The shift to value-based care from a fee-for-service model is putting new pressures on healthcare organizations that are already facing dwindling resources.
In fact, for many providers, administrators and compliance teams, a lack of resources is the No. 1 holdback to a full transition to the value-based care model, according to a recent survey of 1,090 healthcare leaders. In the survey, respondents cited lack of resources as the most significant barrier to value-based care, with over 25% pointing to staffing shortages and insufficient healthcare IT software as their top concerns.1
Of course, a lack of resources is an operational challenge that many healthcare organizations have been navigating for decades. But in the value-based care world of hierarchical condition category (HCC) coding and risk adjustment factor (RAF) scores, inadequate technology and staffing can result in a big hit to the bottom line.
Technology to Improve HCC Coding and Value-Based Care Reimbursement
Value-based care reimbursement is tied directly to HCC coding and patient RAF scores. That means, if provider documentation and coding aren’t done correctly or completely, your healthcare organization is not realizing full reimbursement potential.
Take patient X, for instance. Last year, patient X received an amputation because of diabetes. RAF scores are wiped clean each year, and your current EHR system did not automatically carry over the HCC coding for the amputation into this year. If an open condition, such as an amputation from diabetes, isn’t recorded every year, it can lead to an inaccurate RAF score and negatively impact the rate at which your organization is reimbursed for patient X’s care.
In today’s market, healthcare organizations can’t afford to leave money on the table. But identifying HCC coding gaps from year to year — like the one in our patient X example — and other coding inaccuracies and inconsistencies requires complex analytics and data, expertise and manpower. As the survey showed, these are resources that today’s healthcare organizations simply don’t have.
The solution? Healthcare organizations must go beyond the capabilities of their EHRs and capitalize on new technologies designed to address these resource limitations in the value-based care environment.
For example, healthcare organizations can use predictive analytics software to evaluate provider HCC coding patterns to find gaps and inconsistencies in coding and patient RAF scores. These dynamic tools can instantly analyze provider HCC coding behaviors across your organization — uncovering HCC coding gaps and revealing patients and providers with the most opportunity for improved documentation and reimbursement potential.
Insights for Targeted Provider Education
Predictive analytics technologies not only flag inconsistencies in HCC coding and RAF scores, but they also provide valuable insights for provider education.
Better value-based care documentation starts with providers. But provider education can be a drain on resources. And since most existing EHRs don’t provide sufficient data on provider HCC coding behaviors, healthcare organizations are spending too much of their limited staff time on education initiatives that don’t make a significant impact.
Using predictive analytics software, healthcare organizations can pinpoint provider outliers and use HCC coding behavior data to create targeted provider education programs that will have the most impact on reimbursement potential.
Yes, new technology requires an additional upfront investment, but there can be substantial returns if it’s used the right way. By leveraging technology in a value-based care environment, your organization can overcome resource constraints and realize substantial returns by improving internal processes, creating efficiencies and improving financial performance.
REVEAL/md RAF HCC is a cloud-based subscription service for value-based care organizations. ACOs can use the predictive analytics software to maximize value-based care reimbursement potential through more accurate HCC coding and risk adjustment factor scores.