LECTURE: BIASES IN PATHOLOGY, MOHAMMAD KAMAL MD
Biases are systemic errors resulting in repeated patterns of thinking that lead to inaccurate or unreasonable conclusions. Decision-making under uncertainties can be impacted by biases. Studying biases and heuristics and their effect in economics and other fields has resulted in great changes in decision making processes. In understanding how biases impact the decision-making process, physicians can implement measures to avoid medical errors. Furthermore, pathologists who understand biases are better equipped to communicate uncertainties with the clinicians.
Psychologists and mathematicians study biases with deep interest in their effect on the quality of data. Behavioral economics is a science that is based largely on biases. In his book, Thinking Fast and Slow, Nobel laureate Daniel Kahneman focused on the impact of biases on decision-making under uncertainty. Kahneman’s work in the area resulted in his Nobel prize award.
Recently, Dr. Mohammad Kamal, CEO of OmniPathology has taken up this topic as an area of interest. In this day and age of focus on specialization, there is a need for a multidisciplinary approach to problems. Every day pathologists face uncertainties and situations in which various types of biases can influence their decisions. Dr. Kamal’s interest in teaching resulted in putting together his Biases in Pathology lecture, which he presented at the Grand Round lecture at the department of pathology at Harbor UCLA in April 2019 and at UCLA Department of Pathology Noon Conference in June 2019. Dr. Kamal was invited to present this topic at MD Anderson Department of Pathology on August, 30 2019.
Through a review of a real-life malpractice case, Dr. Kamal provides examples of biases that can impact the decision-making process of a pathologist. He incorporated images of morphological findings of the case and discussed the expert witnesses from the plaintiff and defense sides. He identifies and discusses in detail the biases in the case. The last part of the lecture addresses proposed good practices and quality measures that will help avoid errors produced by biases.
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