Seattle healthcare IT startup KenSci has a tagline that helps simplify the company’s audacious aims: Death versus data science.
Unpack that a little bit and you’ll find a company—banging its drum Wednesday with an $8.5 million Series A funding round led by Ignition Partners—that’s combing of-the-moment machine learning technology and mountains of data to attack some of the wickedest problems in the healthcare industry: problems like high hospital readmission rates, hospital-acquired infections, and over-use of costly emergency room services for conditions that might have been addressed through preventive care before they became acute.
“These are the problems that are very amenable to machine learning and artificial intelligence,” says KenSci CEO Samir Manjure.
KenSci, co-founded in spring 2015 by Manjure and Ankur Teredesai, a University of Washington computer science professor, has already won 11 enterprise customers, including Fullerton Health, St. Luke’s Health Partners, and other large public and private health systems. The funding round, joined by Osage University Partners and Mindset Ventures, will be used for hiring across the company, sales and marketing, and continued research and development.
The company sets machine learning algorithms to work on digitized healthcare data: electronic medical records, claims data, demographics, psycho-social data, and patient-generated data from sensors and personal devices. The company trains scores of separate machine learning models to predict risks for individual patients and entire insured populations across various diseases.
“The idea is to predict who’s going to get sick, and how sick are they going to get,” Manjure says. “If we can understand disease progression and the complex interplay of those variables, then we can help coordinate, proactively, their care in anticipation of those adverse events…This is a radical approach in terms of how we go about saving costs for healthcare, while shooting for better outcomes.”
The startup has big competitors taking similar approaches, such as IBM Watson Health and Health Catalyst. Manjure says one way KenSci can differentiate itself as a startup is through rapid deployment and faster return on investment.
The company—funded until now with customer revenue, Manjure says—is a textbook example of what the Seattle area has to offer when it comes to healthcare IT innovation.
Teredesai, the company’s CTO, is executive director of the Center for Data Science at University of Washington, Tacoma, where the company was incubated. His research over the last half-decade gave KenSci its foundation, and the company has licensed intellectual property from the UW and continues to collaborate with researchers there. Manjure left Microsoft after 17 years to found KenSci, but his startup has benefitted from close ties to the company. KenSci was part of Microsoft’s Seattle startup accelerator class last fall and has been highlighted for its use of Microsoft’s Azure cloud.
Manjure and Teredesai go way back—they grew up in the same neighborhood, and their mothers went to the same high school. They had been talking about launching a project together for some time, Manjure says, and they both wanted it to be more than just a money-making endeavor.
Reducing the death rate from preventable killers like sepsis fits that bill.
Where a doctor might typically diagnose sepsis by looking at a handful of symptoms—temperature, heartrate, respiratory rate—KenSci’s machine learning model crunches hundreds of variables for each patient and provides a predictive score of that individual’s risk of infection.
That score and the other outputs of KenSci’s growing bank of models deliver
