AI’s benefits have reached the revenue cycle, but adoption remains uneven

Artificial intelligence is becoming an increasingly important part of healthcare revenue cycle management, though AI isn’t equally distributed across RCM use cases. According to research from PayZen, the discrepancy has a lot to do with health system size and tends to reflect broader adoption of AI within those organizations. 

The PayZen report, completed in partnership with the Healthcare Financial Management Assocaition (HFMA) and based on a survey of 205 RCM leaders, found that roughly 37% of health systems use generative AI within the revenue cycle. Among those that haven’t yet adopted generative AI, nearly 85% are very interested in it. 

Implementation remains uneven, though. More than 48% of systems with net patient revenue above $5 billion have adopted Ai in RCM, compared to just 24% for systems with revenue under $1 billion. These figures largely mirror recent research from Oliver Wyman, which noted small and/or community-based providers “appear to be under-investing” in AI tools relative to their peers. 

Small systems seek to reduce RCM burdens

PayZen’s survey data illustrates where those investment gaps can be found. Among smaller health systems, denials management (in use at 70% of organizations) and coding support (60%) are the clear frontrunners for AI use cases, followed by predictive modeling at 30%.  

These patterns suggest smaller systems prioritize resource-intensive challenges with the hope of reducing administrative burdens, the report said. Given industry trends, this approach is sensible. Research from Experian has found more than 40% of providers now see at least 10% of claims denied on the first pass – a steep increase from 30% in 2022 – and only 56% of providers believe current technology can meet RCM demands. 

Large systems spread AI throughout RCM workflows

Larger systems, on the other hand, “show a more distributed set of use cases,” as PayZen put it. Managing denials and supporting coding remain the top use cases, but many other functions appear on the list of priorities: Prior authorization, patient access and scheduling, and insurance coverage discovery and financial assistance. This “reflect[s] a broader strategy to enhance patient engagement and streamline front-end processes.” 

These results are also consistent with Oliver Wyman’s research, which reported a “lack of a single dominant use case” in its survey of nearly 300 RCM leaders and end users. That said, the consultancy described a “core demand stack” of “no-regret AI investments” that includes ambient documentation, coding automation, clinical documentation improvement, and prior authorization. 

Organizations tend to prioritize these use cases because they integrate well with existing workflows, produce measurable impacts, and address points of friction in administrative processes. This aligns with an assessment from the American Hospital Association, which noted AI’s potential to decrease time spent on clinical record-keeping and otherwise reduce the reliance on understaffed manual processes.  

Invest now to avoid competitive gaps

Not surprisingly, organizations with broader adoption of AI within the revenue cycle are more likely to increase their spending on the technology, Oliver Wyman found. This will have a positive compounding effect as efficiency and revenue gains increase. It also “may reshape competitive dynamics,” especially if larger organizations continue to make gains while smaller, community-based organizations struggle to keep pace. 

Providers looking to ramp up investments also need to consider the complexities of AI governance and risk management, which are no small matter considering RCM’s dependence on sensitive clinical and financial data. There’s also vendor relationships, as Oliver Wyman noted there’s much to consider when choosing between best-of-breed and platform models. Organizations would be wise to “prioritize deliberately” as they consider their path forward. 

Brian Eastwood is a Boston-based writer with more than 10 years of experience covering healthcare IT and healthcare delivery. He also writes about enterprise IT, consumer technology, and corporate leadership.