Part 2: SDoH capture, coding and analytics to succeed in value-based care

In part one of this series, I talked about how value-based care can allow for greater flexibility in services, while providing accountability for sustainable population health improvements. In part two, I will talk about how health systems can better quantify clinical and social risk to be successful in a value-based care model. An accurate capture of clinical and social risk is essential to understanding your population barriers to improved outcomes.  

One way to ensure you’re capturing accurate data is to deploy technology that can enhance and automate the capture of Z codes. For example, 3M HIS works with health systems to automatically identify social risk data that can be documented in the electronic health record (EHR), even by non-physicians like social workers or case managers. The system will autosuggest the correct Z code to the coder to either accept or reject. 

There are currently limited ways that Z code capture itself directly impacts payment. One way is through enhanced E/M billing for documenting social need and how it affects the care plan. Another challenge is that there is a limit on the number of codes that can be submitted for inpatient claims. Z codes are often left off of claim sheets because they don’t directly impact revenue, even when the information has been collected. That’s why it’s important for health systems to use technology that can help prioritize Z codes for inclusion on claims.  

The indirect way that Z code capture can affect revenue is through understanding your population risk and what needs to be done to improve health outcomes and perform better for your population in value-based payment arrangements. 3M™ Clinical Risk Groups (CRGs) can help health systems quantify the clinical severity of illness (SOI) of its patients based upon coding data drawn from across sites of service to have a true 360-degree view of a patients and a populations total burden of illness.  

3M CRGs group patients based on similar needs, not by diseases, since resource needs and outcomes for a given disease like diabetes, can vary greatly by severity. 3M CRGs also incorporate Z codes, one of the only population health methodologies to do so. They can also form the foundation for comparisons of populations with similar clinical risk, but differing social risk, to understand how those extra clinical factors can affect outcomes. In addition, you can drill down to understand what social risk is the most impactful for outcomes in your population and gain insights into optimal program design and interventions to address that social risk. This approach can help explain that even though care may not be different, social needs that are unmet can contribute to different outcomes and care plans. Conversely, it can show where unwanted variation in care may exist for different socioeconomic groups and where the need to address discrepancies in care quality.  

Choosing the correct and most impactful interventions that will help close health disparity gaps and eliminate unwanted variation in outcomes can ultimately translate to better performance in value-based care contracts. Industry trends like the ACO REACH model and others increase the need for accurate SDoH data capture, coding and analytics to be able to succeed in a value-based care environment. 

Melissa E. Clarke, MD, CMQ, is senior medical director, health care transformation and health equity, at 3M Health Information Systems.