Background

Physician bias ultimately impacts patient care by eroding the physician–patient relationship [1,2,3,4]. To overcome this issue, certain states require physicians to report a varying number of hours of implicit bias training as part of their recurring licensing requirement [5, 6]. Research efforts on the influence of implicit bias on clinical decision-making gained traction after the “Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care” report published in 2003 [7]. This report sparked a conversation about the impact of bias against women, people of color, and other marginalized groups within healthcare. Bias from a healthcare provider has been shown to affect provider-patient communication and may also influence treatment decisions [8, 9]. Nevertheless, opportunities within medical education curriculum are created to evaluate biases at an earlier stage of physician-training and provide instruction to intervene them [10,11,12]. We aimed to identify trends and organize literature on bias training provided during medical school and residency programs since the meaning of ‘bias’ is broad and encompasses several types of attitudes and predispositions [13].

Several reviews, narrative or systematic in nature, have been published in the field of bias research in medicine and healthcare [14,15,16]. Many of these reviews have a broad focus on implicit bias and they often fail to define the patient’s specific attributes- such as age, weight, disease, or condition against which physicians hold their biases. However, two recently published reviews categorized implicit biases into various descriptive characteristics albeit with research goals different than this study [17, 18]. The study by Fitzgerald et al. reviewed literature focused on bias among physicians and nurses to highlight its role in healthcare disparities [17]. While the study by Gonzalez et al. focused on bias curricular interventions across professions related to social determinants of health such as education, law, medicine and social work [18]. Our research goal was to identify the various bias characteristics that are studied within medical student and/or resident populations and categorize them. Further, we were interested in whether biases were merely identified or if they were intervened. To address these deficits in the field and provide clarity, we utilized a scoping review approach to categorize the literature based on a) the bias addressed and b) the study goal within medical students (MS), residents (Res) and a mixed population (MS and Res).

To date no literature review has organized bias research by specific categories held solely by medical trainees (medical students and/or residents) and quantified intervention studies. We did not perform a quality assessment or outcome evaluation of the bias intervention strategies, as it was not the goal of this work and is standard with a scoping review methodology [19, 20]. By generating a comprehensive list of bias categories researched among medical trainee population, we highlight areas of opportunity for future implicit bias research specifically within the undergraduate and graduate medical education curriculum. We anticipate that the results from this scoping review will be useful for educators, administrators, and stakeholders seeking to implement active programs or workshops that intervene specific biases in pre-clinical medical education and prepare physicians-in-training for patient encounters. Additionally, behavioral scientists who seek to support clinicians, and develop debiasing theories [21] and models may also find our results informative.

Methods

We conducted an exhaustive and focused scoping review and followed the methodological framework for scoping reviews as previously described in the literature [20, 22]. This study aligned with the four goals of a scoping review [20]. We followed the first five out of the six steps outlined by Arksey and O’Malley’s to ensure our review’s validity 1) identifying the research question 2) identifying relevant studies 3) selecting the studies 4) charting the data and 5) collating, summarizing and reporting the results [22]. We did not follow the optional sixth step of undertaking consultation with key stakeholders as it was not needed to address our research question it [23]. Furthermore, we used Covidence systematic review software (Veritas Health Innovation, Melbourne, Australia) that aided in managing steps 2–5 presented above.

Research question, search strategy and inclusion criteria

The purpose of this study was to identify trends in bias research at the medical school and residency level. Prior to conducting our literature search we developed our research question and detailed the inclusion criteria, and generated the search syntax with the assistance from a medical librarian. Search syntax was adjusted to the requirements of the database. We searched PubMed, Web of Science, and PsycINFO using MeSH terms shown below.

