9 data points proving AI in healthcare works

Clinician looking at an AI-generated summary in the patient chart to assist documentation.
 Marty Fenn, athenahealth Content Manager
Marty Fenn
December 19, 2025
8 min read

AI-native athenaOne® delivers measurable improvements in efficiency, accuracy, and financial performance

Healthcare leaders face constant pressure to increase efficiency and reduce costs, while preserving the quality of care. Artificial intelligence has long promised to help, but now the results are in: AI in healthcare is providing measurable benefits.

Across athenahealth’s network of participating practices, AI-native athenaOne® is already producing gains in clinical documentation efficiency, billing accuracy, and operational throughput. Each capability — whether it’s our Chart Assistant with Sage™, ChartSync, or AI-powered tools for prior authorization, coding, and denials management — capitalizes on the foundational Advanced Intelligence Layer in athenaOne. That means every AI-enabled tool learns from real encounters and claims to deliver tangible improvements for the organizations that use them every day.

The following 9 data points show how AI capabilities in athenaOne are helping practices reclaim time, strengthen financial stability, and improve the patient experience without additional technology burden. 

1. 60% of clinicians close more encounters within one day

Clinicians using Ambient Notes are seeing increased efficiency in closing same-day encounters. In fact, 3 out of 5 providers close more encounters within one day of the visit, with an average improvement of 28%.1 This is just one of the proof points highlighting the value of AI in clinical efficiency. That’s why we’re going deeper.

We have introduced new functionalities within ChartSync, which helps organize and update patient data automatically in an effort to reduce redundancy and maintain clean, accurate records – including deduplicated external data and vaccine reconciliation. At Thrive 2025, we unveiled our proprietary ambient solution, athenaAmbient in addition to showcasing Chart Assistant with Sage  during a demo of the AI-native encounter. Together, these AI-native capabilities can  help keep documentation and data integrity aligned for less after-hours charting while aiding faster billing readiness, empowering clinicians to end their day on schedule.

2. 98.4% clean-claim submission rate

Customers on athenaOne see on average a 98.4% clean-claim submission rate.2 Automated payer-rule validation – over 30,000 of them – and coding intelligence built into athenaOne help improve the accuracy of claim submission the first time they go out the door. Clean submissions translate to faster reimbursement and lower administrative overhead.

What’s more, Robotic Process Automation (RPA) is becoming a thing of the past. Self-healing agentic AI agents take charge in traversing payer portals and experimenting with payer surveillance to more proactively spot payer policy changes that might affect claim rules. That enables athenahealth to quickly identify and develop the rules that contribute to clean claims. Additionally, AI can help enhance billing efficiency via Express Coding to help accelerate reimbursement cycles.

Together, these AI-driven capabilities are already improving on that industry leading clean-submission rate by leveraging end-to-end automation to help improve accuracy.

3. 26.4% payment recovery improvement for coding-related denials

There’s also value in utilizing intelligent AI to help with resubmitting claims when a coding-related denial occurs. Coding Advice will provide accurate, easy-to-understand recommendations to fix and resubmit coding-related denials. Practices using Coding Advice are already seeing a 26.4% increase in payment recoveries for coding-related denials and are accepting the AI-generated advice 40% more frequently than the prior human-generated advice.3 

Additionally, AI will provide Automated Write-Off Advice by identifying charges that may not be recoverable – helping reduce work related to denials management while also helping to give a clearer picture of net collection rate and aging AR.

4. $45 billion in annual collections data train our AI

Every successful payment that moves through the athenahealth network contributes to model refinement. With over $45 billion in collections analyzed annually,4 the AI adapts continuously to payer trends and best practices, leading to better revenue cycle outcomes for all of our customers.

Across athenahealth’s network of participating practices, AI-native athenaOne® is already producing gains in clinical documentation efficiency, billing accuracy, and operational throughput. 

5. 170K+ providers contribute to AI training data

Not every clinician of the 170K+ on the athenaOne network5 uses every AI feature, but the shared data environment allows models to learn from diverse workflows and specialties. That breadth creates stronger AI pattern recognition, broader applicability, and fairer performance across different practice types.

