Member Data Matching Key To Risk Adjustment Program Success
Poor patient (or member) matching plagues every single component of healthcare, often in different ways and with varying degrees of visibility into the problem. This article focuses on just one healthcare initiative that suffers from poor patient matching: your healthcare organization’s risk adjustment program.
Did I lose you at risk adjustment? Let me explain.
In short, risk adjustment is meant to redistribute funds so that plans with sicker (or higher risk) members receive more than plans with healthier (or lower risk) members. Each line of business (Health Exchange, Medicare and Medicaid) has a slightly different approach, but the goal is the same.
In order to communicate the burden of illness of a payer’s population, each payer must “risk score” each of their patients. To oversimplify, there are two major components to a risk adjustment program: (1) capturing accurate diagnoses for members, and (2) ensuring proper documentation in case of audit. There is a lot that goes into understanding an individual’s risk score, most notably all the diagnoses that each patient may have.
The foundation of the program lies on a critical question, one that must be sorted out primarily through data: What is the current health status of the members enrolled in my plan?
To answer this question precisely, payers need a well-defined view for the clinical status of each member – a record that holds a complete history of that members’ illnesses, lab scores, treatment plans and diagnoses. They need to be able to match lab scores done at one facility to the HCC code taken at the point of care, to other risk adjustment scores given in prior encounters when that member belonged to a different plan, or even to the same plan but with a different member ID.
Typically, a lot of data falls through the many cracks when trying to exchange, gather, and associate all of this data to the correct members. Existing approaches attempt to associate incoming data with existing members by matching the incoming data with member IDs or other demographic data in the payers’ record for that member. But these matching approaches fail whenever a member has moved, changed last names, had a birthdate entered in wrong, or whenever the demographic data is sparse or contains other common errors.
When data falls through the cracks, it ends up hurting the risk adjustment program. For example, by not accurately matching a supportive A1c lab to a member record with a diabetes-specific HCC, payers open themselves up to audits – or worse.
But there is a powerful new matching technology – called Referential Matching – that can match all of your data to the correct members despite radical differences in demographic data (like name and address changes) and despite very sparse demographic data (like on labs). Rather than directly comparing the demographic data from two member records to see if the records match, a Referential Matching approach compares the demographic data from those records to its comprehensive and continuously-updated reference database of identities. This database contains over 300 million identities spanning the entire U.S. population, and each identity contains a complete profile of demographic data – including nicknames, aliases, maiden names, common typos, past phone numbers, and old addresses. Because of this, a lab record with very sparse demographic data and a member record with an old address will both match to the same reference identity – and therefore to each other.
Payers can instantly harness the power of Referential Matching by using a cloud-based plug-in that integrates with MDM technologies to improve member matching. In fact, using Referential Matching automatically resolve 50-75% of the “suspect duplicates” that your MDM has flagged as tasks for manual resolution.
By solving their member matching issues, payers finally have a clearer understanding of their members’ health, and the success of their risk adjustment program will reflect that.
And once payers (and providers for that matter) have a better view of the clinical state of the people in their care, the care delivered can be of much higher quality, and will have lasting impacts on health status far beyond the advantages of a successful risk adjustment program.