Availability bias

A distortion that arises from the use of information which is most readily available, rather than that which is necessarily most representative.


All research questions and decisions, whether considering diagnostic accuracy of a test or effectiveness of an intervention, involve interpretation of data. Clinical decisions are based on data, which may be from routine care, published evidence, guidelines or clinician preference or experience.

Patients decide how to proceed, in health or healthcare, based on information which may come from a variety of sources, including health professionals, published data (particularly lay press) and their environment and experiences. The common thread running through all three areas is their basis in available data.

Availability bias occurs due to the natural human tendency to rely disproportionately upon the most readily available data. It can also occur in the use of artificial intelligence in healthcare if algorithms place greater emphasis on the most readily available data which does not fully represent the target population.

Availability of information can be influenced by spin bias, biases of rhetoric, perception bias and recall bias. Confirmation bias (when information is sought and used to support pre-existing beliefs) may lead to availability bias if data not supporting these beliefs is disregarded and not available for a particular decision or analysis.


Researchers at Erasmus University, Rotterdam, set out to assess whether junior doctors (first- and second-year residents) based their diagnoses on recent clinical experience (the most recently available information). They assessed diagnostic accuracy for cases solved (perfect score=4.0) with or without previous exposure.


Second-year residents scored lower on the cases which they had previously encountered (1.55; 95% confidence interval [CI], 1.15-1.96) than on the other cases (2.19; 95% CI, 1.73-2.66; P =.03). The same trend was not found in the first-year residents (2.03; 95% CI, 1.55-2.51 vs 1.42; 95% CI, 0.92-1.92; P =.046). Therefore, the results support an availability bias (overestimation of the likelihood of a diagnosis based on the ease with which it comes to mind) for the second-year residents, but not for the first-year residents.

Preventive steps

A final phase of the above research involved a reflective stage, where residents were invited to make their diagnoses again after deeper analysis of the clinical features of the case. In both first- (2.31; 95% CI, 1.89-2.73) and second-year residents(2.03; 95% CI, 1.49-2.57; p=0.006) , diagnostic accuracy scores improved.

Availability bias is reduced or mitigated by consideration of the information and data informing any given decision and whether this is sufficient.

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