Refocusing upstream: Operational Research for population health

This paper examines the potential for the application of OR methods to ‘upstream’ problems in health sector planning. These address the underlying causes of ill health, and stand in contrast to ‘downstream’ health service planning which addresses the consequences of ill health. The paper reviews the emergence, globally and locally, of recognition of the importance of upstream planning and policy formation for the health sectors of both developed and developing countries. It presents examples of the use of OR techniques in upstream health planning, with particular illustrations taken from the area of health impact assessment. It argues for a fuller appreciation of the potential contribution of OR in this and in other areas of upstream health sector planning.     

The Blackett Memorial Lecture 18 November 1997

It is argued that movement toward the ‘control’ aspects of management should be effected to expand OR/MS activities beyond their present (almost exclusive) emphasis on ‘planning’ functions. This will involve increasing the amount of empirical-inferential approaches to research (based on ex-post data) as one way to help bring this about. Examples of such empirical-inferential research activities in OR/MS are supplied by reference to the early (founding) work of PMS Blackett and others on high-level policy problems during World War II. Extensions to the control function are evidenced in the work of Robert Fetter and others in the development of Diagnostic Related Groups on which the Prospective Payment Systems are based with large and important impacts on the health care delivery systems in the USA as well as other countries. Other examples covered include the use of OR concepts in the early ‘turnaround’ of Federal Express from failure to success. Other new methods will nevertheless be needed to extend and improve presently available OR/MS approaches. Some of these possibilities are illustrated with Data Envelopment Analysis. Ways to combine DEA with commonly used statistical methods are described to show how new and old methods may be combined to further enhance their power and range. Similar relations of DEA to other OR/MS approaches are now being studied, some of which are presently occurring with ‘fuzzy sets’ and ‘multiple objective programming.’     

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.     

Operations research and environmental planning

This paper aims at specifying the contribution of OR to environmental planning. To this purpose a framework for classifying relevant literature is proposed, according to which the literature has been reviewed and discussed with respect to both thematic and methodological aspects. It is shown that during the last few years, OR has extended its scope to include notably environmental applications. Several OR methods and tools are employed either to identify the type and intensity of environmental problems, or to assist environmental planners to effectively cope with these problems. It is illustrated that OR's relationship with environmental planning is dynamic and interactive in the sense that from the complexity of the issues examined stems the need to develop and/or adapt specific methodological tools and thus contributes to the development of both scientific fields.     

Optimization of planning an advertising campaign of goods and services

A generalization of the mathematical model and operations research problems formulated on its basis, which were presented in [1] in the framework of an approach to planning an advertising campaign of goods and services, is considered, and corresponding nonlinear programming problems with linear constraints are formulated.     

Suitability and managerial implications of a Master Surgical Scheduling approach

Operating room (OR) planning and scheduling is a popular and challenging subject within the operational research applied to health services research (ORAHS). However, the impact in practice is very limited. The organization and culture of a hospital and the inherent characteristics of its processes impose specific implementation issues that affect the success of planning approaches. Current tactical OR planning approaches often fail to account for these issues. Master surgical scheduling (MSS) is a promising approach for hospitals to optimize resource utilization and patient flows. We discuss the pros and cons of MSS and compare MSS with centralized and decentralized planning approaches. Finally, we address various implementation issues of MSS and discuss its suitability for hospitals with different organizational foci and culture.     

What is the role and contribution of models to management and research in the health services? A view from Europe

Modelling in different forms has long been regarded as a cornerstone of health OR as in other fields of application. Models are now tending to become a standard tool in health services management and research. What are the lessons we as operational researchers have learnt during this development? How can health care managers and health service researchers benefit from modelling — with or without OR-analysts to guide them between the pitfalls? After an introductory overview concerning the nature and objectives of modelling, examples will be given of modelling applications from different health service areas in order to illustrate the versatility of the method. Further, the choice of methods and models is discussed with special attention to the problems of interpretation and implementation of results. From this overview some conclusions are drawn with regards to advantages and disadvantages of modelling as tools for policy planning and decision-making in the health area. Finally some observations are made concercing the conditions for the future development in the field.     

A heuristic for direct consumer participation in primary health care planning

Recent attempts at consumer participation in the health care planning process have proved weak in their ability to responsively account for consumer health welfare. This can be attributed, in large part, to the mechanisms employed for identifying and utilizing the consumer's health care views and preferences. A heuristic planning procedure designed to overcome these problems by directly incorporating consumer preferences is developed. It identifies that (primary) health care delivery system which maximizes total incremental health benefit to a community subject to a prespecified budget constraint. The model assumes a methodology (previously developed by the author) for measuring, in aggregable units, the benefit, B_ip, from some health care facility p as perceived by some consumer i. Application of the procedure and subsequent sensitivity analyses demonstrate its ability to generate valid solutions that are robust to disturbances in the planning system.     

