Eric Lefkofsky and Tempus: The Trending Rise of Big Data Analytics in Cancer Care


Roche recently acquired Flatiron, a data analytics health tech that categorizes cancer patients based on processed data. This transaction begged the questions of what other emerging oncology clinical data companies are out there and what are the implications of their rapid expansion and success for both the pharma and healthcare sectors.


Two prominent emerging oncology clinical data startups have been creating a buzz within the pharma and health care industries – Chicago-based Tempus and New York-based Cota. Both are rising rapidly and truly impacting the way cancer as well as cancer data are perceived these days.


Tempus was founded by Chicago’s serial entrepreneur Eric Lefkofsky in 2015. In the short few years of its existence, the enterprise has already earned a spot among the city’s top ten health techs. Tempus focuses on gathering cancer care data through collaborations with nearly all of the nation’s NCI-designated Cancer Care Centers as well as academic institutions. The company was Lefkofsky’s reaction to the poorly structured health care records that are plagued by the vast discrepancy between cancer patients’ clinical and molecular data. This rift between collected patient data and the use of that information in designing effective treatments is because not all patient data is accessible. In hopes to bridge the gap between the two, Lefkofsky based Tempus on a software platform that relies on optical character recognition and natural language processing that gathers electronic healthcare records from medical and academic institutions and transforms them into structured data. Tempus essentially centralizes these vast amounts of data and makes them accessible to health care workers, who in turn can make more educated, optimized and personalized cancer therapy decisions.


The advantage of these algorithms is that they can provide a more precise prognosis for the course of a disease. This, in turn has the potential to entirely reshape treatment for progressive diseases. Algorithms are common in internet commerce, finance and self-driving cars, but are yet to be explored to their full potential in the health care sector. These platforms are promising. If adopted on a broad scale, they have the potential to save lots of time, money and, most importantly, patient lives.


Cota, which is an acronym for “Cancer Outcomes Tracking and Analysis,” also consolidates big data. It relies on automation and manual extraction to gather information from a range of oncologists. It then refines these data in such a way that each patient ends up with a unique category, called the “Cota nodal address” (CNA), which is essentially an integration of the specific prognostic characteristics that are assigned to patients. In other words, Cota consolidates patients into similar CNA categories based on treatment they should be receiving. These categorizations enable comparison of treatment outcomes and costs and as such make it possible for physician organizations as well as payors to determine where the best and most cost effective care originates. The Cota model has become a lot deeper over the years, encompassing more fields and following patients for longer periods of time. This was also in part due to the company’s dashboards that enable tracking of patient progress as well as possible adverse variations in care patterns. According to Tom Gallucci, Cota’s Chief Financial Officer, the firm’s business model is two-fold – it enables delivery of value-based care, while its vast database has curated a pharma-oriented business that includes synthetic cohorts and large registry studies.


And while Cota believes in the efforts of physicians and health care providers to improve outcomes and to drive more efficient costs, Tempus (and Flatiron) are more like a Google model, delivering a useful service cheap or for free while monetizing the back end of the data that is collected. However unique each of the models is, the hope is that the transformative oncology clinical data paradigm they bring to the table can be juxtaposed onto all of medicine. Ideally, the extraction and consolidation of data from electronic health records could lead to improvement of present care and thereby provide valuable data points for future therapy and treatment developments.


For more information, please visit, LinkedIn: ericlefkofsky, Twitter: @lefkofsky or Facebook: @eplefkofsky.

For more information on Tempus, please visit, Facebook: @TempusLabs and Twitter: @TempusLabs.


To find out more about Cota, please visit Cota’s company website.


Please enter your comment!
Please enter your name here