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Targeting Chronic Illness Together

April 18, 2012
by David Raths
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Health plans support providers through predictive analytics

Executive Summary:
Health insurers that used to focus on individual claims are now looking across their entire membership to better define the appropriate level of financial risk to assign and to drive the right kinds of interventions. Many are making a determined effort to share their predictive analytics findings with clinicians.

Can predictive analytics software help health insurers and clinicians identify which patients could most benefit from more attention and better-coordinated care? As the patient-centered medical home and accountable care organization movements get rolling, provider organizations are starting to work more closely with insurers to manage population health for improved clinical outcomes, and the predictive tools may play a key role.

Payers have some experience using predictive analytics (also sometimes called clinical analytics) to stratify group populations by risk, identify high-cost conditions, and approach members for participation in disease management programs. Of course, integrated health systems, especially those with affiliated health plan entities, such as Kaiser Permanente (Oakland, Calif.) and Geisinger Health System (Danville, Pa.), have an advantage when it comes to sharing data. Most payer and provider organizations have to overcome cultural, financial, and technical differences.

On their own, the data systems of providers and insurers each have their strengths and weaknesses, explains Dan Coate, a principal with Pittsburgh-based consulting firm Aspen Advisors. Payers must largely rely on claims data. “The good news is that it is structured data. They can search it and parse it out,” Coate says. “The bad news is that it is often delayed. By the time they do analyses and run a report, it may be five or six months since an identified member was supposed to have that hemoglobin A1c test,” he says.

Providers are starting to use the more real-time data from their clinical systems to search for gaps in quality of care, but the data in those systems is currently less codified, notes Coate. “There is no way to quickly run analytics on it to see gaps in care. You can write a wonderful clinical note, and I could read that about the one patient, but I can’t yet search across all your patients with a large-scale analytics engine.”

To cross the divide, medical executives of some payer organizations are making a determined effort to identify trends and find ways to share their findings with clinicians as those provider organizations take on more risk.

One such pioneer is Brian Wolf, M.D., senior medical director for medical affairs for the Providence-based Blue Cross & Blue Shield of Rhode Island. “There are aspects of healthcare where if we are more efficient in identifying patients and have a free flow of information about best practices, we can eliminate money wasted on missteps such as repeat procedures,” he explains.

Brian Wolf, M.D.

Starting with models developed at Johns Hopkins University, Wolf’s analytics team came up with risk-adjusted scores to identify “impactable” patients who have complex conditions such as cancer or severe behavioral health issues. Starting in 2008, Blue Cross & Blue Shield of Rhode Island began sharing this data and helping groups of local providers set up patient-centered medical homes following the concepts delineated by the National Committee for Quality Assurance (NCQA).

“We now have 200 practitioners in five large physician groups in the patient-centered medical homes,” Wolf says. The insurer also pays to fund on-site case managers and in some cases co-located behavioral health specialists. Although they haven’t published any findings yet, Wolf says the early research suggests that the patient-centered medical homes have been able to lower rates of hospital readmission, especially among the complex patients the predictive analytics identified. “Readmission is a good measure,” Wolf says, “because it tells you that probably there wasn’t good resolution of the complaint initially.”

Tufts Health Plan
For their Medicare Advantage offering, executives at Tufts Health Plan in Watertown, Mass., realize that disease management efforts are labor-intensive and expensive, because they involve more direct interaction with members. “So we want to make sure the interventions work and that we are targeting the right population in order to get the return on investment,” explains Jonathan Harding, M.D., senior medical director of senior products.

“We needed to have a predictive model to identify the members to target,” he recalls. “We went to the vendor of analytic software used on the commercial side but weren’t impressed with what they had because it was not designed for the Medicare population, so we developed our own product.”

Harding and his team had to work through many variables before identifying what was truly predictive. They rejected some ideas that seemed intuitive, such as the number of doctors seen in a given period, which might indicate discontinuity of care. “It didn’t turn out to predict anything,” he says. But recent hospital admission or re-admission, age above 80, or a recent fall all were good predictors. “We came up with this complex report that lists for physicians all patients at high risk of being admitted or re-admitted. And it gives the member a score.”

