Implicit Bias among Physicians and its Prediction of Thrombolysis Decisions for Black and White Patients

Widely documented racial/ethnic disparities are particularly striking in the treatment of cardiovascular disease,1,2 with whites up to twice as likely as blacks to receive thrombolytic therapy for myocardial infarction.3, 4, 5, 6, 7 Whether health professionals’ biases contribute to such disparities in care has been a subject of speculation and study.1,8, 9, 10, 11, 12, 13, 14 For example, physicians might believe that black patients are less likely to adhere to treatment recommendations than whites, and thus offer treatment less often.12 Some researchers speculate that unconscious bias is more likely to underlie treatment disparities than overt prejudice.12,15, 16, 17, 18

The computer-based Implicit Association Test (IAT), first introduced in 1998, is now used widely to measure bias that may not be consciously recognized.19 The IAT measures the time it takes subjects to match representatives of social groups (e.g., age, gender, and race) to particular attributes (e.g., good, bad, cooperative, and stubborn). The IAT operationalizes unconscious bias by hypothesizing that subjects will match a group representative to an attribute more quickly if they connect these factors in their minds, regardless of their awareness of this connection. For instance, the more strongly subjects associate pictures of white persons with good concepts and pictures of black persons with bad concepts, the more quickly they will match them, and vice versa. The computerized IAT measures the aggregate time required for these matching tasks under two conditions (pairings). A difference in average matching speed for opposite pairings (e.g., black+bad/white+good vs black+good/white+bad) determines the IAT score (Fig. 1). Subjects are typically aware that they are making these connections but unable to control them given the rapid response times and structure of the test. To understand the IAT procedure, readers can sample the test at www.implicit.harvard.edu.20

Open image in new window

Although more than 200 studies have employed numerous versions of the IAT,19,20, 21, 22, 23, 24 and data from 5 million tests has accumulated from www.implicit.harvard.edu, the test has not been used to systematically observe the behavior of health care professionals. Given questions about the source of observed disparities in health service use, the IAT might provide insight into the contribution of implicit biases among physicians. In this study, we used a race preference IAT to measure implicit biases among emergency medicine and internal medicine residents. We also developed two new tests to measure stereotypes about general cooperativeness and specific cooperation with medical procedures. We tested whether both preferences and stereotypes affected physicians’ clinical decisions for white and black patients. More specifically, using a case vignette with patient race assigned randomly, three IATs, and a questionnaire, we sought to determine whether implicit or explicit race biases predict physicians’ decisions to give thrombolysis for acute myocardial infarction.

The IAT has been used to study implicit preferences and stereotypes for over a decade. It is a new method in its application to studying health care provider bias as a potential root cause of racial/ethnic disparities in health care. This is the first study to use a sociocognitive measure of bias among physicians, and to correlate this with treatment decisions according to patient race. It also represents the first time that the IAT, first published in 1998,19 has been modified to measure and demonstrate an implicit stereotype specific to medical care (i.e., that black patients are less willing to undergo medical procedures).

Not surprisingly, most physicians did not admit to any racial biases explicitly. However, on the implicit measures of bias (IATs), most nonblack physicians demonstrated some degree of bias favoring whites over blacks. Participants’ scores on the race preference IAT showed a range of implicit race bias similar to previous experiments on nonphysicians.21,26 The new cooperativeness IATs were normally distributed and somewhat correlated with the well-studied race preference IAT, suggesting that they measure different but related components of race bias.

Findings of implicit bias and its effects on clinical decisions may surprise physicians who tend to view their work as both altruistic and evidence-based.27 Implicit race biases are prevalent in the United States in general,26 and as such it should not be surprising that they are prevalent among physicians as well. The neural and cognitive processes underlying these biases are assumed to reflect both evolutionary bases and socially acquired orientations. The content of implicit biases(e.g., that black Americans are less cooperative than white Americans) are assumed to derive from sociocultural learning (e.g., explicit instruction and implicit messages) that accumulate over time. Implicit biases are primarily unconscious and do not imply overt racism. This is supported by the strong dissociation in the average level of expressed, explicit preferences and elicited, implicit ones, as well as the low correlation between explicit and implicit preference observed in this study. Critics of implicit measure of social cognition have asserted that such preferences and beliefs may reflect messages about the state of social groups in the larger culture but cannot be said to reflect an individual’s own preferences. If that were the case, doctors’ own decisions should not have been predicted so clearly by their implicit biases. The fact that they do remind us that implicit biases may affect the behavior even of those individuals who have nothing but the best intentions,24 including those in medical professions.12,13,15 The IAT is but one method for detecting implicit social cognition and it is the first to be put to use in the present study in a medical context. As such, the meaning and significance of implicit biases in health care deserves much greater investigation.

