The current literature indicates that although many physicians, regardless of specialty, demonstrate an implicit preference for white people, this bias does not appear to impact their clinical decision making. Further studies on the impact of implicit racial bias on racial disparities in ED treatment are needed.

Of the 1,154 unique articles identified in the initial search, nine studies ( n = 1,910) met inclusion criteria. Three of the nine studies involved emergency providers including residents, attending physicians, and advanced practice providers. The majority of studies used clinical vignettes to examine clinical decision making. Studies that included emergency medicine (EM) providers had vignettes relating to treatment of acute myocardial infarction, pain, and pediatric asthma. An implicit preference favoring white people was common across providers, regardless of specialty. Two of the nine studies found evidence of a relationship between implicit bias and clinical decision making; one of these studies included EM providers. This one study found that EM and internal medicine residents who demonstrated an implicit preference for white individuals were more likely to treat white patients and not black patients with thrombolysis for myocardial infarction. Evidence from the two studies reporting a relationship between physician implicit racial bias and decision making was low in quality.

Based on PRISMA guidelines, a structured electronic literature search of PubMed, CINAHL, Scopus, and PsycINFO databases was conducted. Eligible studies were those that: 1) included physicians, 2) included the Implicit Association Test as a measure of implicit bias, 3) included an assessment of physician clinical decision making, and 4) were published in peer‐reviewed journals between 1998 and 2016. Articles were reviewed for inclusion by two independent investigators. Data extraction was performed by one investigator and checked for accuracy by a second investigator. Two investigators independently scored the quality of articles using a modified version of the Downs and Black checklist.

Disparities in diagnosis and treatment of racial minorities exist in the emergency department (ED). A better understanding of how physician implicit (unconscious) bias contributes to these disparities may help identify ways to eliminate such racial disparities. The objective of this systematic review was to examine and summarize the evidence on the association between physician implicit racial bias and clinical decision making.

In 1999, the Institute of Medicine (IOM) was asked by Congress to examine healthcare disparities in the United States and make recommendations for areas of improvement. After a review of over 100 studies, the IOM found consistent evidence that healthcare disparities are widespread, even after controlling for possible confounding variables such as socioeconomic status. In their 2002 report Unequal Treatment: Confronting Racial and Ethnic Disparities in Healthcare, the IOM concluded that “some evidence suggests that bias, prejudice, and stereotyping on the part of the healthcare providers may contribute to differences in care.”1 Ten years later, the 2012 National Healthcare Disparities report showed that discrepancies in treatment continued to exist, with black patients receiving worse care than white patients for 40% of quality measures.2 Racial disparities have been well documented in emergency department (ED) care. For instance, a recent analysis of data from 350 U.S. EDs between 2006 and 2010 found that nonwhite patients presenting with abdominal pain were 22% to 30% less likely to receive analgesic medication and 17% to 30% less likely to receive narcotic analgesics compared to white patients. Nonwhite patients were also more likely to have longer wait times and were less likely to be admitted.3 Prior studies have also found that black patients with chest pain were less likely to receive laboratory evaluations for acute coronary syndrome (ACS), and those with identified ACS were less likely to receive percutaneous coronary intervention.4, 5 Given that a number of prior studies have been unable to link explicit bias to physician behavior,6, 7 recent attention has focused on the role of implicit, or unconscious, bias among physicians as a potential contributor to racial disparities in treatment.8, 9 Research has identified advantages of using implicit versus explicit assessments of racial bias. First, whereas explicit racial bias measures are susceptible to socially desirable self‐presentations,10 assessments of implicit racial bias have been shown to be resistant to “faking” because they test associative processes that operate automatically.11 Second, subtle forms of racial bias may not be evident using explicit measures; however, these stereotypes may be expressed unconsciously and captured using implicit tests.12, 13 Therefore, it is critical that studies of physician attitudes and behaviors capture implicit measures of racial bias. Evidence suggests that decision making