import requests
import json
from typing import List, Dict, Optional, Tuple
from dataclasses import dataclass
from datetime import datetime, timedelta
from collections import defaultdict
import logging

@dataclass
class ClaimLine:
    procedure_code: str
    procedure_code_type: str  # HCPCS
    diagnosis_codes: List[str]
    diagnosis_code_type: str  # ICD10CM
    units: int
    charge_amount: float
    service_date: str

@dataclass
class ClaimValidationResult:
    is_valid: bool
    errors: List[str]
    warnings: List[str]
    suggested_codes: List[str]
    fraud_risk_score: float

@dataclass
class EnrichedClaim:
    original_claim: ClaimLine
    additional_diagnosis_codes: List[str]
    alternative_procedure_codes: List[str]
    supporting_relationships: List[Dict]

class InsuranceClaimsProcessor:
    def __init__(self, api_key: str, base_url: str = "https://api.omophub.com"):
        self.api_key = api_key
        self.base_url = base_url
        self.headers = {"Authorization": f"Bearer {api_key}"}
        self.session = requests.Session()
        self.timeout = 10  # seconds
        self.fraud_patterns = {}
        self.prior_auth_requirements = {}
    
    def validate_claim(self, claim: ClaimLine) -> ClaimValidationResult:
        """Comprehensive claim validation including code validation and relationship checks."""
        errors = []
        warnings = []
        suggested_codes = []
        
        # Validate procedure codes
        proc_validation = self._validate_procedure_code(
            claim.procedure_code, 
            claim.procedure_code_type
        )
        if not proc_validation['is_valid']:
            errors.extend(proc_validation['errors'])
        
        # Validate diagnosis codes
        for dx_code in claim.diagnosis_codes:
            dx_validation = self._validate_diagnosis_code(dx_code, claim.diagnosis_code_type)
            if not dx_validation['is_valid']:
                errors.extend(dx_validation['errors'])
        
        # Check procedure-diagnosis relationships
        relationship_check = self._validate_procedure_diagnosis_relationships(
            claim.procedure_code,
            claim.diagnosis_codes
        )
        if not relationship_check['appropriate']:
            warnings.extend(relationship_check['warnings'])
            suggested_codes.extend(relationship_check['suggested_diagnoses'])
        
        # Calculate fraud risk score
        fraud_score = self._calculate_fraud_risk(claim)
        
        return ClaimValidationResult(
            is_valid=len(errors) == 0,
            errors=errors,
            warnings=warnings,
            suggested_codes=suggested_codes,
            fraud_risk_score=fraud_score
        )
    
    def _validate_procedure_code(self, code: str, code_type: str) -> Dict:
        """Validate HCPCS/HCPCS procedure codes."""
        vocabulary_map = {
            'HCPCS': 'HCPCS',
            'HCPCS': 'HCPCS'
        }
        
        if code_type not in vocabulary_map:
            return {
                'is_valid': False,
                'errors': [f"Unsupported procedure code type: {code_type}"]
            }
        
        try:
            response = requests.get(
                f"{self.base_url}/v1/concepts/search",
                headers=self.headers,
                params={
                    'query': code,
                    'vocabulary_ids': vocabulary_map[code_type],
                    'standard_concept': 'S',
                    'page_size': 1
                }
            )
            response.raise_for_status()
            
            data = response.json()
            if data['data']:
                concept = data['data'][0]
                return {
                    'is_valid': True,
                    'concept_name': concept['concept_name'],
                    'concept_id': concept['concept_id']
                }
            else:
                return {
                    'is_valid': False,
                    'errors': [f"Invalid {code_type} code: {code}"]
                }
        except requests.RequestException as e:
            return {
                'is_valid': False,
                'errors': [f"Error validating procedure code: {str(e)}"]
            }
    
    def _validate_diagnosis_code(self, code: str, code_type: str) -> Dict:
        """Validate ICD-10-CM diagnosis codes."""
        vocabulary_map = {
            'ICD10CM': 'ICD10CM'
        }
        
        if code_type not in vocabulary_map:
            return {
                'is_valid': False,
                'errors': [f"Unsupported diagnosis code type: {code_type}"]
            }
        
        try:
            response = requests.get(
                f"{self.base_url}/v1/concepts/search",
                headers=self.headers,
                params={
                    'query': code,
                    'vocabulary_ids': vocabulary_map[code_type],
                    'standard_concept': 'S',
                    'page_size': 1
                }
            )
            response.raise_for_status()
            
            data = response.json()
            if data['data']:
                return {
                    'is_valid': True,
                    'concept_name': data['data'][0]['concept_name']
                }
            else:
                return {
                    'is_valid': False,
                    'errors': [f"Invalid {code_type} code: {code}"]
                }
        except requests.RequestException as e:
            return {
                'is_valid': False,
                'errors': [f"Error validating diagnosis code: {str(e)}"]
            }
    
    def _validate_procedure_diagnosis_relationships(self, procedure_code: str, diagnosis_codes: List[str]) -> Dict:
        """Check if procedure-diagnosis combinations are clinically appropriate."""
        # Get procedure concept
        try:
            proc_response = requests.get(
                f"{self.base_url}/v1/concepts/search",
                headers=self.headers,
                params={
                    'query': procedure_code,
                    'vocabulary_ids': 'HCPCS,HCPCS',
                    'page_size': 1
                }
            )
            proc_response.raise_for_status()
            proc_data = proc_response.json()
            
            if not proc_data['data']:
                return {
                    'appropriate': False,
                    'warnings': [f"Procedure code not found: {procedure_code}"]
                }
            
            procedure_concept_id = proc_data['data'][0]['concept_id']
            
            # Get related conditions for this procedure
            relations_response = requests.get(
                f"{self.base_url}/v1/concepts/{procedure_concept_id}/relationships",
                headers=self.headers,
                params={
                    'relationship_types': 'Has indication,Treats',
                    'include_synonyms': True
                }
            )
            relations_response.raise_for_status()
            relations_data = relations_response.json()
            
