In a recent New York Times article titled "In Constant Battle With Insurers, Doctors Reach for a Cudgel: A.I.," Dr. Azlan Tariq's struggle against insurance denials highlights a growing issue in healthcare.
Physicians, overwhelmed by the increasing reliance of insurers on technology to deny treatments, are turning to AI tools to fight back. Dr. Tariq, for instance, now uses generative AI to synthesize research and bolster his appeals, a practice borne out of necessity as denials rise and pre-approvals become more stringent.
This example underscores the potential of AI not just as a reactive tool, but as a proactive force in Revenue Cycle Management (RCM). Health systems are prioritizing their digital spending on generative AI for RCM and other areas. Here are three use cases demonstrating how generative AI can revolutionize RCM.
1. Revenue Insights
The challenge of extracting actionable insights from revenue cycle data is a significant hurdle for many healthcare organizations. Traditionally, CFOs and RCM leaders have relied on teams of analysts to sift through data, compile reports, and extract meaningful insights. This manual process is not only expensive and time-consuming but also fraught with limitations of our ability to look for patterns and bias.
Generative AI offers a solution to this problem. By feeding revenue cycle metrics and data into a generative AI language model, we can enable users to ask plain language questions and receive insightful answers. Imagine a Revenue Cycle leader querying the system about trends in payer performance, identifying problematic payers, or pinpointing specific issues that have arisen over the past few months. Instead of waiting for a team to compile a report, the AI can provide these insights almost instantaneously. The key here is to use AI language models fine tuned with RCM domain knowledge to understand the context and generate more accurate insights. This approach not only enhances the speed and accuracy of data analysis but also empowers financial leaders to make informed decisions swiftly.
2. A/R Recovery Recommendations
One of the most critical aspects of RCM is determining the best actions to take during tasks such as Accounts Receivable (AR) recovery, billing, and coding. Historically, these decisions have been guided by Standard Operating Procedures (SOPs) and rules-based systems, which often fail to adapt to the dynamic nature of payer policies and practices.
Generative AI, combined with traditional machine learning, can revolutionize this process. By analyzing the unstructured notes and actions documented by RCM professionals, AI can identify which actions lead to successful outcomes. For instance, the AI can discern patterns in past actions that resulted in revenue recovery and recommend similar actions for future claims.
This capability represents a significant shift from static, rules-based systems to a dynamic, learning-based approach. By continuously analyzing and learning from historical data, the AI can provide recommendations that are not only based on established practices but also adaptable to new trends and changes in payer behavior.
3. Proactive Denial Management
Denial management is a perennial challenge in RCM, with denials often resulting from a myriad of complex and varied reasons. Existing solutions typically handle structured data well, but struggle with the unstructured data found in Explanation of Benefits (EOBs) and remittance advice documents.
Generative AI bridges this gap by integrating both structured and unstructured data to provide a comprehensive understanding of each claim and its denial reasons. The AI can read through extensive unstructured data, identify patterns, and discern the root causes of denials. This holistic view allows the AI to build predictive models that forecast potential denials and suggest corrective actions.
By proactively addressing the root causes of denials, revenue cycle leaders can reduce their denial rates and improve their overall revenue cycle efficiency. This proactive approach not only saves time and resources but also ensures that claims are more likely to be paid on the first submission.
The Future of RCM with Generative AI
The integration of generative AI into RCM processes marks a significant advancement in how healthcare organizations manage their revenue cycles. By enhancing insights, optimizing actions, and preventing denials, generative AI offers a powerful tool for navigating the complexities of RCM.
As we move forward, it’s essential to recognize that this technology is still in its early stages. The potential it holds for transforming RCM is immense, but it requires careful implementation and continuous refinement. As we continue to explore and develop these technologies, the future of RCM looks brighter than ever.
Infinx provides scalable AI-driven solutions to optimize the financial lifecycle of healthcare providers across all functions of patient access and revenue cycle management. To stay ahead of ever-changing government regulations and payer guidelines, request a demo here.