How specialty clinical practices can leverage AI for RCM
Technology can make an immediate impact on the business side by assuming some of the burden from staff and facilitating the business office.
Artificial intelligence is revolutionizing many facets of our lives, and the clinical space is no exception.
While the application of AI in direct patient care is still in the early stages, its use in revenue cycle management (RCM) is poised to accelerate operational efficiency, offering promising solutions for common administrative and time-consuming challenges.
A December 2023 study by Akasa indicated that 74 percent of hospitals and health systems use automation in their revenue cycle, with 46 percent reporting they also use AI. Medical practices, however, are a bit slower to adopt. An MGMA study from February 2024 reported that 62 percent of medical practices have automated as much as 40 percent of their RCM operations.
Our first article in this series discussed the considerations for AI in patient experience. This article explores how the application of AI technologies can drive efficiency and improve cash flow in contemporary clinical practices.
AI, machine learning and automation in RCM
Claims processing can be an arduous and repetitive task, but it’s critical for maximum reimbursement. This type of manual data entry and analysis is a textbook use case for AI and machine learning, one that can be done more quickly and accurately by a machine rather than a human.
AI-enhanced claim scrubbing is crucial for increasing accuracy and efficiency of claims processing. Algorithms can increase first-pass clean claims rates by detecting and correcting errors and increase adherence to up-to-date regulatory and payer-specific rules before claims submission.
Machine learning can analyze large amounts of data to identify patterns and enhance decision making. A recent article in Healthcare IT News supports this use case, noting that, “Knowing which scenarios are causing denials and how to catch or predict those denials before they happen is the perfect opportunity to use AI with expertise and claim results data to build and train a model.”
Predictive analytics, powered by AI, can forecast revenue based on historical data and current trends, optimize staff allocation, and anticipate patient payment patterns to improve cash flow management. These algorithms will learn over time, improving accuracy and quality.
Automation can streamline manual data entry and repetitive tasks, such as patient eligibility, claims submission and auto-post explanation of benefits (EOBs) information. AI-enhanced technology can identify, categorize and address claims denials to help reduce resolution times and revenue leakage.
“As market dynamics shift, we see incredible opportunities to create efficiencies upstream to reduce the revenue cycle management process from days to minutes for providers and patients by leveraging both artificial intelligence and machine learning,” says Hannah S. Barber, MBA, vice president of product management for Nextech.
AI and automation drive efficiency, cash flow
AI can significantly reduce the administrative workload on clinical staff by automating routine tasks, giving staff more time to engage in value-added activities and focus on patient care. In addition, automated systems minimize human errors, reducing the need for manual intervention and correction, and enhancing the overall efficiency of the RCM process.
By optimizing RCM, AI can directly impact a practice’s financial health. According to McKinsey & Company, studies suggest deploying AI and automation can save billions in healthcare spending and that “some of these savings would come from administrative functions (including [RCM]) or nonclinical parts of healthcare provisioning (including scheduling, coordinating care with insurers, documentation, and claim or bill adjudication).” For example, increased first-pass clean claims rates can lead to quicker reimbursements and cash flow stability. And predictive analytics can provide more accurate financial planning and budgeting, and better insights into future revenue streams.
Michael Diesenhouse, MD, president of Eye Associates of Tucson, states, “I believe automation is crucial for the financial success of my practice and in improving patient care. Rapidly rising staff costs and limitations on physical space in the practice can negatively impact our ability to scale.”
Leveraging AI from outsourced RCM partners
Outsourcing RCM to a specialized services partner can offer several advantages, including staffing support and scalability. Outsourced partners often use cutting-edge technology and AI tools that are cost-prohibitive for many individual practices. By collaborating with an RCM partner, practices can tap into the power of today's most innovative technologies.
“Emerging AI technologies in healthcare hold transformative potential, but their high costs and complexity often place them out of reach for many individual practices,” says Samantha Akhtarzandi, head of physician RCM for Assembly Health. “By collaborating with an RCM partner, practices can leverage cutting-edge solutions that would otherwise be unattainable, gaining access to the innovations necessary to enhance efficiency and patient care.”
The integration of AI and financial innovation in the clinical space is transforming RCM, making it more efficient, accurate and cost-effective. Automation, AI-driven claim scrubbing and machine learning are key components driving this transformation. Additionally, outsourced RCM can be easy to implement, cost-effective and scalable. By embracing these innovations, medical practices can improve cash flow, reduce administrative burdens and ultimately enhance patient care.
Jason Handza, MD, is chief medical officer at Nextech.