            # Extract indication concepts
            indication_concepts = []
            for relationship in relations_data.get('data', []):
                if relationship['relationship_name'] in ['Has indication', 'Treats']:
                    indication_concepts.append(relationship['related_concept'])
            
            # Check if any diagnosis codes align with indications
            appropriate_diagnoses = []
            for dx_code in diagnosis_codes:
                # Find concept for diagnosis
                dx_response = requests.get(
                    f"{self.base_url}/v1/concepts/search",
                    headers=self.headers,
                    params={
                        'query': dx_code,
                        'vocabulary_ids': 'ICD10CM',
                        'page_size': 1
                    }
                )
                dx_response.raise_for_status()
                dx_data = dx_response.json()
                
                if dx_data['data']:
                    dx_concept_id = dx_data['data'][0]['concept_id']
                    
                    # Check if diagnosis aligns with procedure indications
                    for indication in indication_concepts:
                        if dx_concept_id == indication['concept_id']:
                            appropriate_diagnoses.append(dx_code)
                            break
            
            if not appropriate_diagnoses and diagnosis_codes:
                return {
                    'appropriate': False,
                    'warnings': [f"Procedure {procedure_code} may not be appropriate for provided diagnoses"],
                    'suggested_diagnoses': [concept['concept_code'] for concept in indication_concepts[:5]]
                }
            
            return {
                'appropriate': True,
                'matching_diagnoses': appropriate_diagnoses
            }
            
        except requests.RequestException as e:
            return {
                'appropriate': False,
                'warnings': [f"Error checking procedure-diagnosis relationships: {str(e)}"]
            }
    
    def _calculate_fraud_risk(self, claim: ClaimLine) -> float:
        """Calculate fraud risk score based on various factors."""
        risk_score = 0.0
        
        # High-value procedures get higher scrutiny
        if claim.charge_amount > 10000:
            risk_score += 0.3
        elif claim.charge_amount > 5000:
            risk_score += 0.2
        
        # Unusual unit counts
        if claim.units > 10:
            risk_score += 0.2
        
        # Multiple diagnosis codes might indicate upcoding
        if len(claim.diagnosis_codes) > 5:
            risk_score += 0.1
        
        # Check against known fraud patterns
        procedure_diagnosis_combo = f"{claim.procedure_code}_{','.join(sorted(claim.diagnosis_codes))}"
        if procedure_diagnosis_combo in self.fraud_patterns:
            risk_score += self.fraud_patterns[procedure_diagnosis_combo]
        
        return min(risk_score, 1.0)
    
    def enrich_claim(self, claim: ClaimLine) -> EnrichedClaim:
        """Suggest additional relevant codes to maximize reimbursement."""
        additional_diagnoses = []
        alternative_procedures = []
        supporting_relationships = []
        
        try:
            # Find related diagnosis codes
            for dx_code in claim.diagnosis_codes:
                dx_response = requests.get(
                    f"{self.base_url}/v1/concepts/search",
                    headers=self.headers,
                    params={
                        'query': dx_code,
                        'vocabulary_ids': 'ICD10CM',
                        'page_size': 1
                    }
                )
                dx_response.raise_for_status()
                dx_data = dx_response.json()
                
                if dx_data['data']:
                    concept_id = dx_data['data'][0]['concept_id']
                    
                    # Get hierarchical relationships
                    hierarchy_response = requests.get(
                        f"{self.base_url}/v1/concepts/{concept_id}/hierarchy",
                        headers=self.headers,
                        params={
                            'include_ancestors': True,
                            'include_descendants': True,
                            'max_levels': 2
                        }
                    )
                    hierarchy_response.raise_for_status()
                    hierarchy_data = hierarchy_response.json()
                    
                    # Extract relevant related conditions
                    for item in hierarchy_data.get('data', []):
                        if (item['concept']['vocabulary_id'] == 'ICD10CM' and 
                            item['concept']['concept_code'] not in claim.diagnosis_codes):
                            additional_diagnoses.append(item['concept']['concept_code'])
            
            # Find alternative procedures
            proc_response = requests.get(
                f"{self.base_url}/v1/concepts/search",
                headers=self.headers,
                params={
                    'query': claim.procedure_code,
                    'vocabulary_ids': 'HCPCS,HCPCS',
                    'page_size': 1
                }
            )
            proc_response.raise_for_status()
            proc_data = proc_response.json()
            
            if proc_data['data']:
                proc_concept_id = proc_data['data'][0]['concept_id']
                
                # Get procedure relationships
                proc_relations_response = requests.get(
                    f"{self.base_url}/v1/concepts/{proc_concept_id}/relationships",
                    headers=self.headers,
                    params={
                        'relationship_types': 'Procedure site,Has finding site',
                        'include_synonyms': True
                    }
                )
                proc_relations_response.raise_for_status()
                proc_relations_data = proc_relations_response.json()
                
                supporting_relationships = proc_relations_data.get('data', [])
        
        except requests.RequestException as e:
            logging.error(f"Error enriching claim: {str(e)}")
        
        return EnrichedClaim(
            original_claim=claim,
            additional_diagnosis_codes=additional_diagnoses[:5],  # Limit suggestions
            alternative_procedure_codes=alternative_procedures[:3],
            supporting_relationships=supporting_relationships
        )
    
    def check_prior_authorization(self, claim: ClaimLine, member_id: str, policy_id: str) -> Dict:
        """Check if procedure requires prior authorization."""
        # This would integrate with payer-specific prior auth databases
        # For demo purposes, using simplified logic based on procedure codes
        
        high_cost_procedures = {
        high_cost_procedures = {
            '27447',  # Total knee arthroplasty
            '23472',  # Total shoulder arthroplasty
            '22630',  # Lumbar spinal fusion (one interspace)
            '33533',  # Coronary artery bypass; single arterial graft
        }
        
