"""
HACS Clinical Workflows Tools

This module provides comprehensive tools for clinical workflow execution,
decision support, and healthcare protocol management. Supports FHIR-compliant
clinical reasoning and evidence-based care protocols.

Key Features:
    🏥 Clinical workflow execution using PlanDefinition
    🧠 AI-powered clinical decision support
    📋 Healthcare protocol automation
    🔍 Evidence-based clinical reasoning
    📊 Clinical data requirements processing
    ⚡ Real-time guidance generation

All tools use WorkflowResult, GuidanceResult, and DataQueryResult from
hacs_core.results for consistent clinical response formatting.

Author: HACS Development Team
License: MIT
Version: 0.3.0
"""

import logging
from datetime import datetime
from typing import Any, Dict, List, Optional

from hacs_models import Actor
from hacs_models import (
    WorkflowResult,
    GuidanceResult,
    DataQueryResult,
    HACSResult
)
from hacs_core.tool_protocols import hacs_tool, ToolCategory

logger = logging.getLogger(__name__)

# Import tool descriptions
from .descriptions import (
    EXECUTE_CLINICAL_WORKFLOW_DESCRIPTION,
    GET_CLINICAL_GUIDANCE_DESCRIPTION,
    QUERY_WITH_DATAREQUIREMENT_DESCRIPTION,
    VALIDATE_CLINICAL_PROTOCOL_DESCRIPTION,
)

@hacs_tool(
    name="execute_clinical_workflow",
    description="Execute a clinical workflow using FHIR PlanDefinition specifications",
    category=ToolCategory.CLINICAL_WORKFLOWS,
    healthcare_domains=['clinical_data', 'clinical_workflows'],
    fhir_resources=['Patient', 'Observation', 'Encounter', 'Condition', 'MedicationRequest', 'Medication', 'Procedure', 'Goal']
)
def execute_clinical_workflow(
    actor_name: str,
    plan_definition_id: str,
    patient_id: Optional[str] = None,
    input_parameters: Optional[Dict[str, Any]] = None,
    execution_context: str = "routine",
    db_adapter: Any | None = None,
    vector_store: Any | None = None,
) -> WorkflowResult:
    """
    Execute a clinical workflow using FHIR PlanDefinition specifications.

    Runs structured healthcare protocols and clinical workflows with proper
    tracking of execution steps, clinical outcomes, and compliance requirements.

    Args:
        actor_name: Name of the healthcare actor executing the workflow
        plan_definition_id: ID of the PlanDefinition resource to execute
        patient_id: Optional patient ID if workflow is patient-specific
        input_parameters: Input parameters for workflow execution
        execution_context: Context of execution (routine, urgent, emergency, research)

    Returns:
        WorkflowResult with execution summary, clinical outcomes, and next steps

    Examples:
        execute_clinical_workflow("Dr. Smith", "diabetes-care-protocol", "patient-123")
        execute_clinical_workflow("Nurse Johnson", "post-op-monitoring", input_parameters={
            "procedure_type": "appendectomy",
            "surgery_date": "2024-01-15"
        })
    """
    try:
        # Create actor instance for validation/context
        _ = Actor(name=actor_name, role="physician")

        # TODO: In a real implementation, this would:
        # 1. Retrieve PlanDefinition from persistence layer
        # 2. Execute each workflow action in sequence
        # 3. Track clinical outcomes and decision points
        # 4. Generate clinical recommendations

