Implicit Bias in the Evidence: An Evaluation of Female-Predominant Disease

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This research explores the use of heuristics in diagnostic decision making for conditions that do not have definitive diagnostic tests and rely on a set of diagnostic criteria. Because clinicians are Bayesian by nature, cognitive heuristics are used to estimate the probability of a given condition based on the representativeness of that case among “the average” in diagnostic decision making. Over time, reliance on these shortcuts influences how patients are diagnosed, and in turn, these patients add to the growing evidence base. This may lead to a multi-state confirmation bias in the practice of evidence-based medicine. Our first step to evaluate this complex cycle is to question how the evidence and deviations from the norm influence what diagnosis a clinician gives. We ask: Does the diagnosis a patient receives vary when all that differs is their sex, gender, or race? Our initial pilot shows that clinicians make more diagnostic errors when presented with patients of sex/gender/race that is less common, despite identical clinical signs and symptoms of lupus. We will extend on our experimental approach evaluating implicit bias in the diagnosis of lupus, which builds off of research on discrimination in hiring. This work specifically asks whether when the disease presents the same, does a patient deviating from the “norm” based on the evidence, influence the diagnosis? We will consider additional female-predominant diseases and determine whether a physician's diagnosis is influenced by a patient's sex/gender/race holding everything else constant. First, we will conduct a national internet-based randomized experiment among specialists to assess the role of sex/gender/race in the clinical workup and its contribution to observed heterogeneity and disparities. Second, we will conduct additional randomized experiments in primary care to determine how sex/gender/race influences their clinical workup and referral patterns. Third, we will use mixed methods to characterize and improve our understanding of how clinicians approach diagnostic questions, and identify non-biological, potentially modifiable factors contributing to health disparities. We will conduct focus groups with providers using experimental results as discussion prompts to contextualize findings and inform strategies to address this type of multi-state confirmation bias. This innovative work adapts a novel method by combining methods from sociology, experimental design, and behavioral science. Our findings will increase clinicians' awareness of how cognitive biases that may lead to medical errors, and will demonstrate how an incomplete evidence base may propagate health disparities leading to delayed diagnosis, worsening disease, and irreversible damage.