        authorization_required = claim.procedure_code in high_cost_procedures
        
        if authorization_required:
            # Check if authorization already exists
            auth_exists = self._check_existing_authorization(member_id, claim.procedure_code)
            
            return {
                'authorization_required': True,
                'authorization_exists': auth_exists,
                'status': 'approved' if auth_exists else 'pending',
                'procedure_name': self._get_procedure_name(claim.procedure_code)
            }
        
        return {
            'authorization_required': False,
            'status': 'not_required'
        }
    
    def _check_existing_authorization(self, member_id: str, procedure_code: str) -> bool:
    def _get_procedure_name(self, procedure_code: str) -> str:
        """Get human-readable procedure name."""
        try:
            response = self.session.get(
                f"{self.base_url}/v1/concepts/search",
                headers=self.headers,
                params={
                    'query': procedure_code,
                    'vocabulary_ids': 'HCPCS,HCPCS',
                    'page_size': 1
                },
                timeout=self.timeout
            )
            
            if not response.ok:
                return None
                
            data = response.json()
            
            if data.get('data'):
                return data['data'][0]['concept_name']
            return None
            
        except (requests.RequestException, ValueError, KeyError) as e:
            # Log error or handle as needed
            return None
    
    def process_batch_claims(self, claims: List[ClaimLine]) -> Dict:
        """Process multiple claims efficiently."""
        results = {
            'total_claims': len(claims),
            'valid_claims': 0,
            'invalid_claims': 0,
            'high_risk_claims': 0,
            'claims_requiring_auth': 0,
            'enrichment_opportunities': 0,
            'detailed_results': []
        }
        
        for claim in claims:
            # Validate claim
            validation = self.validate_claim(claim)
            
            # Check prior authorization
            auth_check = self.check_prior_authorization(claim, "member_123", "policy_456")
            
            # Enrich claim
            enriched = self.enrich_claim(claim)
            
            # Update counters
            if validation.is_valid:
                results['valid_claims'] += 1
            else:
                results['invalid_claims'] += 1
            
            if validation.fraud_risk_score > 0.7:
                results['high_risk_claims'] += 1
            
            if auth_check['authorization_required']:
                results['claims_requiring_auth'] += 1
            
            if enriched.additional_diagnosis_codes or enriched.alternative_procedure_codes:
                results['enrichment_opportunities'] += 1
            
            results['detailed_results'].append({
                'claim': claim,
                'validation': validation,
                'authorization': auth_check,
                'enrichment': enriched
            })
        
        return results

# Example usage
def main():
    processor = InsuranceClaimsProcessor("your-api-key")
    
    # Sample claim
    sample_claim = ClaimLine(
        procedure_code="99213",  # Office visit
        procedure_code_type="HCPCS",
        diagnosis_codes=["E11.9"],  # Type 2 diabetes
        diagnosis_code_type="ICD10CM",
        units=1,
        charge_amount=250.00,
        service_date="2024-03-15"
    )
    
    # Validate claim
    validation_result = processor.validate_claim(sample_claim)
    print(f"Claim valid: {validation_result.is_valid}")
    print(f"Errors: {validation_result.errors}")
    print(f"Fraud risk score: {validation_result.fraud_risk_score}")
    
    # Enrich claim
    enriched_claim = processor.enrich_claim(sample_claim)
    print(f"Additional diagnosis suggestions: {enriched_claim.additional_diagnosis_codes}")
    
    # Check authorization
    auth_result = processor.check_prior_authorization(sample_claim, "member_123", "policy_456")
    print(f"Authorization required: {auth_result['authorization_required']}")

if __name__ == "__main__":
    main()

const axios = require('axios');

class InsuranceClaimsProcessor {
    constructor(apiKey, baseUrl = 'https://api.omophub.com') {
        this.apiKey = apiKey;
        this.baseUrl = baseUrl;
        this.headers = { 'Authorization': `Bearer ${apiKey}` };
        this.fraudPatterns = new Map();
    }

    async validateClaim(claim) {
        const errors = [];
        const warnings = [];
        const suggestedCodes = [];

        // Validate procedure code
        try {
            const procValidation = await this.validateProcedureCode(
                claim.procedureCode, 
                claim.procedureCodeType
            );
            if (!procValidation.isValid) {
                errors.push(...procValidation.errors);
            }
        } catch (error) {
            errors.push(`Procedure validation error: ${error.message}`);
        }

        // Validate diagnosis codes
        for (const dxCode of claim.diagnosisCodes) {
            try {
                const dxValidation = await this.validateDiagnosisCode(
                    dxCode, 
                    claim.diagnosisCodeType
                );
                if (!dxValidation.isValid) {
                    errors.push(...dxValidation.errors);
                }
            } catch (error) {
                errors.push(`Diagnosis validation error: ${error.message}`);
            }
        }

        // Check relationships
        try {
            const relationshipCheck = await this.validateProcedureDiagnosisRelationships(
                claim.procedureCode,
                claim.diagnosisCodes
            );
            if (!relationshipCheck.appropriate) {
                warnings.push(...relationshipCheck.warnings || []);
                suggestedCodes.push(...relationshipCheck.suggestedDiagnoses || []);
            }
        } catch (error) {
            warnings.push(`Relationship validation error: ${error.message}`);
        }

        const fraudScore = this.calculateFraudRisk(claim);

        return {
            isValid: errors.length === 0,
            errors,
            warnings,
            suggestedCodes,
            fraudRiskScore: fraudScore
        };
    }

    async validateProcedureCode(code, codeType) {
        const vocabularyMap = {
            'HCPCS': 'HCPCS',
            'HCPCS': 'HCPCS'
        };

        if (!vocabularyMap[codeType]) {
            return {
                isValid: false,
                errors: [`Unsupported procedure code type: ${codeType}`]
            };
        }

