{"id":14694,"date":"2021-04-20T11:22:23","date_gmt":"2021-04-20T15:22:23","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-digit\/submission\/tempus-data-driven-precision-medicine\/"},"modified":"2021-04-20T11:30:35","modified_gmt":"2021-04-20T15:30:35","slug":"tempus-ai-data-driven-precision-medicine","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-digit\/submission\/tempus-ai-data-driven-precision-medicine\/","title":{"rendered":"Tempus AI: Data-Driven Precision Medicine"},"content":{"rendered":"

Introduction<\/strong><\/p>\n

Tempus is a Chicago-based AI company that develops precision medicine[a]<\/a> solutions using patients\u2019 multi-modal data. The company was founded in 2015 and now boasts the world\u2019s largest library of clinical and molecular data. Tempus focused initially on fighting Cancer but is now expanding its solutions into infectious diseases and mental health.<\/p>\n

Product offerings<\/strong><\/p>\n

Originally, Tempus had three main lines of business in a symbiotic cycle.<\/p>\n

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  1. Genomic sequencing<\/u> – Tempus offers a broad range of DNA and RNA sequencing tests that Physicians can order for their patients. In this product, Tempus combines genomic data with clinical data to provide insights on gene alterations associated with specific types of tumor and enable oncologists to prioritize among different treatment options.<\/li>\n
  2. Clinical data structuring <\/u>– Tempus partners with health-care systems and academic institutions to collect, organize and aggregate clinical data primarily but not exclusively from electronic health records. The company uses image recognition, optical character recognition (OCR) and natural language processing (NLP) to transform unstructured information that is buried inside radiology scans, pathology slides, health reports and oncologists notes into structured data.<\/li>\n
  3. Data monetization <\/u>\u2013 Tempus sells curated and anonymous data to pharmaceutical companies for research and development of new drugs.<\/li>\n<\/ol>\n

    Tempus ONE<\/strong><\/p>\n

    Earlier this year, Tempus announced a bold new addition to its product offering. The company launched the beta version of Tempus ONE, a voice-enabled portable smart-speaker designed for helping physicians on real-time. The device is intended to be a physical manifestation of the Tempus library. It can conveniently lie in a doctor\u2019s desk and is small enough to be carried in the pocket.<\/p>\n

    After listening to a question, Tempus ONE searches the answer on the library, organizes the data into a clinical context, and makes it seamlessly available for doctors as they take care of patients. Tempus ONE lets doctors ask a broad range of inquiries, including questions about a patient\u2019s condition, clinical and genomic information, and test status (e.g.: \u201cTempus ONE, what is Karla Sanders\u2019 MSI status?\u201d, \u201cTempus ONE, what alterations were found in Karla Sanders?\u201d).<\/p>\n

    Watch the video below for Tempus ONE Demo:<\/p>\n

    https:\/\/www.youtube.com\/watch?v=Zc2-EFw4txc&t=62s<\/a><\/p>\n

    While the advertised job-to-be-done<\/em> of Tempus ONE is to answer doctors\u2019 questions, it\u2019s likely that Tempus intends to use the device to collect conversational data from doctor-patient interactions. This new category of data would certainly make Tempus\u2019 library even more powerful.<\/p>\n

    Challenges and limitations<\/strong><\/p>\n

    It\u2019s true that the use of AI in healthcare enabled by large sets of historical data is very powerful and exciting. AI perhaps is the single most promising technology to improve people\u2019s health by finding the cure for diseases, developing new drugs and vaccines, and improving therapies. However, a lot of challenges lay ahead. Below, I exemplify the most important ones:<\/p>\n