Correcting Patient Coding Can Save Millions

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Physician burnout is a real thing.  Between seeing increasing numbers of patients, documenting visits and reviewing health records, more tasks are expected of healthcare providers.  According to a Mayo Clinic Study, 54% of physicians reported at least one symptom of burnout. While there are many new technologies to assist with patient care, using new technology does not always integrate well with the face-to-face, personal nature of providing medical care.  Some physicians perceive that technology slows them down, as they do not have the time between patients to update codes and clinical details in documentation.  But the reality is that taking the time to ensure proper coding reduces the likelihood of redundant tests, the risk of medical errors, and can significantly impact value-based contract reimbursements.  So in this sense, taking the time to input patient information, document and code patient conditions is critical.

From a financial standpoint, healthcare CFOs understand the benefits of accurate patient coding.  The transition to value-based payment models in healthcare is accelerating under the Medicare Access and CHIP Reauthorization Act (MACRA).  The intent is to provide financial motivation for providers to manage more patients under risk-bearing or coordinated care contracts.  The two methods of managing physician reimbursement under MACRA—Advanced Alternative Payment Models (APMs) and Merit-Base Incentive Payment Systems (MIPS)—both hinge on correct clinical documentation to assign accurate risk adjustment scores.

From a provider’s standpoint, risk adjustment scores such as the Hierarchical Condition Category (HCC), impact physician documentation practices and requirements.  This means that all chronic conditions need to be reported on an annual basis for all patients, regardless of the care setting, in order to correctly estimate the risk score for a provider’s patients.  Annually, this information is reported to the Centers for Medicare and Medicaid Services (CMS) in a Risk Adjustment Processing System (RAPS) submission.  In many cases, this submission is compiled from claims data; but while claims data carries a great deal of information on a patient’s medical history, it often understates the true “risk” of the patient population. In financial terms, this means that providers who understate their patient risk receive a lower reimbursement from CMS.  In other words, because of inadequate patient coding, healthcare providers are leaving money on the table.

How can healthcare organizations solve the problem of underreported patient risk?  We ventured to take on this problem, and sought to find a solution that any provider group, large or small, could use.  Understanding that not everyone has the budget for an expensive platform or a team of auditors to check through exhaustive patient records, we wanted to see what technology could do to ensure patient risk is accurately coded and reported—without putting an additional burden on physicians. 

We used machine learning and natural language processing of unstructured data, encompassing millions of files, including doctor’s notes and faxes.  Within the many silos of data were notes captured of patient conditions that were not always reflected in the claims coding in the patient’s electronic health record.  This technology enabled us to shortcut the work that would normally be undertaken by an entire auditing team manually reviewing patient files. To give an idea of the efficacy of a process like this, in this case we were able to discover $1.3 million in under-represented risk reimbursements for the provider group. 

For healthcare CFOs who are looking to make sure patient coding is accurately reflecting population risk adjustment for CMS reporting, there 2 options to ensure your organization isn’t leaving money on the table.  1) Invest in hiring additional people to educate, input, and audit correct patient EHR codes or 2) invest in a technological solution to ensure you’re not losing out on reimbursements due to under-reported patient risk.  Healthcare organizations who form a strategy to ensure coding accuracy can see millions in additional deserved reimbursements on their bottom line. 

For a free demo, contact us.

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BetaXAnalytics is a healthcare data consulting firm that helps payers and providers to maximize their CMS reimbursements and helps employers to reduce their healthcare spending through proven strategies to contain costs. For more insights on using data to drive healthcare, pharmacy and wellbeing decisions, follow BetaXAnalytics on Twitter @betaxanalytics, Facebook @bxanalytics and LinkedIn at BetaXAnalytics.

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