A Member-Centric Approach Health Plans Can Use to Improve Hierarchical Condition Categories (HCC) Coding Accuracy

I can recall when chart chasing was the go-to solution for Hierarchical Condition Categories (HCC) coding. In case you missed that era in healthcare, health plans sent teams of nurses to physician offices to review thousands of patient charts to better document care such as assessing adult BMI and measuring hypertension. Suffice it to say that it was a frustrating and expensive process.

There is a better way, a member-centric way that benefits the treating physician, the health plan, and patients in need of care, especially the chronically ill.

As I’m sure you know, chronic disease has reached epidemic levels among Americans. Nearly half of adults have at least one chronic illness, such as diabetes, hypertension, or heart failure. Among seniors, the prevalence is 80 percent, and a staggering 68 percent have two or more chronic conditions.

Chronic illness can decrease the quality and length of life, especially when not appropriately managed. Complicating the efforts to care for the chronically ill is the fact that many people are unaware of their condition. Take diabetes, for example. Of the more than 37 million who have diabetes, one in five doesn’t know they have it. Nearly 100 million have prediabetes, and more than 80 percent don’t know it. Similarly, one-third of adults with hypertension are not aware of their condition and are not being treated to control their blood pressure.

Chances are if the patient is unaware of their condition, the physician and health plan are too. It also likely means that the HCC coding for this patient is inaccurate and may also mean the patient is not receiving the recommended care for his or her condition(s).

Why HCC Coding Matters

HCC coding is a “risk-adjustment model originally designed to estimate future healthcare costs for patients…that helps communicate patient complexity and paint a picture of the whole patient.” HCC coding and the resulting risk scores determine reimbursement. An accurate reflection of the health of a plan’s member population means the payer knows which members need care and that there are sufficient funds to provide the care. In short, accurate HCC coding improves care delivery and aligns health plan reimbursement to member care needs. Given the increasing prevalence of chronic illness, accurate HCC coding benefits just about all of us.

NextHealth’s Medicare Advantage HCC Work with a Regional Blue Plan

A regional Blue plan sought to ensure its reimbursement properly reflected the risk within their Medicare Advantage population. A predictive analysis of its population revealed a gap between the actual and predicted number of patients with HCC codes that would affect risk adjustment. The plan attributed the gap to a lack of a Centers for Medicare and Medicaid Services (CMS) qualifying “face-to-face” visit for risk adjustment based on CMS standards.

The health plan chose NextHealth to help them determine which members to target for engagement to ensure members were visiting their physicians and that those visits were accurately coded. NextHealth used its four-step approach to deliver results:

  1. Identify and baseline: Target members with predicted HCC codes.
  2. Segment population: Use advanced analytics to cluster members who were likely to respond best to different outreach methodologies.
  3. Launch member outreach: Contact members to schedule a face-to-face physician visit.
  4. Measure and optimize: Determine which members scheduled an annual wellness visit via the concierge appointment system, which members scheduled a visit on their own and the unreachable members. Apply learnings to personalize future outreach and engagement efforts.

The results are promising.

Health plan members where outreach efforts were successful saw a fivefold increase in annual wellness visits (AWVs). AWVs yield important patient information, including medical and family history, health risks, and specific vitals like blood pressure, height, weight, body mass index, or waist circumference, and support the diagnosis of chronic conditions such as hypertension and diabetes.  For this plan, the AWVs uncovered numerous missing HCC codes for conditions, with the largest increases found in Cancer, COPD, Congestive Heart Failure, Specified Health Arrhythmias, and Acute Ischemic Heart Disease. Accurate coding of these conditions benefits the plan in the form of reimbursement aligned to the care needs of its members. It also helps members who subsequently receive appropriate care for coded conditions.

This type of client work is particularly gratifying to me. Not only are we identifying the people who need care, but we also are ensuring our health plan clients have the appropriate funds to provide the care. At a higher level, our work is helping to find the story in the health plan’s data and then using those insights to positively impact member behavior.

As a healthcare research analyst at NextHealth, I support our clients when they are designing their statistically valid healthcare intervention programs. I create specifications and proofs-of-concept data queries for deriving healthcare-related metrics and KPIs from client data. I completed my bachelor’s degree in research and experimental psychology at the University of Portland and received my doctorate in Philosophy in Behavioral Neuroscience from Purdue University.

To learn more about my work at NextHealth Technologies, read the case study about the Blue plan that improved its HCC coding.

Matthew Powers, Ph.D., Healthcare Research Analyst

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