# AGENT: ClaimLinc system architecture
# MEDICAL: FHIR R4 validation
# NEURAL: LLM-powered analysis

class ClaimLinc:
    """
    Intelligent claim validation and rejection analysis

    BRAINSAIT: Full audit logging
    """

    def __init__(self, config: ClaimLincConfig):
        self.validator = FHIRValidator()
        self.llm_client = anthropic.Client()
        self.knowledge_base = PolicyKnowledgeBase()

    async def validate_claim(self, claim: Claim) -> ValidationResult:
        """
        Comprehensive claim validation

        Args:
            claim: FHIR Claim resource

        Returns:
            Validation result with specific issues

        MEDICAL: Multi-layer validation
        """
        # Layer validation logic...
        pass

    async def analyze_rejection(
        self,
        claim: Claim,
        rejection_response: dict
    ) -> RejectionAnalysis:
        """
        Analyze rejection and provide recommendations

        AGENT: LLM-powered interpretation
        """
        # Analysis logic...
        pass

# BRAINSAIT: Pre-submission validation workflow
# MEDICAL: FHIR claim validation

from brainsait.agents import ClaimLinc
from fhir.resources.claim import Claim

# Initialize ClaimLinc | تهيئة كليم لينك
claim_linc = ClaimLinc(config=ClaimLincConfig(
    nphies_profile_version="1.0.0",
    payer_rules_enabled=True,
    llm_analysis_enabled=True
))

# Load claim | تحميل المطالبة
claim = Claim.parse_file("claim_example.json")

# Validate before submission | التحقق قبل التقديم
validation_result = await claim_linc.validate_claim(claim)

if not validation_result.is_valid:
    print("❌ Claim validation failed | فشل التحقق من المطالبة:")
    for issue in validation_result.issues:
        print(f"  - {issue.severity}: {issue.message}")
        print(f"    Location: {issue.location}")
        print(f"    Fix: {issue.recommendation}")
else:
    print("✅ Claim is valid for submission | المطالبة صالحة للتقديم")

# AGENT: ClaimLinc rejection analysis
# NEURAL: LLM-powered recommendations

# Claim was rejected by payer | تم رفض المطالبة
rejection_response = {
    "resourceType": "ClaimResponse",
    "status": "rejected",
    "error": [
        {
            "code": "CLN-001",
            "details": {
                "text": "Missing prior authorization for MRI procedure"
            }
        }
    ]
}

# Analyze rejection | تحليل الرفض
analysis = await claim_linc.analyze_rejection(
    claim=claim,
    rejection_response=rejection_response
)

# Display results | عرض النتائج
print(f"Root Cause: {analysis.root_cause['en']}")
print(f"السبب الجذري: {analysis.root_cause['ar']}")

print(f"\nFinancial Impact | الأثر المالي:")
print(f"  Claim Value: {analysis.financial_impact.total_claim_value_sar} SAR")
print(f"  Recovery Probability: {analysis.financial_impact.recovery_probability * 100}%")
print(f"  Potential Loss: {analysis.financial_impact.potential_loss_sar} SAR")

# AGENT: Pattern detection across rejections
# NEURAL: Clustering and recommendations

# Load rejected claims | تحميل المطالبات المرفوضة
rejected_claims = await claim_repository.get_rejected_claims(
    date_from=datetime.now() - timedelta(days=7),
    date_to=datetime.now()
)

# Batch analyze | تحليل الدفعة
batch_report = await claim_linc.batch_analyze(rejected_claims)

print(f"📊 Batch Analysis Report | تقرير تحليل الدفعة")
print(f"Total Claims | إجمالي المطالبات: {batch_report.total_claims}")
print(f"Total Value | القيمة الإجمالية: {batch_report.total_value_sar:,.2f} SAR")
print(f"Potential Loss | الخسارة المحتملة: {batch_report.potential_loss_sar:,.2f} SAR")

print(f"\n🔍 Detected Patterns | الأنماط المكتشفة:")
for pattern in batch_report.patterns:
    print(f"  - {pattern.name}")
    print(f"    Frequency: {pattern.frequency} claims ({pattern.percentage}%)")
    print(f"    Impact: {pattern.financial_impact_sar:,.2f} SAR")