based on heuristics and biases (vs. a more rational approach) is more likely to occur under certain conditions: time pressure, lack of solid knowledge/information to make a decision, cognitive overload, and fatigue.14 Compared to many other specialties, emergency medicine (EM) physicians are often required to make decisions in a limited amount of time and with limited information (incomplete medical record, no previous encounter with patient). Furthermore, EM physicians commonly experience cognitive overload as a result of managing multiple patients at once and dealing with frequent interruptions.15, 16 The combination of these factors, and the fact that the ED serves as the primary source of care for vulnerable population groups, many of whom are racial minorities, makes the ED potential fertile ground for bias‐based decision making. A growing number of studies are examining the relationship between physician implicit racial bias and various healthcare outcomes. In 2015, Hall et al.17 conducted a broad review of the literature on implicit racial bias among healthcare providers and health care outcomes (patient adherence, patient provider communication, physical and mental health outcomes, provider decision making). Although the authors found consistent evidence that provider implicit racial bias has a negative impact on patient–provider interactions, they were unable to draw clear conclusions regarding the relationship between implicit bias and clinical decision making. While this review was fairly comprehensive, the authors failed to assess the literature in terms of quality. Furthermore, since the time of their review, additional studies examining implicit bias and physician decision making have already been published. We aimed to conduct an up‐to‐date, systematic review to assess whether the current evidence supports a relationship between physician racial bias and clinical decision making. Additionally, we wanted to conduct a closer examination of the impact of physician bias as it relates to emergency care. While we were particularly interested in the evidence for implicit bias among emergency providers, we included studies from all specialties, so that any biases described within EM could be framed in the context of bias across the house of medicine.

Methods Search Strategy Following the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) recommendations,18 we developed a predetermined, written protocol and conducted a comprehensive literature search of the PubMed, CINAHL, Scopus, and PsycINFO databases from 1998 to December 2016. With the assistance of a medical librarian, we developed the following search strategy using keywords and Medical Subject Heading terms: 1) physician or doctor; 2) treatment or “treatment decision” or “decision making” or “patient care” or “delivery of healthcare” or “quality of healthcare” or “physician patient relations” or healthcare or “health care”; 3) bias or stereotype or prejudice or discrimination or “healthcare disparities” or “health disparities” or “attitude of health personnel”; 4) racism or “minority group” or “cultural bias” or “unconscious bias” or “racial bias” or “cultural competence” or “cultural competency.” Inclusion Criteria and Study Selection Studies were eligible for inclusion if they: 1) included physicians, including resident physicians and fellows; 2) included the Implicit Association Test (IAT) as measure of physician implicit racial bias; 3) included an assessment of physician clinical decision making; and 4) were written in English and published after the development of the IAT in 1998. Dissertations were eligible, but review articles, editorials, case studies, and letters to the editor were excluded. Bibliographies of relevant studies and reviews were searched to identify any additional studies for inclusion, but were not included in the initial search results. Measure of Implicit Racial Bias The IAT is the most widely used method of assessing implicit bias and has shown strong psychometric properties, especially compared to other implicit measures.19 Since its development in 1998, the IAT has been used extensively to assess racial biases in healthcare. The IAT is a computerized task in which individuals are asked to sort words into their associated categories as quickly as possible. It is believed to assess implicit bias by measuring how quickly people are able to make associations between target categories (e.g., black persons vs. white persons) and evaluations (e.g., good vs. bad) or stereotypes (e.g., athletic, clumsy). The logic behind IAT is that biased individuals are expected to respond more rapidly, on average, to stereotypical pairings (e.g., “good” with a white person and “bad” with a black person) as opposed to counterstereotypical pairings (e.g., “good” with a black person and “bad” with a white person). The IAT score (D‐score) is based on the difference in latency to respond for stereotypical and counterstereotypical pairings. When the stereotypical pairings produce faster responses