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Clinical Professors

Clinical Professors, Mark L. Wess, M.D., M.Sc. at the School of Health Research, Clemson University, Clemson South Carolina

Mark L. Wess, M.D., M.Sc.

Chief Medical Information Officer Physician, General Internal Medicine
Greenville Health System

Contact: 864-455-4308 or mwess@ghs.org


Who is Dr. Wess?

Mark Wess is the CMIO at Greenville Health System (GHS) and has been since 2012. He received his medical training at the University of Cincinnati College of Medicine. He stayed at UC for residency, which was followed by serving as chief resident from 1992-1993. Mark obtained his Masters in Clinical Epidemiology from Harvard School of Public Health.  During his time at UC, he has been involved in many informatics roles. His research activities have included usability testing, computerized provider order entry, and practice quality improvement. Currently, he is collaborating with Dr. Ronald Gimbel, Chair and Associate Professor Department of Public Health Sciences and Ben Martin evaluating the automated electronic medical record activity logging tool using a time motion study.  In addition, he is working with Dr. Ilya Safro and a post graduate fellow, Talayeh Razzaghi. The project is focused on a novel model applied to predict readmission and emergency department recidivism. 

For more information, see his Curriculum Vitae

How Dr. Wess' research is transforming health care

Mark has interest in operational informatics research. He is particularly interested in usability, process improvement, predictive models, and data analysis. One team is working on evaluating an automated activity logging tool of the electronic medical record (EMR). Using a time motion study, they wish to understand the limitations of the logging tool information and validate the results. They anticipate the results will allow identification of providers who are doing very well and those that could benefit from assistance and what areas need to be addressed. Thereby, they could improve provider efficiency and utilization of the EMR. In the other study, the teams wish to develop predictive model that improves the ability to identify patients at high risk of re-admission or repeat emergency visits. This information can improve the care process, reduce health care cost, and improve the patient’s health.


Key Health Research Interest Areas

Clinical Informatics, Electronic Medical Record (EMR), Usability, Data Mining/Analytics, Population Health, Health Care Improvement