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Uncertainty in Medical Decisions
Uncertainty has many definitions and conceptualizations. In decision making, uncertainty refers to unknown or uncertain (probabilistic) outcomes of decisions. Probability is the mathematical expression of the degree of uncertainty. Medicine is fraught with uncertainty, and medical practice involves dealing with it on a day-to-day basis. Due to the uncertainty inherent in the environment, optimal decisions are not guaranteed to give the desired outcome. Furthermore, uncertainty can lead to variability in medical decisions, with the same (type of) patient being treated differently by different physicians. Uncertainty is therefore a central issue in medical decision making. This entry (a) explores uncertainty in the medical tasks of diagnosis, treatment, and prognosis, providing examples; (b) uses a decision analytic framework to identify types of uncertainty in clinical decisions; and (c) identifies ways of coping with uncertainty and facilitating decision making.
Uncertainty in Diagnosis, Treatment Decisions, and Prognosis
Uncertainty characterizes all core activities in medicine: diagnosis, treatment decisions, and prognosis. Differential diagnosis is essentially a process of dealing with uncertainty in the interpretation of information relating to the symptoms and signs of disease and the results of diagnostic tests. Examples include whether a normal electrocardiogram (ECG) can exclude acute coronary syndrome. Uncertainty in treatment decisions relates to the probability that an individual patient will be benefited or harmed, for example, a crucial choice between ventilation and palliative care for a patient with chronic obstructive pulmonary disease who is in acute respiratory distress. In addition, decisions about particular treatments within a healthcare system will revolve around issues of uncertainty in cost-effectiveness, for example, the best strategy for a patient with dyspepsia (test for H-pylori, treat anyway, or just prescribe proton pump inhibitors [PPIs]). Prognosis is probably the most uncertain of all medical tasks due to the unpredictability of future events in relation to specific patients, for example, the risk of developing postoperative complications, the likelihood of cancer recurrence, or a patient's life expectancy. Prognosis influences treatment choice and decisions; therefore, uncertainty in prognosis will increase uncertainty in treatment decisions.
Collecting more information can increase certainty; however, this often implies some cost, financial or otherwise (delay, inconvenience to the patient, pain, risk of injury). On the other hand, if there is no cost involved or the cost is small, physicians may collect more information than required or information that is not guided by specific hypotheses. This may increase physician confidence without necessarily altering the objective probability of the disease or may provide data that are difficult to interpret.
Types and Sources of Uncertainty
From a statistical viewpoint, there are two types of uncertainty: first- and second-order uncertainty. Take the example of whether a patient will benefit more from surgery or medical therapy, where the rates of cure at 5 years are 40% for medical therapy and 60% for surgery. First, one cannot predict precisely whether an individual patient will benefit or be harmed. In spite of the observed mean differences obtained by research (40% vs. 60%), individuals in a population are either “cured” or “not cured,” not “20% cured.” This is first-order uncertainty and is governed by chance and the mathematical laws of probability. There is also the uncertainty about the precision of the estimate of the relative effectiveness of surgery or medical therapy. This is known as second-order uncertainty and can be due to insufficient or conflicting evidence. If there is high second-order uncertainty about a treatment procedure (rates of success or failure, complications), the physician's confidence in its efficacy will be low. Second-order uncertainty is expressed as statistical variation around a point estimate of probability and can be reduced by greater information in terms of research findings. For example, although the absolute risk difference is 20% in favor of surgery, the 95% confidence interval of that estimate may cover the range of a 5% harm to a 45% benefit.
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- Uncertainty in Medical Decisions
- Unreliability of Memory
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