We found no difference in the crude rate of thrombolysis between study participants assigned a black patient versus those assigned a white patient. However, this race equality in treatment occurred in the presence of greater diagnosis of CAD in black than white patients. Equal treatment in the face of unequal diagnosis between the two groups constitutes a disparity.

The result of interest did not depend on demonstrating disparities in treatment. Rather, this study was designed to determine whether physicians’ implicit biases (IAT scores) predicted different patterns of thrombolysis recommendation for black and white patients. We found that implicit bias against blacks (as measured by the race preference IAT) was negatively correlated with likelihood of recommending thrombolysis for black patients and positively correlated with likelihood of recommending thrombolysis for white patients. This finding suggests that unconscious race biases among physicians may influence their decisions about important interventions such as thrombolysis for suspected myocardial infarction. Whereas several studies have pointed to unconscious biases as one potential root cause for racial and ethnic disparities in health care,9, 10, 11, 12, 13, 14 this is the first evidence directly supporting this link. We were encouraged to find most resident physicians open to the idea that unconscious biases could affect their clinical decisions, and that learning more about these biases could improve their care of patients. After completing the IATs, residents acknowledged greater vulnerability to unconscious bias than they did at the start, suggesting that the experience heightened their awareness. Also, those physicians who were aware that the study had to do with racial bias, and who had higher levels of implicit prowhite bias, were more likely to recommend thrombolysis to black patients than physicians with low bias—the opposite of the study’s main effect. This suggests that implicit bias can be recognized and modulated to counteract its effect on treatment decisions. These finding support the IAT’s value as an educational tool.

There are several limitations inherent in this study. Response rates were relatively low and the sample size smaller than ideal, making it difficult to detect smaller effects that may exist. Resident physicians, particularly those at large academic health centers in Boston and Atlanta may differ from physicians who typically make thrombolysis decisions, so it remains to be seen if those with greater experience show the same pattern. Nevertheless, our primary findings are based on an experimental manipulation involving randomized assignment of the physician to a black or white patient vignette, which provides confidence in the causal interpretations that are drawn. A second limitation derives from the use of a computerized presentation of a patient, which may, for reasons that may not be obvious, have contributed to an outcome that may not occur in a typical in-person encounter. The result of predictive validity we report may be an overestimation, but equally likely an underestimation of the role of implicit bias in clinical decision making.

Future studies might do well to examine actual patient-physician interactions, introducing such dimensions as communication, rapport, and other nonverbal behaviors that are known to be related to implicit discrimination. It may in fact be the subtleties of interracial interactions that lay the foundation for differential treatment to occur.28 IATs can be developed to provide a broader range of clinically relevant stereotypes, in addition to the tests we used. Studies should continue to obtain detailed measures of participant awareness because this did show impact on treatment decisions in our study.

In conclusion, our findings suggest that physicians, like others, may harbor unconscious preferences and stereotypes that influence clinical decisions. Further study is needed to confirm our findings, and to determine the extent to which unconscious racial biases contribute to health care disparities. Given the potential existence of these biases, new approaches to addressing disparities might include confidential feedback mechanisms to make physicians aware of disparities in their own cohort of patients, securely and privately administered IATs to increase physicians’ awareness of unconscious bias, and targeted education to mitigate its effects on clinical decision making. We cannot and do not suggest that unconscious bias among health professionals is the largest or most important factor leading to disparities in health care. However, the fact that it is, by its very nature, hidden from conscious awareness suggests that it receive explicit attention.