        try {
            const response = await axios.get(`${this.baseUrl}/v1/concepts/search`, {
                headers: this.headers,
                params: {
                    query: code,
                    vocabulary_ids: vocabularyMap[codeType],
                    standard_concept: 'S',
                    page_size: 1
                }
            });

            if (response.data.data && response.data.data.length > 0) {
                return {
                    isValid: true,
                    conceptName: response.data.data[0].concept_name,
                    conceptId: response.data.data[0].concept_id
                };
            } else {
                return {
                    isValid: false,
                    errors: [`Invalid ${codeType} code: ${code}`]
                };
            }
        } catch (error) {
            return {
                isValid: false,
                errors: [`Error validating procedure code: ${error.message}`]
            };
        }
    }

    async validateDiagnosisCode(code, codeType) {
        const vocabularyMap = { 'ICD10CM': 'ICD10CM' };

        if (!vocabularyMap[codeType]) {
            return {
                isValid: false,
                errors: [`Unsupported diagnosis code type: ${codeType}`]
            };
        }

        try {
            const response = await axios.get(`${this.baseUrl}/v1/concepts/search`, {
                headers: this.headers,
                params: {
                    query: code,
                    vocabulary_ids: vocabularyMap[codeType],
                    standard_concept: 'S',
                    page_size: 1
                }
            });

            if (response.data.data && response.data.data.length > 0) {
                return {
                    isValid: true,
                    conceptName: response.data.data[0].concept_name
                };
            } else {
                return {
                    isValid: false,
                    errors: [`Invalid ${codeType} code: ${code}`]
                };
            }
        } catch (error) {
            return {
                isValid: false,
                errors: [`Error validating diagnosis code: ${error.message}`]
            };
        }
    }

    async validateProcedureDiagnosisRelationships(procedureCode, diagnosisCodes) {
        try {
            // Get procedure concept
            const procResponse = await axios.get(`${this.baseUrl}/v1/concepts/search`, {
                headers: this.headers,
                params: {
                    query: procedureCode,
                    vocabulary_ids: 'HCPCS,HCPCS',
                    page_size: 1
                }
            });

            if (!procResponse.data.data || procResponse.data.data.length === 0) {
                return {
                    appropriate: false,
                    warnings: [`Procedure code not found: ${procedureCode}`]
                };
            }

            const procedureConceptId = procResponse.data.data[0].concept_id;

            // Get related conditions
            const relationsResponse = await axios.get(
                `${this.baseUrl}/v1/concepts/${procedureConceptId}/relationships`,
                {
                    headers: this.headers,
                    params: {
                        relationship_types: 'Has indication,Treats',
                        include_synonyms: true
                    }
                }
            );

            const indicationConcepts = relationsResponse.data.data
                ?.filter(rel => ['Has indication', 'Treats'].includes(rel.relationship_name))
                .map(rel => rel.related_concept) || [];

            // Check diagnosis alignment
            const appropriateDiagnoses = [];
            for (const dxCode of diagnosisCodes) {
                const dxResponse = await axios.get(`${this.baseUrl}/v1/concepts/search`, {
                    headers: this.headers,
                    params: {
                        query: dxCode,
                        vocabulary_ids: 'ICD10CM',
                        page_size: 1
                    }
                });

                if (dxResponse.data.data && dxResponse.data.data.length > 0) {
                    const dxConceptId = dxResponse.data.data[0].concept_id;
                    
                    const matches = indicationConcepts.some(
                        indication => indication.concept_id === dxConceptId
                    );
                    
                    if (matches) {
                        appropriateDiagnoses.push(dxCode);
                    }
                }
            }

            if (appropriateDiagnoses.length === 0 && diagnosisCodes.length > 0) {
                return {
                    appropriate: false,
                    warnings: [`Procedure ${procedureCode} may not be appropriate for provided diagnoses`],
                    suggestedDiagnoses: indicationConcepts.slice(0, 5).map(c => c.concept_code)
                };
            }

            return {
                appropriate: true,
                matchingDiagnoses: appropriateDiagnoses
            };

        } catch (error) {
            return {
                appropriate: false,
                warnings: [`Error checking procedure-diagnosis relationships: ${error.message}`]
            };
        }
    }

    calculateFraudRisk(claim) {
        let riskScore = 0.0;

        if (claim.chargeAmount > 10000) {
            riskScore += 0.3;
        } else if (claim.chargeAmount > 5000) {
            riskScore += 0.2;
        }

        if (claim.units > 10) {
            riskScore += 0.2;
        }

        if (claim.diagnosisCodes.length > 5) {
            riskScore += 0.1;
        }

        const comboKey = `${claim.procedureCode}_${[...claim.diagnosisCodes].sort().join(',')}`;
        if (this.fraudPatterns.has(comboKey)) {
            riskScore += this.fraudPatterns.get(comboKey);
        }

        return Math.min(riskScore, 1.0);
    }

    async enrichClaim(claim) {
        const additionalDiagnoses = [];
        const alternativeProcedures = [];
        const supportingRelationships = [];

        try {
            // Find related diagnosis codes
            for (const dxCode of claim.diagnosisCodes) {
                const dxResponse = await axios.get(`${this.baseUrl}/v1/concepts/search`, {
                    headers: this.headers,
                    params: {
                        query: dxCode,
                        vocabulary_ids: 'ICD10CM',
                        page_size: 1
                    }
                });

                if (dxResponse.data.data && dxResponse.data.data.length > 0) {
                    const conceptId = dxResponse.data.data[0].concept_id;

                    const hierarchyResponse = await axios.get(
                        `${this.baseUrl}/v1/concepts/${conceptId}/hierarchy`,
                        {
                            headers: this.headers,
                            params: {
                                include_ancestors: true,
                                include_descendants: true,
                                max_levels: 2
                            }
                        }
                    );

                    const related = hierarchyResponse.data.data
                        ?.filter(item => 
                            item.concept.vocabulary_id === 'ICD10CM' &&
                            !claim.diagnosisCodes.includes(item.concept.concept_code)
                        )
                        .map(item => item.concept.concept_code) || [];

                    additionalDiagnoses.push(...related);
                }
            }
        } catch (error) {
            console.error('Error enriching claim:', error.message);
        }

        return {
            originalClaim: claim,
            additionalDiagnosisCodes: [...new Set(additionalDiagnoses)].slice(0, 5),
            alternativeProcedureCodes: alternativeProcedures.slice(0, 3),
            supportingRelationships
        };
    }
}

