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Denial Prediction Using Machine Learning: A Practical Tool for Healthcare Leaders

by Sudhir Kshirsagar

Introduction

Imagine a system that can automatically analyze your historical claims data, detect patterns, and present actionable insights about future denials—even before they happen. As an RCM leader, you might wonder, “Is this possible? And if so, how does it work? Is it reliable?”

The short answer is yes. It is not only possible but increasingly accessible. Industries like finance and retail have long leveraged machine learning (ML) and artificial intelligence (AI) to make data-driven predictions, and now healthcare is catching up. Early adopters are already seeing the benefits of these technologies as they efficiently analyze large datasets, categorize claims, and recommend actions to improve your claims acceptance rate.

How Does Machine Learning (ML) Technology Work?

The key to effective denial prediction is your historical claims data. This data holds critical information about patterns, trends, and reasons for past claim denials. Machine learning models thrive on this kind of historical data because they learn from examples, understanding relationships between different claim characteristics (like procedure codes, diagnosis codes, patient demographics, POS, provider type, payer type and specific connections they form) and denial outcomes.

Denial prediction using machine learning can anticipate claims that are prone to being denied by payers at three distinct phases of the claim lifecycle:

Predicting denials before the patient is seen

Before the patient even arrives, ML systems can analyze insurance details to spot potential issues with eligibility and prior authorizations. For example:

If a patient is scheduled for an endoscopy, this procedure is known to require prior authorization from BCBS, ML will flag it before the appointment, ensuring that all necessary approvals are in place.

By cross-referencing patient demographics and insurance plan details, ML models can also predict if services are at risk of denial due to coverage limits. One such as the rule exists where Aetna allows only three ultrasounds during pregnancy unless a medical complication requires more.

This early detection allows healthcare providers to fix any issues before the patient is seen, drastically reducing the chances of denials down the line.

Predicting denials after the patient is seen but before billing

Once the patient has been seen, and the services are documented, ML and AI step in again to catch errors in the coding and billing process. At this stage, the system can:

Flag coding errors, such as missing modifiers or incorrect CPT/ICD codes.

Identify payer-specific rule violations, like bundling issues, frequency limits, or even documentation proof that a payer may potentially request.

This phase ensures that claims are clean and accurate before submission, drastically reducing denials for errors that can be easily avoided.

Predicting denials after the claim is released

Even after the claim is submitted, AI can help by monitoring claims in real-time for issues like timely filing risks or delays in processing. The system might detect patterns of payer behavior that indicate a claim is likely to be delayed or denied, allowing your team to act before it officially gets rejected.

For example, ML could recognize that a UHC consistently requests for medical records when Angioplasties are billed without a 59 modifier

Bucketing the data: classifying and valuing claims

Once the ML system processes the data, it classifies claims into various “buckets.” This involves grouping claims based on:

Claims with missing or incorrect coding (e.g., modifier issues, NCD/LCD mismatches).

Payer-specific rules violations (e.g., Aetna’s ultrasound rules, BCBS out-of-state billing).

High-risk claims, such as those at risk for timely filing violations.

By sorting claims in this way, ML helps RCM teams quickly identify and prioritize claims that need attention, saving time and preventing future denials.

Role of AI: classifying, valuing, and recommending

After claims are bucketed, AI steps in to:

Classify claims based on risk levels.

Value claims, helping teams prioritize them based on financial impact.

Recommend actions, like correcting specific codes, resubmitting claims with missing data, or preparing for an appeal based on historical success.

For instance, if a set of claims tends to be denied due to specific CPT code errors, AI will recommend fixing those codes, reducing the chance of denials and speeding up payments.

The Role of WhiteSpace Health’s RevIntel Module

WhiteSpace Health is at the forefront of this transformation making this advanced tech accessible to larger markets. The RevIntel module leverages ML and AI to help healthcare providers predict denials at all three stages—before the patient is seen, after services are rendered but before billing, and after the claim is submitted. RevIntel offers real-time insights and actionable recommendations that improve claim accuracy and increase approval rates.

Conclusion: A Smarter Approach to Denial Management

Machine learning and AI provide healthcare organizations with a smarter, more proactive approach to denial management. By leveraging advanced tools like WhiteSpace Health’s RevIntel module, RCM teams can predict and prevent denials at every stage of the claim lifecycle, ensuring smoother workflows and stronger financial outcomes.

About Sudhir Kshirsagar

Sudhir Kshirsagar serves WhiteSpace Health as VP of Client Services. Sudhir has deep experience in revenue cycle outsourcing. Based in Atlanta, Sudhir is known for driving successful client implementations that result in strong return on investment.

sudhir.kshirasagar@whitespacehealth.com