Health at Scale lands $16M Series A to bring machine learning to healthcare

Health at Scale, a startup with founders who have both medical and engineering expertise, wants to bring machine learning to bear on healthcare treatment options to produce outcomes with better results and less aftercare. Today the company announced a $16 million Series A. Optum, which is part of the UnitedHealth Group, was the sole investor.

Today, when people look at treatment options, they may look at a particular surgeon or hospital, or simply what the insurance company will cover, but they typically lack the data to make truly informed decisions. This is true across every part of the healthcare system, particularly in the U.S. The company believes using machine learning, it can produce better results.

“We are a machine learning shop, and we focus on what I would describe as precision delivery. So in other words, we look at this question of how do we match patients to the right treatments, by the right providers, at the right time,” Zeeshan Syed, Health at Scale CEO told TechCrunch.

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The founders see the current system as fundamentally flawed, and while they see their customers as insurance companies, hospital systems and self-insured employers, they say the tools they are putting into the system should help everyone in the loop get a better outcome.

The idea is to make treatment decisions more data-driven. While they aren’t sharing their data sources, they say they have information, from patients with a given condition, to doctors who treat that condition, to facilities where the treatment happens. By looking at a patient’s individual treatment needs and medical history, they believe they can do a better job of matching that person to the best doctor and hospital for the job. They say this will result in the fewest post-operative treatment requirements, whether that involves trips to the emergency room or time in a skilled nursing facility, all of which would end up adding significant additional cost.

If you’re thinking this is strictly about cost savings for these large institutions, Mohammed Saeed, who is the company’s chief medical officer and has an MD from Harvard and a PhD in electrical engineering from MIT, insists that isn’t the case. “From our perspective, it’s a win-win situation since we provide the best recommendations that have the patient interest at heart, but from a payer or provider perspective, when you have lower complication rates you have better outcomes and you lower your total cost of care long term,” he said.

The company says the solution is being used by large hospital systems and insurer customers, although it couldn’t share any. The founders also said it has studied the outcomes after using its software and the machine learning models have produced better outcomes, although it couldn’t provide the data to back that up at that point at this time.

The company was founded in 2015 and currently has 11 employees. It plans to use today’s funding to build out sales and marketing to bring the solution to a wider customer set.

Original Article : HERE ; This post was curated & posted using : RealSpecific

This post was curated & Posted using : RealSpecific

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