The purpose of the Section of Decision Sciences and Clinical Systems Modeling (SDS-CSM) is to enhance the analytic research capabilities of the University of Pittsburgh in the core areas listed below.
Decision Analysis: Investigators are increasingly using decision analysis as a technique for combining data from multiple sources and as a method for conducting cost-effectiveness analyses. Formal decision algorithms appear in protocols, pathways, and computerized decision support, and the number of studies using decision models has increased substantially in the scientific literature. We will continue in our work on large-scale Markov processes to model events over time and in our work on linking decision models with empiric datasets to develop quantitative models of natural history.
Cost-Effectiveness Models: Although there is significant economic expertise throughout the University of Pittsburgh, the SDS-CSM has expertise in building models to conduct cost-effectiveness analyses. We serve both as investigators and collaborators on studies. We will continue our work to develop integrated models of resource use and effectiveness that are relatively easy to understand, and we will continue to expand our expertise in the creation and analysis of cost models constructed from administrative databases.
Utility Assessments and Quality of Life: One of the hallmarks of patient-directed research is the ability to directly assess a patient's own preferences for outcomes and treatments and include these preferences in therapeutic decision making. The SDS-CSM has expertise and ongoing research activities in standard methods of utility assessment, the theoretical foundation of utility measurement and quality of life, and the use of preferences to develop quality-adjusted outcomes.
Mathematical Simulation: The SDS-CSM is actively involved in applying multiple types of simulation techniques to problems in health and medicine. We have expertise in discrete event simulation, which provides tools that model specific characteristics of real systems, such as distance, resource constraints, queues, and bottlenecks. Discrete event simulation is designed to analyze problems of optimal throughput under various constraints and is an excellent tool for examining many health policy and resource use questions. We feel that it is the only methodology able to inform resource allocation decisions in which competition for resources and queues are integral to the allocation process—as occurs, for example, with decisions regarding the allocation of donated organs to patients requiring an organ transplant. We have used integer programming as a tool to evaluate the optimal organization of organ procurement organizations into regions, and we have used Markov decision processes to analytically evaluate the optimal time to accept a living donor for liver transplantation.
The SDS-CSM currently engages in three primary types of activities: methodologic research, decision sciences training, and collaboration in research projects to provide cost-effectiveness and decision-modeling expertise.
Methodologic Research: The methodologic research agenda of the section focuses on expanding and extending the methods described above to improve the application of mathematical modeling tools to biologic problems. The SDS-CSM collaborates with the Center for Biomedical Informatics on bayesian inference and networks, with the Department of Industrial Engineering on simulation modeling and operations research, and with the Department of Biostatistics on developing robust survival estimates for simulation models. The goal of our faculty is to advance current knowledge and methodologic capabilities in terms of the operational characteristics of these models and their application to practical policy questions.
Decision Sciences Training: Faculty in our section have developed and teach several courses in programs that are sponsored by the Institute for Clinical Research Education and are related to decision sciences. These courses include the following:
CLRES 2120: Cost-Effectiveness Analysis in Health Care.
CLRES 2121: Clinical Decision Analysis.
CLRES 2122: Advanced Methods in Decision and Cost-Effectiveness Analysis.
Independent study with our faculty is also available.
In addition, our faculty teach national and international course in decision sciences for the Society for Medical Decision Making, the International Society for Pharmacoeconomics and Outcomes Research, and other organizations.
Modeling Expertise for Health Services Research: With appropriate faculty resources, the decision and mathematical modeling group in our section will provide cooperative or consultative expertise to research groups throughout the University to build cost-effectiveness and decision analytic models in conjunction with their research.