Editor's Note: This is the second article in a three-part series on Agent AI in Revenue Cycle Management. Find the first article here and look forward to another article on implementation considerations.
As technology vendors and providers continue to invest in the latest evolution of artificial intelligence, Agent AI is in the early adoption stage, but offers several high-value use cases in revenue cycle management.
Agent AI's use cases in revenue cycle management focus primarily on improving efficiency, accuracy and automation to improve financial health and ultimately patient experience. Important areas where agent AI is applied at this point include claim management, advance approval, denial management, and patient financial involvement.
Health professionals place their faith in Agent AI solutions. According to a recent Salesforce survey of 500 healthcare professionals, AI agents could reduce the burden on administrators by 30% for doctors, 39% for nurses and 28% for administrative staff.
But revenue cycle technology vendors don't think that will stop. Agent AI is poised to disrupt the revenue cycle. Some experts believe the technology can automate 80% of revenue cycle jobs.
Patient eligibility and benefits verification
Agent AI can automate patient eligibility and benefits verification processes and transform the once highly manual and labor-intensive tasks.
AI agents that leverage natural language processing can identify and capture appropriate information from insurance cards, EHRs, other health IT systems, and payer systems for verification.
Through the application program interface, AI agents can verify benefits and eligibility in real time.
Using Agent AI to verify patient eligibility and benefits can accelerate revenue cycles and reduce the likelihood of rejection from eligibility errors. Healthcare organizations can also increase the number of validations and smooth out medical billing and collections.
Patient eligibility and benefits validation is also a top use case cited by medical professionals at Salesforce Survey, with 70% saying they want to use agents in this and other aspects of healthcare.
Previous approval
Previous approvals are one of the biggest challenges in healthcare today. The infamous, troubling task of getting precertified for services is linked to adverse patient events, delays in care, and over-management. However, Agent AI aims to overcome all of that.
Agent AI can autonomously collect clinical documents and patient data from the EHR system, review payer policies and requirements, complete and submit authorization forms, and track requests. This technique can also do this with minimal human intervention.
Additionally, vendors have designed AI agents to proactively identify potential issues that affect approval of previous approvals, whether compliance issues or missing documents.
Similar to AI-based eligibility and verification, using AI agents for advance approval can clear clean claim paths.
“We're thinking about some of the preservation tasks, so we're thinking about a lot of what's all been successful, including eligibility verification, profit verification, pre-certification,” said Dan Parsons, co-founder and chief experience officer of the company, which offers AI-driven revenue cycle technology.
Refusal management and appeal
The refusal of claims continues to rise as the revenue cycle handles increased billing volumes. AI can help fill this gap amidst a lack of staffing. But Agent AI in particular can revolutionize how revenue cycles manage denials.
Its autonomous nature allows Agent AI solutions to analyze claim rejection codes, identify patterns and trends, and retrieve the data they need to correct errors. Additionally, Agent AI can prioritize rejection based on revenue impact, allowing healthcare institutions to get the most out of their profits.
Vendors are also seeing opportunities to use agent AI to automate the appeal process.
Perhaps one of the most powerful use cases at the moment, Agent AI has completed the claim refusal appeal process from initial denial to filing an appeal.
“We believe the rejection appeal process is another really good use case,” explained John Landy, Finthrive's Chief Technology Officer. “If we can think about it today, humans are essentially dealing with denials, calling the payer, knowing what happened, packaging packets of sue, and resubmitting the claim to the payer.
Agent AI can eliminate its manual work and scale from appealing faster and greater negation overturned operations.
Billing Management
What makes billing management so complicated is part of the ripeness of AI confusion is the act of analyzing payer contracts.
With data, there's a hockey stick curve for use cases and the tool is now incredible.
John Randy, CTO, acquired
Agent AI can automatically analyze payer agreements to learn the rules and requirements for submitting clean claims and gather the information needed to file a claim. Once an AI agent passes the process, it can also learn what may be paid or denied, and adjust the practice to suit those trends.
“Loading contracts from payers is a really good use case,” Randy said. “The industry generally wants to do it very quickly and it's very manual, so there are a lot of error-prone steps to it.”
Using Agent AI in billing management can reduce approval times by streamlining and automating billing processing and submissions. More accurate pre-build scrubbing provides mid- and back-end efficiency.
Patient financial communication
Consumers encounter AI agents in many industries for customer service. Healthcare can leverage this solution to get better patient financial communications. Agent AI can streamline interactions with healthcare consumers and patients while providing a personalized experience.
AI agents can handle daily billing inquiries. These are processed online by phone or email. Agents can answer frequently asked questions about fees and payment options and process payments. Agents can even explain the bill to the patient and provide personalized billing information including deductible status and outstanding balances.
Additionally, agents with agent AI can provide multilingual support.
The vendor has already achieved success with AI agents at patient contact centers, and Judson Ivy, founder and CEO of Ensemble Health Partners, reports a higher one-touch resolution rate. This means that more patients will be able to answer questions at first contact.
These agents are typically designed to escalate communication when AI agents no longer meet the needs of users. Thus, agent AI can streamline patient call center workflows, but it also allows humans to recognize when it is necessary for solutions.
Beyond use cases
Several use cases emerged during this early adoption phase, which appears to be the tip of the revenue cycle agent AI iceberg.
“We got the data, so we have a hockey stick curve for our use case and the tools are now incredible,” Randy explained. “So we can scale applications and move from ideas to delivery very quickly. In the past, it was previously a science project, testing models for a year and sometimes it didn't work.
However, according to Ivy, Agent AI doesn't necessarily just find use cases.
“I hate the word use cases because the goal is not to develop a case that works. It's to develop a goal that allows enterprise deployments to be made,” Ivy explained.
Agent AI's orchestration components rely on a wider range of applications across the organization. If AI agents can not only communicate with other AI agents, but also learn from each other, their revenue cycles can move faster.
Jacqueline Lapointe is a graduate of Brandeis University and King's College London. She has written about healthcare finance and revenue cycle management since 2016.
