INCONGRUENT DEFINITIONS OF RISK AND UNCERTAINTY AMONG CLINICAL ONCOLOGISTS AND ITS IMPLICATIONS FOR DECISION MAKING
Author(s)
Molnar R1, Carrera P2, Terris DD2, Bridges JFP21 Case School of Medicine, Cleveland, OH, USA; 2 University of Heidelberg, Heidelberg, Baden Wuerttembe, Germany
OBJECTIVES: The purpose of this study was to examine the meaning given to a number of concepts of risk and uncertainty by clinical oncologists and to better understand how physicians use these terms in their day to day practice. METHODS: Open ended semi-structural interviews were used to elicit definitions of ‘Risk,' ‘Uncertainty,' and ‘Ambiguity,' as well as the methods used to express the concepts and the barriers that exist in communicating these concepts. Participants included board registered practicing oncologists at two major academic teaching hospitals. Purposive sampling was used to obtain a range of specialties and levels of seniority within the target population, with a total of 14 interviews being conducted. RESULTS: A broad range of meanings were associated with these three concepts, with little consistency, if any, in the actual definitions given to the key concepts. Methods for communicating risks also varied, but all respondents found significant barriers to communicating these concepts with patients. A number of other key themes emerged relating to risk and certainty, including the differences between population and individual risk, issues of sample size and power, and the importance of conditioning risk to the individual patient. CONCLUSION: This paper demonstrates that there is not a set of clear cut definitions to cover issues of risk and uncertainty and that this leads to difficulties in interpretation and communication, even among physicians and especially to patients. There is a need for a common language to express concepts of risk and uncertainty.
Conference/Value in Health Info
2005-05, ISPOR 2005, Washington, DC, USA
Value in Health, Vol. 8, No. 3 (May/June 2005)
Code
PCN27
Topic
Methodological & Statistical Research
Topic Subcategory
Modeling and simulation
Disease
Oncology