  • Bias* [ti] OR prejudice*[ti] OR racism[ti] OR homophobia[ti] OR mistreatment[ti] OR sexism[ti] OR ageism[ti]) AND (prejudice [mh] OR "Bias"[Mesh:NoExp]) AND (Education, Medical [mh] OR Schools, Medical [mh] OR students, medical [mh] OR Internship and Residency [mh] OR “undergraduate medical education” OR “graduate medical education” OR “medical resident” OR “medical residents” OR “medical residency” OR “medical residencies” OR “medical schools” OR “medical school” OR “medical students” OR “medical student”) AND (curriculum [mh] OR program evaluation [mh] OR program development [mh] OR language* OR teaching OR material* OR instruction* OR train* OR program* OR curricul* OR workshop*

Our inclusion criteria incorporated studies which were either original research articles, or review articles that synthesized new data. We excluded publications that were not peer-reviewed or supported with data such as narrative reviews, opinion pieces, editorials, perspectives and commentaries. We included studies outside of the U.S. since the purpose of this work was to generate a comprehensive list of biases. Physicians, regardless of their country of origin, can hold biases against specific patient attributes [17]. Furthermore, physicians may practice in a different country than where they trained [24]. Manuscripts were included if they were published in the English language for which full-texts were available. Since the goal of this scoping review was to assess trends, we accepted studies published from 1980–2021.

Our inclusion criteria also considered the goal and the population of the study. We defined the study goal as either that documented evidence of bias or a program directed bias intervention. Evidence of bias (EOB) had to originate from the medical trainee regarding a patient attribute. Bias intervention (BI) studies involved strategies to counter biases such as activities, workshops, seminars or curricular innovations. The population studied had to include medical students (MS) or residents (Res) or mixed. We defined the study population as ‘mixed’ when it consisted of both MS and Res. Studies conducted on other healthcare professionals were included if MS or Res were also studied. Our search criteria excluded studies that documented bias against medical professionals (students, residents and clinicians) either by patients, medical schools, healthcare administrators or others, and was focused on studies where the biases were solely held by medical trainees (MS and Res).

Data extraction and analysis

Following the initial database search, references were downloaded and bulk uploaded into Covidence and duplicates were removed. After the initial screening of title and abstracts, full-texts were reviewed. Authors independently completed title and abstract screening, and full text reviews. Any conflicts at the stage of abstract screening were moved to full-text screening. Conflicts during full-text screening were resolved by deliberation and referring to the inclusion and exclusion criteria detailed in the research protocol. The level of agreement between the two authors for full text reviews as measured by inter-rater reliability was 0.72 (Cohen’s Kappa).

A data extraction template was created in Covidence to extract data from included full texts. Data extraction template included the following variables; country in which the study was conducted, year of publication, goal of the study (EOB, BI or both), population of the study (MS, Res or mixed) and the type of bias studied. Final data was exported to Microsoft Excel for quantification. For charting our data and categorizing the included studies, we followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews(PRISMA-ScR) guidelines [25]. Results from this scoping review study are meant to provide a visual synthesis of existing bias research and identify gaps in knowledge.

Results

Study selection

Our search strategy yielded a total of 892 unique abstracts which were imported into ‘Covidence’ for screening. A total of 86 duplicate references were removed. Then, 806 titles and abstracts were screened for relevance independently by the authors and 519 studies were excluded at this stage. Any conflicts among the reviewers at this stage were resolved by discussion and referring to the inclusion and exclusion criteria. Then a full text review of the remaining 287 papers was completed by the authors against the inclusion criteria for eligibility. Full text review was also conducted independently by the authors and any conflicts were resolved upon discussion. Finally, we included 139 studies which were used for data extraction (Fig. 1).

Fig. 1
figure 1

PRISMA diagram of the study selection process used in our scoping review to identify the bias categories that have been reported within medical education literature. Study took place from 2021–2022. Abbreviation: PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses

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Publication trends in bias research

First, we charted the studies to demonstrate the timeline of research focused on bias within the study population of our interest (MS or Res or mixed). Our analysis revealed an increase in publications with respect to time (Fig. 2). Of the 139 included studies, fewer studies were published prior to 2001, with a total of only eight papers being published from the years 1985–2000. A substantial increase in publications occurred after 2004, with 2019 being the peak year where most of the studies pertaining to bias were published (Fig. 2).

