HHS' partnership this month with OptumLabs is likely to add momentum to the push to use big data to lower healthcare costs and improve the quality of care delivery.
But the announcement also highlights the divide between health systems that are ahead of the curve in using sophisticated data analytics and those that are still learning how to collect and use the information.
For several decades, many industries, such as financial services, have used data to determine strategies for everything from marketing to maintenance scheduling. But the healthcare sector as a whole has lagged in using analytics to become more informed about its customers, their behavior and how to influence it.
“Clearly there are some that are really innovative, but most systems are just starting this journey,” said Murtuza Mukadam, global head of healthcare strategy and solutions at consulting firm Virtusa.
Through the recent partnership, HHS agencies will gain access to OptumLabs' big-data resources, including de-identified medical claims and clinical data. OptumLabs is a research and innovation center established by UnitedHealth Group's Optum health-services division and the Mayo Clinic.
The first research project, which will be led by the Agency for Healthcare Research and Quality, will compare the results of the government's Medical Expenditure Panel Survey to OptumLabs' real-world claims data.
Large systems like Mayo and Geisinger Health System, which in 2013, launched its own technology and analytics group xG Health Solutions, are starting to apply their research findings to changing their standards of care. Geisinger has spent $200 million on information technology over the past two decades. But by using data to identify its sickest patients, it has been able to reduce hospital admissions by 27%, the system reported at a 2014 health IT conference.
Others are just starting to dabble in data analytics. Until recently, healthcare IT efforts have centered primarily on adopting electronic health records, or the digital infrastructure, as the government has pushed that transition through meaningful-use payments, Mukadam said.
Only recently have providers shifted their focus to analytics. As they take on more financial risk for care outcomes, they are finally seeing a financial incentive to make the investment.
“People are waking up,” said Munzoor Shaikh, a director in the healthcare practice at consulting firm West Monroe. “People are just starting to understand the problem a little bit better.”
Yet one of the biggest barriers to effectively using data analytics is confusion over how much and what type of data to collect, according to a survey from Stoltenberg Consulting, which collected responses at this year's Health Information and Management Systems Society meeting.
Just over half of respondents (51%) cited “not knowing how much or what data to collect” as the biggest hindrance to using data analytics, while 33% said their organizations didn't know what to do with the data they collected. Even the term "big data" elicited confusion, because it can cover anything from administrative data, to clinical data, to patient-generated data.
Health systems are generating terabytes upon terabytes of data each day through many sources, Mukadam said, from claims data to physicians' EHR notes, to data from consumer mobile and wearable devices.
“If there's no good, low-cost way to capture that data, it's going to be pointless,” he said. “I don't think hospitals are even there yet. A lot of them don't even have the right skill set.”
A number of vendors have stepped in to help providers manage the load. Mukadam pointed to Hadoop, an open-source platform, which makes it cheaper and simpler to build data lakes.
Quest Diagnostics is targeting the physician market through a partnership with Inovalon, an analytics software company. Quest reaches about half of the country's physicians through its laboratory services.
The partnership's on-demand Data Diagnostics service goes live at the end of the year, said Dan Rizzo, Inovalon's chief innovation officer.
“You're still in this world of big-data application,” he said. “It's amazing how much data are still in a disconnected EHR. Where you will see it picking up is when you have entities bringing together different sources.”
While bigger health systems and insurers tend to have more data, they're not necessarily using it more effectively, Shaikh said. For organizations both large and small, the best place to start is by zeroing in on just one question they're trying to answer, like identifying the cost of care for a given condition in the Eastern region of the U.S., for example.
“We call it small data,” he said. “You have to prove the value in a small and directed way. Don't invest in everything all at once; invest in something where you can really make a difference.”