// Example usage
async function main() {
    const processor = new InsuranceClaimsProcessor('your-api-key');

    const sampleClaim = {
        procedureCode: '99213',
        procedureCodeType: 'HCPCS',
        diagnosisCodes: ['E11.9'],
        diagnosisCodeType: 'ICD10CM',
        units: 1,
        chargeAmount: 250.00,
        serviceDate: '2024-03-15'
    };

    // Validate claim
    const validation = await processor.validateClaim(sampleClaim);
    console.log('Claim valid:', validation.isValid);
    console.log('Errors:', validation.errors);
    console.log('Fraud risk score:', validation.fraudRiskScore);

    // Enrich claim
    const enriched = await processor.enrichClaim(sampleClaim);
    console.log('Additional diagnosis suggestions:', enriched.additionalDiagnosisCodes);
}

if (require.main === module) {
    main().catch(console.error);
}

module.exports = { InsuranceClaimsProcessor };

library(httr)
library(jsonlite)
library(dplyr)
library(purrr)

InsuranceClaimsProcessor <- R6::R6Class(
  "InsuranceClaimsProcessor",
  
  public = list(
    api_key = NULL,
    base_url = NULL,
    headers = NULL,
    fraud_patterns = NULL,
    
    initialize = function(api_key, base_url = "https://api.omophub.com") {
      self$api_key <- api_key
      self$base_url <- base_url
      self$headers <- add_headers(Authorization = paste("Bearer", api_key))
      self$fraud_patterns <- list()
    },
    
    validate_claim = function(claim) {
      errors <- character(0)
      warnings <- character(0)
      suggested_codes <- character(0)
      
      # Validate procedure code
      proc_validation <- tryCatch({
        self$validate_procedure_code(claim$procedure_code, claim$procedure_code_type)
      }, error = function(e) {
        list(is_valid = FALSE, errors = paste("Procedure validation error:", e$message))
      })
      
      if (!proc_validation$is_valid) {
        errors <- c(errors, proc_validation$errors)
      }
      
      # Validate diagnosis codes
      for (dx_code in claim$diagnosis_codes) {
        dx_validation <- tryCatch({
          self$validate_diagnosis_code(dx_code, claim$diagnosis_code_type)
        }, error = function(e) {
          list(is_valid = FALSE, errors = paste("Diagnosis validation error:", e$message))
        })
        
        if (!dx_validation$is_valid) {
          errors <- c(errors, dx_validation$errors)
        }
      }
      
      # Check relationships
      relationship_check <- tryCatch({
        self$validate_procedure_diagnosis_relationships(
          claim$procedure_code,
          claim$diagnosis_codes
        )
      }, error = function(e) {
        list(
          appropriate = FALSE,
          warnings = paste("Relationship validation error:", e$message)
        )
      })
      
      if (!relationship_check$appropriate) {
        if (!is.null(relationship_check$warnings)) {
          warnings <- c(warnings, relationship_check$warnings)
        }
        if (!is.null(relationship_check$suggested_diagnoses)) {
          suggested_codes <- c(suggested_codes, relationship_check$suggested_diagnoses)
        }
      }
      
      fraud_score <- self$calculate_fraud_risk(claim)
      
      list(
        is_valid = length(errors) == 0,
        errors = errors,
        warnings = warnings,
        suggested_codes = suggested_codes,
        fraud_risk_score = fraud_score
      )
    },
    
    validate_procedure_code = function(code, code_type) {
      vocabulary_map <- list(
        "HCPCS" = "HCPCS",
        "HCPCS" = "HCPCS"
      )
      
      if (!code_type %in% names(vocabulary_map)) {
        return(list(
          is_valid = FALSE,
          errors = paste("Unsupported procedure code type:", code_type)
        ))
      }
      
      response <- GET(
        paste0(self$base_url, "/v1/concepts/search"),
        self$headers,
        query = list(
          query = code,
          vocabulary_ids = vocabulary_map[[code_type]],
          standard_concept = "S",
          page_size = 1
        )
      )
      
      if (status_code(response) != 200) {
        return(list(
          is_valid = FALSE,
          errors = paste("Error validating procedure code:", status_code(response))
        ))
      }
      
      data <- fromJSON(content(response, "text"))
      
      if (length(data$data) > 0) {
        list(
          is_valid = TRUE,
          concept_name = data$data$concept_name[1],
          concept_id = data$data$concept_id[1]
        )
      } else {
        list(
          is_valid = FALSE,
          errors = paste("Invalid", code_type, "code:", code)
        )
      }
    },
    
    validate_diagnosis_code = function(code, code_type) {
      vocabulary_map <- list("ICD10CM" = "ICD10CM")
      
      if (!code_type %in% names(vocabulary_map)) {
        return(list(
          is_valid = FALSE,
          errors = paste("Unsupported diagnosis code type:", code_type)
        ))
      }
      
      response <- GET(
        paste0(self$base_url, "/v1/concepts/search"),
        self$headers,
        query = list(
          query = code,
          vocabulary_ids = vocabulary_map[[code_type]],
          standard_concept = "S",
          page_size = 1
        )
      )
      