In the fall of 2010, Tufts began sharing the data with 100 provider entities. Only a small number are NCQA-certified medical homes, but that number is increasing. For the most part, the response from physicians has been positive, Harding says. “Some were doing similar things on their own and stopped because they liked our list,” he says. “Some complain about the three- or four-month time lag. They want to get upstream of that. But we can only give them what we have. They have to supplement that with their own data that is more current.”

QualChoice, a small health plan in Arkansas that had been hospital-owned but is now independent, is piloting a patient-centered medical home, and the idea is to get the primary care providers information about their patient population before they reach the stage of being catastrophic, says Richard Armstrong, M.D., the plan’s vice president of medical affairs. QualChoice is just starting to use Clinical CareAdvance, a solution from the Denver-based TriZetto Group, which allows payers to manage members who have chronic diseases by coordinating resources, automating manual processes, and identifying and stratifying at-risk members for support across the care continuum.

But for any of these tools to help clinicians, Armstrong believes, the flow of data between payers and providers has to improve. “For ACOs to flourish, they need to be able to do analyses and have actionable data,” he explains. “The reason capitation didn’t work before is because we just threw the money over the wall and said good luck. The providers couldn’t analyze how they were doing.” Payers have always had better health IT infrastructure for doing analyses, he adds. The trick is to figure out how to put it to use for clinical purposes.

TriZetto’s payer clients are still in the early stages of using these clinical analytics tools, says Jerry Osband, M.D., vice president of product management at The TriZetto Group Inc. “What we found in the past was that different parts of payer organizations had different business intelligence needs, so we might find one payer organization with five or six different analytics vendors,” he notes. “Now they are starting to aggregate that data in a data warehouse and reducing the number of vendors they work with to one or two.”

Those payers who used to focus on individual claims are now looking across their entire membership to better define the appropriate level of financial risk to assign and to drive the right kinds of interventions, he says. “They can reach down to patients at lower levels of acuity,” Osband says, “and plan mitigations that will help prevent members from poor and expensive outcomes.”

Analytics for Wellness Programs
One integrated health system used its own employees in a test to see whether predictive analytics could have an impact on its wellness and disease management efforts.

Starting in 2007, Optima Health, the insurance arm of Sentara Healthcare in Norfolk, Va., used Risk Navigator analytics from the Orlando-based Elsevier/MEDai to support Sentara employees with customized prevention programs.

Karen Bray, Ph.D., R.N., vice president of clinical care services for Optima, reports that the “Mission Health” program both improved awareness and treatment of health risks such as high blood pressure and high cholesterol and bent the cost curve.

Karen Bray, Ph.D., R.N.

Optima officials created their own risk models. “We looked around the industry and didn’t really see a blueprint,” Bray says. “We took the claims and lab data we had access to and ran a bunch of what-if scenarios, and that allowed us to create some of the parameters and definitions for the program.”

Optima found that three conditions accounted for 14 percent of its costs: diabetes, coronary artery disease, and congestive heart failure. Employees identified as being at risk for such conditions received incentives of up to $600 per year for reducing health risks and complying with evidence-based guidelines. The employees worked with nurse case managers and health coaches.

By 2009 the program paid for itself and by 2010 Sentara saw $3.4 million in healthcare cost savings, Bray says. Since then, the program has been expanded to six or seven large employer groups in the area representing about 90,000 members.

Now Optima is using the analytics to show physician groups interested in becoming patient-centered medical homes some of the gaps in care they can address with Optima members. “Physician groups are doing a good job,” Bray says, “but once you pull the data apart, you can see where gaps in care are and reinforce the changes the physicians are making to their practices.”

Looking for gaps in care sounds good as a concept, but Aspen Advisors’ Dan Coate warns that is easier said than done. “We know a diabetic patient should have an A1c test every three months and it is easy enough to identify a population of diabetics and urge an intervention. But that is probably the easiest, most clear-cut example,” he says. Defining other gaps in care is not so easy. “I would say to health system CIOs you have to be careful with the definitions. I would push back to clinician teams that they must make sure the scenarios are defined in enough detail. You can say you want to look for high-risk members, but high risk of what? High medical costs? Low-quality treatment? Not being treated with evidence-based protocols? Before you send in five business intelligence guys to start drilling into the data, be sure you have your definitions and goals clear.”

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