory model of a service-oriented Internet with price and quality competition among network transport providers

This paper develops a game theory model of a service-oriented Internet in which profit-maximizing service providers provide substitutable (but not identical) services and compete with the quantities of services in a Cournot–Nash manner, whereas the network transport providers, which transport the services to the users at the demand markets, and are also profit-maximizers, compete with prices in Bertrand fashion and on quality. The consumers respond to the composition of service and network provision through the demand price functions, which are both quantity and quality dependent. We derive the governing equilibrium conditions of the integrated game and show that it satisfies a variational inequality problem. We then describe the underlying dynamics, and provide some qualitative properties, including stability analysis. The proposed algorithmic scheme tracks, in discrete-time, the dynamic evolution of the service volumes, quality levels, and the prices until an approximation of a stationary point (within the desired convergence tolerance) is achieved. Numerical examples demonstrate the modeling and computational framework.     

Memra: An equilibrium model for resource allocation in a health care system

The classical approach to resource allocation in Health Care Systems seems inappropriate as it does not take into account the principle of equilibrium between supply and demand, a concept widely used in other areas, e.g. transportation. We propose and validate a model which uses this concept of equilibrium to reproduce medical practice and to predict the quantity of resources consumed and the number of medical services performed given an amount of physician resources available. The formulation reduces to a convex function to be minimized and is very easy to solve. It has been tested on data from the province of Québec with excellent results.     

Object technology software selection: a case study

The goal of this paper is to present a case study focusing on object technology assessment in a computer service industry. We develop decision models to give proper recommendations for object-oriented software projects. The assessment uses a quantitative approach, in which a mixed-integer linear programming model and a multi-objective model were formulated and applied. By reducing the element of subjectivity, these formal models led to consistent tool selection. By separating the data and models, the models can be reused in subsequent software development projects. Finally, by allowing users to specify their objectives and requirements and by providing a sensitivity analysis of the results, this approach also increases customer orientation.     

Two-resource stochastic capacity planning employing a Bayesian methodology

We examine a stochastic capacity-planning problem with two resources that can satisfy demand for two services. One of the resources can only satisfy demand for a specific service, whereas the other resource can provide both services. We formulate the problem of choosing the capacity levels of each resource to maximize expected profits. In addition, we provide analytic, easy-to-interpret optimal solutions, as well as perform a comparative statics analysis. As applying the optimal solutions effectively requires good estimates of the unknown demand parameters, we also examine Bayesian estimates of the demand parameters derived via a class of conjugate priors. We compare the optimal expected profits when demands for the two services follow independent distributions with informative and non-informative priors, and demonstrate that using good informative priors on demand can significantly improve performance.     

Performance Monitoring and Competence Assessment in Health Services

Health and health service monitoring is among the most promising research area today and the world work towards efficient and cost effective health care. This paper deals with monitoring health service performance using more than one performance outcome variable (multi-attribute processes), which is common in most health services. Although monitoring whether a health service changes or improves over time is important this is well covered in the current literature. Therefore this paper focuses on comparing similar health services in terms of their performance. The proposed procedure is based on an appropriate control chart. The paper deals with firstly the case when no risk adjustment is required because the health services being compared treat the same patient case-mix which does not vary over time. Secondly it deals with comparing health services where risk adjustment is required because the patient case-mix they service do differ because they service either very different geographical locations or service very different demographics of the same population. The technology developed in this paper could be used for example to assess and compare health practitioners’ competence over time, i.e. to decide if two doctors are equivalent in terms of their outcome performances. The waiting time random variable associated with the run length distribution of the control charts (as well as to competence testing) is studied using a Markov Chain embedding technique. Numerical results are provided that exhibit the value of the proposed procedures.     

The capacitated maximal covering location problem with backup service

The maximal covering location problem has been shown to be a useful tool in siting emergency services. In this paper we expand the model along two dimensions — workload capacities on facilities and the allocation of multiple levels of backup or prioritized service for all demand points. In emergency service facility location decisions such as ambulance sitting, when all of a facility's resources are needed to meet each call for service and the demand cannot be queued, the need for a backup unit may be required. This need is especially significant in areas of high demand. These areas also will often result in excessive workload for some facilities. Effective siting decisions, therefore, must address both the need for a backup response facility for each demand point and a reasonable limit on each facility's workload. In this paper, we develop a model which captures these concerns as well as present an efficient solution procedure using Lagrangian relaxation. Results of extensive computational experiments are presented to demonstrate the viability of the approach.