PALO ALTO, Calif. – CloudMedx, a health care artificial intelligence company, is collaborating with the University of California San Francisco Department of Orthopaedic Surgery to study how patient-generated health care data collected from wearable sensors can predict clinical outcomes following hip and knee replacement surgery. By looking at structured and unstructured data from patient medical records, as well as from wearable devices, the UCSF research team, advised by the National Science Foundation’s Center for Disruptive Musculoskeletal Innovations, aims to create a new class of algorithms that can predict a patient’s individual outcome and recovery following surgery, said Dr. Stefano Bini, one of the researchers. “There currently is a huge cache of unstructured information in the medical records in the form of physician notes, nurse progress notes, discharge summaries, radiology notes and patient-reported outcomes that is being overlooked due to lack of resources,” said Bini in a statement. “By using CloudMedx’s robust AI to read clinical notes using machine-assisted natural language processing, we aim to surface insights in real time to improve patient outcomes.” CloudMedx uses evidence-based algorithms to provide real-time clinical insights to the health care industry with the goal of improving clinical and operational outcomes.
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