Fig. 2
figure 2

Studies matching inclusion criteria mapped by year of publication. Search criteria included studies addressing bias from 1980–2021 within medical students (MS) or residents (Res) or mixed (MS + Res) populations. *Publication in 2022 was published online ahead of print

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Overview of included studies

We present a descriptive analysis of the 139 included studies in Table 1 based on the following parameters: study location, goal of the study, population of the study and the category of bias studied. All of the above parameters except the category of bias included a denominator of 139 studies. Several studies addressed more than one bias characteristic; therefore, we documented 163 biases sorted in 11 categories over the 139 papers. The bias categories that we generated and their respective occurrences are listed in Table 1. Of the 139 studies that were included, most studies originated in the United States (n = 89/139, 64%) and Europe (n = 20/139, 20%).

Table 1 Charting of all included studies fitting our search strategy with references (n = 139). Studies mapped based on bias(es) studied may belong to more than one or more category. All other parameters (location, goal, population) contain mutually exclusive criteria
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Sorting of included research by bias category

We grouped the 139 included studies depending on the patient attribute or the descriptive characteristic against which the bias was studied (Table 1). By sorting the studies into different bias categories, we aimed to not only quantitate the amount of research addressing a particular topic of bias, but also reveal the biases that are understudied.

Through our analysis, we generated 11 descriptive categories against which bias was studied: Age, physical disability, education level, biological sex, disease or condition, LGBTQ + , non-specified, race/ethnicity, rural/urban, socio-economic status, and weight (Table 1). “Age” and “weight” categories included papers that studied bias against older population and higher weight individuals, respectively. The categories “education level” and “socio-economic status” included papers that studied bias against individuals with low education level and individuals belonging to low socioeconomic status, respectively. Within the bias category named ‘biological sex’, we included papers that studied bias against individuals perceived as women/females. Papers that studied bias against gender-identity or sexual orientation were included in its own category named, ‘LGBTQ + ’. The bias category, ‘disease or condition’ was broad and included research on bias against any patient with a specific disease, condition or lifestyle. Studies included in this category researched bias against any physical illnesses, mental illnesses, or sexually transmitted infections. It also included studies that addressed bias against a treatment such as transplant or pain management. It was not significant to report these as individual categories but rather as a whole with a common underlying theme. Rural/urban bias referred to bias that was held against a person based on their place of residence. Studies grouped together in the ‘non-specified bias’ category explored bias without specifying any descriptive characteristic in their methods. These studies did not address any specific bias characteristic in particular but consisted of a study population of our interest (MS or Res or mixed). Based on our analysis, the top five most studied bias categories in our included population within medical education literature were: racial or ethnic bias (n = 39/163, 24%), disease or condition bias (n = 29/163, 18%), weight bias (n = 22/163, 13%), LGBTQ + bias (n = 21/163, 13%), and age bias (n = 16/163, 10%) which are presented in Table 1.

Sorting of included research by population

In order to understand the distribution of bias research based on their populations examined, we sorted the included studies in one of the following: medical students (MS), residents (Res) or mixed (Table 1). The following distributions were observed: medical students only (n = 105/139, 76%), residents only (n = 19/139, 14%) or mixed which consisted of both medical students and residents (n = 15/139, 11%). In combination, these results demonstrate that medical educators have focused bias research efforts primarily on medical student populations.

Sorting of included research by goal

A critical component of this scoping review was to quantify the research goal of the included studies within each of the bias categories. We defined the research goal as either to document evidence of bias (EOB) or to evaluate a bias intervention (BI) (see Fig. 1 for inclusion criteria). Some of the included studies focused on both, documenting evidence in addition to intervening biases and those studies were grouped separately. The analysis revealed that 69/139 (50%) of the included studies focused exclusively on documenting evidence of bias (EOB). There were fewer studies (n = 51/139, 37%) which solely focused on bias interventions such as programs, seminars or curricular innovations. A small minority of the included studies were more comprehensive in that they documented EOB followed by an intervention strategy (n = 19/139, 11%). These results demonstrate that most bias research is dedicated to documenting evidence of bias among these groups rather than evaluating a bias intervention strategy.

Research goal distribution

Our next objective was to calculate the distribution of studies with respect to the study goal (EOB, BI or both), within the 163 biases studied across the 139 papers as calculated in Table 1. In general, the goal of the studies favors documenting evidence of bias with the exception of race/ethnic bias which is more focused on bias intervention (Fig. 3). Fewer studies were aimed at both, documenting evidence then providing an intervention, across all bias categories.

