Overdiagnosis is difficult to define, demanding to measure, and hard to eliminate. One of the reasons for this is that overdiagnosis is closely connected to uncertainty. This presentation will investigate what kind of uncertainty that is at play in overdiagnosis. Probability, ambiguity, and complexity have been identified as key sources of uncertainty in health care. More specifically, diagnostic uncertainty has been explained in terms of various characteristics, such as heuristics and biases, uncertainty about defining disease, measurement error, lack of evidence, uncertainty about observing outcomes, uncertainty framed by degrees of beliefs (prior probability), and failure to include all diagnostic possibilities. Although overdiagnosis is a type of diagnostic uncertainty, there is little agreement on what diagnostic uncertainty is and how it can be measured. While many types of diagnostic uncertainty are connected to present events and conditions, overdiagnosis is related to future uncertainties. In overdiagnosis you are not uncertain about what you have got, but about what you will get. Hence, overdiagnosis is characterized by prognostic uncertainty. This relates it more to treatment than to diagnostics. However, there is one major difference, i.e., outset severity. In cases where overdiagnosis is of great concern, the initial severity is zero, as most people are healthy. Uncertainty therefore relates the ethics of overdiagnosis more to the ethics of treatment than to the ethics of diagnostics. However, there is an important distinction, as you rarely treat healthy people. The key ethical issue becomes how much risk you can expose (presumably unaffected) people to in order to obtain a potential future health benefit.
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