6. 12.8% fewer insurance-related denials

Multiple functionalities within athenaOne revenue cycle management (RCM) and practice management toolset help drive denials down. Express Authorizations help automate prior authorization determinations to streamline authorization processes across various processes. With standardization in place, practices can obtain prior authorization approval without leaving athenaOne via direct electronic integrations with payers. Meanwhile, our AI-powered claims engine scrubs and flags claims to help support complete claim submission. Scrubbing can also verify payer-specific checks. All these elements help prevent denials upstream. athenaOne customers had a median monthly claims denial rate of 5.7% in 2024, meeting the industry standard of 5-10%.6

Verifying patient insurance and ensuring correct insurance details on file is another crucial component in helping prevent denials upstream. This is where AI optical character recognition (OCR) can make a difference. The Automated Insurance Selection workflow in athenaOne uses AI OCR to help identify and automate insurance selection upon uploading of patient insurance cards. Practices using the Automated Insurance Selection workflow have seen a 12.8% reduction in patient insurance-related denials after claims submission.7

7. 35% fewer insurance-related rule hold-rates

If the athenaOne rules engine determines a claim is at risk of payer rejection, that claim is flagged and held for further action. While this pause is critical to send clean claims and help prevent denials, too many holds can slow down your revenue cycle and create a backlog for billing staff. athenaOne customers using the Automated Insurance Selection workflow see a 35% reduction in insurance-related rule-holds for claims compared to manual insurance selection8 – accelerating revenue cycles and bolstering the case for leveraging AI RCM tools.

Helping practices reduce insurance-related denials and rule holds becomes more of a premium when done efficiently and conveniently, hence the release of Insurance Capture in athenaOne. This feature automatically initiates patient outreach to obtain the insurance card image before or after an appointment and triggers Automated Insurance Selection. Insurance Capture can help track which patients have responded by providing a card on file and which have not – subsequently using any new information to support resubmission of claims.

8. 315 million claims processed annually

athenahealth processes more than 315 million claims every year,9 creating a feedback loop that helps to continuously strengthen the AI models behind athenaOne. Each transaction contributes to training algorithms that recognize payer behavior, detect denial trends, and predict claim outcomes with increasing accuracy. This scale gives every participating practice the advantage of collective intelligence—where insights learned from millions of encounters translate into smarter, faster, and more reliable workflows and payments across the network. 

9. 89% reduction in document processing time

athenahealth has utilized AI in helping automate document processing for incoming external faxes for several years. Why? Several statistics highlight the value of AI-powered fax labeling in helping improve document management efficiency. 

Customers using Document Services AI workflows have seen an 89% reduction in document processing time.10 And, since the 25.7 release of our Predicted Document Labels for Admin Documents feature, athenaOne customers using the feature have saved over 10+ million clicks11 … and counting. 

Building a smarter, more sustainable practice 

Each of these metrics reflects a practical benefit: fewer clicks, faster claims, steadier revenue, and better patient experiences. But what truly sets athenaOne® apart is how those capabilities work together and evolve. 

Because the platform is cloud-based and AI-native, new features are rolled out automatically when they become available. That means there’s no need to manage upgrades, add-ons or downtime. Customers always have access to the latest tools without disrupting daily operations. 

Practices can also join alpha and beta programs, gaining early access to emerging features such as athenaAmbient.™ These early participants help guide development, ensuring each release addresses real clinical and operational needs before it scales across the network. 

Innovation advances continuously, shaped by the people who use athenaOne every day. 

Proof that AI-native EHR and RCM tools are working 

AI-native technology is now proving its value across every dimension of practice performance. By embedding intelligence directly into athenaOne, athenahealth helps organizations streamline documentation, strengthen revenue integrity, and simplify operations.

For healthcare administrators and physician-owners, these results mark the first of many proof points that AI is making a measurable difference — clinically, operationally, and financially — and the advantages will continue to expand with every update and as more users across the network use tools.

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  1. Based on athenahealth data as of May 2025 for providers who onboarded on Ambient Notes from Feb-May 2025, with three months of consistent Ambient Notes usage and at least 10 encounters per month; M264
  2. Based on athenahealth data for 12 months ending March 2025; results compared to competitors' self-reporting of clean claim submission rates; M164
  3. Based on athenahealth data from May-July 2025; M277
  4. Based on athenahealth data for 12 months ending Dec. 2024; M015
  5. Based on athenahealth data as of Sept. 2025; M010
  6. Based on athenahealth data as of May 2025; M246
  7. Based on athenahealth data for 12 months ending June 2025. athenaOne customers who created claims with new insurance policies selected using the Automated Insurance Selection workflow saw a 12.8% reduction in patient insurance-related denials on those claims compared to those using other methods; M236
  8. Based on athenaOne data between July 2023 – June 2025, customers who created claims with new insurance policies selected using the Automated Insurance Selection workflow saw a 35% reduction in patient insurance related rule-hold rates on those claims compared to their claims with new insurance policies created without using the Automated Insurance Selection workflow; M242
  9. Based on athenahealth data for 12 months ending Dec. 2024; M016
  10. Based on athenahealth data as of Dec. 2024, comparing median processing time for documents in 2018 to 2024; M239
  11. athenahealth internal data 2025