14 research outputs found
Application of constrained optimization methods in health services research: Report 2 of the ISPOR Optimization Methods Emerging Good Practices Task Force
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
What is the Most Cost-Effective Strategy for Treating Chronic Myeloid Leukemia After Imatinib Loses Patent Exclusivity in Europe?
PSS22 A Cost-Effectiveness Analysis of Off-Label Biologics to Treat Sarcoid Posterior Uveitis Versus Standard of Care: Comparing Infliximab to Methotrexate and Systemic Steroids
Comparing Five Alternative Methods of Breast Reconstruction Surgery: A Cost-Effectiveness Analysis
Recommended from our members
Cost-effectiveness of Compression Therapy With Early Endovenous Ablation in Venous Ulceration for a Medicare Population
Importance: Venous leg ulcers (VLU) are the most common cause of lower extremity ulceration that commonly occur among older individuals and are characterized by a slow healing trajectory and frequent recurrence; in the United States, VLUs affect more than 600 000 people per year with an estimated cost of 100 000/QALY. Univariate and probabilistic sensitivity analyses were performed to test uncertainty of model results. Results: This model used a simulated cohort of patients with VLU aged 65 years and older enrolled in Medicare. Early ablation dominated, with a lower per-patient cost of 15 208 and 1.985 QALYs gained. At a 5226 per patient in favor of early ablation. Probability of healing, followed by the probability of recurrence, was the parameter with greatest impact on model uncertainty. The probabilistic sensitivity analysis showed that early ablation was cost-effective in 59.2% of simulations at the $100 000/QALY threshold. Conclusions and Relevance: In this economic evaluation of compression therapy with early endovenous ablation, early intervention was dominant, as it was cost saving and generated greater QALYs over 3 years from the US Medicare perspective. Payers should prioritize coverage for early ablation to prevent VLU complications rather than treat a costly outcome that also reduces patient well-being.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]
Implementation Of Quality Improvement Interventions For Pressure Ulcer Prevention In University Healthsystem Consortium Hospitals
PHS97 An Off-the-Shelf Catalogue of Quality Improvement Interventions to Implement in the Prevention of Pressure Ulcers for Hospital Settings
Constrained Optimization Methods in Health Services Research-An Introduction: Report 1 of the ISPOR Optimization Methods Emerging Good Practices Task Force.
Providing health services with the greatest possible value to patients and society given the constraints imposed by patient characteristics, health care system characteristics, budgets, and so forth relies heavily on the design of structures and processes. Such problems are complex and require a rigorous and systematic approach to identify the best solution. Constrained optimization is a set of methods designed to identify efficiently and systematically the best solution (the optimal solution) to a problem characterized by a number of potential solutions in the presence of identified constraints. This report identifies 1) key concepts and the main steps in building an optimization model; 2) the types of problems for which optimal solutions can be determined in real-world health applications; and 3) the appropriate optimization methods for these problems. We first present a simple graphical model based on the treatment of "regular" and "severe" patients, which maximizes the overall health benefit subject to time and budget constraints. We then relate it back to how optimization is relevant in health services research for addressing present day challenges. We also explain how these mathematical optimization methods relate to simulation methods, to standard health economic analysis techniques, and to the emergent fields of analytics and machine learning