How AI can power a zero-defect approach to the revenue cycle
AI tools and a zero-defect philosophy toward claims submission can untangle revenue cycles, reducing denials and accelerating revenue.
The healthcare provider revenue cycle remains complex, inefficient and expensive. Provider financials often suffer from a backlog of denied claims and difficulties in collecting amounts due from patients.
The reasons for ongoing revenue challenges are myriad. They include the complexities of medical coding, the need to comply with the unique submission requirements of multiple payer contracts and prior authorization disagreements. The bottom line is that rejected and denied claims, whether through payer or provider error, are expensive to appeal, rework and reprocess.
While these issues may seem to present an impenetrable snarl, AI tools and a zero-defect philosophy toward claims submission provide golden threads healthcare providers can tug on to untangle their revenue cycle and experience fewer denials and faster revenue realization.
Applying zero defect thinking
Many aspects of the claims, billing and denials management processes should be repeatable and standardized – much like a manufacturing production cycle. Industrial production lines feature pods of experts who specialize in a part or process and who can immediately identify and remedy a potential or current issue. Pod leaders even have the authority to shut down a line immediately when a defect occurs.
Healthcare providers can apply similar thinking to their revenue operations, aided by current AI tools. The industry’s complexities make a zero-defect goal ambitious. However, AI co-pilots, agentic AI workflows, generative AI tools and analytics can offer insights providers can use to streamline the revenue cycle and mitigate issues causing claim rejections and denials.
Here’s a quick look at where and how providers may apply AI to drive defects out of their revenue cycle.
Coding, billing and appeals
AI co-pilot tools can augment the expertise of experienced coders, reviewing medical records and notes in seconds and identifying appropriate diagnostic and procedural codes supported by a patient’s clinical data. Coders can work more efficiently while the tools create audit trails supporting the coding choices.
Similarly, generative AI tools can quickly retrieve specific contract requirements and verify whether a claim complies with them. Then, the provider’s revenue team can correct any issues before submitting the claim.
Providers may automate claim status checks with AI. When a claim is rejected or denied, AI tools can generate an appeals letter and manage resubmissions autonomously. More complex cases may be routed to business office associates, with AI co-pilots locating required documentation to shorten the appeals process.
Root cause analysis
Understanding why claims fall out of a payer’s adjudication process is essential for correcting issues and preventing their recurrence.
Most providers already categorize their accounts receivable (AR) inventory by payer and specialty, service type, patient type and more. Analytics tools plus AI enable providers to go further and evaluate as many claims subcategories as possible.
One example would be examining all claims with a specific CPT code across a specific dollar range. Generative AI could summarize the commonalities and discrepancies among the patient records for analytics tools to investigate. This effort could reveal that a payer always rejects a specific code, or that the payer’s rejection code is erroneous. Quickly recognizing and mitigating these issues minimizes their impact on submitted claims and subsequent reimbursements.
Tracing dependencies
Root cause analysis can reveal where issues outside coding and claims submission processes are leading to claims denials. Eligibility and prior authorization are common issues that AI tools can help mitigate.
AI tools can streamline patient pre-registration and eligibility verification and guide patients through payment option selections. For prior authorization, AI tools can query payer portals, identify which procedures require prior approval, then generate the request and attach supporting documentation.
Dependencies may extend into providers’ clinical practices, such as clinical documentation variances. Supporting evidence from AI and analytic tools revealing the financial impact of these variances can help persuade clinicians to adjust their methods.
Developing new proficiencies
As providers analyze their receivables and better understand root causes of denied claims, they also can begin to see what “expert pods” they may develop.
Providers could choose one large group of denied claims (such as cardiology-related claims), conduct a root cause analysis, then train coders on mitigating those issues, assisted by AI coding tools.
Every provider is likely to require different AI and coding proficiencies depending on their payer mix and claim types and volumes. Some providers may need their coders and billers to be generalists. Others may have the claims volumes to justify specializing in specific claim types, such as radiology or pathology.
Getting started
A zero-defect approach to claims may seem overwhelming. Here’s how to make it less daunting.
Start small. Zero in on specific pain points to tackle first, rather than overhauling the entire revenue cycle. Most providers can identify their most time-consuming claims work and denial problem areas. AI tools and analysis can help providers further stratify which group of claims to focus on initially, whether by payer or specialty or CPT code.
Emphasize AI is for augmentation, not replacement. AI agents and co-pilots are digital team members that exist to make coders’ work more efficient and rewarding by taking over tedious and time-consuming tasks, such as looking up payer procedures. While business office staff will need to adjust to different workflows when AI tools are introduced, it should soon be obvious how AI is helping them work smarter and recover more revenue.
Follow governance and compliance guardrails. When using AI tools, providers must follow all regulatory requirements for security and privacy and ensure their vendors do the same.
Benchmark and measure. Improved actual dollars reimbursed against AR valuation is a good baseline and evaluation metric. With clear improvement in reimbursements for the initial group of claims, providers can then explore other areas in which AI can support the zero-defect approach to the revenue cycle.
Proposed legislation affecting Medicare and Medicaid payments and rising utilization rates are putting financial pressure on the entire industry. Adopting a production line-inspired zero-defect approach to the revenue cycle, powered by AI tools, will help providers avoid denied claims, maximize reimbursements and strengthen their balance sheets.
Yvonne Perez is global head of provider operations for Firstsource.