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Diagnosing patients’ illnesses is the essence of medicine. Patients present to doctors seeking an answer to the question, “What is wrong with me?” Ideally, no clinician would want to treat a patient without knowing the diagnosis or, worse yet, erroneously treat a misdiagnosed illness. From the earliest moments of medical school, the defining quest toward becoming a knowledgeable and proficient physician is learning how to put a diagnostic label on patients’ symptoms and physical findings, and clinicians pride themselves on being “good diagnosticians.” Yet the centuries-old paradigm of mastering a long list of diseases, understanding their pathophysiology, and knowing the cardinal ways they manifest themselves in signs and symptoms, while still of fundamental importance, is being challenged by new insights illuminated by the glaring spotlight of diagnostic errors. Basic internal medicine diseases, such as asthma, pulmonary embolism, congestive heart failure, seizures, strokes, ruptured aneurysms, depression, and cancer, are misdiagnosed at shockingly high rates, often with 20–50% of patients either being mislabeled as having these conditions (false-positive diagnoses) or having their diagnosis missed or delayed (false negatives). How and why do physicians so often get it wrong, and what can we do to both diagnose and treat the problem of delayed diagnosis or misdiagnosis?

Diagnosis is both an ancient art and a modern science. The current science of diagnosis, however, goes far beyond what typically comes to clinicians’ and patients’ minds when they conjure up images of state-of-the-art molecular, genetic, or imaging technologies. Improvements in diagnosis are just as likely to come from other areas, many with origins outside of medicine, as they are from advanced diagnostic testing modalities. These diverse sciences that the field of diagnostic safety has, and must, draw from include systems and human factors engineering, reliability science, cognitive psychology, decision sciences, forensic science, clinical epidemiology, health services research, decision analysis, network medicine, learning health systems theory, medical sociology, team dynamics and communication, risk assessment and communication, information and knowledge management, and health information technology, especially artificial intelligence and clinical decision support. A clinician reading this chapter is likely to find this list of overlapping and intersecting domains quite daunting. However, rather than feeling overwhelmed, we urge readers to view them as the basic science supports that will ultimately make their lives easier and diagnosis more accurate and timely. Rather than feeling intimidated, clinicians should feel a sense of relief and assurance in understanding that good diagnosis does not rest entirely on their shoulders. Instead, it is a systems property, where an infrastructure and a team, one that especially includes the patient, can in a coordinated way work together to achieve more reliable and optimal diagnosis.


Over the past decade, a series of studies culminating in a landmark report from the U.S. National Academy of Medicine (NAM), Improving Diagnosis in Health Care, have shone a spotlight on diagnostic ...

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