Biomedical subjects

Stephanie R Earnshaw

Publications and source records attributed to Stephanie R Earnshaw.

2 recordsLinked to original sources

PubMed2003

Integer/linear mathematical programming models: a tool for allocating healthcare resources.

In today's environment, the demand for efficient healthcare resource allocation is increasing. As new technologies become available, allocation decisions become more complex and tools to assist decision makers in determining efficient allocations of healthcare resources are encouraged. Mathematical programs have multiple properties that are desirable for healthcare decision makers such as the simultaneous consideration of multiple constraints and a built-in sensitivity analysis. These models have been well researched and are considered invaluable in other industries. Mathematical programming has also become increasingly visible in facilitating the allocation of healthcare resources in the health services research sector. However, the use of mathematical programming tools has been limited in economic evaluations of new technologies. Budget allocations, such as formulary, drug development, and pricing decisions may benefit greatly from the use of mathematical programs. As an increasing number of expensive new technologies become available and pressure grows to contain healthcare costs, these tools may help guide a more efficient allocation of resources for technologies under budgetary and other constraints.

Decision Making↗

PubMedDate not supplied

Optimal allocation of resources across four interventions for type 2 diabetes.

BACKGROUND: Several interventions can be applied to prevent complications of type 2 diabetes. This article examines the optimal allocation of resources across 4 interventions to treat patients newly diagnosed with type 2 diabetes. The interventions are intensive glycemic control, intensified hypertension control, cholesterol reduction, and smoking cessation. METHODS: A linear programming model was designed to select sets of interventions to maximize quality-adjusted life years (QALYs), subject to varied budget and equity constraints. RESULTS: For no additional cost, approximately 211,000 QALYs can be gained over the lifetimes of all persons newly diagnosed with diabetes by implementing interventions rather than standard care. With increased availability of funds, additional health benefits can be gained but with diminishing marginal returns. The impact of equity constraints is extensive compared to the solution with the same intervention costs and no equity constraint. Under the conditions modeled, intensified hypertension control and smoking cessation interventions were provided most often, and intensive glycemic control and cholesterol reduction interventions were provided less often. CONCLUSIONS: A resource allocation model identifies trade-offs involved when imposing budget and equity constraints on care for individuals with newly diagnosed diabetes.

Adult↗