I read an article the other day that has me reminiscing. The article compared the experience of two medical groups adjusting to the accountable care organization (ACO) model. One group will receive nearly a $1M bonus for achieving a variety of quality outcome metrics while the other remains uncertain how they will be affected – good or bad. Executive vice president of the latter organization, Tony Slonim, M.D. lamented, “It sounds ridiculous, but we have no sense of how much we might get. We know our average spend and we know our performance, but with case mix adjustment, it remains theoretical.” Dr. Slonim and colleagues are not alone.
Accountable care is the latest incarnation of a decades old concept to reward care quality over quantity. In the nineties, insurers attempted to transfer risk and reward for managing patient populations to providers with “capitated care.” Under this model, providers received a fixed fee per member per month, whether or not they saw each patient. Logic dictated that physicians would have the financial motivation to improve patient health thus reducing visits and procedure volumes and lowering the total cost of care.
Unfortunately, where capitation excelled in theory, it struggled in practice. While payers were equipped to assess their own risk, providers, lacking actuarial skills and data, were faced with taking on unknown financial risk. Consequently, many physicians elected not to participate and capitation lost its fan club.
Today, physicians are experiencing déjà vu as accountable care organizations (ACOs) are reintroducing the premise of shared risk for population health management. However, thanks to the adoption of electronic health records (EHRs) and advances in information technology, there are reasons to believe that the ACO model can achieve coordination of care, improved outcomes, and lower total costs – without asking physicians to leap blindly into shared risk.
Technology like Clinithink’s CLiX Clinical Natural Language Processing (CNLP) platform is one of the more auspicious advances to help providers profitably assume and manage risk under an ACO model. CLiX can provide the information needed to help providers successfully assume and manage risk under an ACO model. Consider one way in which CNLP may enable providers to analyze the collective data of their patient population:
A paradigm for the ACO movement, the Centers for Medicare and Medicaid Services (CMS) is using the HCC Model (Hierarchical Conditions Categories) to define risk scores for the Medicare Shared Savings Programs, which is essentially a government sponsored ACO. Understanding that insurers would be inclined to cherry-pick the healthiest seniors, CMS leveraged its vast data resources to assess patient populations and establish risk–adjusted premiums based on individual patient health status. Using the CMS-HCC risk adjustment methodology, CMS is able to offer higher fees for the care of “sicker” patients thus compensating physicians who choose to accept relatively more risk. Yet, until providers like Dr. Slonim are able to assess how each patient measures up to a risk-adjusted model, they are exposed to risk with little control in the matter.
Using the power of the CLiX CNLP platform, a provider can establish appropriate risk premiums for their patients by identifying all relevant diagnosis regardless of where that information is stored in the medical record – analyzing clinical narrative, medical history, as well as codified data. This is an impossibly large and expensive task for humans to perform. Once a complete analysis of the patient’s risk profile is complete, providers can match the profile to the risk-adjusted payment schema and ensure that they are optimizing their compensation.
While the ACO path may feel like a walk down memory lane, today’s providers are better able to equip themselves for effective risk-sharing with payers. Armed with technology like CLiX, providers no longer need to blindly leap into accountable care but rather can prepare to boldly step forward, comfortable with well calculated risk.