Fig. 3
figure 3

Sorting of total biases (n = 163) within medical students or residents or a mixed population based on the bias category. Dark grey indicates studies with a dual goal, to document evidence of bias and to intervene bias. Medium grey bars indicate studies which focused on documenting evidence of bias. Light grey bars indicate studies focused on bias intervention within these populations. Numbers inside the bars indicate the total number of biases for the respective study goal. *Non-specified bias includes studies which focused on implicit bias but did not mention the type of bias investigated

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Furthermore, we also calculated the ratio of EOB, BI and both (EOB + BI) within each of our population of interest (MS; n = 122, Res; n = 26 and mixed; n = 15) for the 163 biases observed in our included studies. Over half (n = 64/122, 52%) of the total bias occurrences in MS were focused on documenting EOB (Fig. 4). Contrastingly, a shift was observed within resident populations where most biases addressed were aimed at intervention (n = 12/26, 41%) rather than EOB (n = 4/26, 14%) (Fig. 4). Studies which included both MS and Res (mixed) were primarily focused on documenting EOB (n = 9/15, 60%), with 33% (n = 5/15) aimed at bias intervention and 7% (n = 1/15) which did both (Fig. 4). Although far fewer studies were documented in the Res population it is important to highlight that most of these studies were focused on bias intervention when compared to MS population where we documented a majority of studies focused on evidence of bias.

Fig. 4
figure 4

A ratio of the study goal for the total biases (n = 163) mapped within each of the study population (MS, Res and Mixed). A study goal with a) documenting evidence of bias (EOB) is depicted in dotted grey, b) bias intervention (BI) in medium grey, and c) a dual focus (EOB + BI) is depicted in dark grey. *N = 122 for medical student studies. bN = 26 for residents. cN = 15 for mixed

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Discussion

Addressing biases at an earlier stage of medical career is critical for future physicians engaging with diverse patients, since it is established that bias negatively influences provider-patient interactions [171], clinical decision-making [172] and reduces favorable treatment outcomes [2]. We set out with an intention to explore how bias is addressed within the medical curriculum. Our research question was: how has the trend in bias research changed over time, more specifically a) what is the timeline of papers published? b) what bias characteristics have been studied in the physician-trainee population and c) how are these biases addressed? With the introduction of ‘standards of diversity’ by the Liaison Committee on Medical Education, along with the Association of American Medical Colleges (AAMC) and the American Medical Association (AMA) [173, 174], we certainly expected and observed a sustained uptick in research pertaining to bias. As shown here, research addressing bias in the target population (MS and Res) is on the rise, however only 139 papers fit our inclusion criteria. Of these studies, nearly 90% have been published since 2005 after the “Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care” report was published in 2003 [7]. However, given the well documented effects of physician held bias, we anticipated significantly more number of studies focused on bias at the medical student or resident level.

A key component from this study was that we generated descriptive categories of biases. Sorting the biases into descriptive categories helps to identify a more targeted approach for a specific bias intervention, rather than to broadly intervene bias as a whole. In fact, our analysis found a number of publications (labeled “non-specified bias” in Table 1) which studied implicit bias without specifying the patient attribute or the characteristic that the bias was against. In total, we generated 11 descriptive categories of bias from our scoping review which are shown in Table 1 and Fig. 3. Furthermore, our bias descriptors grouped similar kinds of biases within a single category. For example, the category, “disease or condition” included papers that studied bias against any type of disease (Mental illness, HIV stigma, diabetes), condition (Pain management), or lifestyle. We neither performed a qualitative assessment of the studies nor did we test the efficacy of the bias intervention studies and consider it a future direction of this work.

