Outcomes Research

Abstract

Outcomes research has only recently been recognized as a critical method of arriving at a health care decision which utilizes value to the patient as a guiding principle. In general, an outcome is a change in health status that a patient can notice, including such parameters as amelioration of symptoms, improvement in quality of life, and prolongation of life, while excluding anatomic, physiologic, and laboratory measures. Outcomes research, therefore, studies patient outcomes related to therapeutic intervention and attempts to incorporate the patient’s perspective together with measurements of cost and treatment effectiveness.

The ultimate goal of outcomes research is to study outcomes under daily clinical practice settings. Controlled clinical trials are, therefore, not an ideal way of conducting such research. However, clinical trials can be used as a means of collecting baseline data in order to mirror the impact of an intervention on resource utilization. When using data for clinical trials, physicians should evaluate to what extent the results can be generalized to the individual patient, and whether the outcomes that have been measured are important. In today’s health care climate the final analysis often involves balancing the probability of benefit and the associated costs and risks. Outcomes research is sometimes confounded with health economics; however, health economics is only one aspect of outcomes research and relates costs to medical effectiveness.

The practical application of outcomes research can take on various forms, e.g., outcomes management. Outcomes management is a population-based approach to generating desirable health outcomes. Great reliance is placed on standards and guidelines that physicians can use in selecting appropriate interventions. It is also a system which routinely and systematically measures the functioning and well-being of patients, along with disease-specific clinical outcomes, at appropriate time intervals. Under outcomes management programs, clinical and outcomes data are pooled on a massive scale and used as a basis for decision making. A related approach is disease management in which information systems are used to generate a cycle of continuous improvement in all aspects of care, including prevention, treatment, and management.

Let us agree that good clinical medicine will always blend the art of uncertainty with the science of probability. But let us also hope that the blend can be weighted heavily towards science, whenever and wherever evidence is brought to light. WILLIAM OSLER