The Critical Imperative for Health Plans to Tackle Late Payment Interest

Today’s health plans continue to pay healthcare providers significant penalties and interest when they violate prompt pay laws for clean claim-delayed processing due to various processes, disparate systems, and dependencies on functional groups. This includes, for example, authorization requirements, coordination of benefits details, external pricing, provider data management, itemized bills, and medical records requirements. In fact, major payers have paid $150 million to $60 million in penalties annually and are estimated to pay an additional 20% to 30% every year. More specifically, as the cost of working capital is increasingly a focal point, so is the need for cost avoidance and improving operating cash flow issues. 

Late claim payment interest and penalties determination and tracking comprise a manual process that is highly cumbersome and inefficient. This is due to reporting suite deficiencies and a claims settlement process that involves member eligibility and benefits verification, prior-authorization, provider contracts/reimbursements, external pricing, and many other steps. The process spans functional areas and disparate systems, resulting in incorrect processing and rework. 

Technology solutions like AI/ML-based predictive analytics can be precisely focused on identification of claims with high propensity to pay and late payment interest (LPI), incorrect processing avoidance, and work reduction by automation of manual verification process steps. Health plans can reduce late payment interest by up to 25% year over year. The journey starts by reaching out to a healthcare services expert with a Late Payment Interest Center of Excellence. With this sharpened capability set, payers must focus on three critical areas:

Predictive analytics can be analyzed to master payment with standardized forecasting and accuracy

Creation of a claims data lake and robust platforms can analyze large scale historical claims data set and identify high frequency claims independent variables. These include bill types, places of service, CPT vs. revenue code vs. DRG, and provider network types to identify propensity to LPI dependent variables (as a combination of incorrect denial and process delays). These platforms leverage data science techniques using AI/ML modelling to predict claims with high probability of incurring LPI and penalty. With these techniques, healthcare payers can optimize decision-making and generate new insights that lead to more effective and profitable business outcomes like:

• 20-25% reduction in enterprise wide LPI amount for claims processed on multiple platforms
• Projected healthcare organization savings of nearly $8-10 million a year annually
• Reduction in rework rate by 20 to 30% on current baseline
• Significantly reduced provider/member abrasion

Process/task mining platforms take aim at bottlenecks through end-to-end claims journey

Process and task mining software provides data-driven process discovery from enterprise transaction systems and provides detailed — and data-driven — information about how key processes are performing. This tech enablement analyzes event logs as work is done. Take, for example, claims data pended for eligibility/benefit and provider verification and then routed to contract reimbursement for pricing calculation, with medical records request, adjustment, and, finally, payment. The logs make visible:

• How computer-mediated work is really happening, including who performed the task, how long it takes, and how it departs from the average turnaround time (TAT)/cycle-time
• Process mining applications with the ability to create process digital twins to map out the claims journey and provide an overview of project conformance and variations
• AI algorithms to detect the root cause of variation and alert/send notification to respective functions
• Process analytics with creation key performance indicators for the process, to enable health plans to focus on the priority steps to improve and drive positive business outcomes and improved provider and member experiences
• Task mining capture and collection of user applications interaction logs over time, as well as user actions to provide robotic process automation opportunities improve operational efficiencies
  

Automated workflow application

Web-based app monitoring of claims through the claims journey is supported by a LPI tracking application dashboard. The current LPI-management process involves identifying aging claims with potential for incurring LPI from the inventory file by the Production Control Team, followed by sharing, assigning and following up for resolution. While a good process, it is labor-intensive and requires a high possibility of inefficiencies/cracks in the system which can result in leakage.

An LPI tracking application, combined with process re-engineering, accomplishes a robust real-time claims metrics management system, thereby ensuring that the LPI expenditure is significantly reduced. The application evaluates a claim based on various parameters (historical and mapping it to the current claim) and predicts the likelihood to accrue late claim interest and penalty. This could be integrated into the claims system with hotspots or warning messages enabled to help processors in identifying potential LPI claims.

These three tech enablers work together in an accelerated way to drive significant outcomes for payers and improve experience for customers, physicians, hospitals, and members in critical LPI problem areas, with results including:

• Fast-tracked claims processing and settlement and cycle time reduction 
• Incorrect processing and unwanted rework and additional staff reduction 
• Potential reduction in provider and member calls on follow ups and incorrect denial inquiries  
• Payer adherence to states prompt pay law compliance
• Improved ratings on state and federal indices
• Reduction in downstream volume such as complaints, appeals, and grievances

These integrated solutions not only drive significant operational improvements but also pave the way for a more efficient and responsive healthcare system.

Photo: Topp_Yimgrimm, Getty Images

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