than the counterstereotypical pairings, results suggest an implicit preference for one group (e.g., white persons) over another (e.g., black persons). Given the goals of this article, we are interested in studies using the race IAT. Two independent reviewers evaluated all titles and abstracts identified from the initial search for inclusion in the study. If at least one investigator determined the study was potentially eligible, then the study was included in the full‐text review. Full‐text articles were independently reviewed for final inclusion by two reviewers. Any disagreements among reviewers were resolved through discussion. Data Abstraction Data extracted from each selected article included authors, year of publication, sample size and characteristics, methodology, main findings, and limitations. One investigator extracted all of the information for each study, and the accuracy of information was checked by a second investigator. Quality Assessment Articles meeting inclusion criteria were assessed for methodologic quality by two independent investigators using a modified version of the Downs and Black checklist.20 The Downs and Black checklist is a 27‐item instrument that evaluates study quality of nonrandomized studies in the following categories: 1) reporting, 2) external validity, 3) internal validity, and 4) power. The authors created a modified nine‐item checklist, since not all items on the original checklist were relevant to the studies included in this review. Each study was assigned a final quality rating ranging from 0 to 9. Scores 0 to 3 were considered low quality; 4 to 6, moderate quality; and 7 to 9, high quality. (Full details regarding the scoring criteria are provided in Data Supplement S1, available as supporting information in the online version of this paper, which is available at http://onlinelibrary.wiley.com/doi/10.1111/acem.13214/full). Disagreements among investigators were resolved through discussion.

Results The study selection process is shown in Figure 1. The initial search identified 1,154 unique articles. After titles and abstracts were reviewed, a total of 35 articles were selected for full‐text review. Among the 35 articles selected for full‐text review, 10 studies met inclusion criteria. Furthermore, the full‐text review of the articles revealed that two21, 22 of the 10 articles meeting inclusion criteria were analyses of the same data. Therefore, this review includes a total of nine studies. Figure 1 Open in figure viewer PowerPoint PRISMA diagram. Table 1 provides further detail about the methodology and results of each study, including the quality assessment score. Of the nine studies, six were rated as moderate quality and three as high quality. Quality ratings ranged from 4 to 8 (mean quality score = 6.40). Inter‐rater reliability for the quality assessment scores was high with four discrepancies out of 90 data points (96% concurrence). The four disagreements were resolved through discussion. Table 1. Summary of Research Methods and Findings From Studies Examining the Relationship Between Implicit Racial Bias and Physician Decision Making Source Sample Method of Assessing Clinical Decision Making Mean IAT D‐scorea Relationship Between IAT and Clinical Decision Making Major Limitations QASb Blair et al., 23 N = 138 primary care physicians Charts were reviewed to assess physician's decision to intensify treatment. Treatment intensification was measured using pharmacy dispensing records from 4,794 patients with hypertension. 0.30 (SD = 0.29) No significant relationship between IAT and treatment intensification. Low response rate (60%); Pharmacy refills are only a proxy for decision to intensify treatment 6 Cassell, 24 c N = 216 internal medicine, EM, and family medicine residents One case vignette about a patient presenting with chest pain and an electrocardiogram suggestive of anterior myocardial infarction. Participants rated the likelihood that chest pain was due to coronary artery disease and whether to give the patient thrombolysis. 0.40 (SD = 0.43) No significant relationship between IAT and treatment decisions. Unknown response rate; Vignettes may not reflect real‐world decision making 7 Green et al., 25 c N = 287 internal medicine and EM residents One case vignette about a patient presenting with chest pain and an electrocardiogram suggestive of anterior myocardial infarction. Participants rated the likelihood that chest pain was due to coronary artery disease and whether to give the patient thrombolysis. 0.36 (SD = 0.40) Implicit preference for whites was significantly associated with not treating blacks with thrombolysis. Low response rate (51%); Vignettes may not reflect real‐world decision making; Questionable interpretation of results 6 Haider et al., 26 N = 248 trauma surgeons Four case vignettes focused on common trauma scenarios (i.e., pain management following car crash, disorientation and alcohol use, self‐harm risk assessment, decision to CT scan a patient with lower quadrant pain). After each vignette participants responded to three clinical management questions. 