When Alex Barclay received his Ph.D. in mathematics from the University of California San Diego in 1999, he was already well on his way to a career focused on big data. Barclay brought his interest and expertise in analytics to Fair Isaac, a software analytics company, and then Experian, the credit reporting company.
Then, two years ago, he joined UnitedHealthcare and brought his experience with Big Data and a mature analytic environment to help advance the company’s Payment Integrity analytic capabilities.
UnitedHealthcare offers the full spectrum of health benefit programs to individuals, employers, military service members, retirees and their families, and Medicare and Medicaid beneficiaries, and contracts directly with more than 850,000 physicians and care professionals, and 6,000 hospitals and other care facilities nationwide.
For Barclay, as Vice President of Advanced Analytics for the company’s Payment Integrity organization, these numbers translated into huge amounts of data flowing into and through the company. When he first surveyed the internal big data landscape, he found an ad hoc approach to analytics characterized by data silos and a heavily rule-based, fragmented data environment.
“In the other environments I’ve been in, we had an analytic sandbox development platform and a comprehensive, integrated analytic delivery platform,” said Barclay. “This is what I wanted to set up for UnitedHealthcare. The first thing I did was partner with our IT organization to create a big data environment that we didn’t have to re-create every year – one that would scale over time.”
The IT and analytic team members used Hadoop as the basic data framework and built a single platform equipped with the tools needed to analyze information generated by claims, prescriptions, plan participants and contracted care providers, and associated claim review outcomes. “We spent a about a year pulling this together – I was almost an IT guy as opposed to an analytics guy,” Barclay added.
Rather than tackling a broad range of organizational entities, Barclay has concentrated his team’s efforts on a single, key function – payment integrity. “The idea is to use analytics to ensure that once a claim is received we pay the correct amount, no more, no less, including preventing fraudulent claims,” he said.
The Payment Integrity organization handles more than 1 million claims every day. The footprint for this data is about 10 terabytes and growing, according to Barclay. It is also complex; data is generated by 16 different platforms, so although the claim forms are similar, they are not the same and must be rationalized.
Another major challenge to revamping the organization’s approach to big data was finding the right tools.
“The tools landscape for analytics is very dynamic – it changes practically every day,” said Barclay. “What’s interesting is that some of the tools we were looking at two years ago and rejected because they didn’t yet have sufficient capability for our purposes, have matured over time and now can meet our needs.”
“It’s working,” said Barclay. “We have been able to help identify mispaid claims in a systematic, consistent way. And we have encouraged the company to embrace big data analytics and move toward broadening the landscape to include other aspects of our business including clinical, care provider networks, and customer experience.
“We emphasize innovation. For example, we apply artificial intelligence and deep learning methodologies to better understand our customers and meet their needs. We are broadening our analytic scope to look beyond claims and understand our customers’ health care needs. We’re really just at the beginning of a long and rewarding Big Data journey.”
When asked if he had any advice for his fellow data analysts who might be implementing a big data analytic solution, Barclay replied: “Be patient. Start slow and grow with a clear vision of what you want to accomplish. It you don’t have a clearly defined use case, you can get lost in the mud in a hurry. With Payment Integrity, in spite of some early challenges, we created something that is real and has tangible payback. We always knew what the end game was supposed to look like.”
Originally published in Datanami:
Keeping an Eye on the Analytic End Game at UnitedHealthcare
September 7, 2015