We create and study methods and tools to support unstructured clinical data reuse. Reuse of clinical data is essential to fulfill the promises for high quality healthcare, improved healthcare management, and effective clinical research. Accurate and detailed clinical information, as found in patient Electronic Health Records (EHR), rather than existing but often biased and insufficiently detailed diagnostic and procedure codes assigned for reimbursement and administrative purposes only are needed for effective clinical research and high quality and efficient healthcare. We use Natural Language Processing (NLP) to extract this clinical information from EHRs, and to automatically de-identifying clinical notes and protect patient privacy, also providing user-friendly and easy to use tools for researchers and clinicians to browse, query, visualize, and obtain clinical data. Our research group was part of the Department of Biomedical Informatics at the University of Utah, and is now part of the Translational Biomedical Informatics center in the Biomedical Informatics Center (BMIC) at the Medical University of South Carolina College of Medicine.

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Lab work on the slopes of Brighton, UT

Meystre lab – latest news

Dr. Meystre inducted Fellow of AMIA

Dr. Meystre inducted Fellow of AMIA
Stephane Meystre was inducted as Fellow of the American Medical Informatics Association (FAMIA), a recognition of the contributions and professional accomplishments of AMIA members who apply...
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Dr. Meystre elected fellow of IAHSI

Dr. Meystre elected fellow of IAHSI
The International Medical Informatics Association (IMIA) and the International Academy of Health Sciences Informatics (IAHSI) announced the 2020 class of Fellows including Stephane Meystre. The...
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Data Science Best Paper Award Winner

Data Science Best Paper Award Winner
Our paper on “A Comparative Analysis of Speed and Accuracy for Three Off-the-Shelf De-Identification Tools” has been awarded the Data Science Best Paper Award at the AMIA 2020...
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