Kevin Krauth grew up in a healthcare family — his father was a doctor and his mother was a nurse. Krauth ended up working in finance and data analytics, but there was often a healthcare element involved. While employed at Blackstone, for instance, he worked on a deal between healthcare companies.
“Healthcare has followed me around,” he said in a phone interview.
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As he noticed an influx in the number of health apps in the market, he started considering how complicated it is for patients to manage all their healthcare data.
Thus, he co-founded Orderly Health, where he serves as CEO. The Denver, Colorado-based company, which is an alum of Techstars and 500 Startups, sought to offer a solution that allowed patients to aggregate their existing healthcare information.
“We got into Techstars on the idea of Mint.com for healthcare,” Krauth said.
Though Orderly Health was moving along, there were a few hiccups along the way. The company had to recruit every single data partner to feed the information into the application.
Krauth explained: “[T]he overwhelming sentiment was, ‘While the data you’ve given me was interesting, I don’t know what to do with it. I want somebody to help me navigate this mess.'”
So the Denver company began offering a tool that let patients get their healthcare questions answered. Users could text the startup’s AI-driven chatbot and get help finding a doctor, looking for insurance or shopping for medications.
Although users subscribed to Orderly and the company secured contracts with insurers, Krauth said that overall, the solution was “extraordinarily difficult to sell.”
Still, the company knew the following about its users: “The number one reason they’d come to us at all was to find a doctor,” he noted. “If our data was wrong, we were doing them a disservice. We had to come up with a way to find out that the data was accurate.”
That’s why Orderly Health is shifting its focus to improving provider data accuracy through its solution called OrderlyData. The tool leverages more than 30 public and proprietary data sources to ensure information is up-to-date. It then utilizes machine learning to pinpoint errors and repair the data. Finally, it returns the updated and corrected information.
“We correct doctor data for insurance companies,” Krauth summarized.
The ultimate goal is not only to assist insurers as they deal with this problem but also to ensure patients have the most accurate information as they make health decisions.
Photo: Jane_Kelly, Getty Images