0.41 (95% CI = 0.35–0.47) No significant relationship between IAT and clinical management decisions. Vignettes may not reflect real‐world decision making 8 Haider et al., 27 N = 215 attending surgeons, residents, fellows, and interns Four case vignettes focused on common trauma scenarios. After each vignette participants responded to three clinical management questions. 0.42 (95% CI = 0.37–0.48) No significant relationship between IAT and clinical management decisions. Vignettes may not reflect real‐world decision making 8 Hirsh et al., 28 N = 129 residents and fellows Twelve virtual patients presenting with acute pain. Participants rated the patient's level of pain and the likelihood that they would use a parenteral opioid analgesic, oral opioid analgesic, or oral nonopioid analgesic. 0.50 (SD = 0.42) No significant relationship between IAT and clinical management decisions. Unknown response rate; Videos/vignettes may not reflect real‐world decision making 6 Oliver et al., 29 N = 543 family and internal medicine physicians One case vignette about a patient presenting with knee pain. Participants rated the likelihood that the patient's knee pain was because of severe osteoarthritis and whether they would recommend total knee replacement. 0.43 (SD = 0.34) No significant relationship between IAT and vignette based assessments. Vignettes may not reflect real‐world decision making. 6 Puumala et al., 30 N = 48 EM providers (physicians and advanced practice providers) Four pediatric case vignettes focused on treatment recommendations for pain and asthma control. Participants selected their treatment recommendation from a list that included one ideal option and an adequate option. NA (Black‐White IAT not used) No significant relationship between IAT and treatment recommendations. Low response rate (38%); No sample size calculation; Vignettes may not reflect real‐world decision making 5 Sabin et al., 21, 22 d N = 86 pediatricians (attendings, fellows, and residents) Four pediatric case vignettes focused on treatment recommendations for pain control, UTI, ADHD, and asthma control. Participants selected their treatment recommendation from a list of potential treatments that included one ideal option and other “adequate” and “good enough” options. 0.18 (SD = 0.44) No significant relationship between IAT and treatment recommendations for UTI, ADHD, and asthma. Implicit preference for whites was associated with not prescribing blacks or whites narcotic medication for pain. Low response rate (58%); No sample size calculation; Vignettes may not reflect real‐world decision making; Conclusions not supported by results. 721422 All nine studies were conducted in the United States and published between 2007 and 2016. Two studies involved EM residents along with residents from other specialties; only one study focused entirely on the ED, including EM physicians and advanced practice providers. The remaining studies addressed implicit bias within general surgery, trauma surgery, internal medicine, pediatrics, and family medicine. The number of participants ranged from 48 to 287 (total n = 1,910). All nine studies used differential response times (D‐scores) on the IAT to assess implicit bias. The Black‐White Race IAT was used in the eight studies examining a preference for white people compared to black people.21-29 The researchers in one study30 developed their own IAT to examine implicit bias toward American Indians versus white people, although it is important to note that the IAT used in this study was not validated. In addition to the Black‐White IAT, several studies incorporated other versions of the IAT, including the social class IAT, medical compliance IAT, and quality care IAT. Eight of the nine studies used clinical vignettes to examine physician clinical decision making. Since the clinical scenarios had to be tailored to the physician's specialty and experience, the vignettes and measures of clinical decision making varied from study to study. The studies that included ED physicians had vignettes relating to treatment of acute myocardial infarction, pain, and pediatric asthma. Two studies involved possible myocardial ischemia, two the management of acute pain, two on posttrauma care, and two on management of common pediatric medical conditions. One study used pharmacy dispensing records to assess decision making. A summary of the key characteristics of the included studies can be found in Table 1. The IAT effect is represented by a D‐score which has a possible range of –2 to +2. A D‐score of 0.15 to 0.34 indicates slight preference/bias; 0.35 to 0.63, moderate preference/bias; and 0.64 or higher, strong preference/bias.31 Of the eight studies that used the Black‐White IAT, implicit preference favoring white people was common across physicians from all specialties (mean IAT D‐score = 0.33), although pediatricians demonstrated a weaker implicit bias (mean IAT D‐score = 0.18).21, 22 Two of the nine studies found evidence of