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Background: Constrained optimization methods are already widely used in health care to solve problems that represent traditional applications of operations research methods, such as choosing the optimal location for new facilities or making the most efficient use of operating room capacity.Objectives: In this paper we illustrate the potential utility of these methods for finding optimal solutions to problems in health care delivery and policy. To do so, we selected three award-winning papers in health care delivery or policy development, reflecting a range of optimization algorithms. Two of the three papers are reviewed using the ISPOR Constrained Optimization Good Practice Checklist, adapted from the framework presented in the initial Optimization Task Force Report. The first case study illustrates application of linear programming to determine the optimal mix of screening and vaccination strategies for the prevention of cervical cancer. The second case illustrates application of the Markov Decision Process to find the optimal strategy for treating type 2 diabetes patients for hypercholesterolemia using statins. The third paper (described in Appendix 1) is used as an educational tool. The goal is to describe the characteristics of a radiation therapy optimization problem and then invite the reader to formulate the mathematical model for solving it. This example is particularly interesting because it lends itself to a range of possible models, including linear, nonlinear, and mixed-integer programming formulations. From the case studies presented, we hope the reader will develop an appreciation for the wide range of problem types that can be addressed with constrained optimization methods, as well as the variety of methods available.Conclusions: Constrained optimization methods are informative in providing insights to decision makers about optimal target solutions and the magnitude of the loss of benefit or increased costs associated with the ultimate clinical decision or policy choice. Failing to identify a mathematically superior or optimal solution represents a missed opportunity to improve economic efficiency in the delivery of care and clinical outcomes for patients. The ISPOR Optimization Methods Emerging Good Practices Task Force's first report provided an introduction to constrained optimization methods to solve important clinical and health policy problems. This report also outlined the relationship of constrained optimization methods relative to traditional health economic modeling, graphically illustrated a simple formulation, and identified some of the major variants of constrained optimization models, such as linear programming, dynamic programming, integer programming, and stochastic programming. The second report illustrates the application of constrained optimization methods in health care decision making using three case studies. The studies focus on determining optimal screening and vaccination strategies for cervical cancer, optimal statin start times for diabetes, and an educational case to invite the reader to formulate radiation therapy optimization problems. These illustrate a wide range of problem types that can be addressed with constrained optimization methods. [ABSTRACT FROM AUTHOR]

In developing countries, including India, the role of the private sector in the provision of basic healthcare services is gradually expanding, since the public sector provides limited services and covers only limited areas. Using location-allocation models (LAM), this paper (1) examines the changing geographic access to and locational efficiency of basic public healthcare vis-a`-vis private healthcare services in two districts located in northwestern part of India, and (2) interrogates the factors that govern their geographic accessibility and locational-efficiency.Although this research confirms regional inequalities in geographic accessibility and locational efficiency of both public and private healthcare services in the selected districts, the locational efficiency of private health services is significantly lower than that of public health services. This paper further demonstrates the use of LAM for new site identification (keeping the existing healthcare sites intact) that will, in the future, improve locational efficiency of these services. This paper not only recommends improved geographic access to both public and private health services and their enhanced complementary role, but also stresses the need to evaluate geographic access from the service-users’ perspective and the use of more realistic data on demand and supply in future research. The findings of this paper can be extended to areas with similar geographic settings, and socio-economic and demographic conditions. [Copyright &y& Elsevier]

The paper presents a stylised model of contracting for a specific health service. The benefit of this service differs across patients. A purchaser, National Health Service (NHS, insurer) offers a contract to providers (hospitals, GP's), under the constraint of limited information about the provider's costs and the contract specifies the payment as a function of the number of cases treated. A number of features of the optimal contract are derived. Some of these are surprising: for example, the price per case generally increases with the efficiency of the provider. [ABSTRACT FROM AUTHOR]