Financial health must be a top tier priority for today’s healthcare organizations. Faced with some of the greatest revenue challenges in recent history, the c-suite is increasingly replacing traditional revenue cycle management practices with more effective revenue integrity models to ensure accurate capture of all reimbursement opportunities.
And while this is an important step forward to an optimal bottom line, many executives may find that they are still behind the eight ball in terms of capitalizing on the latest tools and emerging best practices. The healthcare industry has witnessed an explosion of new technologies—such as artificial intelligence (AI) and machine learning—that bridge the divide between massive data repositories and actionable insights.
Revenue integrity is no exception.
Healthcare organizations still engaged in error-prone manual auditing processes are missing a huge opportunity to move process improvement from reactive to proactive. The reality is that the revenue integrity landscape of yesterday is quickly evolving to a holistic framework that considers the full claim lifecycle—past, present and future. Processes characterized by painstaking line-by-line analyses of claims will not fit the bill.
Let’s consider the progression of revenue integrity practices over the past decade. A concept that originated with chargemaster review and making sure all codes were up to date quickly morphed to include tactics that ensured accurate front-end capture of all services and procedures to minimize revenue leakages.
Recognizing the value of revenue integrity, healthcare organizations continued expanding auditing practices to encompass greater collaboration around documentation review between health information management and clinical departments. Today, revenue integrity has become an integral branch of compliance among forward-thinking providers, and the best strategies draw on the power of automation and analytics to promote bring compliance and billing functions together in support of well-honed retrospective auditing.
But, while the evolution of revenue integrity has dramatically improved revenue capture, it shouldn’t stop there.
Today, automation and analytics working in tandem with AI, natural language processing and machine learning techniques can help healthcare organization move beyond retrospective auditing to daily risk monitoring and more prescriptive analytics.
With the right tools, provider organizations can add the strengths of prospective auditing (front-end) to retrospective auditing (back-end) to detect anomalies in at-risk claims in near real-time. For example, algorithms may predict that 40% of COVID-19 related cases will be denied around two main causes: medical necessity and length of stay. Equipped with this foresight, prospective auditing can be designed to spot check an appropriate sample of those claims before they are submitted.
The University of Utah Health used MDaudit Enterprise, Hayes’ flagship revenue integrity platform, to build this type of holistic strategy and get ahead of potential fall-out from COVID-19 claim issues. Compliance professionals leverage the platform to audit entities and perform corrective actions to mitigate compliance risks, while revenue cycle professionals leverage the analytics modules to identify revenue risks and take further actions. This allowed Utah Health to recapture nearly $60 million in at-risk charges and revenues.
Revenue integrity has come a long way over the past decade thanks to technological advancement. The business case for investing in emerging technologies such as AI and natural language processing to improve revenue capture is an easy one to make. How will your organization position itself for optimal bottom-line health going forward?