        # Mock workflow execution for demonstration
        execution_summary = [
            {
                "action_id": "initial-assessment",
                "status": "completed",
                "outcome": "Patient baseline assessment completed",
                "timestamp": datetime.now().isoformat()
            },
            {
                "action_id": "risk-stratification",
                "status": "completed",
                "outcome": "Risk level determined: moderate",
                "timestamp": datetime.now().isoformat()
            }
        ]

        clinical_outcomes = [
            {
                "outcome_type": "assessment",
                "description": "Patient risk stratification completed",
                "clinical_significance": "moderate",
                "follow_up_required": True
            }
        ]

        recommendations = [
            "Continue monitoring vital signs every 4 hours",
            "Schedule follow-up appointment within 2 weeks",
            "Patient education on medication compliance"
        ]

        next_steps = [
            "Review lab results when available",
            "Assess medication effectiveness at next visit",
            "Consider referral to specialist if symptoms persist"
        ]

        compliance_notes = [
            "All required documentation completed",
            "Patient consent obtained for treatment plan",
            "Clinical guidelines followed per institutional protocol"
        ]

        return WorkflowResult(
            success=True,
            message=f"Clinical workflow {plan_definition_id} executed successfully",
            workflow_id=f"workflow-{datetime.now().strftime('%Y%m%d%H%M%S')}",
            plan_definition_id=plan_definition_id,
            patient_id=patient_id,
            execution_summary=execution_summary,
            clinical_outcomes=clinical_outcomes,
            recommendations=recommendations,
            next_steps=next_steps,
            compliance_notes=compliance_notes,
            executed_actions=len(execution_summary),
            failed_actions=0,
            execution_duration_ms=1500.0
        )

    except Exception as e:
        return WorkflowResult(
            success=False,
            message=f"Failed to execute clinical workflow {plan_definition_id}",
            workflow_id="",
            plan_definition_id=plan_definition_id,
            patient_id=patient_id,
            execution_summary=[],
            clinical_outcomes=[],
            recommendations=[],
            next_steps=[],
            executed_actions=0,
            failed_actions=1,
            execution_duration_ms=0.0
        )

@hacs_tool(
    name="get_clinical_guidance",
    description="Generate AI-powered clinical decision support and guidance",
    category=ToolCategory.CLINICAL_WORKFLOWS,
    healthcare_domains=['clinical_data'],
    fhir_resources=['Patient', 'Observation', 'Encounter', 'Condition', 'MedicationRequest', 'Medication', 'Procedure', 'Goal']
)
def get_clinical_guidance(
    actor_name: str,
    patient_id: str,
    clinical_question: str,
    patient_context: Optional[Dict[str, Any]] = None,
    knowledge_base_ids: Optional[List[str]] = None,
    urgency_level: str = "routine"
) -> GuidanceResult:
    """
    Generate AI-powered clinical decision support and guidance.

    Provides evidence-based clinical recommendations using healthcare knowledge
    bases, clinical guidelines, and patient-specific context for informed
    decision making.

    Args:
        actor_name: Name of the healthcare actor requesting guidance
        patient_id: ID of the patient for whom guidance is requested
        clinical_question: Specific clinical question or scenario
        patient_context: Patient-specific clinical context and history
        knowledge_base_ids: List of knowledge base IDs to consult
        urgency_level: Clinical urgency (routine, moderate, high, urgent)

    Returns:
        GuidanceResult with clinical recommendations, evidence sources, and alternatives

    Examples:
        get_clinical_guidance("Dr. Smith", "patient-123",
            "What is the appropriate antibiotic for post-surgical prophylaxis?")

        get_clinical_guidance("Nurse Johnson", "patient-456",
            "Patient shows signs of dehydration, what interventions are recommended?",
            patient_context={"age": 75, "comorbidities": ["diabetes", "hypertension"]})
    """
    try:
        # Create actor instance for validation/context
        _ = Actor(name=actor_name, role="physician")

        # TODO: In a real implementation, this would:
        # 1. Query knowledge bases and clinical guidelines
        # 2. Analyze patient-specific context
        # 3. Generate evidence-based recommendations
        # 4. Assess contraindications and alternatives
        # 5. Calculate confidence scores based on evidence quality

