How Predictive Editing Can Boost Revenue Cycle Performance

Numerous threats, including chronic staff shortages and rising expenses, may further impact many hospitals and health systems’ narrow operating margins. More than ever, provider organizations need to maximize reimbursement opportunities. Unfortunately, denied claims—and the associated adjudication process—remain among the leading causes of financial shortfalls and inefficient revenue cycle management.

A recent analysis found an increase in initial denial rates from 11.2 percent in 2022 to 11.99 percent in the first three quarters of 2023. Another study found that it costs about $25 to rework a claim. This situation negatively impacts revenue cycle performance, adds to the volatility of health systems’ accounts receivable, and depletes cash reserves. Furthermore, denied claims delay reimbursement and add workload to the appeals team. This causes adverse effects on care delivery and patient experience.

However, the value of the unpaid claim and the time it takes to rework it are only part of the overall impact on the bottom line. Historically, denied claims require provider organizations to invest heavily in reactive solutions—point technologies, staffing surges, and complex appeal processes. These tools may help “manage” denials, but true cost savings lie in prevention.

Work smarter with an AI-powered prediction engine that analyzes claims for errors before routing them to the payer

Reducing avoidable claim denials is a top priority in optimizing revenue cycle management for hospitals and health systems. Many systems offer packaged or custom edits to claims before submission, but these are often built retrospectively. This requires costly analysis to determine the root cause of the denials, along with ongoing maintenance as payers’ adjudication rules shift in response to external forces. Implementing AI technology to improve the denial management process is a better approach. Identifying and preventing denied claims that could help reduce administrative rework and lost revenue for a health system’s facilities. 

Predictive editing, a new approach to denial management leveraging artificial intelligence (AI), uses an algorithm that focuses on the subset of denials likely to be avoidable and correctable. The algorithm’s predictive capabilities lie in its ability to analyze claims data across a broad network of provider organizations, as well as policies specific to individual health plans, to predict the probability of denials. If the likelihood of denials is 98 percent or higher, the predictive editing solution returns the predicted denial reason and code.

AI analyzes the constantly changing stream of data, eliminating the need for manual writing and maintenance of edits. This enables health systems to respond more quickly to changes—before submitting claims and facing a new wave of denials to analyze. Furthermore, AI not only streamlines the denial management process and prevents denied claims but also helps providers submit claims correctly on the first attempt. It achieves this by delivering insights and analytics that help organizations identify and address areas for payer-specific, data-driven adjudication improvements. This capability further reduces avoidable and actionable denials, enhancing reimbursement outcomes for the health system.

By applying responsible predictive editing technology, providers reduce administrative costs associated with reworking claims. They increase edit coverage by capturing complex, payer-specific edit scenarios that traditional front-end edit engines cannot address. Providers also reduce the administrative effort required to maintain manual rules and save on implementation costs by integrating predictive editing into their existing edit/error management tools, especially if they use Availity Essentials Pro.

Conclusion

The path toward a sustainable and healthy revenue cycle requires tools, insights, and analytics to help providers submit claims right the first time. The potential impact of artificial intelligence on the $43 billion spent each year on healthcare’s revenue cycle could be transformative. By leveraging automation and AI in denial prevention, providers can streamline administrative tasks and ultimately improve the patient experience.

To learn more about Predictive Editing and other revenue cycle services, please contact me at [email protected] or visit www.availity.com.

About the Author

Linda Perryclear is the Senior Director of Product Management at Availity. Since joining Availity in 2004, Linda has held various positions in the organization including roles in client experience and account management. Throughout her career, Linda has worked in healthcare in both administrative and clinical roles. She also served in the US Navy for six years as a hospital corpsman. She holds a bachelor’s degree in finance and risk management from UNC Charlotte and is based in North Carolina.