Leaders from Janus recently attended the HFMA Revenue Cycle conference, where they attended insightful panel discussions and networked with revenue cycle leaders from across the country. Here, our experts have come up with some key takeaways from the week that we’d like to share.
1. Denials in Focus: AI Emerges as a Key Asset
Denials persist as a primary concern, as evidenced by 46% of conference attendees recognizing AI’s potential in denial management. The surge in denials, along with payers leveraging AI to deny en masse, underscores the critical need for health systems to utilize this technology for their advantage. With tailored solutions addressing claim status, account prioritization, and denial insights, the realm is ripe for AI and automation to redefine how health systems optimize their revenue cycle. Attendees’ acknowledgment of AI’s significant potential in analyzing Revenue Cycle Management (RCM) workflows indicates a shifting landscape. Process Mining, a proven practice in various industries, is gaining traction in healthcare as a potent tool to reinforce process gaps and elevate overall efficiencies. This moment marks an exciting juncture where the rising potential use cases for AI spark industry interest.
2. AI Adoption: Early Stages and Mixed Sentiments
The majority of health systems find themselves in the nascent stages of their AI journey, with fewer than 25% of attendees currently employing more than two AI solutions in their revenue cycle. Despite the excitement and perceived necessity surrounding AI, 8% of attendees either haven’t contemplated its adoption or harbor reservations. A Brookings Article underscores healthcare’s lag in AI adoption compared to other industries. The imperative for vendors lies in educating and assuaging concerns among health systems to fully unlock AI’s potential. Providers are keen on understanding the implementation timeline and achieving ROI recognition before making comprehensive commitments. As Jaren Day, Insights Director at KLAS Research, highlighted to Rev Cycle Intelligence, “Overall, enthusiasm for AI is growing, but some people are still skeptical. Mainly, it depends on whether or not they have an established AI strategy or what type of organization you’re talking to.”
3. Education Is Key: Navigating the AI Landscape
In the early stages of adoption, a concentrated effort on education becomes imperative. Many health systems are still navigating the diverse landscape of AI use cases and grappling with implementation requirements. “Those of us that are living and breathing these things need to continue to help demystify these technologies and work with revenue cycle leaders to understand the impact and how they can change their businesses using these tools and platforms,” notes Chris Gervais, CTO at Janus, “To leverage AI effectively, it’s important to clarify the areas where you’re willing to champion the experimentation and application of it with system leaders.” Clarifying distinctions like machine learning (ML) versus large language models (LLM) and recognizing their limitations in addressing issues with broken processes is essential. In the highly regulated healthcare industry, the synergy between AI and human expertise is pivotal. When coupled with bold change management, this collaboration has the potential to substantially enhance business performance, unlocking limitless possibilities.
4. Data Dilemma: Recognizing the Reign of Data
Acknowledging that “data is king,” health systems face the challenge of transforming data into actionable insights. As we heard from one of our customers, “we are drowning in data and we need your help turning it into information,” this echoes a common struggle across health system executives. Revenue cycle leaders seek vendors capable of extracting meaning from data, understanding that reliance solely on EHR systems may not suffice. Integration with existing EHR systems is key, as external partners should strive to streamline rather than contribute to the existing data silos.