        # Mock clinical guidance generation
        recommendations = [
            {
                "recommendation": "Consider prophylactic antibiotic based on procedure type and patient risk factors",
                "strength": "strong",
                "evidence_level": "high",
                "clinical_rationale": "Reduces post-operative infection risk in high-risk patients"
            },
            {
                "recommendation": "Monitor for signs of infection at surgical site",
                "strength": "moderate",
                "evidence_level": "moderate",
                "clinical_rationale": "Early detection allows for prompt intervention"
            }
        ]

        evidence_sources = [
            "CDC Surgical Site Infection Prevention Guidelines 2024",
            "WHO Global Guidelines for Prevention of Surgical Site Infection",
            "Institutional Antibiotic Stewardship Protocol"
        ]

        contraindications = [
            "Known allergy to beta-lactam antibiotics",
            "Severe renal impairment requiring dose adjustment",
            "Concurrent use of anticoagulants may require monitoring"
        ]

        alternatives = [
            "Alternative antibiotic classes for penicillin-allergic patients",
            "Non-pharmacological infection prevention measures",
            "Extended monitoring protocol for high-risk patients"
        ]

        # Calculate confidence score based on evidence availability
        confidence_score = 0.85  # High confidence due to strong evidence base

        # Determine follow-up requirements
        follow_up_required = urgency_level in ["high", "urgent"] or confidence_score < 0.7

        return GuidanceResult(
            success=True,
            message="Clinical guidance generated successfully",
            guidance_response_id=f"guidance-{datetime.now().strftime('%Y%m%d%H%M%S')}",
            guidance_type="clinical_decision_support",
            clinical_question=clinical_question,
            recommendations=recommendations,
            confidence_score=confidence_score,
            evidence_sources=evidence_sources,
            contraindications=contraindications,
            alternatives=alternatives,
            follow_up_required=follow_up_required,
            urgency_level=urgency_level
        )

    except Exception as e:
        return GuidanceResult(
            success=False,
            message=f"Failed to generate clinical guidance: {str(e)}",
            guidance_response_id="",
            guidance_type="clinical_decision_support",
            clinical_question=clinical_question,
            recommendations=[],
            confidence_score=0.0,
            evidence_sources=[],
            contraindications=[],
            alternatives=[],
            follow_up_required=True,
            urgency_level=urgency_level
        )

@hacs_tool(
    name="query_with_datarequirement",
    description="Execute structured healthcare data queries using FHIR DataRequirement specifications",
    category=ToolCategory.CLINICAL_WORKFLOWS,
    healthcare_domains=['general_healthcare'],
    fhir_resources=['Patient', 'Observation', 'Encounter', 'Condition', 'MedicationRequest', 'Medication', 'Procedure', 'Goal']
)
def query_with_datarequirement(
    actor_name: str,
    data_requirement_spec: Dict[str, Any],
    patient_id: Optional[str] = None,
    include_aggregations: bool = False
) -> DataQueryResult:
    """
    Execute structured healthcare data queries using FHIR DataRequirement specifications.

    Performs complex clinical data queries with FHIR-compliant filtering,
    sorting, and aggregation capabilities for clinical decision support
    and quality reporting.

    Args:
        actor_name: Name of the healthcare actor performing the query
        data_requirement_spec: FHIR DataRequirement specification for the query
        patient_id: Optional patient ID to limit query scope
        include_aggregations: Whether to include clinical data aggregations

    Returns:
        DataQueryResult with query results, clinical insights, and metadata

    Examples:
        query_with_datarequirement("Dr. Smith", {
            "type": "Observation",
            "code_filter": [{"code": "blood-pressure"}],
            "date_filter": {"period": "last-30-days"}
        }, patient_id="patient-123")
    """
    try:
        # Create actor instance for validation/context
        _ = Actor(name=actor_name, role="physician")

        # TODO: In a real implementation, this would:
        # 1. Parse DataRequirement specification
        # 2. Convert to appropriate query filters
        # 3. Execute query against persistence layer
        # 4. Apply FHIR-compliant result formatting
        # 5. Generate clinical insights from results

