Using NLP (Natural Language Processing) to Extract Billing Data from Unstructured Clinical Notes

Medical billing is evolving rapidly as healthcare organizations strive for accuracy, efficiency, and compliance. One of the most significant challenges in modern revenue cycle management (RCM) is extracting billing-relevant information from unstructured clinical notes. Unlike structured fields in Electronic Health Records (EHRs), unstructured notes contain free-text narratives written by physicians, nurses, and other healthcare providers. These notes often hold crucial data for accurate coding, documentation, and claim submission.

Natural Language Processing (NLP)—a subset of artificial intelligence—offers a transformative solution. By interpreting, analyzing, and extracting key information from free-text clinical documentation, NLP streamlines the billing workflow, reduces errors, and maximizes revenue recovery.

At Right Medical Billing, we leverage NLP-driven systems alongside human expertise to ensure practices can extract accurate billing data from unstructured notes, optimize claim submissions, and comply with regulatory standards.

The Challenge of Unstructured Clinical Notes

Clinical notes are rich in detail but inherently complex:

• Physicians often write in narrative form, mixing abbreviations, shorthand, and clinical jargon.

• Notes include diverse data: patient history, symptoms, diagnostic impressions, procedures performed, and follow-up plans.

• Key billing elements such as CPT, ICD-10, or HCPCS codes may not be explicitly stated.

Traditional billing relies on human coders manually reviewing notes—a time-consuming, error-prone process. Errors in extracting correct codes or missing documentation can result in claim denials, delayed payments, or underbilling.

How NLP Solves the Problem

Natural Language Processing uses AI algorithms to understand human language and extract structured data from unstructured text. For medical billing, NLP can:

1. Identify Relevant Clinical Data: Extract diagnosis, procedures, medications, and lab results from physician notes.

2. Map to Standard Codes: Translate clinical terms into ICD-10, CPT, or HCPCS codes for claims submission.

3. Detect Missing or Inconsistent Information: Flag documentation gaps, mismatched codes, or insufficient clinical justification.

4. Accelerate Workflow: Automate routine coding tasks, allowing human coders to focus on complex or ambiguous cases.

For example, a pulmonology note stating, “Patient with chronic obstructive pulmonary disease (COPD) underwent spirometry and medication adjustment”, can be analyzed by NLP to extract:

• ICD-10 code: J44.9 (COPD, unspecified)

• CPT code: 94010 (spirometry)

• Modifiers: If performed in conjunction with another procedure, NLP may flag required modifier codes.

Key Benefits of NLP in Billing

1. Increased Accuracy

NLP reduces coding errors by standardizing how clinical data is interpreted. This minimizes denied claims due to incorrect or incomplete coding.

2. Faster Turnaround Time

Automated extraction significantly shortens the time between note creation and claim submission, improving cash flow.

3. Improved Compliance

By ensuring all relevant documentation is captured and properly coded, NLP supports HIPAA compliance and auditing readiness.

4. Scalable Across Specialties

NLP is adaptable across specialties like internal medicine, cardiology, pulmonology, nephrology, and behavioral health, even for practices with high patient volumes.

Relevant CPT Codes NLP Helps Capture

For common specialties, NLP can extract codes such as:

Internal Medicine:

• 99213 – Office or other outpatient visit, established patient

• 99214 – Higher-complexity outpatient visit

• 81002 – Urinalysis, non-automated, without microscopy

Pulmonology:

• 94010 – Spirometry, including graphic record

• 94640 – Pressurized or non-pressurized inhalation treatment

• 94664 – Demonstration and/or evaluation of patient utilization of an aerosol generator

Cardiology:

• 93000 – Electrocardiogram, routine EKG with interpretation

• 93306 – Echocardiography, transthoracic, complete

• 93224 – External electrocardiographic recording up to 48 hours

These codes are critical for accurate billing, and NLP ensures that procedural or diagnostic mentions in free-text notes are captured and mapped correctly.

Implementing NLP in Medical Billing Workflow

At Right Medical Billing, our approach integrates NLP into the RCM process as follows:

Step 1: Data Ingestion

Clinical notes are collected from EHR systems, telehealth platforms, and other documentation sources.

Step 2: NLP Processing

Advanced NLP algorithms analyze notes for key billing data, identify relationships between symptoms, diagnoses, and procedures, and extract relevant entities.

Step 3: Coding and Mapping

Extracted data is mapped to standardized codes (ICD-10, CPT, HCPCS), generating preliminary claims for review.

Step 4: Human Review

Experienced coders validate NLP-extracted data, resolving complex or ambiguous cases.

Step 5: Submission and Reporting

Clean, validated claims are submitted to payers, and analytics dashboards provide insights into trends, denials, and coding performance.

This hybrid approach combines AI speed with human expertise, ensuring both accuracy and efficiency.

Use Cases Across Specialties

1. Internal Medicine

NLP identifies routine office visits, lab orders, and chronic condition management, ensuring correct CPT and ICD coding for high-volume primary care practices.

2. Pulmonology

Extracts detailed spirometry, inhalation therapy, and COPD management data, linking procedures with diagnoses to prevent claim denials.

3. Cardiology

Captures procedural codes for echocardiograms, stress tests, and ECG monitoring, ensuring complete billing for complex cardiovascular services.

Challenges and Considerations

While NLP offers tremendous benefits, successful implementation requires attention to:

• Data Quality: Poorly documented notes can limit NLP accuracy. Encouraging consistent clinical documentation practices improves results.

• Continuous Model Training: NLP models must be updated with new codes, payer rules, and specialty-specific terminology.

• Integration: Seamless integration with EHRs, billing platforms, and RCM systems is critical for workflow efficiency.

At Right Medical Billing, our team ensures that NLP systems are continuously refined and integrated into our multi-specialty billing workflows.

Final Takeaway

Natural Language Processing is revolutionizing medical billing by transforming unstructured clinical notes into structured, billable data. By extracting accurate information for ICD-10, CPT, and HCPCS coding, NLP reduces errors, accelerates reimbursement, and improves compliance. Practices that adopt NLP, especially when combined with expert billing teams like Right Medical Billing, can maximize revenue, scale operations efficiently, and focus more on patient care instead of paperwork.

The future of medical billing lies at the intersection of AI, data analytics, and human expertise, and NLP is a critical tool for achieving that balance.