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The presence of implicit biases among healthcare professionals and the effect on quality of clinical care is a cause for concern [13,14,15]. In the US, racial healthcare disparities are widely documented and implicit race bias is one possible cause. Two excellent literature reviews on the issue of implicit bias in healthcare have recently been published [16, 17]. One is a narrative review that selects the most significant recent studies to provide a helpful overall picture of the current state of the research in healthcare on implicit bias [16]. The other is a systematic review that focusses solely on racial bias and thus captures only studies conducted in the US, where race is the most prominent issue [17]. Our review differs from the first because it poses a specific question, is systematic in its collection of studies, and includes an examination of studies solely employing the vignette method. Its systematic method lends weight to the evidence it provides and its inclusion of the vignette method enables it to compare two different literatures on bias in healthcare. It differs from the second because it includes all types of bias, not only racial; partly as a consequence, it captures many studies conducted outside the US. It is important to include studies conducted in non-US countries because race understood as white/black is not the source of the most potentially harmful stereotypes and disparities in all cultural contexts. For example, a recent vignette study in Switzerland found that in the German-speaking part of the country, physicians displayed negative bias in treatment decisions towards fictional Serbian patients (skin colour was unspecified, but it would typically be assumed to be white), but no significant negative bias towards fictional patients from Ghana (skin colour would be assumed to be black) [18]. In the Swiss German context, the issue of skin colour may thus be less significant for potential bias than that of country of origin.Footnote 2

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3767 titles were retrieved and independently screened by the two reviewers (SH and CF). The titles that were agreed by both after discussion to be ineligible according to our inclusion criteria were discarded (3498) and the abstracts of the remaining articles (269) were independently screened by both reviewers. Abstracts that were agreed by both reviewers to be ineligible according to our inclusion criteria were discarded (241). When the ineligible abstracts were discarded, the remaining 28 articles were read and independently rated by us both. Out of these, 27 articles were agreed after discussion to merit inclusion in the final selection. One article was excluded at this stage because it did not fit our inclusion criteria (it did not employ the assumption method or an implicit measure). Additionally, the reference lists of these 27 articles were manually scanned by CF, and the full text articles resulting from this were independently read by both reviewers, resulting in the inclusion of a further 11 articles that both reviewers agreed fitted the inclusion criteria. After a repeat process of scanning the reference lists of the 11 articles from the second round, the final number of eligible articles was 42. All disagreements were resolved through discussion.

The most convincing studies from our review are those that combine the IAT and a method measuring the quality of treatment in the actual world. These studies provide some evidence for a relationship between bias as measured by the IAT and behaviour by clinicians that may contribute to healthcare disparities. More studies using real-world interaction measures would be helpful because studies using vignettes remain open to the criticism that they do not reveal the true behaviour of healthcare professionals. In this respect, the three studies using measures of physician-patient interaction are exemplary [22,23,24], in particular when using independent evaluators of the interactions [23]. Overall, our review reveals the need for discussion of methodology and for more interaction between different literatures that focus on different biases.