Skip to content Skip to navigation

Healthcare Analytics: Moving from Setup to Use Cases

July 22, 2015
by David Raths
| Reprints
Pioneer patient care organizations are moving rapidly to leverage analytics for population health and accountable care. What have they been learning in the process?

Over the last several years, Healthcare Informatics has had the privilege of covering the birth and toddler stages of the data analytics movement in healthcare. Our editors have spoken with the chief information officers and chief medical information officers of some of the pioneering organizations as they set up data warehouses and data marts, the robust business intelligence capabilities, and data governance and quality initiatives. The exciting thing now is that even though many organizations continue to struggle with setting up programs, those leading organizations are moving from talking about analytics to actually applying it to multiple use cases.

Keith Figlioli, senior vice president of healthcare informatics at Premier Inc., recently spoke with Healthcare Informatics during the meeting of his organization’s Data Alliance Collaborative, which includes representatives from 12 of the largest health systems across the country.

In the past year things have matured fairly rapidly, said Figlioli, who heads up Premier’s enterprise technology and data initiatives. “The discussions we have been having over the past three years have all been about big data and merging claims and clinical, for both operational and population health reasons. The discussions we have had over the last 48 hours have been so fundamentally different from even a year ago,” he said. The provider organizations are moving away from just setting up the infrastructure. In large systems, they invest six months to a year to set up the infrastructure to begin to do analytics. It takes a considerable amount of time to get enterprise data management and governance in place, he said, but these organizations are now beyond that. “You are seeing them proliferate the use cases throughout the whole organization,” Figlioli said. “It is exciting.”

Keith Figlioli

One example Healthcare Informatics has highlighted is the 42-hospital Carolinas HealthCare System, which has devoted considerable resources to its efforts to leverage analytics capabilities to support population health initiatives. The organization has done some impressive work with predictive analytics on cutting readmissions related to chronic obstructive pulmonary disease.

But if the leading health systems are poised to make great progress, other organizations still have hurdles to get over. Most have developed some strategy they are trying to execute that involves an enterprise data warehouse, and combining claims, clinical and supply chain data, says Judy Hanover, research director at research firm IDC Health Insights. “They are trying to assimilate that information. Most have a number of different repositories and different degrees of success combining that information.”

Data Quality and Governance

Data quality is a huge issue, Hanover adds. Free-text notes in the EHR are unstructured, and the approaches to unstructured data are much further behind approaches that look at structured data, she says. “The value in unstructured data is clearly there for organizations that choose to tackle it, whether through text analytics or natural language processing.”

“It is astounding how many organizations are creating enterprise-wide data warehouses and dumping in massive amounts of data without knowing about the stewardship of each data source,” says Larry Yuhasz, senior director of innovation and population health at Truven Health Analytics. There are huge challenges around attribution, matching data to the right patient and the right physician, he says.

Figlioli agrees that data quality is a key problem. “We just interviewed 50 to 75 people, ranging from executives down to analysts, and the No. 1 issue they mentioned by far is data quality. It was two standard deviations away from all the other issues. Even somebody with a single instance of Epic or Cerner, and a single instance of an ERP, once they pull all that together, they start realizing how bad that clinical data is,” he says. Even more complicated is pulling data from affiliates into your system. “They are having all sorts of challenges intermingling with other pieces of data. It is a pretty complicated set of issues once you start getting into it,” Figlioli says. By far the No. 1 thing we talk about with members is data governance. We are seeing professionals come from other industries, such as consumer product goods and insurance, migrate to healthcare because they understand data governance.”

Analytics Talent Shortage

In fact, the dearth of data analysts is a massive problem, Figlioli adds. “It is the single biggest gap across the industry right now. Even if you get the infrastructure and data governance in place, then what?” he asks. “The data is served up. Do you have front-line analysts who know how to do this?” He jokes that if you changed your title to data scientist on LinkedIn, you would have 50 job offers by the end of the day.

One reason these jobs are tough to fill is that the executives need not only analytics training, but also clinical and operational expertise and skills in being able to wed together data from different functions, Hanover says. “They have to understand the origins and elements that are contained in that data and how to be sensitive to that and put it together in a way that yields meaningful results,” she adds. “That is a rare skill set, and most successful programs have a really skilled person at the helm.”