As a board-certified ophthalmologist and glaucoma specialist, I have more than 15 years of clinical experience caring for patients with different types and complexities of glaucoma. In addition to my clinical experience, as a health services researcher, I have developed experience and expertise in several disciplines including performing analyses using large health care claims databases to study utilization and outcomes of patients with ocular diseases, racial and other disparities in eye care, associations between systemic conditions or medication use and ocular diseases. I have learned the nuances of various data sources and ways to maximize our use of these data sources to answer important and timely questions. Leveraging my background in HSR with new skills in bioinformatics and precision medicine, over the past 2-3 years I have been developing and growing the Sight Outcomes Research Collaborative (SOURCE) repository, a powerful tool that researchers can tap into to study patients with ocular diseases. My team and I have spent countless hours devising ways of extracting electronic health record data from Clarity, cleaning and de-identifying the data, and making it linkable to ocular diagnostic test data (OCT, HVF, biometry) and non-clinical data. Now that we have successfully developed such a resource here at Kellogg, I am now collaborating with colleagues at > 2 dozen academic ophthalmology departments across the country to assist them with extracting their data in the same format and sending it to Kellogg so that we can pool the data and make it accessible to researchers at all of the participating centers for research and quality improvement studies. I am also actively exploring ways to integrate data from SOURCE into deep learning and artificial intelligence algorithms, making use of SOURCE data for genotype-phenotype association studies and development of polygenic risk scores for common ocular diseases, capturing patient-reported outcome data for the majority of eye care recipients, enhancing visualization of the data on easy-to-access dashboards to aid in quality improvement initiatives, and making use of the data to enhance quality of care, safety, efficiency of care delivery, and to improve clinical operations. .
Kevin J. Dombkowski, DrPH., MS, is Research Professor with the Child Health Evaluation and Research (CHEAR) Center within the University of Michigan Department of Pediatrics. He is a health services researcher working extensively with public health information systems and large administrative claims databases.
Kevin’s primary research focus is conducting population-based interventions aimed at improving the health of children, especially those with chronic conditions. Much of his work has focused on evaluating the feasibility and accuracy of using administrative claims data to identify children with chronic conditions by linking these data with clinical and public health systems. Many of these projects have linked claims, immunization registries, newborn screening, birth records and death records to conduct population-based evaluations of health services. He has also applied these approaches to assess the statewide prevalence of chronic conditions such as asthma, sickle cell disease, and inflammatory bowel disease in Michigan as well as other states. Kevin is currently collaborating with Michigan State University on the design and development of the Flint Registry information architecture.
Kevin’s research interests also include registry-based interventions to improve the timeliness of vaccinations through automated reminder and recall systems. He has led numerous collaborations with the Michigan Department of Health and Human Services (MDHHS), including several CDC-funded initiatives using the Michigan Care Improvement Registry (MCIR). Through this collaboration, Kevin tested a statewide intervention aimed at increasing influenza vaccination among children with chronic conditions during the 2009 influenza pandemic. Kevin is currently collaborating with MDHHS to evaluate MCIR data quality as immunization providers across Michigan adopt real-time, bi-directional messaging between electronic health records (EHRs) and MCIR. As PI of a CDC-funded project, Kevin is evaluating the costs and benefits of electronic interoperability between EHRs and MCIR. He is also conducting a statewide evaluation of blood lead testing result data reported by electronic laboratory systems to MDHHS.
Jeffrey S. McCullough, PhD, is Associate Professor in the department of Health Management and Policy in the School of Public Health at the University of Michigan, Ann Arbor.
Prof. McCullough’s research focuses on technology and innovation in health care with an emphasis on information technology (IT), pharmaceuticals, and empirical methods. Many of his studies explored the effect of electronic health record (EHR) systems on health care quality and productivity. While the short-run gains from health IT adoption may be modest, these technologies form the foundation for a health information infrastructure. As scientists are just beginning to understand how to harness and apply medical information, this problem is complicated by the sheer complexity of medical care, the heterogeneity across patients, and the importance of treatment selection. His current work draws on methods from both machine learning and econometrics to address these issues. Current pharmaceutical studies examine the roles of consumer heterogeneity and learning about the value of products as well as the effect of direct-to-consumer advertising on health.
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.
My research is focused on developing and using identifiability and parameter estimation methods in mathematical modeling to generate insights into disease mechanisms, forecasting, and intervention design. My work blends mathematics, statistics, and epidemiology to both develop new methods and theory and to translate this theory to directly improve population health.