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Mastering the Complexities of MSSP ACO Payment at Janesville, Wisconsin’s Mercy Health System

June 14, 2016
by Mark Hagland
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Ladd Udy of Mercy Health System shares his learnings around optimizing coding under the MSSP program

Things are moving forward on a number of fronts at Mercy Health System, a five-hospital, 80-clinic integrated health system based in Janesville, Wisconsin. In January 2015, the former Rockford (Illinois) Health System merged with Mercy to create the combined, community hospital-based system.

Significantly, both the old Mercy Health and the old Rockford Health were participants in the Medicare Shared Savings Program; the old Mercy had joined the MSSP program on Jan. 1, 2014, while the old Rockford had joined on Jan. 1, 2015. Senior leaders at the merged health system are currently preparing to bring the two ACOs together as a single ACO in 2017. That ACO will coordinate the care of 20,500 Medicare beneficiaries—11,000 from the Mercy ACO and 9,500 from the Rockford ACO.

Given the extensive involvement in the Medicare ACO program, there naturally is ample motivation among Mercy Health’s senior executives to optimize their organization’s reimbursement from Medicare. But as the health system’s executives have been learning, along with the leaders of all the other MSSP ACOs, there are nuances and complexities around MSSP benchmarking. One of those has been around hierarchical condition categories, or HCCs, which assess the health condition of the individual patient. Originally introduced by the federal Centers for Medicare & Medicaid Services (CMS) to risk-adjust Medicare Advantage payments to participating health plans, the use of HCCs in the MSSP program is proving challenging to participating ACOs.

The challenges built into the CMS-HCC model are ones that Ladd Udy, director of population health and ACO, and his colleagues at Mercy Health System, have steadily been unwrapping as they’ve been delving into care management under the MSSP program. Udy and his colleagues have partnered with the Salt Lake City-based 3M Health Information Systems, in efforts to optimize reimbursement under the system. Udy will be speaking on the topic “Using Hierarchical Condition Categories to Manage Population Health,” on June 28 along with Donna Smith, a senior 3M consultant, at the Healthcare Financial Management Association’s annual ANI Institute, to be held at the Venetian Sands Convention Center in Las Vegas.

Udy recently shared with HCI Editor-in-Chief Mark Hagland some of the learnings that have been gleaned so far from this work at Mercy Health System, and which will be the subject of his and Smith’s HFMA ANI presentation this month. Below are excerpts from that interview.

Let’s begin by discussing the core issues around hierarchical condition categories. What are the fundamental issues for ACO leaders?

Well, to begin with, our efforts are still relatively young, and it takes a year before you even have the data back from CMS. But the focus has been around the MSSP program’s use of the hierarchical condition categories, which show the prevalence of certain types of illnesses in our population. They use that model, which encompasses both quality and cost outcomes, and the payment element of the model involves the cost outcomes tied to our benchmarks. They [Medicare officials] look at three years of historical spending on a patient, and also apply a risk-adjustment factor to determine how healthy or sick your population looks based on your claims.

And so the HCC model tells them whether you’re hitting your cost benchmarks?

Well, they use the model to determine what your benchmark is in the first place. Then they give us our target, our benchmark, saying, based on the HCC and based on your claims, this is your benchmark.

So you’re using analytics to determine your accuracy of your spending on patients?

Well, to determine how accurately we’re documenting how healthy or sick each patient is.

So inevitably, becoming successful in this area takes you back to coding, then, correct?

Yes, it does. And the other tricky part of this model is that it resets each January 1. So for example, let’s say that a patient has a diabetes and has their foot amputated. And then we see them in the clinic and assess their condition and document that, and it goes into the encounter diagnosis. So the patient will be newly risk-adjusted. But on January 1 of the next year, that resets, and goes essentially to zero. So if we don’t assess a wound or amputation, it drops off the risk adjustment for that patient. So we have to make sure that we’re correctly risk-adjusting each patient.

Tell me about the mechanics of applying the solution. Are they difficult?

Well, getting it accurate and having accurate measurement, has been a significant challenge, not just for us, but for ACOs everywhere. Everybody who hasn’t participated in a Medicare Advantage arrangement until now is struggling with this. There is a small handful of providers who’ve been getting capitated payments from Medicare—and they probably have a pretty good handle on this, because they’ve worked with this. And this could be a difference between $800 PMPM [per member per month], versus $2,500 PMPM. On a fee-for-service basis, if we simply put down “diabetic,” we’ll get paid for the claim, but overall, our risk adjustment will be off. And that’s the problem we’re facing. Our data is telling us that our Medicare population is healthier than the average Medicare population by our significant amount.

So right now, you’re digging into this, and figuring out where the problems and gaps are, correct?


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