Revenue cycle management automation and AI: three use cases

Key takeaways:

• Revenue cycle teams can use automation to reduce repetitive work and apply AI where data-driven prediction, matching or prioritization can improve a workflow.
• Experian Health’s 2025 State of Claims survey found that 41% of providers now face denial rates of 10% or higher, while 68% say submitting clean claims is more challenging than a year ago.
• Patient Access Curator (PAC) uses AI to support front-end data validation and insurance discovery, while AI Advantage helps teams predict denial risk and prioritize denial follow-up.

Artificial intelligence (AI) and automation can support administrative work in healthcare. In the revenue cycle, teams depend on accurate information, timely decisions and efficient follow-up to keep claims moving.

In revenue cycle management, AI and automation can help organizations reduce manual checks, find data gaps, predict denial risk and prioritize work queues. These tools are most useful when they support staff judgment, payer expertise and compliance oversight. They can handle repetitive, data-heavy tasks so staff can focus on exceptions and decisions that need human review.

In 2023, McKinsey & Company reported that research suggests effectively deploying automation and analytics could eliminate $200 billion to $360 billion of spending in U.S. healthcare. For revenue cycle leaders, the practical question is where to apply those capabilities first.

The case for applying AI and automation in healthcare

Revenue cycle teams juggle many daily tasks. Staff collect and verify patient information, confirm eligibility, identify the right payer, submit clean claims, monitor status, work denials and manage collections. Small data gaps at the beginning of the process can create downstream rework and delays.

Rework also consumes staff time, adding to these operational pressures. As costs rise and revenue cycles tighten, there is increasing pressure to do more with less. Experian Health’s 2025 State of Claims survey found that 54% of providers say claim errors are increasing and 90% of claim denials are reworked with at least some human review before resubmission.

Providers are also managing broader financial and administrative pressures. The American Hospital Association has reported that prior authorization requirements, claim audits, denials and other payer policies add administrative burden and cost for hospitals and health systems. These requirements also consume staff time to appeal denials and manage payer processes.

AI and automation are different but complementary. Automation follows defined rules to complete repeatable work. AI models can identify patterns in data, predict risk and help teams decide where to focus attention. When used together, they can support more consistent revenue cycle workflows.

How AI and automation can support revenue cycle workflows

Revenue cycle management automation and AI are most useful when tied to a specific workflow and a measurable operational problem. The goal is to help teams act earlier, reduce avoidable rework and focus staff time where judgment is needed most.

For example, automation can complete rule-based eligibility checks. AI can help identify claims with a higher likelihood of denial. In insurance discovery workflows, AI can also help identify coverage that wasn’t captured at registration. When these tools fit into existing workflows, they can support more consistent decisions and reduce manual work.

Three practical applications include:

1. Improving front-end data quality with Patient Access Curator

Patient and coverage information collected early in the revenue cycle can affect downstream claim outcomes. Incomplete or outdated demographic details, eligibility responses, coordination of benefits or Medicare Beneficiary Identifier information can create problems that lead to claim delays or denials later in the cycle.

Experian Health’s Patient Access Curator helps prevent claim denials by validating demographics, eligibility, insurance discovery, coordination of benefits and Medicare Beneficiary Identifier data in seconds. PAC’s AI and machine learning capabilities help improve match accuracy, coverage sequencing and data confidence by writing the validated data back into the host system and sequencing payers before the claim is created. This automates work that would otherwise require manual coverage checks.

2. Using insurance discovery to find coverage not captured at registration

When active coverage isn’t identified during registration, claims may be delayed or submitted with incomplete insurance information. Insurance discovery looks for coverage that may not have been captured during registration.

Patient Access Curator includes insurance discovery as part of its front-end validation workflow. It can help identify and correct missing or incorrect insurance information so claims can be submitted with more complete coverage data.

3. Using AI to prevent and prioritize denials

Even with strong front-end processes, some claims still require additional attention. AI can help claims teams decide which claims to review before submission and which denials to work first after payer response.

This approach can help teams prioritize with more confidence. Rather than treating every claim or denial the same way, teams can use predictive models to focus on the work that needs the most attention.

Potential benefits of AI and automation in the revenue cycle

A high-performing revenue cycle depends on timely, accurate and consistent work. AI and automation can help providers modernize that work without losing the expertise of the people who manage complex payer and patient situations every day.

When applied to the right workflows, these tools can help organizations:

• Reduce manual data searches that take staff away from higher-value work
• Improve front-end data quality before claims are created
• Identify missing or incorrect coverage information earlier
• Spot claims that may be at higher risk of denial
• Prioritize denied claims by potential reimbursement
• Reduce rework caused by inaccurate or incomplete information
• Give staff more consistent information for follow-up decisions

A focused AI strategy starts with the workflow problem, uses data that is relevant to that problem and keeps staff in control of judgment-based decisions.

The Total Economic Impact of Experian Health Patient Access Curator, a Forrester study commissioned by Experian Health, modeled fewer coverage-related denials and less manual discovery work for a composite health system when using PAC.

Download the study >

FAQs

A more proactive approach to revenue cycle management

Revenue cycle teams can move from reactive work toward a more proactive approach: catch errors earlier, validate coverage before claims are created and prioritize the claims and denials that need the most attention.

Experian Health offers revenue cycle solutions that use AI and automation in targeted ways to support front-end data quality, reduce rework and manage denials. Patient Access Curator supports registration and coverage validation, while AI Advantage supports denial prediction and triage.

Learn more about Experian Health’s Patient Access Curator and AI Advantage.

The post Revenue cycle management automation and AI: three use cases appeared first on Healthcare Blog.