KenSci Raises $8.5M, Delves into Data to Predict Who Will Get Sick

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results to various individuals in the healthcare system—from front-line care providers to insurance CFOs—through data visualizations, reports, and application programming interfaces. Manjure showed off a 23-year-old piece of software used by hospital discharge planners to track who is getting better. Through an API, KenSci’s scores are integrated into that tool.

“The discharge planners and the doctors don’t have to change their workflow, but yet are able to get this very advanced insight from machine learning algorithms in a way that is natural to them,” Manjure says.

But are doctors ready to defer to the judgement of a complex algorithm, whose inner workings they may or may not understand?

Greg McKelvey, a doctor and KenSci’s head of clinical insights, says the company emphasizes transparency in all of its design decisions.

“We cannot be operating in black boxes,” he says. “We have to show why we’re making predictions [and] how well they’re performing on the populations that are actually being cared for. For a clinician—to really trust this—they need to see this for the patients that are in front of them, is it actually performing as described?”

When a new customer begins using KenSci, the company’s machine learning models are refined using two years of data from the customer’s specific population. The models then make predictions for a third year, and those predictions are validated against actual outcomes for that year.

The performance of KenSci’s predictions are shown against baseline clinical prediction models that may already be in use in another bid to show physicians how this new tool compares to their status quo practices.

For now, KenSci’s predictive models are meant to be “natural extensions of the tools that physicians are already using,” McKelvey says. “They’re merely an adjunct to their clinical judgement.”

But it’s easy to envision a system like this going from prediction to prescription, which will open “a much thornier discourse” about things like the autonomy of physicians, reimbursement, regulation, and liability, he says.

The insights KenSci promises to physicians are multiplied for large healthcare organizations—such as accountable care organizations and health insurance providers—which are struggling to better manage their covered populations and control costs. Healthcare payers are increasingly tying reimbursements to patient and population outcomes, rather than simply paying for procedures performed.

“Imagine the world where you can predict [the] 1,000 people that are at the highest risk for you next year, and these are the reasons why they are at the risk,” Manjure says. “Now you can enroll them into the right kind of programs.”

That might mean identifying someone at imminent risk of cardiac arrest before they arrive at an emergency room with chest pain, and before they incur a $25,000 medical bill. “If you can predict about this patient up front, then you can actually treat them, even if it means sending a nurse or a doctor to their home, which may cost less than $1,000,” Manjure says. “So right there, you’re having not only a better medical outcome for a patient, but also reducing significant costs in healthcare.”

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