This article appears in the November issue of HealthLeaders magazine.
Without robust analytics technology, the goals of accountable care and population health cannot fully be achieved, good intentions notwithstanding. ACOs must correlate clinical data and claims data and use analytics technology to produce the actions needed to manage the health of a population. The data is there, but the healthcare industry does not have an evenly distributed knowledge of how to use it effectively.
With potential savings of up to $300 billion a year, according to the consulting firm McKinsey & Company, the upside of industrywide analytics to manage a population is considerable.
And, increasingly, providers have the raw data they need to feed an analytics system. But it is not as simple or quick as installing electronic health record technology—no small feat in itself for many organizations—and must be accompanied by solid governance and education, according to leading providers.
These providers are using analytics to bring a more intense focus on gaps in care, to discover cost outliers, and to put a magnifying glass on efficiency. But the use of such healthcare analytics has yet to reach maturity.
Early in the process
"Our organization is facing what most of the industry is facing, and that is the need to build a bridge to the future through analytics; so unlike some other industries that may be high users of data and very sophisticated, the healthcare industry is just in a different point," says Aric Sharp, vice president of the accountable care organization at UnityPoint Health, a West Des Moines, Iowa–based integrated health system with 3,026 licensed beds across 15 hospitals and total operating revenue of $2.7 billion.
"We're still in the process, as an industry, of going through implementing electronic health records and achieving meaningful use and those types of things. At the same time, with a lot of the new efforts around accountable care organizations, for one of the first times many providers have an opportunity to collect claims data by working with payers," Sharp says. "We felt it necessary to build a platform where we can mesh together both claims data and data out of our electronic health records, because there's a lot more that's able to be learned in that type of an environment. The type of intelligence that we can glean is at a much more informed level than if we're just dealing with one of those data sets in isolation."
UnityPoint Health typifies numerous providers, having initiated analytics for its population health initiative only a couple of years ago. "The primary lesson is, this is really difficult, and there's a lot to learn along the way," Sharp says. "And yet, we can certainly see that as we continue to enhance the work, there's more and more benefit with every step. The big learning is that there's just a lot to be learned, and it's exciting, because with every step of the process, we are better able to identify opportunities to improve care, and we're able to become more efficient at this type of work."
At the heart of population health analytics is the concept of risk stratification: understanding, through various inputs such as claims data, surveys, and EHRs, which members of a given healthcare organization's customer base represent a level of risk for which intervention offers the greatest possibility of preventing future hospital admissions, reducing readmissions, improving overall health, and lowering costs.
UnityPoint Health selected analytics technology from Explorys, a data spinoff of Cleveland Clinic founded in 2009.
"Explorys is able to pull data from a variety of sources—multiple electronic health records, our own billing systems, claims data from CMS or other payers—and assimilate that all together," Sharp says. "Explorys is really what sits on top of that and gives us an ability to slice and dice and analyze it and probe it and report quality metrics, identify gaps in care, and in the future even use that to do outreach to patients and do registry-type functions."
UnityPoint Health still counts the time until the big payoff in years. "We're not yet ready to say that it has an impact on our global per-member per-month spent," says Vice President of Operations Kathleen Cunningham. "It will, but we are so early in our innovation that some of our results are really based on the pilot type of innovation programs that we're working on."
Starting with employee populations
In many healthcare systems, population health analytics success stories are just beginning to emerge, but some providers have used their own employee populations as a proof of concept for the effectiveness of the effort.
For the past 11 years, employees of Adventist HealthCare—a nonprofit network based in Gaithersburg, Md., with three acute care and three specialty hospitals, 6,263 employees, and 2012 revenue of $726 million—have been managed for risk by the self-insured provider.
"It got started with the idea that a healthier population is going to be a more effective employee population, and it's going to also be a lower-cost population," says Bill Robertson, president and CEO of Adventist HealthCare.