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.

Reducing Days in Accounts Receivable with Machine Learning
In the healthcare industry, revenue cycle management is crucial for maintaining the financial stability of hospitals and clinics. One of the most significant challenges in this process is reducing the number of days in accounts receivable (AR), which is the amount of time it takes for a healthcare provider to collect payment for services rendered. Reducing AR days is essential for maintaining a positive cash flow and ensuring the financial health of healthcare organizations. Machine learning can help healthcare providers identify bottlenecks in their revenue cycle and enhance cash flow by optimizing their AR process.
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Combating Healthcare Fraud with Machine Learning
Healthcare fraud is a persistent issue in the industry that leads to significant financial losses and negatively affects patient care. With the rise of electronic health records and the increasing complexity of medical billing, identifying and preventing fraudulent activity has become more challenging. However, machine learning is proving to be a powerful tool in detecting billing irregularities and preventing healthcare fraud.
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Improving Clean Claim Rates Using Data Analytics
Healthcare providers face several challenges when it comes to managing revenue cycle operations. One of the biggest challenges is the high rate of claim denials and rejections. According to a report published by the American Medical Association (AMA), the average denial rate for medical claims is around 5% to 10%. However, the cost of resubmitting denied or rejected claims can be expensive and can impact the financial stability of healthcare organizations. Therefore, improving clean claim rates is critical for the financial health of healthcare providers.
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Streamlining Prior Authorization Processes with Data-Driven RCM
The prior authorization process is a critical step in the revenue cycle management (RCM) process for healthcare organizations. This process ensures that patients receive the appropriate care they need while also ensuring that the healthcare provider gets paid for their services. However, the prior authorization process can often be a source of frustration for both patients and providers due to delays and denials. In recent years, healthcare organizations have turned to data-driven RCM solutions to streamline prior authorization processes, reduce delays, and ensure coverage.
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Best-practices in Data-Driven Denial Management
As healthcare costs continue to rise, it is essential for healthcare providers to effectively manage their revenue cycle to remain financially stable. One of the significant challenges in revenue cycle management is managing claim denials. According to a recent study, approximately 9% of claims are denied, which can lead to significant financial losses for healthcare providers. However, leveraging data, machine learning, analytics, and automation can help healthcare providers sustain and improve their denial management processes.
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Why Visual Analytics is Only Part of the Story
As healthcare organizations seek to optimize their revenue cycle operations, there is growing interest in the use of machine learning to generate data insights. While visual analytics tools are often used to present these insights, there is a growing understanding that narrative-based communication may be more effective for conveying complex insights and driving action.
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Why 835/837 Analysis is not Enough
The healthcare industry has always been complex, and one of the most significant challenges facing healthcare providers is revenue cycle management. One of the most common issues that healthcare providers face is claim denials, which can be caused by a variety of factors, including coding errors, lack of documentation, and eligibility issues. To address these denials, healthcare providers often rely on the 835/837 analysis, but is this enough?
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Resolving Credit Balances with Data-Driven Analytics
In healthcare revenue cycle management, credit balances are a common occurrence. A credit balance is an amount of money that a healthcare provider owes to a patient or insurance company for overpayment or over-adjustment of claims. Resolving credit balances is crucial in maintaining financial stability and compliance for healthcare providers. Traditionally, healthcare providers have relied on manual methods to resolve credit balances, which can be time-consuming and error-prone. However, with the help of machine learning and data analytics, credit balance resolution can be optimized, improving efficiency and accuracy.
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Enhancing Patient Payment Predictability
In today's healthcare landscape, one of the biggest challenges for healthcare providers is collecting patient payments. The rise in high-deductible health plans has left patients with a larger portion of the bill to pay, and as a result, healthcare providers are seeing an increase in patient responsibility for payment. This has led to a rise in uncollected debt and a need for healthcare providers to improve their collection efforts. In order to improve collection efforts, healthcare providers can leverage data to enhance patient payment predictability.
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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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