Real-Time Medical Coding

Automating the assignment of ICD-10, CPT, and HCPCS codes during or shortly after patient encounters.

How it Works

AI models process clinical notes, EHR data, and physician documentation to recommend or assign correct medical codes in real time.

Benefits

• Faster turnaround time for claims.
• Reduced manual coding effort.
• Increased coding accuracy.

Use Case: Automating coding for emergency department visits or outpatient care.

Natural Language Processing (NLP) for Clinical Notes

Extracting clinical terms and relevant coding information from unstructured data such as physician notes, transcriptions, or discharge summaries.

How it Works

NLP tools process large volumes of free-text data to identify diagnosable conditions, procedures, and treatments.

Benefits

• Streamlined coding from unstructured EHR content.
• Reduced human error.
• Scalability across specialties.

Use Case: Identifying pneumonia diagnosis and corresponding treatment codes from physician’s notes.

Predictive Coding Recommendations

AI models recommend appropriate medical codes based on historical data, patient demographics, and visit types.

How it Works

Machine learning predicts codes most likely to align with a given encounter, assisting coders.

Benefits

• Enhances coder productivity.
• Reduces decision fatigue for manual coders.

Use Case: Predicting potential procedure codes for a common outpatient visit.

Handling High-Volume Claims

Automating coding for high-volume, routine encounters such as wellness visits, vaccinations, or lab tests.

How it Works

AI identifies repetitive patterns and applies standardized codes automatically.

Benefits

• Saves manual effort for routine coding.
• Allows coders to focus on complex cases.

Use Case: Automating lab result coding for preventive health screening

Integrating AI Coding with RPA (Robotic Process Automation)

Automating end-to-end coding workflows, including data extraction, code generation, and claim submission.

How it Works:

RPA bots handle repetitive tasks while AI ensures coding accuracy and compliance.

Benefits

• Full process automation.
• Improved operational efficiency.

Use Case: Coding and submitting claims for radiology reports without manual intervention.

Identification of Documentation Gaps

Automatically identifying incomplete or ambiguous clinical documentation that could impact coding accuracy or reimbursement.

How it Works

AI and Natural Language Processing (NLP) analyze clinical notes to identify missing diagnoses, procedures, or severity information.

Benefits

• Ensures documentation specificity.
• Reduces claim denials due to incomplete information.

Use Case: Flagging missing “cause and effect” relationships for conditions like sepsis.

Optimizing Clinical Documentation for Value-Based Care

Ensuring documentation supports value-based care programs such as HCC and quality measure reporting.

How it Works

AI identifies documentation opportunities to capture risk-adjusted conditions and outcomes.

Benefits

• Enhances performance in risk-based contracts.
• Improves quality measure reporting accuracy.

Use Case: Highlighting missed HCC codes like diabetes with complications.