Revenue cycle management AI: Work smarter, not harder

Under pressure to collect outstanding revenue, healthcare billing teams face fragmented systems that can force them to spend valuable time simply identifying what needs attention.

They may search for unpaid balances or spot-check claim statuses randomly across multiple platforms. Without a consistent way to prioritize work, specialists may follow up wherever they identify a potential reimbursement opportunity.

This is called “work hunting,” and it’s a far cry from effective and efficient revenue cycle management (RCM). The problem happens when too much focus is on downstream revenue collection activities instead of upstream process improvements. That is, if healthcare organizations become overly preoccupied with chasing individual payments instead of developing a more strategic process for RCM workflows, they’re likely to create burdens and miss opportunities.  

At the same time, implementing RCM process improvements is a big ask for teams working with limited resources. Fortunately, artificial intelligence (AI) can help. Used responsibly, AI can help billing specialists go from hunting work to proactively identifying, prioritizing, routing, and executing it. They can also help anticipate payer trends that inform coding and billing practices to reduce wasted work downstream.  

All of this leads to more value gleaned in both revenue capture and time saved. Here’s how. 

The cost of inefficient revenue cycle operations

Some teams approach RCM tasks manually and selectively. Those tasks often include finding non-payment, picking which claims to follow up on or resubmit, and toggling different systems to identify and resolve issues individually.

It’s a siloed and often disorganized approach that stretches teams’ bandwidth and yields plenty of frustration with very little value.  

And yet, it’s understandable why work hunting happens. Healthcare financial operations teams are doing their best with limited resources and continued financial strain. These challenges may be especially acute for smaller and rural practices, where staffing constraints can magnify the effects of inefficient workflows.

Still, work hunting is a solution that can exacerbate the very problem it’s trying to solve: Inefficient RCM is one contributor to the up to 5% of revenue that providers fail to capture.1 Spinning wheels looking for overdue amounts or claim submissions creates administrative burdens and costs in a time when many healthcare teams can’t afford either.  

That’s because manual work discovery takes up time that should be spent on the areas with the greatest financial impact. For instance, consider these more insidious but potentially significant impacts of work hunting:

• Staff capacity gets consumed with low-value searches.
• Specialists work the wrong claims at the wrong times.
• Interventions get delayed.
• There’s no mechanism in place to learn from recurring payer problems.

Ultimately, when RCM workflows are fragmented, there is no clear direction or task delegation for billing staff. Without the benefit of that direction, it’s a matter of manually determining which work needs to be completed next rather than relying on a strategic process in which work is effectively triaged and routed to billing teams.

So, there’s the problem: RCM requires teams to continuously identify what needs attention, prioritize it, route it, act on it, and iteratively learn from that experience. While legacy RCM systems have basic filtering capabilities that help with this, they can’t necessarily parse out the high-value tasks (such as knowing which denials should be appealed, and how) from the rest.  

That’s the space where AI excels, which makes revenue cycle management such a strong use case for AI. 

Using AI in healthcare RCM

AI like that enabled through athenaOne® supports a more efficient and smarter RCM approach in a few different ways.  

First, it can reduce work hunting. Agents act like project managers that help determine what work needs to happen next, prioritize it, and execute or route work to RCM specialists. These tools can, for example, automate the scrubbing of claims and send alarms when certain actions are required. With that support, billing teams don’t have to waste time searching for outstanding tasks that need to be done — the work gets routed directly to them.

Another way AI helps is through predictive intelligence. This capability gives billing teams a special opportunity to anticipate, navigate, and even preempt payer or payment challenges.  

For example, the technology can:

• Analyze payer processing trends to recommend optimal follow-up windows.
• Automate denial advice with suggested changes before claim resubmission.
• Surface potential write-off candidates for authorized staff review, based on organization-defined policies and thresholds.  
• Review denial trends to support denial prevention in the future.
• Support coders through technology that identifies CPT codes, like Express Coding does in athenaOne.

Having AI tools integrated within a single AI-native RCM system, as with athenaOne, makes all the difference. Any healthcare team can combine different point solutions for different purposes, but if the point is to reduce work hunting, that also means reducing the need to cross-check multiple platforms for multiple pieces of information. Also, with an integrated system, there’s the benefit of tools that get iteratively better over time as more data is fed into the models for analysis. 

RCM workflow automation frees up staff for high-value work

Once RCM workflows become smarter and more operationalized with AI, teams may start noticing the benefits over time. Operational burdens can start to feel less heavy as they phase out. Teams save time in their search for tasks, and they can experience more purposeful work day-to-day as outstanding needs are triaged, delegated, and completed based on priority.

RCM decision-making may also become more informed. When staff know when to follow up or how to resubmit claims, they’re more likely to move those claims toward successful reimbursement.

When specialists are equipped with more time and clearer priorities, they can then focus on other, more meaningful areas such as patient interaction, which is increasingly important as AI tools assume tasks elsewhere. They can also save their bandwidth for human review of the more complex claims that are on the rise as care delivery and payer policies evolve.

Achieve smarter healthcare financial operations

Stronger revenue capture starts with a more coordinated RCM process. By helping teams identify and prioritize work, surface payer patterns, and automate routine tasks, AI can reduce avoidable manual effort and support more informed follow-up. An integrated RCM system can help staff focus their time where judgment and intervention matter most.  

Learn how athenaOne uses AI to support RCM workflows.