We'll also see increased participation in Alternative Payment Models (APMs) such as CPC+. And if the current federal approach persists, providers will feel the impact of MACRA—even for their populations that aren't at risk.
For many providers, the idea of taking on risk is scary and complicated. But in our work helping health systems across the country optimize their EHRs to better manage risk, we've found that managing risk can be quite straightforward if you can do three things well: 1. Document (and get credit for) the care already being provided to at-risk populations; 2. Identify the unrecognized risk within the patient population through proactive care management; and 3. Hardwire chronic care management for at-risk populations.
Step 1: Document (and get credit for) the care already being provided to at-risk populations
I almost always recommend that health systems jumpstart their entire risk management strategy by focusing on improving the accuracy of their hierarchical condition categories (HCC) documentation. For those unfamiliar with HCCs, they are the mechanism used by Medicare and some commercial payers to determine patients' risk adjustment factor (RAF) scores based on their demographics and chronic conditions.
Accurate HCC capture can inflect everything from care quality to greater reimbursement. It is typically a low impact and scalable initiative to get off the ground, and health systems can use any additional revenue from HCC documentation to fund other components of their long-term risk strategy.
The best way to improve the accuracy of risk documentation is by optimizing a tool nearly all providers already use—the EHR. I'll share an example of this in action.
At one of our health system clients in the Northeast, a patient was going into pre-op for a surgery when the physician received HCC alert in the EHR. The alert informed the physician of an aneurysm the patient had years ago. Though this physician had never treated the patient for the aneurysm, and was previously unaware of it, the HCC alert enabled the physician to adjust the patient's care plan to account for the aneurysm moving forward.
Step 2: Identify unrecognized risk within the patient population
Trying to manage a population's care gaps without first identifying the full burden of chronic illnesses is like checking the box without reading the fine print. You can have a million-mile view of your population's relative health and risks, but actually managing patient care over the long term will be difficult without a clear understanding of the individual patients' health challenges.
One way to identify underlying patient risk is through the Annual Wellness Visit (AWV). AWVs are visits covered by Medicare during which providers perform a health-risk assessment and discuss a patient's preventative services for the coming year. This brings patients into the physician's office on a regular basis, where a provider can address any of the patients' chronic conditions and identify and monitor other at-risk conditions.
Once patients come in for the AWV and their conditions are identified, it's important to accurately document that risk. This is another reason I recommend health systems tackle improving documentation accuracy first, so when they proactively bring patients in for their AWV, the documentation solution is up and running.
Step 3: Hardwire chronic care management for at-risk populations
Mechanisms like HCC capture and AWVs are designed to be checked off on a yearly basis to support how Medicare calculates RAF scores for reimbursement. Each year's payment rates are based on the prior year's performance, and HCCs must be documented every year to contribute to total RAF.
The ultimate objective, though, is not about enhancing RAF capture. The focus is on enhancing patient care by consistently managing chronic conditions, and to truly master chronic care management, providers must use EHR-enabled HCC capture, AWV capture, and other tools to proactively identify and treat the highest risk patients within a population. That means developing an infrastructure that supports risk stratification of an entire population, and helps providers prioritize patients based on the highest risk and unmanaged burden of care.
Taking a data-driven approach to monitoring and managing patients with the highest disease burden is a foreign process to many providers, but it can drive value. For example, my team builds a series of high risk targeting reports that help providers identify and treat patients who may be missing diagnoses for chronic conditions.
One of those reports specifically identifies patients who are on oxygen but don't have any recorded conditions that would justify that treatment. With the report, providers can prioritize outreach to these patients to schedule follow up visits as necessary to correctly diagnose and document care gaps.
But technology is not adequate by itself; there are never enough discrete data in the records, and natural language processing systems aren't sophisticated enough to be highly accurate. We still need clinicians to review charts, and they are a much scarcer resource. The right technology just helps us to efficiently leverage this resource to make the most of their efforts.