      if (status_code(response) != 200) {
        return(list(
          is_valid = FALSE,
          errors = paste("Error validating diagnosis code:", status_code(response))
        ))
      }
      
      data <- fromJSON(content(response, "text"))
      
      if (length(data$data) > 0) {
        list(
          is_valid = TRUE,
          concept_name = data$data$concept_name[1]
        )
      } else {
        list(
          is_valid = FALSE,
          errors = paste("Invalid", code_type, "code:", code)
        )
      }
    },
    
    validate_procedure_diagnosis_relationships = function(procedure_code, diagnosis_codes) {
      # Get procedure concept
      proc_response <- GET(
        paste0(self$base_url, "/v1/concepts/search"),
        self$headers,
        query = list(
          query = procedure_code,
          vocabulary_ids = "HCPCS,HCPCS",
          page_size = 1
        )
      )
      
      if (status_code(proc_response) != 200) {
        return(list(
          appropriate = FALSE,
          warnings = paste("Error fetching procedure:", status_code(proc_response))
        ))
      }
      
      proc_data <- fromJSON(content(proc_response, "text"))
      
      if (length(proc_data$data) == 0) {
        return(list(
          appropriate = FALSE,
          warnings = paste("Procedure code not found:", procedure_code)
        ))
      }
      
      procedure_concept_id <- proc_data$data$concept_id[1]
      
      # Get related conditions
      relations_response <- GET(
        paste0(self$base_url, "/v1/concepts/", procedure_concept_id, "/relationships"),
        self$headers,
        query = list(
          relationship_types = "Has indication,Treats",
          include_synonyms = TRUE
        )
      )
      
      if (status_code(relations_response) != 200) {
        return(list(
          appropriate = FALSE,
          warnings = "Error fetching procedure relationships"
        ))
      }
      
      relations_data <- fromJSON(content(relations_response, "text"))
      
      indication_concepts <- relations_data$data %>%
        filter(relationship_name %in% c("Has indication", "Treats")) %>%
        pull(related_concept)
      
      # Check diagnosis alignment
      appropriate_diagnoses <- character(0)
      
      for (dx_code in diagnosis_codes) {
        dx_response <- GET(
          paste0(self$base_url, "/v1/concepts/search"),
          self$headers,
          query = list(
            query = dx_code,
            vocabulary_ids = "ICD10CM",
            page_size = 1
          )
        )
        
        if (status_code(dx_response) == 200) {
          dx_data <- fromJSON(content(dx_response, "text"))
          
          if (length(dx_data$data) > 0) {
            dx_concept_id <- dx_data$data$concept_id[1]
            
            matching <- any(map_lgl(indication_concepts, ~ .x$concept_id == dx_concept_id))
            if (matching) {
              appropriate_diagnoses <- c(appropriate_diagnoses, dx_code)
            }
          }
        }
      }
      
      if (length(appropriate_diagnoses) == 0 && length(diagnosis_codes) > 0) {
        suggested_diagnoses <- if (length(indication_concepts) > 0) {
          head(map_chr(indication_concepts, ~ .$concept_code %||% ""), 5)
        } else {
          character(0)
        }
        
        list(
          appropriate = FALSE,
          warnings = paste("Procedure", procedure_code, "may not be appropriate for provided diagnoses"),
          suggested_diagnoses = suggested_diagnoses
        )
      } else {
        list(
          appropriate = TRUE,
          matching_diagnoses = appropriate_diagnoses
        )
      }
    },
    
    calculate_fraud_risk = function(claim) {
      risk_score <- 0.0
      
      if (claim$charge_amount > 10000) {
        risk_score <- risk_score + 0.3
      } else if (claim$charge_amount > 5000) {
        risk_score <- risk_score + 0.2
      }
      
      if (claim$units > 10) {
        risk_score <- risk_score + 0.2
      }
      
      if (length(claim$diagnosis_codes) > 5) {
        risk_score <- risk_score + 0.1
      }
      
      combo_key <- paste(claim$procedure_code, paste(sort(claim$diagnosis_codes), collapse = ","), sep = "_")
      if (combo_key %in% names(self$fraud_patterns)) {
        risk_score <- risk_score + self$fraud_patterns[[combo_key]]
      }
      
      min(risk_score, 1.0)
    },
    
    enrich_claim = function(claim) {
      additional_diagnoses <- character(0)
      alternative_procedures <- character(0)
      supporting_relationships <- list()
      
      tryCatch({
        # Find related diagnosis codes
        for (dx_code in claim$diagnosis_codes) {
          dx_response <- GET(
            paste0(self$base_url, "/v1/concepts/search"),
            self$headers,
            query = list(
              query = dx_code,
              vocabulary_ids = "ICD10CM",
              page_size = 1
            )
          )
          
          if (status_code(dx_response) == 200) {
            dx_data <- fromJSON(content(dx_response, "text"))
            
            if (length(dx_data$data) > 0) {
              concept_id <- dx_data$data$concept_id[1]
              
              hierarchy_response <- GET(
                paste0(self$base_url, "/v1/concepts/", concept_id, "/hierarchy"),
                self$headers,
                query = list(
                  include_ancestors = TRUE,
                  include_descendants = TRUE,
                  max_levels = 2
                )
              )
              
              if (status_code(hierarchy_response) == 200) {
                hierarchy_data <- fromJSON(content(hierarchy_response, "text"))
                
                related_codes <- hierarchy_data$data %>%
                  filter(
                    concept$vocabulary_id == "ICD10CM",
                    !concept$concept_code %in% claim$diagnosis_codes
                  ) %>%
                  pull(concept) %>%
                  map_chr(~ .$concept_code)
                
                additional_diagnoses <- c(additional_diagnoses, related_codes)
              }
            }
          }
        }
      }, error = function(e) {
        message("Error enriching claim: ", e$message)
      })
      
      list(
        original_claim = claim,
        additional_diagnosis_codes = unique(additional_diagnoses)[1:min(5, length(unique(additional_diagnoses)))],
        alternative_procedure_codes = alternative_procedures[1:min(3, length(alternative_procedures))],
        supporting_relationships = supporting_relationships
      )
    }
  )
)