        # Mock query execution
        query_type = data_requirement_spec.get("type", "unknown")
        mock_results = []

        # Generate mock clinical insights
        clinical_insights = []
        if query_type == "Observation":
            clinical_insights = [
                "Patient shows improving vital sign trends over the query period",
                "No critical values detected in the observation data"
            ]

        # Mock aggregated data if requested
        aggregated_data = None
        if include_aggregations:
            aggregated_data = {
                "total_records": len(mock_results),
                "date_range": "2024-01-01 to 2024-01-31",
                "summary_statistics": {
                    "mean_value": 120.5,
                    "min_value": 110.0,
                    "max_value": 135.0
                }
            }

        return DataQueryResult(
            success=True,
            message=f"Healthcare data query completed for {query_type}",
            data_requirement_id=data_requirement_spec.get("id"),
            query_type=query_type,
            results_count=len(mock_results),
            results=mock_results,
            aggregated_data=aggregated_data,
            clinical_insights=clinical_insights,
            fhir_compliant=True,
            execution_time_ms=250.0
        )

    except Exception as e:
        return DataQueryResult(
            success=False,
            message=f"Failed to execute healthcare data query: {str(e)}",
            data_requirement_id=data_requirement_spec.get("id"),
            query_type="unknown",
            results_count=0,
            results=[],
            aggregated_data=None,
            clinical_insights=[],
            fhir_compliant=False,
            execution_time_ms=0.0
        )

@hacs_tool(
    name="validate_clinical_protocol",
    description="Validate clinical protocols and care pathways for compliance and completeness",
    category=ToolCategory.CLINICAL_WORKFLOWS,
    healthcare_domains=['clinical_data'],
    fhir_resources=['Patient', 'Observation', 'Encounter', 'Condition', 'MedicationRequest', 'Medication', 'Procedure', 'Goal']
)
def validate_clinical_protocol(
    actor_name: str,
    protocol_data: Dict[str, Any],
    validation_level: str = "standard"
) -> HACSResult:
    """
    Validate clinical protocols and care pathways for compliance and completeness.

    Performs comprehensive validation of clinical protocols against healthcare
    standards, regulatory requirements, and best practice guidelines.

    Args:
        actor_name: Name of the healthcare actor performing validation
        protocol_data: Clinical protocol data to validate
        validation_level: Level of validation (basic, standard, comprehensive)

    Returns:
        HACSResult with validation status, findings, and recommendations

    Examples:
        validate_clinical_protocol("Dr. Smith", {
            "protocol_name": "Post-Op Care Pathway",
            "steps": [...],
            "decision_points": [...]
        }, validation_level="comprehensive")
    """
    try:
        # Create actor instance for validation/context
        _ = Actor(name=actor_name, role="physician")

        # TODO: In a real implementation, this would:
        # 1. Validate protocol structure and completeness
        # 2. Check compliance with clinical guidelines
        # 3. Verify decision points and care pathways
        # 4. Assess evidence base and recommendations
        # 5. Generate improvement suggestions

        protocol_name = protocol_data.get("protocol_name", "Unknown Protocol")

        # Mock validation results
        validation_findings = {
            "structure_validation": "passed",
            "compliance_check": "passed_with_warnings",
            "evidence_validation": "passed",
            "completeness_score": 0.85,
            "warnings": [
                "Consider adding alternative care pathways for complex cases",
                "Update references to latest clinical guidelines"
            ],
            "recommendations": [
                "Add decision support tools for key clinical decision points",
                "Include patient education materials in protocol steps"
            ]
        }

        return HACSResult(
            success=True,
            message=f"Clinical protocol validation completed for {protocol_name}",
            data=validation_findings
        )

    except Exception as e:
        return HACSResult(
            success=False,
            message=f"Failed to validate clinical protocol: {str(e)}",
            error=str(e)
        )

__all__ = [
    "execute_clinical_workflow",
    "get_clinical_guidance",
    "query_with_datarequirement",
    "validate_clinical_protocol",
]