Speakers: Monica Gaddis, PhD
Monica Gaddis, PhD
Assistant Teaching Professor
Department(s) of Biomedical and Health Informatics, Emergency Medicine
University of Missouri Kansas City
Monica Gaddis, PhD is an Associate Professor with the Department of Biomedical and Health Informatics (DBHI) at UMKC School of Medicine. Within the DBHI, Dr. Gaddis teaches Applied Biostatistics (Beginning and Advanced), and is the lead instructor for the Weekly Department Multidisciplinary Seminar. She also oversees and conducts project analysis for the Neuroscience Unit research project, which educates year three medical students in the basics of the research process. Outside of the DBHI, she serves as the Research Director for the Department of Emergency Medicine (DEM), Truman Medical Center – Hospital Hill, Kansas City, MO. Dr. Gaddis is involved as a PI or co-PI in several DEM research projects, leveraging both prospective clinical and retrospective secondary dataset analyses, addressing various questions regarding the diagnosis, treatment and outcomes of patients with sepsis. Dr. Gaddis is also a Co-PI for several Emergency Medical System research projects within the DEM, assessing policy and outcomes regarding delivery of pre-hospital care. Further, Dr. Gaddis oversees the research and scholarly activity of the Emergency Medicine Residents, assists the DEM faculty with their research and produces and co-directs the monthly Department of EM Journal Club. Finally, Dr. Gaddis is a recognized expert in emergency medicine research program development for areas with limited resources, having spoken internationally on three continents.
The Electronic Health Record: What You Ask For May Not Be What You Get
As the transition to ICD–11 codes occurs and the number and complexity of available codes increase, this disconnect is likely to only be magnified. This talk will inform non-clinical researchers regarding the selection of diagnoses by clinicians, using real life examples. With this, researchers will better understand why the data that is obtained may lack the precision that is desired.