To charge or not to charge?

Discussions on the success or failure of OR practice tend to focus on specific case studies which often highlight major dilemmas that face OR professionals: Model robustness, simplicity vs complexity, optimising vs satisficing, conflict between criteria, who is the ‘client’?, and so on. But above all, the following issues predominate: Should OR be concerned with tactical or strategic problems? Where should it be placed in the organisational structure and who should it report to? These issues inevitably raise the questions of accountability, coupled with that of charging for OR services. It is this issue in particular, whether to charge or not to charge for OR services, that the paper seeks to explore.     

Approximation and optimization of higher order discrete and differential inclusions

This paper is mainly concerned with the necessary and sufficient conditions of optimality for Cauchy problem of higher order discrete and differential inclusions. Applying optimality conditions of problems with geometric constraints, for arbitrary higher order (say s-order) discrete inclusions optimality conditions are formulated. Also some special transversality conditions, which are peculiar to problems including third order derivatives are formulated. Formulation of sufficient conditions both for convex and non-convex discrete and differential inclusions are based on the apparatus of locally adjoint mappings. Furthermore, an application of these results is demonstrated by solving the problems with third order linear discrete and differential inclusions.     

Critical heuristics of social systems design

Health care planning, city and regional planning, energy and transportation planning, environmental design and other areas of social systems design are becoming issues of increasing concern to policy makers. Faced with complex steering problems that may have far-reaching societal implications, they except help from applied disciplines such as Operational Research, systems science, technology assessment, program evaluation, cost-effectiveness analysis, and similar approaches to scientifically informed planning. But these approaches offer little help in critically reflecting on the normative implications of the problem definitions and solution proposals they inspire.Critical Heuristics seeks to provide both the involved planners and affected citizens with a conceptual framework for identifying and discussing the normative implications of problem definitions, systems designs, program evaluations, etc. The paper introduces some key concepts of Critical Heuristics and briefly discusses their significance to ‘rational’ planning.     

Planning for a Single Specialty in a Health District

This paper discusses planning in the National Health Service. An outline of a general method for planning at the district level is illustrated with a case study in which a strategic plan was produced for the maternity services in Cambridge Health District. Some of the problems of ensuring implementation of proposals are then discussed.     

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.     

Planning midwifery services to deliver continuity of care

This paper describes a simulation model of maternity services which was developed to support the planning of midwifery services to meet demands for continuity of care. A Government report of maternity services in 1992 emphasised the need to replace the fragmented care offered to pregnant women by a more personalised type of approach. One way of doing this is by the implementation of team midwifery in which care is provided, antenatally, during labour, and then postnatally by either the same midwife or, more usually a small team of midwives.The model described in this paper attempts to assess the effects of team size upon the continuity of care provided to pregnant women under different implementations. Continuity is measured in terms of the percentage of occasions a team midwife will be available to meet the health care demands of pregnant women. The model may be used not only for the evaluation of services already in use, but also for the planning of alternative and better services.     

OR contributions to flexibility improvement in production/inventory systems

Recently the notion of flexibility (especially with respect to Flexible Manufacturing Systems) has attracted considerable attention of OR workers. The majority of the contributions is focussed on operational aspects of production systems. A further very important issue concerns the design of production systems and investment decisions with respect to these systems.The aim of this paper is to illustrate how OR models can contribute to quantify effects of, for instance, investment in set-up time reduction or investment in supply leadtime reduction on logistic performance of production systems. Of course there are many more opportunities to improve logistic performance. The approach in this paper offers the opportunity to quantify the integral effects of these improvements and shows the direction for logistic innovation. In this way both design of production systems and quality of related investment decisions can be improved significantly.Some simple examples are worked out in order to illustrate the ideas and to give impetus for further research in this field.     

