Julia E. Szymczak, PhD
Dr. Szymczak’s research interests include medical sociology, work and organizations, patient safety, quality improvement and implementation science. She aims to understand how the social organization of medical work influences the uptake of standards, guidelines and best practices. Most of her research utilizes qualitative methodology (particularly ethnography) to uncover the mediating conditions that influence whether a practice will be adopted consistently or not. She also collaborates with clinician-scientists and health policy researchers. She strongly believes that the theories and methodological tools of sociology can be brought to bear in order to better understand significant real-world problems in population health.
She has examined various case studies of efforts to improve healthcare quality and patient safety in the U.S., including resident duty-hour restrictions and infection-prevention practices. Her current research focuses on the nonclinical factors that shape antibiotic prescribing and the social factors that influence success in antibiotic stewardship. She has received funding from the U.S. Centers for Disease Control and Prevention (CDC), the National Cancer Institute (NCI), the Agency for Healthcare Research and Quality (AHRQ), the Patient-Centered Outcomes Research Institute (PCORI) and the University of Pennsylvania School of Arts and Sciences Graduate Division. Her research has appeared in journals such as Social Science and Medicine, The Milbank Quarterly, JAMA Pediatrics and the Journal of Health and Social Behavior.
She completed her doctoral training in sociology at Penn and a postdoctoral fellowship in the Division of Infectious Diseases and the Center for Pediatric Clinical Effectiveness at the Children's Hospital of Philadelphia.
Medical sociology, patient safety and quality improvement, implementation science, antimicrobial stewardship, infection prevention and control
Ethnography, in-depth interviewing, qualitative content analysis, computer-assisted qualitative data analysis