# Example usage
example_usage <- function() {
  processor <- InsuranceClaimsProcessor$new("your-api-key")
  
  sample_claim <- list(
    procedure_code = "99213",
    procedure_code_type = "HCPCS",
    diagnosis_codes = c("E11.9"),
    diagnosis_code_type = "ICD10CM",
    units = 1,
    charge_amount = 250.00,
    service_date = "2024-03-15"
  )
  
  # Validate claim
  validation_result <- processor$validate_claim(sample_claim)
  cat("Claim valid:", validation_result$is_valid, "\n")
  cat("Errors:", paste(validation_result$errors, collapse = ", "), "\n")
  cat("Fraud risk score:", validation_result$fraud_risk_score, "\n")
  
  # Enrich claim
  enriched_claim <- processor$enrich_claim(sample_claim)
  cat("Additional diagnosis suggestions:", paste(enriched_claim$additional_diagnosis_codes, collapse = ", "), "\n")
}

# Process a complex surgical claim
processor = InsuranceClaimsProcessor("your-api-key")

complex_claim = ClaimLine(
    procedure_code="27447",  # Total knee replacement
    procedure_code_type="HCPCS",
    diagnosis_codes=["M17.11", "Z96.651"],  # Osteoarthritis, knee implant status
    diagnosis_code_type="ICD10CM",
    units=1,
    charge_amount=45000.00,
    service_date="2024-03-15"
)

# Full processing pipeline
validation = processor.validate_claim(complex_claim)
auth_check = processor.check_prior_authorization(complex_claim, "member_456", "policy_789")
enrichment = processor.enrich_claim(complex_claim)

print("=== Claim Processing Results ===")
print(f"Valid: {validation.is_valid}")
print(f"Fraud Risk: {validation.fraud_risk_score:.2f}")
print(f"Authorization Required: {auth_check['authorization_required']}")
print(f"Additional Codes Suggested: {len(enrichment.additional_diagnosis_codes)}")

=== Claim Processing Results ===
Valid: True
Fraud Risk: 0.30
Authorization Required: True
Additional Codes Suggested: 3

# Process multiple claims efficiently
claims_batch = [
    ClaimLine("99213", "HCPCS", ["E11.9"], "ICD10CM", 1, 250.00, "2024-03-15"),
    ClaimLine("27447", "HCPCS", ["M17.11"], "ICD10CM", 1, 45000.00, "2024-03-15"),
    ClaimLine("93000", "HCPCS", ["I25.9"], "ICD10CM", 1, 150.00, "2024-03-15")
]

batch_results = processor.process_batch_claims(claims_batch)

print(f"Total Claims: {batch_results['total_claims']}")
print(f"Valid Claims: {batch_results['valid_claims']}")
print(f"High Risk Claims: {batch_results['high_risk_claims']}")
print(f"Authorization Required: {batch_results['claims_requiring_auth']}")

Total Claims: 3
Valid Claims: 3
High Risk Claims: 1
Authorization Required: 1

# Analyze potentially fraudulent claim patterns
suspicious_claim = ClaimLine(
    procedure_code="99215",  # High-level office visit
    procedure_code_type="HCPCS",
    diagnosis_codes=["M79.601", "M79.602", "M79.603", "M25.511", "M25.512"],  # Multiple pain codes
    diagnosis_code_type="ICD10CM",
    units=15,  # Unusual high units
    charge_amount=12000.00,  # High charge
    service_date="2024-03-15"
)

validation = processor.validate_claim(suspicious_claim)
print(f"Fraud Risk Score: {validation.fraud_risk_score:.2f}")
print(f"Risk Factors: High charge amount, excessive units, multiple similar diagnoses")

class PayerClaimsSystem:
    def __init__(self, omophub_processor, payer_config):
        self.processor = omophub_processor
        self.payer_config = payer_config
        
    def process_claim_submission(self, claim_data):
        # Convert payer format to OMOPHub format
        omophub_claim = self.convert_to_omophub_format(claim_data)
        
        # Validate and enrich
        validation = self.processor.validate_claim(omophub_claim)
        enrichment = self.processor.enrich_claim(omophub_claim)
        
        # Apply payer-specific business rules
        payer_validation = self.apply_payer_rules(claim_data, validation)
        
        return {
            'claim_id': claim_data['claim_id'],
            'status': 'approved' if validation.is_valid and payer_validation else 'denied',
            'omophub_validation': validation,
            'suggested_codes': enrichment.additional_diagnosis_codes,
            'payer_notes': payer_validation.get('notes', [])
        }

class EMRClaimsValidator {
    constructor(omophubProcessor, emrConfig) {
        this.processor = omophubProcessor;
        this.emrConfig = emrConfig;
    }

    async validateBeforeSubmission(encounterData) {
        const claims = this.extractClaimsFromEncounter(encounterData);
        const validationResults = [];

        for (const claim of claims) {
            const validation = await this.processor.validateClaim(claim);
            const enrichment = await this.processor.enrichClaim(claim);

            validationResults.push({
                claim,
                validation,
                enrichment,
                recommendedActions: this.generateRecommendations(validation, enrichment)
            });
        }

        return {
            overallStatus: validationResults.every(r => r.validation.isValid) ? 'ready' : 'needs_review',
            claims: validationResults,
            totalPotentialReimbursement: this.calculatePotentialReimbursement(validationResults)
        };
    }

    generateRecommendations(validation, enrichment) {
        const recommendations = [];

        if (!validation.isValid) {
            recommendations.push({
                type: 'error',
                message: 'Fix coding errors before submission',
                actions: validation.errors
            });
        }