a relationship between implicit bias and physician clinical decision making. One of these two studies included EM providers. This moderate‐quality study, which included both EM and internal medicine residents, found that an implicit preference for whites was significantly associated with treating white patients and not treating black patients with thrombolysis for myocardial infarction.25 Another moderate‐quality study21, 22 found evidence of bias influencing pediatricians’ clinical decision making in one of four vignettes. This study reported that implicit preference for white people was associated with not prescribing black patients narcotic medication for postsurgical pain. However, in this same study, implicit preference for white people was also associated with prescribing white patients ibuprofen and not the ideal treatment (oxycodone). There was no evidence of a relationship between physician clinical decision making and any of the additional IAT measures, including the social class IAT, medical compliance IAT, and quality care IAT.

Discussion There is widely documented evidence of an association between race and differential treatment decisions across clinical settings. In the ED, black patients have been shown to be less likely to receive electrocardiographs and chest x‐rays for suspected ACS, and those with identified ACS were less likely to receive percutaneous coronary intervention.4, 5 Additionally, black patients with pain are less likely to be treated with narcotic analgesics.3 Although the underlying cause of these treatment disparities remains unclear, existing literature suggest implicit racial bias may play a role. Recent studies have investigated the role of implicit racial bias as it relates to physician clinical decision making. The purpose of this review was to evaluate the current state of the evidence regarding the relationship between physician implicit racial bias and clinical decision making. Furthermore, we aimed to examine implicit racial bias as it related to EM physicians and emergency care. This systematic review of nine studies involving 1,772 physicians found that physician implicit bias is common, albeit at levels similar to that of the general population.32 Observations of implicit bias among EM physicians seemed similar to those observed in other specialties, including internal medicine, family medicine, and surgery. Despite the prevalence of implicit racial biases among physicians, the evidence available does not support the hypothesis that implicit racial bias impacts physicians’ clinical decision making as assessed by clinical vignettes (n = 8) and electronic medical record review (n = 1). Although the majority of studies (seven of nine) concluded that physicians’ implicit racial biases do not influence clinical decision making, two studies reached different conclusions. In the study by Green et al.,25 implicit preference for whites was identified as a significant predictor of treating whites and not treating blacks with thrombolysis. However, the findings of this study were not straightforward. As Dawson and Arkes33 point out in their critique, the differences in treatment were found among the physicians who scored lowest on implicit racial bias, whereas the physicians with the highest scores on racial bias measures actually treated blacks and white patients relatively equally. In other words, the physicians who were the least racially biased were the ones identified as treating black and whites patients unequally, and those with the highest levels of racial bias were found to treat black and white patients equally.33 Similarly, in the study by Sabin et al.21, 22 an implicit preference for whites was associated with selecting the less ideal treatment for both black and white pediatric pain patients. Therefore, findings from these two studies do not support the conclusion that implicit racial bias is a clear predictor of physicians’ decision making. Our review revealed several opportunities for growth in this area of research. Although this review, which was limited to studies that used the IAT, captured the majority of studies examining implicit bias and physician decision making, we identified one study that used an implicit measure other than the IAT. In this study,34 the authors used a subliminal priming test to assess implicit bias and a case vignette of a patient with chest pain. They found that under time pressure, implicit biases about blacks and Hispanics impacted physicians’ diagnosis and decision to refer to a specialist. However, these results should be interpreted with caution since subliminal priming methods have been found to have relatively low reliability, especially compared to the IAT.20 A full discussion of the criticisms of the IAT and alternative measures of implicit bias is well beyond the scope of this review. Nonetheless, future studies may benefit from incorporating other reliable and valid methods of measuring implicit bias. Additionally, eight of the nine included studies used clinical vignettes to