How Clinical NLP Entity Extraction Is Improving Medical Coding Accuracy for a Health System

The Challenge

Coders were experts at the code sets, but much of their day went to hunting through free-text notes for the evidence each code required.

The existing approach made it difficult to:

• Find every documented diagnosis and procedure across long, multi-author encounter notes.
• Distinguish confirmed conditions from negated, historical or family-history mentions.
• Spot documentation gaps early enough for clinical documentation integrity (CDI) queries.
• Apply coding guidelines consistently across facilities and coders.
• Show auditors the exact note text behind each assigned code.

From a technical perspective, clinical text is dense with abbreviations, misspellings, copy-forward content and templated sections. Meaning often depends on negation, uncertainty and temporality ("rule out pneumonia", "no history of diabetes"). All processing had to stay within HIPAA-aligned controls, and results had to land in the EHR and encoder the coding team already used.

The Solution

InnovationM designed a clinical NLP layer that turns encounter documentation into structured, evidence-linked findings. Notes are pulled from the EHR, sectioned, analyzed for clinical entities and context, and mapped to candidate codes that coders review and finalize. The solution included:

• Note ingestion and sectioning: HL7/FHIR-based extraction of encounter documents with section detection for history, assessment and plan.
• Clinical entity extraction: a domain-adapted transformer model that identifies diagnoses, procedures, medications and findings.
• Context detection: negation, uncertainty, temporality and subject classification so only confirmed, current, patient-specific conditions drive suggestions.
• Terminology mapping: normalization of extracted concepts to standard vocabularies and candidate ICD-10-CM and CPT codes.
• Evidence-linked review: each suggested code shown with highlighted source sentences, so coders accept, edit or reject with one click.
• CDI query support: flags for missing specificity (laterality, acuity, stage) routed to CDI specialists, with pipelines and HIPAA-aligned controls built with InnovationM's data engineering team.

The key focus was not simply automating code assignment, but giving coders the evidence for every code so accuracy and audit readiness improved together.

The Impact

Coding moved from reading every note end to end to reviewing evidence-linked suggestions, and CDI teams began receiving specificity gaps while the patient was still in-house. The solution supported:

• Higher coding accuracy on audited encounters.
• Fewer coding-related claim denials.
• Shorter time per chart for inpatient and outpatient coding.
• More CDI queries raised before discharge.
• Consistent, auditable evidence trail for every code.

By bringing together clinical entity extraction, context detection, terminology mapping and evidence-linked review, the health system gave its coders a faster and more defensible way to code.