A multi-objective production smoothing model with compressible operating times

Real life multi-product multi-period production planning often deals with several conflicting objectives while considering a set of technological constraints. The solutions of these problems can provide deeper insights to the decision makers/managers than those of single-objective problems. Some managers want to use from a production plan that is corresponding to minimum change in production policy along with minimum total cost simultaneously as possible. On the other hand, these two objectives have intrinsic conflicts such that producing in a fixed rate will cause huge costs than producing economically or according to JIT. So this paper presents a novel multi-objective model for the production smoothing problem on a single stage facility that some of the operating times could be determined in a time interval for. The model is to: (a) smooth the variations of production volume, and (b) minimize total cost of the corresponding production plan, while satisfying a set of technological constraints such as limited available time. The proposed model is developed in a real case study and is solved by a new genetic algorithm. The proposed genetic algorithm uses a novel achievement function for exploring the solution space, based on LP-metric concepts. Computational experiences reveal the sufficiency and efficiency of the proposed approach in contrast to previous researches.     

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.     

Integrating long-term care insurance purchase decisions with saving and investment for retirement

Risk related to long-term care (LTC) is high for the elderly. Planning for LTC is now regarded as the ‘third leg’ of retirement planning. In this paper, planning for LTC is integrated with saving and investment decisions for an integrated approach to retirement planning. Optimal LTC insurance purchase decisions are obtained by developing a trade-off between post-retirement LTC costs and LTC insurance premiums paid and coverage received. Integrating insurance purchase with wealth evolution, consisting of saving and investment decisions, allows addressing affordability issues.Two-way branching models are used for the stochastic health events and asset returns. The problem, formulated as a nonlinearly constrained mixed-integer optimization problem, is solved using a heuristic. Sensitivity analyses are performed for initial health and wealth status. Some important aspects of an individual’s behavioral preferences are also addressed in this framework to provide more robust decision support.     

Decision Making and Optimization in Changeable Spaces, a New Paradigm

This paper proposes a new decision making/optimization paradigm, the decision making/optimization in changeable spaces (DM/OCS). The unique feature of DM/OCS is that it incorporates human psychology and its dynamics as part of the decision making process and allows the restructuring of the decision parameters. DM/OCS is based on Habitual Domain theory, the decision parameters, the concept of competence set, and the mental operators 7-8-9 principles of deep knowledge. The covering and discovering processes are formulated as DM/OCS problems. Some illustrative examples of challenging problems that cannot be solved by traditional decision making/optimization techniques are formulated as DM/OCS problems and solved. In addition, some directions of research related to innovation dynamics, management, artificial intelligence, artificial and e-economics, scientific discovery, and knowledge extraction are provided in the conclusion.     

Data-stories about (im)patient customers in tele-queues

Credible queueing models of human services acknowledge human characteristics. A prevalent one is the ability of humans to abandon their wait, for example while waiting to be answered by a telephone agent, waiting for a physician’s checkup at an emergency department, or waiting for the completion of an internet transaction. Abandonments can be very costly, to either the service provider (a forgone profit) or the customer (deteriorating health after leaving without being seen by a doctor), and often to both. Practically, models that ignore abandonment can lead to either over- or under-staffing; and in well-balanced systems (e.g., well-managed telephone call centers), the “fittest (needy) who survive” and reach service are rewarded with surprisingly short delays. Theoretically, the phenomenon of abandonment is interesting and challenging, in the context of Queueing Theory and Science as well as beyond (e.g., Psychology). Last, but not least, queueing models with abandonment are more robust and numerically stable, when compared against their abandonment-ignorant analogues. For our relatively narrow purpose here, abandonment of customers, while queueing for service, is the operational manifestation of customer patience, perhaps impatience, or (im)patience for short. This (im)patience is the focus of the present paper. It is characterized via the distribution of the time that a customer is willing to wait, and its dynamics are characterized by the hazard-rate of that distribution. We start with a framework for comprehending impatience, distinguishing the times that a customer expects to wait, is required to wait (offered wait), is willing to wait (patience time), actually waits and felt waiting. We describe statistical methods that are used to infer the (im)patience time and offered wait distributions. Then some useful queueing models, as well as their asymptotic approximations, are discussed. In the main part of the paper, we discuss several “data-based pictures” of impatience. Each “picture” is associated with an important phenomenon. Some theoretical and practical problems that arise from these phenomena, and existing models and methodologies that address these problems, are outlined. The problems discussed cover statistical estimation of impatience, behavior of overloaded systems, dependence between patience and service time, and validation of queueing models. We also illustrate how impatience changes across customers (e.g., VIP vs. regular customers), during waiting (e.g., in response to announcements) and through phases of service (e.g., after experiencing the answering machine over the phone). Our empirical analysis draws data from repositories at the Technion SEELab, and it utilizes SEEStat—its online Exploratory Data Analysis environment. SEEStat and most of our data are internet-accessible, which enables reproducibility of our research.