Speakers: Dinesh Pal Mudaranthakam, MS, MBA
Dinesh Pal Mudaranthakam, MS, MBA
Director of Research Information Technology
Department of Biostatistics & Data Science
University of Kansas Medical Center
Dinesh Pal Mudaranthakam is Director of Research Information Technology at The University of Kansas Medical Center, within the Department of Biostatistics & Data Science. Mr. Mudaranthakam is also the co-director of the Investigator-Initiated Trial within The University of Kansas Cancer Center. Before 2013, Dinesh worked for CERNER, as a Senior Software Developer in the pharmacy department. Dinesh has obtained his master’s in computer science from Kansas State University and his Master’s in Business Administration with a specialization in leadership from the University of Kansas Business School. Currently, Mr. Mudaranthakam is working towards his Ph.D. degree in Health Policy and Management. Research interest involves working with large health care data sets, optimizing higher education/ online education and building innovative eco-systems to enhance informatics infrastructure.
Automated Data transfer from the Electronic Health Record to Electronic Data Capture System
Research plays a vital role in discovering groundbreaking medications and treatments. Time is of the essence when it comes to oncology since oncology patients need medication and treatment now! One can’t wait for eight or ten years. One of the major elements of conducting a clinical trial is capturing data in Electronic Case Report Forms (eCRFs), which is then analyzed and used as a proof to justify the need for new drug or treatment. KUMC has taken a new approach to autopopulate eCRFs by utilizing a Cancer Curated Clinical Outcomes database (C3OD). Literature suggests countries such as Belgium, London, France, have already tried implementing this approach. Our contribution to the literature is not just auto-populating the forms for our internal needs but also to automatically push and disseminate data to the sponsor (Industry sponsor). Results from our approach suggest a two-prong approach where the structured data elements are straight forward to transmit and populate. The free text (or unstructured) data elements would need some manual verification or a Natural Language Processing (NLP) methodology to interpret/parse the data to answer study-specific questions. Depending upon the phase, design and disease of the clinical trial, we noticed a significant improvement in the quality of data and cost/time savings.