Disease Categorisation Assistant: Structuring Medical Complexity for Underwriting & Claims

Neutrinos Platform AI/ML with ADA
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The Problem: Transforming Raw Medical Evidence into Consistent Risk Insights

In insurance underwriting and claims, every medical condition is more than just a label — it’s a risk factor, a potential exclusion, and a determinant for policy benefits or pricing.

Applicants can submit:

  • Symptom descriptions: e.g., “chest pain,” “high blood sugar”

  • Diagnostic codes: ICD-10 E11.9 (Type 2 Diabetes) or I10 (Hypertension)

  • Medical evidence: discharge summaries, lab panels, imaging reports, bills, and physician notes

Manually classifying these conditions into clinically meaningful disease categories is slow, error-prone, and inconsistent. Challenges include:

  • Long underwriting cycles

  • Ambiguous claims adjudication

  • Increased reliance on individual expertise

  • Poor audit traceability

Insurers require fast, standardized, auditable disease classification — and that’s the purpose of the Disease Categorisation Assistant (DCA+). :high_voltage:

Reimagining Medical Understanding: The Disease Categorisation Assistant

The DCA+ is an AI-powered agent that converts raw medical evidence into structured, clinically relevant disease categories, enabling consistent risk stratification, benefit mapping, and claims routing.

Key features:

  • CPT-First Procedure Extraction: Identifies all medical procedures first (CPT codes, modifiers, facility settings, surgery flags) to inform benefits mapping, prior-authorisation checks, and medical necessity evaluations.

  • Diagnosis Classification: Maps ICD-10, ICD-11, and SNOMED CT codes into disease categories (chronic, acute, infectious, hereditary, lifestyle-related).

  • Policy & Insurance Logic: Evaluates pre-existing conditions, waiting periods, exclusions, and severity/control metrics to generate underwriting or claims recommendations.

  • Structured, Machine-Readable Output: JSON outputs compatible with underwriting engines, claims triage modules, and policy rules engines.

  • Rule-Based Governance: Ensures consistency, accuracy, and audit-readiness.

It’s not just classification — DCA+ bridges clinical understanding and policy logic, enabling fully automated decision-making while preserving explainability.

How It Works: From Raw Evidence to Actionable Insights

Step 1: Intake & Normalization

The agent collects inputs from:

  • Document Extraction Modules: Discharge summaries, lab panels, imaging reports, physician notes, claims bills

  • Condition Normalization Agents: Converts free-text symptoms or diagnoses into ICD/SNOMED codes

  • Applicant Disclosures: Health questionnaires or forms

OCR and multilingual support ensure scanned or non-English documents are fully usable while preserving original evidence spans for audit purposes.

Step 2: CPT-First Procedure Analysis

Before diagnoses, DCA+ extracts procedures to establish benefit eligibility and medical necessity:

  • Detects CPT codes, modifiers, units, global periods, and facility settings

  • Evaluates major surgeries (global period ≥ 90 days) to flag potential underwriting postponements

  • Maps procedures to product benefits and identifies prior-authorisation requirements

Step 3: Diagnosis Classification & Disease Mapping

  • Each condition code is mapped to predefined disease categories (chronic, acute, hereditary, infectious, lifestyle-related)

  • Severity, control, complications, and onset dates are determined using clinical heuristics (e.g., HbA1c bands for diabetes, eGFR for CKD, TNM staging for oncology)

  • Synonyms and regional language variants are handled to improve accuracy

  • Multiple symptoms per applicant are individually categorized

Step 4: Insurance Logic & Policy Evaluation

The agent evaluates each mapped condition against:

  • Pre-existing condition lookback windows

  • Policy waiting periods and coverage rules

  • Potential exclusions or special underwriting treatment

  • Risk-adjusted benefit eligibility (from CPT and ICD integration)

  • Document sufficiency for additional evidence requests

Step 5: Output Generation & Forwarding

Outputs include:

  • Disease family, category, ICD/SNOMED codes

  • Status, severity, control, complications

  • Confidence scores for each classification

  • Policy-specific flags (exclusions, loading recommendations, pending evidence)

Structured JSON outputs are directly usable by:

  • Underwriting Decision Engines (risk stratification, eligibility checks)

  • Claims Triage Modules (routing based on disease category and severity)

  • Policy Rules Engines (exclusions, benefit mapping, waiting periods)

Governed by Intelligent Rules

DCA+ follows a rule-driven, agentic orchestration framework:

  • Symptom-to-Category Mapping: NLP with confidence thresholds, ambiguous cases flagged

  • Policy Coverage Filtering: Excluded conditions flagged immediately

  • Category Granularity: Adjustable for underwriting or claims processes

  • Historical Data Cross-Check: Ensures consistency with prior cases

  • Taxonomy Maintenance: Periodic remapping to current ICD/SNOMED standards

  • CPT Bundling & Medical Necessity Rules: Prevent misaligned benefits or duplicate procedures

These rules ensure automation aligns with human-understood workflows, preserving auditability and regulatory compliance. :white_check_mark:

Technical Ecosystem Integration

DCA+ works seamlessly within the Neutrinos AI Hub and insurer core systems:

Connected System Function
MedParse-AI Structured entity extraction (CPT/ICD/labs/meds/dates)
Condition Normalization Agent Standardized codes for classification
CoverageCheck Validates benefits against ICD/CPT
RulesVerifier Applies market- and product-specific rule packs
DocSufficiency Suggests missing medical evidence
DecisionRulesEngine Generates underwriting/claims recommendations
PayoutCalculator Resolves claims (if applicable)

External knowledge sources include UMLS, MedlinePlus, regulatory disease databases, and insurer-specific policy schemas, ensuring up-to-date and clinically aligned categorization.

Quantitative & Qualitative Impact

Operational Efficiency:

  • 85–95% accurate disease categorization

  • 50–65% reduction in manual effort

  • 30–45% faster processing of ambiguous medical inputs

Business Benefits:

  • Consistent, auditable disease classification across underwriting and claims

  • Fewer disputes and faster turnaround

  • Enables automated risk scoring, benefit determination, and fraud detection

  • Traceable evidence spans support regulatory and internal audits

Broader Perspective: Building a Smarter Insurance Core

The Disease Categorisation Assistant is part of Neutrinos’ AI Agent Ecosystem, which includes:

  • Health Risk Scoring Agent

  • Health Claims Summarization Agent

  • Drug Classification Assistant

Together, they transform raw medical and claims data into structured intelligence, enabling insurers to automate decisions intelligently while remaining transparent, compliant, and scalable.

:light_bulb: Explore our other AI Agents to see how Neutrinos is reimagining insurance workflows — one intelligent agent at a time.

Drug Classification Assistant
Health Risk Scoring Agent
Claims Summarisation Agent

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