"""
HACS Healthcare Analytics Tools

This module provides comprehensive healthcare analytics and population health
tools for clinical quality measurement, outcomes analysis, and healthcare
performance monitoring. Supports clinical decision support and quality improvement.

Key Features:
    📊 Clinical quality measure calculation
    🏥 Population health analytics and reporting
    📈 Healthcare outcomes analysis and trending
    🎯 Risk stratification and predictive analytics
    📋 Clinical dashboard and KPI generation
    ⚡ Real-time healthcare performance monitoring

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

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

from hacs_models import Actor
from hacs_models import HACSResult
from hacs_core.tool_protocols import hacs_tool, ToolCategory

logger = logging.getLogger(__name__)

# Import tool descriptions
from .descriptions import (
    CALCULATE_QUALITY_MEASURES_DESCRIPTION,
    ANALYZE_POPULATION_HEALTH_DESCRIPTION,
    GENERATE_CLINICAL_DASHBOARD_DESCRIPTION,
    PERFORM_RISK_STRATIFICATION_DESCRIPTION,
)

@hacs_tool(
    name="calculate_quality_measures",
    description="Calculate clinical quality measures for healthcare performance monitoring",
    category=ToolCategory.HEALTHCARE_ANALYTICS,
    healthcare_domains=['general_healthcare'],
    fhir_resources=['Patient', 'Observation', 'Encounter', 'Condition', 'MedicationRequest', 'Medication', 'Procedure', 'Goal']
)
def calculate_quality_measures(
    actor_name: str,
    measure_set: str,
    patient_population: List[str],
    measurement_period: Dict[str, str],
    include_benchmarks: bool = True
) -> HACSResult:
    """
    Calculate clinical quality measures for healthcare performance monitoring.

    This tool computes clinical quality measures such as HEDIS, CMS, and custom
    quality indicators for patient populations with benchmarking, trending,
    and improvement opportunity identification.

    Args:
        actor_name: Name of the healthcare actor calculating measures
        measure_set: Quality measure set (HEDIS, CMS, MIPS, custom)
        patient_population: List of patient IDs for calculation
        measurement_period: Period for calculation (start_date, end_date)
        include_benchmarks: Whether to include industry benchmarks

    Returns:
        HACSResult with calculated quality measures and performance analytics

    Examples:
        calculate_quality_measures("Dr. Smith", "HEDIS", patient_ids, {"start": "2024-01-01", "end": "2024-12-31"})
        calculate_quality_measures("Quality Director", "CMS", population, period_dict, True)
    """
    try:
        # Create actor instance for validation/context
        _ = Actor(name=actor_name, role="physician")

        # TODO: In a real implementation, this would:
        # 1. Retrieve patient data for specified population
        # 2. Apply quality measure specifications and algorithms
        # 3. Calculate numerator, denominator, and exclusions
        # 4. Generate quality scores and performance metrics
        # 5. Compare against benchmarks and identify opportunities

        # Mock quality measure calculation
        quality_results = {
            "measure_set": measure_set,
            "measurement_period": measurement_period,
            "population_size": len(patient_population),
            "measures": [
                {
                    "measure_id": "DM-HbA1c-Control",
                    "measure_name": "Diabetes HbA1c Control",
                    "numerator": 142,
                    "denominator": 180,
                    "rate": 78.9,
                    "benchmark": 75.0,
                    "performance": "Above Benchmark",
                    "improvement_opportunity": 22
                },
                {
                    "measure_id": "HTN-BP-Control",
                    "measure_name": "Hypertension Blood Pressure Control",
                    "numerator": 156,
                    "denominator": 200,
                    "rate": 78.0,
                    "benchmark": 82.0,
                    "performance": "Below Benchmark",
                    "improvement_opportunity": 8
                }
            ],
            "summary": {
                "total_measures": 2,
                "measures_above_benchmark": 1,
                "measures_below_benchmark": 1,
                "overall_performance_score": 78.45
            }
        }

        return HACSResult(
            success=True,
            message=f"Quality measures calculated for {len(patient_population)} patients",
            data=quality_results,
            actor_id=actor_name
        )

    except Exception as e:
        return HACSResult(
            success=False,
            message=f"Failed to calculate quality measures: {str(e)}",
            error=str(e),
            actor_id=actor_name
        )

@hacs_tool(
    name="analyze_population_health",
    description="Analyze population health patterns, trends, and outcomes for healthcare management",
    category=ToolCategory.HEALTHCARE_ANALYTICS,
    healthcare_domains=['general_healthcare'],
    fhir_resources=['Patient', 'Observation', 'Encounter', 'Condition', 'MedicationRequest', 'Medication', 'Procedure', 'Goal']
)
def analyze_population_health(
    actor_name: str,
    population_criteria: Dict[str, Any],
    analysis_type: str = "comprehensive",
    include_social_determinants: bool = True,
    time_period_months: int = 12
) -> HACSResult:
    """
    Analyze population health patterns, trends, and outcomes for healthcare management.