        if (enrichment.additionalDiagnosisCodes.length > 0) {
            recommendations.push({
                type: 'enhancement',
                message: 'Consider adding additional diagnosis codes',
                actions: enrichment.additionalDiagnosisCodes
            });
        }

        if (validation.fraudRiskScore > 0.7) {
            recommendations.push({
                type: 'warning',
                message: 'High fraud risk - review documentation',
                actions: ['Verify medical necessity', 'Check supporting documentation']
            });
        }

        return recommendations;
    }
}

# Required packages: pip install tenacity pybreaker
from tenacity import retry, stop_after_attempt, wait_exponential
from pybreaker import CircuitBreaker
import time

class RobustClaimsProcessor(InsuranceClaimsProcessor):
    def __init__(self, api_key, base_url="https://api.omophub.com"):
        super().__init__(api_key, base_url)
        self.retry_config = {
            'max_retries': 3,
            'backoff_factor': 2,
            'timeout': 30
        }
        self.circuit_breaker = CircuitBreaker(fail_max=5, reset_timeout=60)
    
    @retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=1, max=10))
    def validate_claim_with_fallback(self, claim):
        try:
            return self.validate_claim(claim)
        except requests.RequestException as e:
            # Fallback to basic validation
            return self._basic_validation_fallback(claim, str(e))
    
    def _basic_validation_fallback(self, claim, error_msg):
        """Fallback validation when API is unavailable."""
        basic_errors = []
        
        # Basic format validation
        if not claim.procedure_code or len(claim.procedure_code) < 3:
            basic_errors.append("Invalid procedure code format")
        
        if not claim.diagnosis_codes or len(claim.diagnosis_codes) == 0:
            basic_errors.append("No diagnosis codes provided")
        
        return ClaimValidationResult(
            is_valid=len(basic_errors) == 0,
            errors=basic_errors,
            warnings=[f"API unavailable: {error_msg}"],
            suggested_codes=[],
            fraud_risk_score=0.0
        )

from functools import lru_cache
import requests
from collections import defaultdict

# Module-level cached function for code validation
@lru_cache(maxsize=1000)
def _cached_code_validation(code: str, vocabulary: str, base_url: str, api_key: str) -> bool:
    """Cache frequently validated codes."""
    try:
        headers = {"Authorization": f"Bearer {api_key}"}
        response = requests.get(
            f"{base_url}/v1/concepts/search",
            headers=headers,
            params={'query': code, 'vocabulary_ids': vocabulary, 'page_size': 1}
        )
        return len(response.json().get('data', [])) > 0
    except:
        return False

class OptimizedClaimsProcessor(InsuranceClaimsProcessor):
    def __init__(self, api_key, base_url="https://api.omophub.com"):
        super().__init__(api_key, base_url)
        self.cache = {}
        self.batch_size = 50
    
    def process_claims_optimized(self, claims: List[ClaimLine]) -> Dict:
        """Optimized batch processing with caching and concurrent requests."""
        # Group similar claims for batch processing
        claim_groups = self._group_claims_by_similarity(claims)
        
        results = []
        for group in claim_groups:
            # Process each group with cached lookups
            group_results = self._process_claim_group(group)
            results.extend(group_results)
        
        return self._aggregate_results(results)
    
    def _group_claims_by_similarity(self, claims):
        """Group claims with same procedure/diagnosis combinations."""
        groups = defaultdict(list)
        for claim in claims:
            key = f"{claim.procedure_code}_{','.join(sorted(claim.diagnosis_codes))}"
            groups[key].append(claim)
        return list(groups.values())
    
    def validate_code_cached(self, code: str, vocabulary: str) -> bool:
        """Validate code using cached lookup."""
        return _cached_code_validation(code, vocabulary, self.base_url, self.api_key)

class HIPAACompliantClaimsProcessor(InsuranceClaimsProcessor):
    def __init__(self, api_key, base_url="https://api.omophub.com"):
        super().__init__(api_key, base_url)
        self.audit_logger = self._setup_audit_logging()
        self.phi_detector = PHIDetector()
    
    def process_claim_secure(self, claim: ClaimLine, user_context: Dict) -> ClaimValidationResult:
        """HIPAA-compliant claim processing with full audit trail."""
        audit_id = str(uuid.uuid4())
        
        try:
            # Log access attempt
            self.audit_logger.info({
                'audit_id': audit_id,
                'action': 'claim_validation_start',
                'user_id': user_context['user_id'],
                'timestamp': datetime.utcnow().isoformat(),
                'claim_id': getattr(claim, 'claim_id', 'unknown')
            })
            
            # Detect and mask PHI
            sanitized_claim = self._sanitize_phi(claim)
            
            # Process with standard validation
            result = self.validate_claim(sanitized_claim)
            
            # Log successful completion
            self.audit_logger.info({
                'audit_id': audit_id,
                'action': 'claim_validation_complete',
                'status': 'success',
                'timestamp': datetime.utcnow().isoformat()
            })
            
            return result
            
        except Exception as e:
            # Log error
            self.audit_logger.error({
                'audit_id': audit_id,
                'action': 'claim_validation_error',
                'error': str(e),
                'timestamp': datetime.utcnow().isoformat()
            })
            raise
    
    def _sanitize_phi(self, claim: ClaimLine) -> ClaimLine:
        """Remove or mask potential PHI from claim data."""
        # Implementation would depend on specific PHI detection requirements
        return claim
    
    def _setup_audit_logging(self):
        """Configure HIPAA-compliant audit logging."""
        logger = logging.getLogger('hipaa_audit')
        logger.setLevel(logging.INFO)
        
        # Configure secure, encrypted log storage
        handler = SecureFileHandler('/var/log/hipaa/claims_audit.log')
        formatter = logging.Formatter(
            '%(asctime)s - %(levelname)s - %(message)s'
        )
        handler.setFormatter(formatter)
        logger.addHandler(handler)
        
        return logger