Revolutionizing Revenue Cycle Management in Healthcare: How Adaptive Chief Revenue Officers Harness AI for Success

Economic factors like higher interest rates, persistent inflation and clinical staffing shortages continue to put downward pressure on private equity investments in healthcare services businesses. To alleviate this pressure, addressing and solving revenue cycle leakage, which can often amount to millions of dollars, is one of the most immediate and impactful solutions.  

PE sponsors are increasingly investing in portfolio company chief revenue officers to lead and transform revenue cycle management in their healthcare investments. And artificial intelligence solutions are rapidly emerging as the go-to tool for CROs, helping establish airtight revenue cycles capable of recapturing those millions in lost revenue and bolstering the bottom line. 

AI is Critical to Revenue Cycle Automation 

Historically, the revenue cycle function has depended on numerous manual tasks that are time-consuming and prone to human error. Though automating these efforts is not a new goal in healthcare, adaptive CROs are re-prioritizing automation initiatives by leveraging AI solutions to achieve their goals, especially in the following areas:  

  • Fulfilling insurance company requests: Healthcare providers continue to receive a significant number of requests from insurance companies, ranging from clinical information to authorizing coverage throughout the patient experience. As HFMA reports, Banner Health in Arizona implemented AI chatbots to respond to different types of insurance inquiries, instead of their limited RCM staff. “As we put in improvements in workflows, there are clear opportunities where we could insert a bot as we make those workflows more streamlined and consistent,” said Jacci Schavone, revenue cycle automation program director for Banner Health. “It happened organically from applying continuous improvement methodologies.” This improved response times to insurance companies and minimized the number of manual responses required of the healthcare staff, ultimately improving the patient experience. 
  • Decreasing claim denials: Eric Eckhart, director of patient financial services for Community Medical Centers in Fresno, California found that increasing claim volumes were constantly putting added pressure on the RCM team. By incorporating an AI tool that could track past denials and analyze them by type and payer, they could better predict future denials and avoid prematurely submitting insurance claims or generating patient bills. After implementing AI into its RCM for claim denials, HFMA reports that Community Medical Centers decreased its lack of prior authorization denials by 22% and services not covered denials by 18%, saving as many as 35 staff hours per week without the need to write large amounts of back-end appeals.
  • Optimizing coding processes: Computer-assisted coding programs use AI and natural language processing to identify specific terms within provider documentation and apply the appropriate diagnosis, procedure codes and CPT codes to patient records. This reduced errors and saved time for coding staff by allowing them to focus on accuracy instead of manually entering the information, with the Cleveland Clinic reportedly experiencing a 22% reduction in coding time.
  • Monitoring and incorporating changing billing codes: The American Medical Association updates the CPT code set annually. The 2024 update resulted in 349 changes including the consolidation of 50 previous codes related to COVID-19 immunizations, now totaling 11,163 codes. Documenting these changes can quickly overwhelm coding staff, especially in high-volume settings. To combat this, implementing an AI tool that can learn, monitor and accurately incorporate code changes into RCM technologies can streamline manual entry, reduce potential errors by coding staff and deliver results more quickly. 
  • Predicting payment trends: When AI-predictive analytics review and assess payment histories instead of humans, CROs benefit from a more complete and timely picture of future payment prospects and behaviors. These insights allow organizations to prioritize more complex accounts and have their staff focus on other prioritized tasks, while also enabling CFOs in PE-backed healthcare services businesses to generate more accurate cash flow forecasts because of it. For example, if AI-predictive analytics identify that a payer is unlikely to provide payment until day 14, the team won’t receive a prompt to work the account until day 15 or 16.  

By limiting the manual effort required to keep revenue cycles running smoothly and reducing the number of human errors that directly address leakage gaps, AI solutions allow the remaining RCM staff to focus on more critical, strategic and patient-centered areas of work, resulting in increased employee and patient satisfaction. However, it’s important to recognize that while AI can help streamline many aspects of RCM processes, it doesn’t solve all pre-authorization issues. In cases where clinical data is complex and nuanced, manual input may be required to ensure accuracy and compliance. 

RCM Executives View AI as a Tremendous Opportunity 

According to the 2022 CAQH Index, RCM automation presents a $22.3B savings opportunity in the medical industry. Beckers Hospital Review recently engaged with seven RCM executives to gauge their feelings on AI solutions around RCM. While there was a consensus that the implementation of AI-based tools requires proper oversight and planning, some executives have already experienced impressive results. 

Michael Mercurio, VP of Revenue Cycle Operations at Mass General Brigham in Boston feels that AI will have an enormous impact on healthcare RCM. After implementing AI solutions to automate repetitive tasks around coding and billing, Mercurio celebrated their results saying, “The result for us has been a far more efficient revenue cycle, a shorter time to payment, fewer payer denials, and a reduced cost to collect (in addition to a reduced burden for our providers, coders and billers).” 

Given their understanding that AI technologies are evolving, leaders view the potential of AI in RCM as undeniable and something that requires close monitoring, demonstrating yet another way that AI is irrevocably impacting the business landscape. 

Healthcare Organizations Should Discuss AI with CRO Candidates 

There is a growing awareness among CROs that healthcare organizations unwilling to invest in AI technologies can impact their ability to succeed in the role, especially when they are already having to deal with lingering economic uncertainties. This can lead to prioritized candidates rejecting offers, making it essential that interviewers, hiring managers and talent acquisition leaders proactively discuss AI’s importance to their RCM during the recruitment process to avoid any misunderstandings.  

These conversations can be pivotal in attracting top talent and getting them across the hiring finish line. But, if a CRO candidate shows little interest in adapting to AI for RCM improvement, it could indicate a potential hiring mismatch for a healthcare organization ready to invest in AI. In such cases, continuing the search for a more adaptive CRO may be more beneficial.  

As AI adoption and application in healthcare is just beginning, the ability to identify candidates who will drive this journey towards efficiency, with security and practicality in mind, will be crucial. The next era of healthcare will be powered by AI and led by revolutionary and adaptive leaders.