Condition Normalization Agent
Standardize applicant health conditions into ICD/SNOMED codes for structured and interoperable medical data
Description
Challenge:
Health underwriting and claims adjudication often rely on unstructured or ambiguous condition inputs from applicants and claimants. Free-text declarations such as “heart pain” or “stomach issues” cause inconsistent mapping, missed exclusions, and frequent manual rework. These inefficiencies create delays in eligibility checks, policy misalignment risks, and increase exposure to fraudulent claims when provider authenticity or diagnostic evidence isn’t validated. Without condition standardization, insurers face costly errors and poor decision accuracy.
How It Works:
The Condition Normalization Agent uses NLP and medical ontologies to convert free-text health conditions into standardized clinical codes (ICD-10, ICD-11, SNOMED). It validates mapped conditions against policy definitions, exclusions, and mandatory evidence requirements such as labs or imaging. The agent also cross-verifies provider authenticity and ensures prescriptions align with declared conditions. By generating structured, validated outputs, it enables underwriters, claims adjudicators, and predictive models to operate with accuracy, consistency, and fraud safeguards.
Benefits:
Achieves 94%+ accuracy in mapping conditions to ICD/SNOMED codes
Reduces manual rework from unclear declarations by 70%
Cuts processing delays from ambiguous health inputs by 45%
Improves fraud detection by 30% through provider/evidence validation
Ensures 100% structured outputs ready for underwriting and claims systems
Resources
Features
The Condition Normalization Agent ensures every applicant and claimant condition is standardized, validated, and policy-aligned before progressing to underwriting or claims adjudication.
Features & Capabilities:
Free-Text Recognition: Extracts health conditions from raw applicant or claimant inputs.
Clinical Code Mapping: Standardizes inputs to ICD-10, ICD-11, SNOMED, or insurer codes.
Coverage Alignment: Validates mapped conditions against policy coverage and exclusions.
Evidence Checks: Flags missing diagnostics such as labs, imaging, or prescriptions.
Provider Verification: Confirms authenticity of healthcare providers submitting evidence.
Drug–Condition Validation: Cross-checks prescriptions with declared health conditions.
Multilingual Normalization: Converts layman or multilingual terms into clinical equivalents.
Low-Confidence Handling: Routes uncertain matches to manual review.
Structured Output: Delivers coded, audit-ready data for claims and underwriting systems.
Fraud Safeguards: Detects anomalies in evidence or provider authenticity.
Operating Blueprint
Knowledge Sources:
Health Declarations & Records: Capture conditions from forms, chats, or scanned medical documents.
OCR & Parsing Engine: Extract and interpret clinical details from unstructured inputs.
ICD-10/ICD-11 Mapping: Standardize conditions into global clinical taxonomies.
Policy & Evidence Repositories: Align conditions with coverage rules, labs, and prescriptions for validation.
Business Rules:
Condition Mapping Rule: All inputs must map to ICD/SNOMED or insurer code.
Evidence Requirement Rule: Flag if mandatory diagnostics (labs, imaging, prescriptions) are missing.
Coverage Alignment Rule: Cross-check mapped condition against policy coverage and exclusions.
Provider Validation Rule: Confirm legitimacy of medical provider submitting records.
Drug–Condition Match Rule: Cross-reference prescriptions with declared condition for consistency.
Confidence Threshold Rule: If mapping confidence <80%, route for manual review.
Synonym Expansion Rule: Translate layman terms into standardized medical terminology.
Multi-Condition Rule: Detect and validate multiple declared conditions separately.
Tool Workflow:
Capture applicant/claimant condition input (free-text, form, or document extract).
Apply NLP to detect probable medical condition(s).
Normalize terms into ICD-10/11, SNOMED, or insurer-specific codes.
Validate mapped condition against policy definitions and exclusions.
Check for presence of mandatory supporting evidence; flag gaps if any.
Verify prescribing or diagnosing provider against authorized registries.
Cross-check prescribed drugs with normalized conditions for consistency.
Generate structured, coded condition record with metadata and validation log.
Deliver output to underwriting, claims adjudication, or predictive modeling engines.
Badges
Classification
Capabilities
