Dr. Katz’s research addresses cancer treatment communication, decision-making, and quality of care. His work aims to examine the dynamics of how precision medicine presents itself in the exam room via provider and patient communication and shared decision-making. Dr. Katz leads the Cancer Surveillance and Outcomes Research Team (CanSORT), an interdisciplinary research program centered at the University of Michigan and focused on population and intervention studies of the quality of care and outcomes of cancer detection and treatment in diverse populations. Dr. Katz and CanSORT have been collaborating with Surveillance, Epidemiology, and End Results (SEER) cancer registries since 2002 to study breast cancer treatment decision making at the population level. We obtain patient clinical and demographic information from SEER and combine this with surveys of patients and physicians to create comprehensive data sets that enable us to study testing and treatment trends and the challenges of individualizing treatments for breast cancer patients. In 2015 we added a new dimension to our research by partnering with evaluative testing firms to obtain tumor genomic and germline genetic test results for over 30,000 breast and ovarian cancer patients in the states of California and Georgia. We are also pursuing insurance claims data to assist with our analysis of physician network effects.
Research in my lab occurs within a multidisciplinary and translational space that promotes greater understanding of issues in public health, clinical rehabilitation, human performance, and physiology. My specific research interests have been devoted to physical activity epidemiology and behavioral interventions for the treatment/prevention of obesity and related cardiometabolic diseases, frailty, functional motor declines, and early mortality. Although predictive models based on healthy cohorts have a certain degree of generalizability, it is necessary to better understand populations at heightened risk. Our current and future research efforts are therefore directed at understanding and identifying precision strategies to prevent metabolic dysregulation and secondary musculoskeletal pathology among children and adults with neuromuscular impairments, as well as a variety of frailty syndromes. Our primary data collection occurs through typical clinical and basic laboratory studies, high throughput imaging, and remote sensing/tracking of human movement and various biomarkers. Numerous secondary, large-data analysis efforts are also incorporated for epidemiologic studies utilizing nationally-representative samples.
Data science applications: Connecting statistical models and disease data; design of behavioral interventions with remote sensing and messaging; understanding pediatric cardiometabolic and muscle health; sports analytics and human performance; population-based epidemiology/surveillance; secondary disease disparities among acquired and chronic frailty syndromes