What is Clinical Economics?
Clinical economics is founded on the principle that choices must be made among alternative uses of limited resources, and thus decision making in the health care arena should consider both costs and health benefits (i.e. improvements in the health status of a target population). Clinical economic analyses are performed primarily to assess the health outcomes achieved with alternative health care interventions relative to the costs involved.
Clinical economics is not the study of cost containment; in fact, most new technologies increase medical costs while providing additional health benefits. Rather, the ultimate goal of clinical economic analysis is to maximize net health benefits for all persons in a target population given a range of health care interventions and known resource constraints.
What It Does and Doesn't Do
What it does do
When properly performed, economic analyses identify, measure, value, and compare the costs and health outcomes associated with the health care interventions under consideration. These studies provide useful information to decision-makers, but they comprise only one piece of the total picture and are most appropriate when preceded by evaluations of efficacy and, ideally, effectiveness.
What it doesn't do
Overall, clinical economics is not intended to provide clear, absolute solutions to health care problems.
Rather, it is performed to foster the process of setting priorities and making decisions about the best possible care in a resource-constrained environment. Results of an economic analysis are just one possible component of the decision-making process--social, legal, political, and ethical issues, among others, also are involved.
Clinical Economics and Decision Making
Inevitably, the decision to implement or eliminate a health care program or to expand or reduce its use requires making judgments about the value or desirability of the outcomes achieved and about the validity and reliability of all relevant information, including clinical and economic data. Different decision-makers may reach different conclusions about the adoption of a new technology or intervention depending, in part, on their goals and constraints.