Risk adjustment has been around for many years now, but the terms “data governance,” “data integrity,” and “data definitions” were not mainstream until recently. Why is that?
The Affordable Care Act (ACA) forced plans to think outside the box when it came to many areas within their organization. One of those areas was related to data. Organizations needed to learn from a Medicare and Commercial perspective how to incorporate technology into their day-to-day activities to be more efficient and competitive. The advancements in healthcare technology and the usage of data analytics to drive business decisions have provided a new perspective about the way organizations view data. Coupled with the increasing demand from the Centers for Medicare & Medicaid Services (CMS) and the Department of Health & Human Services (HHS) around technological advancements and pristine data submissions, the demand for health plans to understand their data is accurate has skyrocketed.
This advancement within the industry has led to a deeper understanding for a lot of health plans about what truly drives risk adjustment. When you think about risk adjustment, most people gravitate to retrospective chart reviews, prospective assessments, provider education, and other various initiatives to ensure a member’s risk score is appropriately reflected. The common denominator with all of those initiatives is they utilize analytics to drive results, and analytics needs data against which to run the algorithms to calculate probabilities and outcomes―it’s a domino effect.
By utilizing “clean” data for analytics, the outcomes will be more accurate, leading to more valuable initiatives with less wasteful spending on probability gaps that may not exist. Clean data can be defined as data that is consistently verified to be up to date and accurate. This can be achieved in various ways but most successfully by the following three methods:
- Internal Controls
Internal controls focusing on end-to-end risk adjustment should be established. These controls will provide checks and balances to the data as it flows through the various transformations it encounters. Having controls in place allows a health plan to identify anomalies or possibly systemic data issues sooner so corrections to the problem can be implemented and clean data can be utilized for downstream processes. From an auditing perspective, having internal controls and corresponding processes in place shows a health plan is taking the necessary measures to ensure the data being utilized is accurate.
At first pass, having definitions for data almost seems silly because it is so basic. Sometimes the simplest of things tends to be what people overlook because it is assumed to be basic common knowledge everyone interprets the same way. However, when it comes to data storage and retrieval, sometimes what you perceive the data to be isn’t what it truly is. Take, for instance, a health plan’s term “billed premium.” It seems like a rather straightforward term with a clear definition of what it means, but does it mean the same thing for everyone throughout the organization? Probably not. Finance and Accounting may have a different interpretation than Actuarial and Risk Adjustment. So if different departments have varying definitions, what is the data called in the warehouse when an analyst goes to retrieve the information? Having a clear understanding about terminology throughout the organization helps ensure everyone is speaking the same language to progressively move forward towards the common goal.
As with all requirements, oversight to ensure the rules are being followed has to be conducted. This is where data governance comes in. There has to be a classified group of individuals responsible for ensuring the internal controls, data definitions, and corresponding processes established throughout the company are being followed.
Focusing on these three methods will set any organization on the right path to achieving accurate data submissions, more actionable analytics to drive interventions, and, ultimately, data integrity.
Selecting the right partners and implementing the right tools to produce valuable and actionable data will allow clinicians and health plans to prioritize interventions, stratify the populations they are managing and identify those most at risk. Contact us to learn how we can help your organization >>
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