Missed, delayed, or inaccurate medical diagnoses (i.e., diagnostic errors) are responsible for up to 80,000 hospital deaths in the United States annually. Some medical conditions are not easy to diagnose because symptoms are ambiguous or easy to confuse with other conditions, which contributes to diagnostic errors. This is especially the case for rare diseases with which physicians may not have much experience. This project examines whether marginalized status (e.g., racially and ethnically minoritized people and women) and the structure of patient-physician network ties are associated with delays in diagnosis of medical conditions with ambiguous symptoms. The findings from this project can lead to better healthcare interventions that could reduce health disparities related to diagnostic errors. This project determines the social and structural predictors of delays in diagnosis of medical conditions with ambiguous symptoms. Drawing on theories from social psychology, this study hypothesizes that marginalized people are more likely to experience longer delays in diagnosis of diseases with ambiguous symptoms because the diagnostic process can be affected by racial, gender, and other stereotypes. Drawing on theories from network science, this study hypothesizes that patient-physician network structures that encourage exposure to non-redundant information (new possible diagnoses or tests to consider), together with physicians? increased willingness to accept mistakes and correct misdiagnoses are associated with faster times to diagnoses of medical conditions with ambiguous symptoms. The project combines Longitudinal Medicaid Analytic eXtract (MAX) medical claims data with the American Medical Association (AMA) Physician Masterfile data to quantitatively test these hypotheses. Patient-physician networks are constructed through direct links between patients and physicians based on clinical encounters and indirect links between physicians through the patients they share. This project is important for understanding how social networks influence the patient-physician relationship and diagnostic processes, and for reducing the number of diagnostic errors in the U.S. healthcare system. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.