According to our Mindsets 2026 Healthcare Executive Leadership Report, revenue cycle challenges are one of the top concerns for healthcare leaders, and 72% say their organizations are investing in additional technology solutions to address these challenges and improve performance.
Advancements in AI, analytics, and automation are creating new opportunities and reducing traditional barriers to performance improvement, and many leaders recognize the potential. When we asked executives where AI could have the greatest impact on their daily operations, revenue cycle management—specifically claims billing and follow-up—was the top response. In addition, 42% of executives say their organization is currently investing to embed AI solutions in the revenue cycle.
However, technology adoption remains uneven across the healthcare industry, with some organizations much further along in their journey than others. Organizations looking to leverage automation and AI in their revenue cycle management strategy should establish a strong analytics foundation while evaluating and prioritizing more advanced use cases to harness the value of these evolving capabilities.
Revenue Cycle Leaders Should Establish a Strong Foundation of Performance Reporting & Analytics
Revenue cycle analytics and performance reporting remain critical to monitor performance and address issues. Many organizations struggle to identify key metrics for departments, monitor trends, and drill into detail. As organizations consider implementing AI solutions to help automate and enhance these efforts, strong foundational reporting and clear data priorities, including focus on the areas below, are vital first steps:
- Accounts receivable (AR), denials, and payment variances
- Charges, payments, and adjustments
- Department metrics (patient access, revenue integrity, billing, collections, etc.)
How Can Revenue Cycle Leaders Evaluate & Prioritize New Advanced AI, Analytics, & Automation Use Cases?
Organizations should evaluate new AI revenue cycle use cases by applying a strategic framework to support prioritization that asks key questions across the following areas:
- Patient Care: How can automation and AI tools help improve access, affordability, or continuity of care?
- Operational Performance: How can automation and AI tools help improve revenue, reduce cost, or mitigate organizational risk?
- Requirements: What resources, investment, and timeline are needed to achieve and sustain results?
- Strategic Fit: Does the AI use case support and align with the organization’s mission, top goals, and priorities?
Prioritizing use cases for AI, analytics, and automation is dependent on each individual organization’s profile and goals.
What Are Potential Use Cases for Advanced Analytics, Automation, & AI in Revenue Cycle Management?
We have observed several themes and specific examples of adoption of analytics, automation, and AI solutions for revenue cycle management across our hospital and health system clients. Below, we explore a few.
Advanced Queries of Operational & Financial Data
Some hospitals and health systems are using AI, analytics, and automation for advanced queries and harmonization of 837 (claim), 835 (remit), and electronic medical record (EMR) system details to help uncover revenue or system issues. Examples include:
- Identifying Neglected AR: Querying AR, worklist, and note details to identify accounts not actively worked or falling into a system worklist
- Identifying Claims Without Remittances: Querying claims submitted (837) with no corresponding remittance (835) to identify re-bill and payor receipt issues
Uncovering Compliance & Regulatory Insights
Another common use case for revenue cycle analytics is monitoring the organization’s adherence to regulatory requirements that can have significant impacts on revenue or carry risk of penalties for noncompliance. Examples include:
- Identifying No Surprises Act Independent Dispute Resolution (IDR) Candidates: Using analytics to identify out-of-network claims for which IDR may help increase revenue, as well as to monitor resolution status
- Monitoring Price Transparency Compliance: Using analytics to continuously validate published rates, machine-readable files, and payor-specific pricing data to avoid potential penalties and adhere to regulatory requirements
Automating & Enhancing Revenue Cycle Workflows
Hospitals and health systems are also using AI and automation to help enhance and automate core revenue cycle management tasks in an effort to increase revenue, reduce cost, and improve patient care. Examples include:
- Pre-Service Financial Clearance: Automating and enhancing tasks to schedule and register patients, including capturing insurance, obtaining payor authorizations, and screening for medical necessity
- Denials & AR Management: Automating and enhancing tasks to collect unpaid balances, including retrieving claim statuses, filing appeals, and other claim follow-up resolution activities
How Forvis Mazars Can Help With AI, Analytics, & Automation in the Revenue Cycle
Our revenue cycle team combines extensive operational, technological, and compliance experience to help organizations navigate the evolving digital landscape and turn emerging analytics, automation, and AI capabilities into measurable performance improvement. If you have questions or would like assistance with implementing advanced technology solutions or identifying potential use cases at your organization, please reach out to our professionals today.