The effectiveness of value-based care models is measured based on how this type of reimbursement can improve the quality of a patient’s overall health. Reducing hospital readmissions and improvement in preventive care are good examples of critical factors to assess the effectiveness of these models. U.S. legislation is clearly moving towards the adoption of value-based care models; in 2015, the Medicare Access and CHIP Reauthorization Act (MACRA) put into place multiple quality programs under the new merit based incentive payments system (MIPS). The Health & Human Services Department (HHS) established the objective to convert 30% of Medicare charges into value-added payment models by the end of 2016. By 2018, the Agency’s objective was to make a transition to 50 percent of conventional payments.
Revenue Cycle Management (RCM) systems will eventually assist the industry in transitioning from fee-for-service to value-based reimbursement. The analytics of many of these RCM systems enable payers and providers to look at their patient population in more detail, such as the number of chronic disorders in their patients, allowing payors and providers to monitor claims data and identify potential abnormalities.
Revenue cycle managers are betting on data analytics to try to improve every phase in the revenue cycle process, in order to eventually obtain more cash flow from each phase. Information is being consolidated and evaluated to drive business process changes across the revenue cycle process, from scheduling appointments to claims denials. RCM systems will in due course, streamline routine processes such as claims processing and payer denials prevention.
Challenge One: Lack of integration
Revenue cycle managers work with historical data from differing financial and clinical information systems. However, many hospitals and health systems have not been integrated to this level. The number of disparate information systems can continue to increase when hospitals merge and purchase physician practices and other healthcare entities. The incorporation of data from external sources canalso contribute to the challenges of standardization. Large-scale analytics is still a considerable challenge, resulting in a lack of an all-inclusive view of a patient’s record.
Challenge two: Lack of preventive education
Many reimbursement problems arise from human errors, such as incorrect coding, missing items on a patient's account, and problems with insurance eligibility. Health organizations that proactively promote appropriate coding techniques, comprehensive chart documentation, and financial policy reinforcement, have consistently reduced turnover and medical errors, which results in a reduced amount of denied claims.
Challenge three: Lack of visibility and control on claim submission-denial processFollowing up on a claim throughout its lifetime is another major challenge for healthcare revenue management. Healthcare professionals should closely monitor claims processes to identify errors. Revenue may be lost unless providers can identify where problems arise and quickly resolve mistakes. Providers can usually receive automated alerts or have a dedicated data analysis resource to produce custom and automated reporting, with the objective of identifying why payers deny claims for certain methods or codes routinely.