assess clinical decision making. Importantly, clinical vignettes have been criticized because they may not reflect real world clinical decision making and may be subject to social desirability, whereby physicians' responses reflect what they “should” do but not what they would actually do in clinical practice.35 Some studies have provided support for this view,36 whereas other studies have found that physician responses to clinical vignettes reliably predict real‐world decision making.37 Nonetheless, future studies should identify other methods (e.g., standardized patients, electronic medical records) of assessing clinical decision making. Even though one of the studies used electronic medical records to measure provider decision making, this study was conducted with primary care physicians who had long‐term relationships with many of their patients. This is not an ideal scenario for this type of study, since implicit bias is more likely to have an influence on decision making when there is a lack of information about the patient. None of the studies examined the impact of bias on actual physician decision making in the ED. The ED is an important setting to study the impact of racial bias on decision making, since there is well‐documented evidence of differential treatment by race. Furthermore, evidence suggests that EM physicians often work under conditions that make them more susceptible to the influence of biases. For one, unlike primary care physicians, EM physicians often do not have longstanding relationships with their patients and have to make decisions based on limited information about the patient. Additionally, EM physicians are often required to make decisions under time pressure, frequent interruptions, stress, and fatigue, all of which increase the risk of relying on cognitive shortcuts, including racial biases.14-16 It is worth mentioning that a recent study found that even though EM physicians consistently assigned lower pretest probabilities of ACS risk for nonwhite patients with chest pain, they evaluated all patients the same. The authors suggested that physicians’ decisions may have been influenced more by the presence of a standardized protocol or fear of legal consequences.38 In designing future studies, disparities researchers should consider examining physician decision making for chief complaints that are not associated with a standardized protocol.

Limitations Limitations should be considered when interpreting the findings of this review. First, we limited our included articles to studies that used the IAT to assess implicit racial bias. While this allowed for comparison across studies, it somewhat narrowed the number of eligible studies for this review. Given that we are aware of only one study that used a measure other than the IAT, it is unlikely that broadening our inclusion criteria to include other implicit bias measures would significantly impact the results. Second, even though our literature search procedures were extensive, it is possible that we may have missed studies that should have been included either because our search terms failed to identify them or because we inadvertently overlooked them in our review. It is also possible that we missed unpublished studies that had relevant data. Also, any studies published after December 31, 2016, would not be included in this review. Finally, although the quality assessment measure that we used was developed using nine of 27 items from a widely recognized and frequently utilized checklist for systematic reviews, it is not a validated measure and the cutoff points we used were established arbitrarily.

Conclusions The current state of the evidence suggests that physicians’ implicit biases do not impact their clinical decision making. However, these results are based on a limited number of available studies. Furthermore, these findings should not take away from existing evidence showing that implicit racial bias negatively impacts patient–provider interactions and patient satisfaction,17 which can have a downstream effect on patient adherence to medical advice.39 Therefore, even though we did not find evidence of a direct relationship between physician bias and disparate treatment decisions, physician bias may indirectly contribute to healthcare disparities. Efforts should be made to further examine the impact of implicit bias on racial disparities in the ED. Recommendations for future research are to focus on addressing major gaps in the literature. For one, studies should incorporate methods other than vignettes to measure physician decision making and focus on areas where disparities in emergency treatment have been reported (e.g., pain treatment, laboratory evaluation for acute coronary syndrome). Additionally, to increase the likelihood of detecting an effect, future studies should focus on examining implicit bias and physician decision making using ambiguous clinical scenarios that are not associated with a standardized protocol.

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