Article Title: Tempus And Eric Lefkofksy At The Precision Medicine World Conference

Article Text:

Eric Lefkofksy was recently interviewed for the Interview.net website titled, “Q&A: CEO Eric Lefkofksy and his Cancer Data Health tech Tempus Labs.” The interview took place at the 2018 Precision Medicine World Conference. Eric Lefkofksy was one of the speakers who discussed adapting big data into actionable information for the medical market.

He believes the success of Tempus is due to the problem-focused strategy. The company was designed to solve the data issue that takes place in healthcare and cancer care. He believes it is a problem that should be solved because there is a lot of data that can be mined to help physicians use the information to create a more individualized treatment plan. When he saw the difficulty of treatment first hand, he was shocked at how little data the physicians were using to solve the problem. He has spent the last twenty years creating businesses that help markets bring technology and unstructured data into the market by structuring the data at scale. He wanted to bring a similar solution to the cancer treatment market.

When he created Tempus, he used large medical systems to funnel the data and structure it. Tempus has partnered with more than 50 different cancer centers out of 69 in the U.S. Eric Lefkofsky believes they can use the data and combine it with molecular and clinical data for each patient to create a better treatment plan for each patient. The next step was creating an algorithm and analytics tool that could silo the data volume while also helping physicians understand it in real time. Working with the academic medical centers and the cancer treatment centers might have been tricky, but they believe it can have a huge impact. They created a demonstration that shows how the tool works by sifting through the clinical variability of the different drugs and therapies to create a precise treatment plan for the patient.

Tempus devised a strategy to collect data from the different patients which help oncologists learn about the best treatments for the different cancers. They can see what treatment has worked and what hasn’t. They use drug-response data and phenotypic data to see how the patients respond to different drugs.

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