The future of AI-enabled intelligent revenue management.

Best-practice revenue cycle leaders use healthcare's most advanced AI-Enabled Financial Performance Tools to improve margin, accelerate cash and optimize staff performance.

in numbers

Providers see results with Etyon's data, insights and workflow automation tools.


Healthcare Organizations


Years of Healthcare Expertise


Data Points Analyzed


ROI Driven to Date


Revenue Cycle Leakage Stops Here.

Etyon's AI-enabled Financial Performance tools help leaders enhance insights and automation practices in some of the following areas:

Front-Office Denial Prediction

Predict denials prior to service to optimize front-end workflows and increase first pass payment rates.

Accounts Receivable Prioritization

Risk score every clean or denied claim in your accounts receivable every night to ensure appropriate aging policies and resource allocations across work-queues.

Staff Productivity / Touch Optimization

Know exactly how many touches and what cost resources are having on buckets or invoices to ensure optimal follow-up performance.

Order and Referral
Leakage Mining

Automatically find and quantify all order mismatch and missing referral leakage across physicians, services lines and more.

Payment Accuracy and Scorecarding

Determine payment accuracy on mixes of claims, payers, services and denials to ensure accurate and timely reimbursement.

Leakage Mining

Automatically find and quantify all registration leakage across locations, representatives and payer plans.

Automated Authorization
Status Review

Automatically audit the authorization request and delivery process to ensure all claims have appropriate authorizations prior to service.

POS Patient
Estimate Accuracy

Capture more cash and financially clear more patients prior to billing.

Leakage Mining

Automatically find and quantify dollars lost through non-covered services and claims management inefficiencies.

Vendor Placement
Recovery Audit

Automatically reconcile and audit claims placed with Vendors to ensure maximum external recovery efforts.

Treasury Reserve

Automatically determine reimbursement and recovery risk to determine accurate and consistent financial reserves.

ED Throughput

Identify optimal discharge times, schedule staff according to acuity, and understand ordering turnaround times.

Start simple, scale to sophistication

Leading the Way to AI-Enabled Revenue Cycle

Drive your revenue cycle to be more dynamic, collaborative, and intelligent with connected data, people and processes.

Data Chaining

Surface valuable insights when you collect, store, and clean your data.

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AI and MLE

Automatically find answers in your data without the need for manual analysis.

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Analytics-as-a-service (AaaS) automates 75% of data insights work.

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Smart Workflows

Smarter decision support directly in your Electronic Medical Record.

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Information is Everything.

Harnessing Machine Learning for Workforce Optimization
Healthcare organizations are turning to machine learning to revolutionize revenue cycle management. Machine learning enhances workforce performance by optimizing resource allocation, prioritizing claim follow-up activities, setting competitive wage rates, designing effective performance incentives, enabling career planning, and guiding Performance Improvement Plans (PIPs). By leveraging data-driven insights, healthcare providers can improve efficiency, reduce costs, and ensure high-quality patient care in an increasingly complex healthcare landscape.
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Future-Proofing Denials Management: Leveraging AI and Analytics in Revenue Cycle Management
As the realms of artificial intelligence (AI) and automation continue to evolve, their potential to redefine revenue cycle management (RCM) in healthcare is becoming increasingly apparent. However, despite these advancements, the revenue cycle best-practice methodology behind managing account inventory in health systems has remained largely static and inefficient. This creates significant revenue and cost risk, particularly in an era characterized by shifts in payer policies, workforce shortages, and loss of institutional knowledge, all of which exacerbate the challenge of overturning denials. To counter this risk, leading revenue cycle teams are adopting a more strategic approach to denials management—one that harnesses analytics in groundbreaking ways to bolster inventory workflow and optimize revenue.
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What Can the AI Buzz Actually Do for RCM?
Artificial intelligence (AI) has become a buzzword in recent years, but what does it actually mean? Artificial Intelligence (AI) is a branch of computer science that seeks to create machines and computer programs that can simulate human intelligence, including the ability to learn, reason, and make decisions. Over the last few decades, AI has made significant progress, transforming industries such as finance, retail, and transportation. Healthcare is one industry that has been particularly impacted by AI, with machine learning playing a critical role in optimizing the revenue cycle management process.The revenue cycle is the process of generating revenue for healthcare providers, which includes everything from billing and coding to claims processing and payment collection. For healthcare providers, the revenue cycle is a critical function that ensures that they are paid for the services they provide. However, the revenue cycle process can be lengthy, complicated, and prone to errors, which can lead to payment delays, denials, and lost revenue.This is where AI and machine learning come into play. By automating processes and using data analytics to identify patterns and trends, healthcare organizations can streamline the revenue cycle process and reduce errors, leading to faster reimbursement times and increased revenue.
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Using Data and Automated Insights to Help Guide Third-party Patient Financing
The cost of healthcare in the United States has risen steadily over the years, leading to an increasing number of patients unable to pay for their medical expenses. This has created a significant challenge for healthcare providers, who must find ways to ensure that they are paid for their services while also ensuring that their patients have access to the care they need. One solution to this challenge is the use of third-party patient financing, which can help providers manage their revenue cycle while also providing patients with affordable payment options.
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Using Machine Learning in RCM to Overcome Staff Shortages
Accounts receivable revenue cycle management is an essential function of any healthcare provider, as it directly impacts the organization's cash flow. However, managing accounts receivable can be challenging, especially when there are staff shortages. Fortunately, advances in technology have made it possible to use machine learning to overcome these staffing challenges and improve the revenue cycle management process.
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Why Using Machine Learning to Analyze Human Performance is as Important as RPA
In healthcare, the accounts receivable follow-up process is an essential component of financial management. However, it is a complex and time-consuming task that often requires extensive resources, including skilled labor and technology. In recent years, many healthcare organizations have been turning to robotic process automation (RPA) to streamline their accounts receivable processes. While RPA can help to automate repetitive tasks and reduce the workload of human workers, it is not always enough to ensure accurate and timely reimbursement. This is where machine learning comes in.
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Best-practices in Visual Analytics
Visual analytics design is the process of creating effective visual representations of complex data in order to gain insights, communicate information, and facilitate decision-making. As the amount of data available to organizations continues to grow, visual analytics is becoming increasingly important. However, not all visual analytics designs are created equal. In this article, we will explore some best practices in visual analytics design to help you create effective and impactful visualizations.
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Navigating ICD-11 Implementation
The International Classification of Diseases (ICD) is a globally recognized diagnostic tool used by healthcare providers to classify and code diseases, injuries, and other medical conditions. The World Health Organization (WHO) recently released the 11th revision of the ICD, which includes new codes, updates to existing codes, and improved functionality. As healthcare organizations transition to the new version, there are steps they can take to ensure a smooth and successful transition, including the use of data analytics.
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Data-Driven RCM in Telehealth
The COVID-19 pandemic has brought about a significant shift in the healthcare industry, with a growing emphasis on telehealth services. While remote care has been available for some time, it has become increasingly important in light of the pandemic as a way to reduce the risk of transmission and ensure patient safety. However, the shift to telehealth has presented new challenges when it comes to billing and reimbursement, which has led to the adoption of data-driven revenue cycle management (RCM) solutions.
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Partners say

Don’t take our word for it - hear from your provider peers

"We're now working the right accounts at the right times to where our timely filing write-offs reduction have drastically reduced and that's great."


"When you combine amazing technology with folks that know revenue cycle challenges inside and out, you get tremendous results."

Lab Director

" I knew we had denial challenges, but could never uncover them as quickly as Etyon's solutions allows us to without all the effort."

Director Process Improvement

AI-Enabled financial performance tools

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