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Developing Strong Revenue Protection with AI Tools

18 December 2025
Blog
4.5 minute read

TriZetto Provider Solutions

This blog is presented by TriZetto Provider Solutions as part of their sponsorship of the 2025 eClinicalWorks® and healow® National Conference.

Image of a doctor in a white lab coat leaning up against a wall with AI medical icons overlaid on top of that photo

Healthcare practices face more reimbursement challenges than ever. New legislation is changing eligibility rules for Affordable Care Act health plans. Payers are tightening networks in their attempts to curtail Medicare Advantage plan losses. Congress is still eyeing cuts to Medicare in the wake of reducing federal funding for the Medicaid program. These pressures on reimbursement rates make it imperative for health practices to optimize revenue recovery and minimize leakage.

Many practices primarily deal with reimbursement issues by trying to manage denials and appeals in the back office. That’s a bit like taking two legs off a three-legged stool, then trying to balance on it. Front-end and midstream activities have a huge impact on revenue realization. Based on our analysis of denied claims, we found that 41% of denials are due to front-end issues; 25% to charge capture and coding; and 34% to claims preparation and submission.

With problems in any of these phases resulting in patient friction, denied claims and financial harm, health practices need to consider developing a revenue protection strategy. Revenue protection is a holistic approach that helps your practice receive optimal reimbursements on all services delivered. Today’s AI capabilities give medical practices of all sizes practical, powerful ways to protect revenue at each stage of the revenue cycle. We explore below how AI tools deliver effective options for revenue protection tactics during the eligibility, charge capture and coding and claims preparation and submission phases of the revenue cycle.

Reducing Errors in Eligibility and Prior Authorization

With more than 40% of claim denials citing incorrect eligibility and lack of prior authorization, protecting revenues clearly should begin the minute a medical practice engages a patient. AI tools can streamline eligibility verification and prior authorization requests and help ensure accurate results. Medical groups and practices can also incorporate payment options at this stage to set up collections of co-pays and patient balances.

In addition, providers can lay strong foundations for AI-driven prior authorization workflows by adopting tools that incorporate FHIR-based application programming interfaces (APIs) today. The various FHIR-compliant APIs provide real-time payer data and guidance within office workflows so that providers can identify payer requirements and documentation needs before they create prior authorization requests.

AI-enhanced eligibility and prior auth procedures will help your practice avoid denied claims and bad debt write-offs. Clearer communication up front about patient responsibility also should reduce questions about coordination of benefits (COB) statements later.

Optimizing Charge Capture and Coding

While it’s probably obvious accurate coding and charge capture are cornerstones of revenue protection, what may be less clear is just how complicated these tasks have become. Complex payer contracts, bundled payment arrangements, new compliance requirements all require expert, experienced coders. But this talent is increasingly difficult to find.

That’s one reason AI tools are poised to fill larger roles in capturing charges and coding claims. AI-based medical scribes can quickly capture physician notes, and AI tools can point out keywords within the transcriptions and suggest correct codes. Automated and continuous chargemaster monitoring can help ensure coding is accurate and avoid missed charges and underpayment while improving compliance.

Getting Claims Paid on the First Pass

Far too much time is spent on managing denied claims—and some research indicates many group practices don’t even try to appeal these claims. Using revenue protection tactics at the front and middle of the revenue cycle should help make claims preparation and billing operate more smoothly. In addition, today’s AI and automation tools excel at helping practices generate clean claims that conform to payer requirements for first-pass reimbursement.

That capability includes helping group practices catch any issues with claims before they are submitted. For example, today’s tools can automatically run an eligibility verification check that confirms the patient had the appropriate coverage on the date care was delivered. If the claim fails that test, the provider can decide how to proceed. In fact, some tools can prioritize claims by their value and likelihood of payment so providers can decide how many resources to devote to pended claims.

AI claims editing tools can also help ensure the accuracy of codes and that they conform to a specific payer’s requirements. For example, our Advanced Claims Editing tool incorporates more than 30,000 rules that validate everything from medical necessity and modifier usage to age and gender appropriateness. These edits ensure claims are coded correctly before submission, helping prevent denials and delays.

Here’s just a sampling of the pre-submission issues AI tools can flag for correction: a claim line billed with a V2784 HCPCS code for polycarbonate lenses that is missing information detailing medical necessity or a required prior authorization is absent; a duplicate claim that will get flagged as an A1-N522; a claim with potential coordination of benefits issues (reason code 22) because another payer is involved; a claim line that may snag an A1-M15 or 97 denial code because it was already billed as part of a bundled procedure.

In addition, AI tools built on machine learning models train on data sets to learn specific tasks. Our Advanced Claim Editing tool is trained on hundreds of millions of historical claims and remits across geographies, payers and providers to learn how to predict the potential for a claim to be denied and why. The tool also analyzes CARC and RARC issues. With those insights, practices can take steps to amend a claim and possibly make changes to front end and midstream processes to solve and prevent future denials.

AI as Revenue Protection Collaborator

Health practices can embed AI tools in users’ existing workflows, from eligibility through claim editing. The tools are assistants, helping employees throughout the revenue cycle work more efficiently and with less frustration. By protecting revenue at each phase of the cycle with intelligence and automation, AI tools help improve a practice’s financial health while freeing clinicians and office staff to focus on the most important job of all: caring for their patients.

Want to see how eClinicalWorks users are proactively protecting revenue? Explore how TriZetto Provider Solutions can support your goals.