Medical claims adjudication determines whether a payer will pay, deny, or further query a claim. Traditional, rule-based methods struggle with coding complexities and stricter compliance rules, letting payers barely reach 85% accuracy. Errors and fines alone cause $36 billion in losses every year across the industry. These issues slow down payments, drain resources, and frustrate both payers and providers. 

The good news is that, in this chaos, machine learning (ML) has been pushing the accuracy rate to the brink of 99% and streamlining adjudication workflows in real time. In this blog, you’ll explore common claims adjudication challenges, how ML and AI tackle them, and what measurable financial benefits payers can expect.

Scenarios Where Errors Happen in Medical Claims Adjudication

Around 80% of medical bills contain errors, significantly impacting the claims adjudication process. These errors can arise from multiple points in the workflow, and some occur far more often than others. This could be anything from incomplete personal or insurance policy details to wrong doctor or provider information.

Coding mistakes are another significant challenge. Healthcare providers often take part in complex medical coding, especially when a case has too many procedures during a single hospitalization. Medical codes like ICD-10 diagnostics, CPT procedures, and HCPCS supply codes must match the records perfectly. One wrong digit in the procedure code, and a good claim can turn into a denial.

Errors in policy interpretation worsen this problem. Regulatory policies change quite frequently, and both manual reviewers and automated systems require timely updates to maintain accuracy. Some of the error-prone areas are authorization needs, coverage limits, deadlines, and benefit exclusions.

Furthermore, medical fraud detection and duplicate claim submissions create security issues. False positive alerts create significant challenges for investigation teams, potentially impacting their ability to focus on genuine fraud patterns. 

How Machine Learning Helps Payers Achieve 99%+ Accuracy

Fortunately, intelligent machine learning technologies can fix each error source in claims adjudication with automated tools. They constantly learn from past data patterns, modify their processes accordingly, and adapt to new situations, all in real-time.

Here’s how ML addresses each error source: |
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1. Intelligent Data Processing and Validation

Machine learning models excel at checking and fixing data quality. You can leverage Natural Language Processing (NLP) to pull important information from medical documents, patient information, and historical patterns and fill data gaps automatically that would otherwise cause denials.

Machine learning algorithms that are driven by artificial intelligence (AI) can cross-reference patient information across multiple databases and flag mismatches to reduce repeat submissions. For instance, if a patient's insurance ID is missing, ML can often locate it by matching demographic data across databases.

2. Automated Coding Verification

Advanced AI models are trained on millions of medical records in order to achieve greater accuracy in checking codes. They can analyze clinical documentation and verify ICD-10, CPT, and HCPCS code assignments in real-time. NLP understands medical terms in context, finds coding errors that manual reviewers might miss, and suggests correct codes.

ML can also detect unbundling attempts by verifying procedure code combinations against standard medical practices. This helps prevent overpayment and ensures proper reimbursement for complicated cases and procedures.

3. Compliance Alignment and Policy Enforcement

CMS and state regulation policies are frequently updated across multiple payer networks. When these policy changes are integrated into the system, Intelligent algorithms can rapidly apply new coverage guidelines, prior authorization requirements, and benefit limits across claims adjudication workflows.

They help evaluate claims against policy requirements before processing begins, and use historical approval patterns to identify which claims will likely pass review and which ones need more scrutiny.

4. Medical Fraud Detection and Prevention

Deceitful billing patterns usually hide in plain sight, completely invisible to traditional rule-based systems. By analyzing historical data, AI and ML algorithms can predict and flag suspicious claims by analyzing providers’ billing habits, usage trends of patients, and relationships between diagnostic codes.

Models use behavioral analytics to compare individual providers against similar practitioners to spot unusual activities. Anomaly detection feature learns standard billing patterns for each specialty and region, then flags only genuine irregularities. This way, investigators get alerts that matter, making medical fraud detection faster and more accurate.

5. Adaptive Learning and Error Protection

Each adjudicated claim, whether human or ML reviewed, feeds back into the system automatically, which helps it improve its decision-making algorithms on its own. With each decision, more training data is added, which further refines accuracy. Meanwhile, new error patterns are spotted quickly, and validation rules get updated accordingly. In addition, high-risk claims are flagged before the process starts.

This early warning lets providers correct issues at submission, cutting admin work for both sides and preventing problems before they escalate.

6. Measurable Benefits for Payers and Providers

Integrating machine learning into claims adjudication can help payers achieve near-perfect accuracy of 99%. However, it depends upon the solution they choose. For example, HOM RCM’s AI-powered claims adjudication process delivers 24-hour turnaround time and 99%+ payment accuracy. This directly translates into faster reimbursements and reduced operational costs.

Here’s a recent case study that illustrates the impact: A Florida-based third-party administrator (TPA) partnered with HOM to automate its adjudication workflow. Within the agreed timeframe, error rates fell by 95% while manual review hours dropped by 525 per month. The TPA also cut adjudication handling time by 75%.

Over time, the company’s claim processing team reported 35% less manual intervention with more free resources available for higher-value tasks. Improvement in employee productivity and confidence was also noted, which further strengthened the operational performance.

Final Thoughts

Healthcare payers ready to achieve 99%+ accuracy in claims adjudication must adopt machine learning technologies strategically. Start with structured data validation, then layer on automated coding checks, real-time payer policy alignment, medical fraud detection, and adaptive learning systems.

Work with a technology provider who knows healthcare regulations and compliance and choose solutions that integrate smoothly with your existing systems instead of requiring a full infrastructure overhaul.

HOM offers this very approach, delivering state-of-the-art claims adjudication services that combine advanced AI and machine learning capabilities with human expertise and deep healthcare industry knowledge.

This dual approach enables HOM RCM to process routine claims efficiently, while offering personalized attention to complex cases.

Additionally, you can leverage real-time dashboards to monitor claim status and performance metrics and robust denial management to recover potential lost revenue.

Request a free audit if you wish to learn how HOM RCM’s claims adjudication services can optimize your revenue cycle workflow and help you achieve 99% accuracy.