Thomas Chong, MD, MS
- Pathology

Associate Director of Pathology Informatics and Informatics Education
Assistant Professor of Pathology & Immunology, Baylor College of Medicine
Phone:
832-824-5105
Languages: English
Departments:
Get to know Thomas Chong, MD, MS
Personal Statement
My goal as a pathology informatician is to serves as a liaison between pathologists, administrators, and technologists (laboratory information systems analysts, data analysts, and researchers) by communicating the pathologists’ needs to the technical experts and the technical solution possibilities to the pathologists and administration. I teach informatics education to residents and fellows and support clinical research.
Clinical Interests
My current interests have been in clinical decision support methods to reduce lab test mis-orders by clinicians, in health record interoperability and data standardization by defining, validating, and maintaining LOINC assignments to lab tests, digital pathology workflows, and in defining interface requirements between medical records and pathology slide image systems.
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* Texas Children’s Hospital physicians’ licenses and credentials are reviewed prior to practicing at any of our facilities. Sections titled From the Doctor, Professional Organizations and Publications were provided by the physician’s office and were not verified by Texas Children’s Hospital.
Chong, T., Palma-Diaz, M. F., Fisher, C., Gui, D., Ostrzega, N. L., Sempa, G., ... & Khacherian, C. (2019). The California Telepathology Service: UCLA's experience in deploying a regional digital pathology subspecialty consultation network. Journal of Pathology Informatics 10.
Sue, L. Y., Kim, J. E., Oza, H., Chong, T., Woo, H. E., Cheng, E. M., & Leung, A. M. (2019). REDUCING INAPPROPRIATE SERUM T3 LABORATORY TEST ORDERING IN PATIENTS WITH TREATED HYPOTHYROIDISM. Endocrine Practice 25(12), 1312-1316.
Rivenson, Y., Wang, H., Wei, Z., de Haan, K., Zhang, Y., Wu, Y., Günaydın, H., Zuckerman, J.E., Chong, T., Sisk, A.E. and Westbrook, L.M., 2019. Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning. Nature Biomedical Engineering 3(6), p.466.