Implicit Bias In Cardiology: A Call to Action

Chest pain, nausea, and vomiting; this was the chief complaint of a disheveled middle aged Black woman as she presented to her local emergency department. When she was brought in via emergency medical services (EMS), the patient was uncomfortable, complaining of pain, and restless. She had a history of homelessness, IV drug abuse, and was known as a “frequent flier” in the emergency room. EMS assessed that her chest pain appeared non-cardiac and provided supportive care while en route to the hospital. She was then triaged to the appropriate care and an emergency provider assessed the patient. 

The patient continued to have chest pain and nausea while in the emergency department. The medical team agreed with the EMS assessment and deemed her symptoms as atypical for a cardiac etiology, but recognized the utility of basic cardiac screening. She underwent an EKG which showed very subtle ST-segment elevations in her inferior leads as well as faint reciprocal changes. High sensitivity troponin ultimately revealed a modest enzyme leak and the cardiology team was consulted. After hours in the emergency department, repeat EKG assessment showed that the ST segment elevation continued to evolve and become more pronounced. The patient was urgently taken to the cath lab to reveal an acute lesion of her proximal right coronary artery. She suffered acute cardiogenic shock during the case requiring mechanical support prior to revascularization. 

 If our patient had no history of drug abuse, homelessness, or was white, would her acute myocardial infarction have been treated faster? Implicit bias refers to the attitudes or stereotypes that affect our understanding, actions and decisions in an unconscious manner. These biases are activated involuntarily, without an individual’s awareness or intentional control. Unfortunately, implicit bias is ubiquitous. All healthcare professionals have implicit biases that affect how they view the world and interact with others. There is extensive evidence showing how bias can lead to differential treatment of patients by race, gender, age, weight, language, socioeconomic status, and insurance status.  

A seminal 2007 study of internal medicine and emergency medicine residents found that while the medical participants reported no explicit racial bias, Implicit Association Tests (IATs) indicated an implicit preference towards white Americans. Further, the higher the preference, the more likely that physician was to treat Whites and not treat Blacks with early thrombolysis in the setting of acute myocardial infarction 1. 

While the effects of implicit bias in medicine are clear, it is also clear that implicit bias is malleable. Once we recognize our own internal biases, there are a number of validated approaches  for combating implicit bias. These include stereotype replacement, counter-stereotypic imaging, individuation, perspective-taking, and increasing opportunities for contact with individuals from different groups 2. Further, our current models for teaching implicit bias to medical students and trainees are sparse and outdated. New research must be conducted to find more innovative techniques for managing our implicit bias. By leveraging technology we can make strides in reducing the biases affecting healthcare disparities. The American Heart Association is founded upon the guiding principle of ensuring equitable health for all, thus, it is essential that we come together as a community of healthcare leaders to address bias in medicine. 

References

1. Green AR, Carney DR, Pallin DJ, Ngo LH, Raymond KL, Iezzoni LI, Banaji MR. Implicit bias among physicians and its prediction of thrombolysis decisions for black and white patients. J Gen Intern Med. 2007 Sep;22(9):1231-8. doi: 10.1007/s11606-007-0258-5. Epub 2007 Jun 27. PMID: 17594129; PMCID: PMC2219763.
   
2. Byrne A, Tanesini A. Instilling new habits: addressing implicit bias in healthcare professionals. Adv Health Sci Educ Theory Pract. 2015 Dec;20(5):1255-62. doi: 10.1007/s10459-015-9600-6. Epub 2015 Mar 15. PMID: 25771742.