Big data analytics startup adding healthcare channel

For healthcare reform to make the shift from the fee for service to outcomes based reimbursement that its architects envision, providers need to have a way to analyze big data that can make accurate predictions about patient populations, particularly people with chronic conditions. It’s going to take a lot of data scientists to respond to […]

For healthcare reform to make the shift from the fee for service to outcomes based reimbursement that its architects envision, providers need to have a way to analyze big data that can make accurate predictions about patient populations, particularly people with chronic conditions. It’s going to take a lot of data scientists to respond to that huge need.

In a phone interview with Predictify.me co-founder and CEO Robert Burns, he talked about its approach to predictive analytics to understand human behavior and its plans to roll out a predictive analytics tool for healthcare in June.

One of the things that makes the Research Triangle Park business unusual is that the four-month old company already has a team of data scientists at a time when supply falls far short of demand. Burns and co-founder and CTO Zeeshan-ul-hassan Usmani were Eisenhower Fellows. They were more interested in applying their predictive analytics platform to education issues such as identifying the cause of North Carolina universities’ dropout rate. The methodology behind Its flagship product, Hourglass, combining publicly available data and private data with a focus on human behavior resonates with other industries, particularly retail. It was approached by a national health system, impressed with the credentials of its fast growing staff of data scientists. About 20 have Masters degrees and five have PhDs and its continuing to add staff.

Since then it has attracted clients interested in using its services for everything from detecting Medicare fraud to predicting and improving patient outcomes. They use the data in different scenarios to predict outcomes. It’s worked with a clinic with a large percentage of no shows for follow-up appointments. For people who rely on public transportation, rainy days can delay transportation so we thought that might be a factor. But it turned out the reason was not the rain or transportation at all. It found there were a disproportionate number of cancellations by patients who had appointments with a couple of doctors or a nurse practitioner. There could be a lot of reasons for this beyond demeanor but the important thing is the tool was able to point to a potential problem at the clinic.

Predictify.me had started to raise a $1 million seed round but Burns said it stopped at $250,000, according to a Form D filing. Burns said it stopped because it just didn’t need all that funding. The number of clients it has gained in recent months minimized its capital needs.

Burns is very specific about the kind of clients it likes to work with. The size of the company isn’t an issue — it is comfortable providing a product with a price tag that’s approachable for small and medium-sized businesses. It prefers clients that are interested in collaborating and it prioritizes these clients. It makes sense since the contracting company can offer more insight on the data. “Someone who will pay us 5 times as much but doesn’t want to talk with us isn’t as valuable as a client that doesn’t mind a conversation as part of the process.”

“When you are looking at simulation environments that combine internal and external data requires a bigger commitment. It doesn’t mean that they need to have all the answers but they have to be willing to talk.”

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Burns doesn’t come across as someone who underestimates the challenge of making accountable care organizations and other tenets of healthcare reform work. I point out that if the data scientist need in healthcare is as significant as he thinks, it will take many years before ACOS can be effective on a wide scale. But he’s also an optimist. “If outcomes based care is going to work, companies have to be flexible, agile and it will require a tremendous amount of hard work and collaboration.”

Predictify.me is a global company, Burns emphasizes. Its healthcare division will have members in the US, but it’s just as likely have an office in Cambodia and other countries. It is also looking at adding 21 countries.  Having a global strategy will no doubt help its data scientist hiring and puts it in a solid position to grow its healthcare business.

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