Tempus Labs had three patents in digitalization during Q4 2023. Tempus Labs Inc filed patents in Q4 2023 for methods and systems involving processing unstructured data to generate structured patient records, validating them, displaying errors, and receiving revisions. Another patent involves audibly broadcasting responses to user queries about patient reports by executing data operations on different sets of data and providing audible responses via a speaker. GlobalData’s report on Tempus Labs gives a 360-degreee view of the company including its patenting strategy. Buy the report here.
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Tempus Labs grant share with digitalization as a theme is 0% in Q4 2023. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Transforming unstructured patient data streams using schema mapping and concept mapping with quality testing and user feedback mechanisms (Patent ID: US20230360752A1)
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Tempus Labs Inc. has developed a method and system for improving automated quality assurance testing of structured patient data by transforming unstructured data streams. The method involves receiving unstructured patient data, processing it to create structured patient records through schema and concept mapping, validating these records using quality tests, displaying errors or incomplete information, and allowing for revisions to the unstructured data. This process is repeated until the structured patient records meet a specified accuracy threshold, with the option to generate a digital abstraction of a patient record if needed. The system includes processors and memory storing instructions for executing these steps, while the computer-readable medium contains instructions for receiving, processing, validating, displaying errors, and accepting revisions to unstructured patient data.
The method also involves identifying medical ontological terms within the unstructured data using natural language processing, determining mappings between data elements and values, and performing data quality tests using recursive or iterative algorithms. Additionally, the system can process structured patient records to identify clinical attributes for potential inclusion in clinical trials, with decisions based on data quality test results. Trigger events prompt the method, which can include on-demand requests, new data receipt, or real-time analysis during data creation. The system can also train machine learning models based on structured patient records and generate definitive clinical records for patients. Overall, Tempus Labs Inc.'s method and system aim to enhance the accuracy and quality of structured patient data through automated processes and user interaction.
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