Evidence suggests that medical educators and healthcare professionals are struggling to find the appropriate approach to intervene biases [175,176,177] So far, bias reduction, bias reflection and bias management approaches have been proposed [26, 27, 178]. Previous implicit bias intervention strategies have been shown to be ineffective when biased attitudes of participants were assessed after a lag [179]. Understanding the descriptive categories of bias and previous existing research efforts, as we present here is only a fraction of the challenge. The theory of “cognitive bias” [180] and related branches of research [13, 181,182,183,184] have been studied in the field of psychology for over three decades. It is only recently that cognitive bias theory has been applied to the field of medical education medicine, to explain its negative influence on clinical decision-making pertaining only to racial minorities [1, 2, 15,16,17, 185]. In order to elicit meaningful changes with respect to targeted bias intervention, it is necessary to understand the psychological underpinnings (attitudes) leading to a certain descriptive category of bias (behaviors). The questions which medical educators need to ask are: a) Can these descriptive biases be identified under certain type/s of cognitive errors that elicits the bias and vice versa b) Are we working towards an attitude change which can elicit a sustained positive behavior change among healthcare professionals? And most importantly, c) are we creating a culture where participants voluntarily enroll themselves in bias interventions as opposed to being mandated to participate? Cognitive psychologists and behavioral scientists are well-positioned to help us find answers to these questions as they understand human behavior. Therefore, an interdisciplinary approach, a marriage between cognitive psychologists and medical educators, is key in targeting biases held by medical students, residents, and ultimately future physicians. This review may also be of interest to behavioral psychologists, keen on providing targeted intervening strategies to clinicians depending on the characteristics (age, weight, sex or race) the portrayed bias is against. Further, instead of an individualized approach, we need to strive for systemic changes and evidence-based strategies to intervene biases.

The next element in change is directing intervention strategies at the right stage in clinical education. Our study demonstrated that most of the research collected at the medical student level was focused on documenting evidence of bias. Although the overall number of studies at the resident level were fewer than at the medical student level, the ratio of research in favor of bias intervention was higher at the resident level (see Fig. 3). However, it could be helpful to focus on bias intervention earlier in learning, rather than at a later stage [186]. Additionally, educational resources such as textbooks, preparatory materials, and educators themselves are potential sources of propagating biases and therefore need constant evaluation against best practices [187, 188].

This study has limitations. First, the list of the descriptive bias categories that we generated was not grounded in any particular theory so assigning a category was subjective. Additionally, there were studies that were categorized as “nonspecified” bias as the studies themselves did not mention the specific type of bias that they were addressing. Moreover, we had to exclude numerous publications solely because they were not evidence-based and were either perspectives, commentaries or opinion pieces. Finally, there were overall fewer studies focused on the resident population, so the calculated ratio of MS:Res studies did not compare similar sample sizes.

Future directions of our study include working with behavioral scientists to categorize these bias characteristics (Table 1) into cognitive error types [189]. Additionally, we aim to assess the effectiveness of the intervention strategies and categorize the approach of the intervention strategies.

Conclusion

The primary goal of our review was to organize, compare and quantify literature pertaining to bias within medical school curricula and residency programs. We neither performed a qualitative assessment of the studies nor did we test the efficacy of studies that were sorted into “bias intervention” as is typical of scoping reviews [22]. In summary, our research identified 11 descriptive categories of biases studied within medical students and resident populations with “race and ethnicity”, “disease or condition”, “weight”, “LGBTQ + ” and “age” being the top five most studied biases. Additionally, we found a greater number of studies conducted in medical students (105/139) when compared to residents (19/139). However, most of the studies in the resident population focused on bias intervention. The results from our review highlight the following gaps: a) bias categories where more research is needed, b) biases that are studied within medical school versus in residency programs and c) study focus in terms of demonstrating the presence of bias or working towards bias intervention.

This review provides a visual analysis of the known categories of bias addressed within the medical school curriculum and in residency programs in addition to providing a comparison of studies with respect to the study goal within medical education literature. The results from our review should be of interest to community organizations, institutions, program directors and medical educators interested in knowing and understanding the types of bias existing within healthcare populations. It might be of special interest to researchers who wish to explore other types of biases that have been understudied within medical school and resident populations, thus filling the gaps existing in bias research.

Despite the number of studies designed to provide bias intervention for MS and Res populations, and an overall cultural shift to be aware of one’s own biases, biases held by both medical students and residents still persist. Further, psychologists have recently demonstrated the ineffectiveness of some bias intervention efforts [179, 190]. Therefore, it is perhaps unrealistic to expect these biases to be eliminated altogether. However, effective intervention strategies grounded in cognitive psychology should be implemented earlier on in medical training. Our focus should be on providing evidence-based approaches and safe spaces for an attitude and culture change, so as to induce actionable behavioral changes.