Siting of HIV/AIDS diagnostic equipment in South Africa: a case study in locational analysis

This paper describes a practical application of locational analysis to the siting of HIV/AIDS diagnostic equipment in laboratories across South Africa. Classical location analytical techniques were extended to ensure that laboratories are sited as close as possible to major centres of demand from hospitals and clinics. A particular advantage of the modified set covering algorithm developed is that choices between laboratory sites are made in a transparent manner. In order to find appropriate numbers and ideal placement of CD4 laboratories, runs were undertaken for various scenarios based on maximum travel time from health facilities to laboratory sites. Results demonstrated to decision makers showed close comparisons with pilot review projects undertaken in four health districts of South Africa. The research has potential to impact health care delivery to HIV sufferers in the poorest rural regions of the country

Programmatic implications of implementing the relational algebraic capacitated location (RACL) algorithm outcomes on the allocation of laboratory sites, test volumes, platform distribution and space requirements

Introduction: CD4 testing in South Africa is based on an integrated tiered service delivery model that matches testing demand with capacity. The National Health Laboratory Service has predominantly implemented laboratory-based CD4 testing. Coverage gaps, over-/under-capacitation and optimal placement of point-of-care (POC) testing sites need investigation. Objectives: We assessed the impact of relational algebraic capacitated location (RACL) algorithm outcomes on the allocation of laboratory and POC testing sites. Methods: The RACL algorithm was developed to allocate laboratories and POC sites to ensure coverage using a set coverage approach for a defined travel time (T). The algorithm was repeated for three scenarios (A: T = 4; B: T = 3; C: T = 2 hours). Drive times for a representative sample of health facility clusters were used to approximate T. Outcomes included allocation of testing sites, Euclidian distances and test volumes. Additional analysis included platform distribution and space requirement assessment. Scenarios were reported as fusion table maps. Results: Scenario A would offer a fully-centralised approach with 15 CD4 laboratories without any POC testing. A significant increase in volumes would result in a four-fold increase at busier laboratories. CD4 laboratories would increase to 41 in scenario B and 61 in scenario C. POC testing would be offered at two sites in scenario B and 20 sites in scenario C. Conclusion: The RACL algorithm provides an objective methodology to address coverage gaps through the allocation of CD4 laboratories and POC sites for a given T. The algorithm outcomes need to be assessed in the context of local conditions

Operational Research techniques applied throughout cancer care services: a review

Cancer is a disease affecting increasing numbers of people. In the UK, the proportion of people affected by cancer is projected to increase from 1 in 3 in 1992, to nearly 1 in 2 by 2020. Health services to tackle cancer can be grouped broadly into prevention, diagnosis, staging and treatment. We review examples of Operational Research (OR) papers addressing decisions encountered in each of these areas.In conclusion we find many examples of OR research on screening strategies, as well as on treatment planning and scheduling. On the other hand, our search strategy uncovered comparatively few examples of OR models applied to reducing cancer risks, optimising diagnostic procedures and staging. Improvements to cancer care services have been made as a result of successful OR modelling. There is potential for closer working with clinicians to enable the impact of other OR studies to be of greater benefit to cancer sufferers

OR in developing countries: A review

The relevance of Operational Research (OR) in developing countries has increasingly engaged the attention of operational researchers in both the industrialised and less developed countries over the last 50 years. With this, there has been a considerable amount of interest in the potential for using OR in developing countries. One sign of this is the emergence of a number of initiatives to promote OR in developing countries and the number of new societies for OR that have emerged from the developing world. This paper is an attempt at providing an overall picture of the state of OR in the developing countries. In particular, it will look at the coverage in terms of countries and methods. It will also highlight the contribution OR is making towards the theme of poverty, the reduction of which is regarded as the key focus of development policy interventions as reflected in the Millennium Development Goals (MDGs

Whole blood or apheresis donations? A multi-objective stochastic optimization approach

In the blood supply chain, several alternative technologies are available for collection and processing. These technologies differ in cost and efficiency: for example, collection by apheresis requires very expensive machines but the yield of blood products is considerably greater than whole blood collection. Blood centre managers are faced with the difficult strategic problem of choosing the best combination of technologies, as well as the equally difficult operational problem of assigning donors to collection methods. These decisions are complex since so many factors have to be taken into account, including stochastic demand, blood group compatibilities, donor availability, the proportions of blood types in both donor and recipient populations, fixed andvariable costs, and process efficiencies. The use of deterministic demand forecasts is rarely adequate and a robust decision must consider uncertainty and variability in demand as well as trade-offs between several potentially conflicting objectives. This paper presents a multi objective stochastic integer linear programming model to support such decisions. The model treats demand as stochastic and seeks to optimize two objectives: the total cost and the number of donors required. To solve this problem, we apply a novel combination of Sample Average Approximation and the Augmented Epsilon-Constraint algorithm. This approach is illustrated using real data from Bogota, Colombia

Location modelling for community healthcare facilities

Proceedings of the 33rd International Conference on Operational Research Applied to Health Services (ORAHS 2007

Choosing where to set the threshold between low- and high-risk patients: Evaluating a classification tool within a simulation

Health service providers must balance the needs of high-risk patients who require urgent medical attention against those of lower-risk patients who require reassurance or less urgent medical care. Based on their characteristics, we developed a tool to classify patients as low- or high-risk, with correspondingly different patient pathways through a service. Rather than choosing the threshold between low- and high-risk patients solely considering classification accuracy, we demonstrate the use of discrete-event simulation to find the best threshold from an operational perspective as well. Moreover, the predictors in classification tools are often categorical, and may be inter-dependent. Defining joint distributions of these variables from empirical data assumes that missing combinations are impossible. Our new approach involves using Poisson regression to estimate the joint distributions in the underlying population. We demonstrate our methods on a practical example: setting the threshold between low- and high-risk patients with proposed different pathways through a breast diagnostic clinic.</p

Planning sustainable community health schemes in rural areas of developing countries

In this research, we consider the planning of community health schemes by non-governmental or faith-based organisations in rural areas of developing countries, from both top-down and ground level viewpoints. We conclude that both types of planning approach are valid and necessary for sustainability of such developments. With top-down planning in mind, we describe our hierarchical models especially designed for location of community health facilities, with objectives pertaining to both efficiency and equity of provision. As an additional case study, we present modelling of the location of a maximal number of self-sustainable primary healthcare workers in a rural region of India.OR in developing countries Location OR in health

A generalized symbiotic simulation model of an emergency department for real-time operational decision-making

We describe the design of a generalizable simulation model of an emergency department (ED) that forms part of a symbiotic simulation tool designed to improve short-term decision-making. While the paper will give an overview of the planned symbiotic simulation tool, our focus here is on the generalizability of the simulation model. The model is coded such that the routing logic of patient pathways are not explicitly defined but are instead included as an input parameter. By structuring the model this way, the pathways can instead be discovered through process mining methods on standard healthcare transactions data. This enables the simulation model to be applied to other EDs without redesigning all of the logical flows within the model. As symbiotic simulation tools are designed for ongoing use within the system they model, utilizing process mining also allows for automating recalibration of the patient pathways if changes occur in the physical system