Redesign of Health Care Systems to Reduce Diagnostic Errors: Leveraging Human Experience and Artificial Intelligence

iagnostic errors are defined by the National Academies of Sciences, Engineering, and Medicine (NASEM) as the failure to either establish an accurate and timely explanation of the patient's health problem(s) or communicate that explanation to the patient. 1 According to a report by the Institute of Medicine, diagnostic errors account for a substantial number of adverse events in health care, affecting an estimated 12 million Americans each year. 1 Diagnostic errors are a common and serious issue in health care systems, with studies estimating that 5% to 15% of all diagnoses are incorrect. 1Such errors can result in unnecessary treatments, delays in necessary treatments, and harm to patients.The high prevalence of diagnostic errors in primary care has been identified as a global issue. 2 While many factors contribute to diagnostic errors, the complex nature of health care systems, the limited processing capacity of human cognition, and deficiencies in interpersonal patient-clinician communication are primary contributors. 3,4iscussions around the redesign of health care systems to reduce diagnostic errors have been at the forefront of medical research for years. 2,4To decrease diagnostic errors in health care, a comprehensive strategy is necessary.This strategy should focus on utilizing both human experience (HX) in health care and artificial intelligence (AI) technologies to transform health care systems into proactive, patient-centered, and safer systems, specifically concerning diagnostic errors. 1