    This tool performs comprehensive population health analysis including
    demographic patterns, clinical outcomes, social determinants, and
    health equity assessment for population health management.

    Args:
        actor_name: Name of the healthcare actor performing analysis
        population_criteria: Criteria for population selection (age, conditions, geography)
        analysis_type: Type of analysis (comprehensive, outcomes, equity, trends)
        include_social_determinants: Whether to include SDOH analysis
        time_period_months: Time period for trend analysis

    Returns:
        HACSResult with population health analysis and insights

    Examples:
        analyze_population_health("Population Health Director", {"age_range": [65, 85]}, "comprehensive")
        analyze_population_health("Dr. Smith", {"condition": "diabetes"}, "outcomes", True, 24)
    """
    try:
        # Create actor instance for validation/context
        _ = Actor(name=actor_name, role="physician")

        # TODO: In a real implementation, this would:
        # 1. Identify population based on criteria
        # 2. Analyze clinical outcomes and utilization patterns
        # 3. Assess social determinants of health impact
        # 4. Identify health disparities and equity gaps
        # 5. Generate actionable population health insights

        # Mock population health analysis
        population_analysis = {
            "population_criteria": population_criteria,
            "analysis_period": f"{time_period_months} months",
            "population_size": 2450,
            "demographics": {
                "age_distribution": {"18-34": 15.2, "35-49": 22.8, "50-64": 31.5, "65+": 30.5},
                "gender_distribution": {"male": 48.2, "female": 51.8},
                "race_ethnicity": {"white": 65.4, "hispanic": 18.2, "african_american": 12.1, "other": 4.3}
            },
            "clinical_outcomes": {
                "chronic_conditions_prevalence": {
                    "diabetes": 12.4,
                    "hypertension": 28.7,
                    "heart_disease": 8.9,
                    "depression": 15.3
                },
                "healthcare_utilization": {
                    "primary_care_visits_per_year": 3.2,
                    "specialty_visits_per_year": 1.8,
                    "emergency_visits_per_year": 0.4,
                    "hospitalizations_per_year": 0.1
                }
            },
            "social_determinants": {
                "food_insecurity": 18.5,
                "housing_instability": 12.3,
                "transportation_barriers": 15.7,
                "social_isolation": 22.1
            } if include_social_determinants else {},
            "health_equity_gaps": [
                "Hispanic population shows 15% higher diabetes prevalence",
                "Rural patients have 25% lower primary care access",
                "Low-income patients have 30% higher emergency utilization"
            ],
            "recommendations": [
                "Implement targeted diabetes prevention programs for high-risk populations",
                "Expand telehealth services for rural patient access",
                "Develop social determinants screening and intervention programs"
            ]
        }

        return HACSResult(
            success=True,
            message=f"Population health analysis completed for {population_analysis['population_size']} individuals",
            data=population_analysis,
            actor_id=actor_name
        )

    except Exception as e:
        return HACSResult(
            success=False,
            message=f"Failed to analyze population health: {str(e)}",
            error=str(e),
            actor_id=actor_name
        )

@hacs_tool(
    name="generate_clinical_dashboard",
    description="Generate interactive clinical dashboards for healthcare performance monitoring",
    category=ToolCategory.HEALTHCARE_ANALYTICS,
    healthcare_domains=['clinical_data'],
    fhir_resources=['Patient', 'Observation', 'Encounter', 'Condition', 'MedicationRequest', 'Medication', 'Procedure', 'Goal']
)
def generate_clinical_dashboard(
    actor_name: str,
    dashboard_type: str,
    clinical_specialty: str = "general",
    time_period: str = "current_month",
    include_alerts: bool = True
) -> HACSResult:
    """
    Generate interactive clinical dashboards for healthcare performance monitoring.

    This tool creates customized clinical dashboards with key performance indicators,
    quality metrics, patient outcomes, and real-time alerts for healthcare
    professionals and administrators.

    Args:
        actor_name: Name of the healthcare actor generating dashboard
        dashboard_type: Type of dashboard (clinical, quality, operational, executive)
        clinical_specialty: Medical specialty focus (cardiology, primary_care, etc.)
        time_period: Dashboard time period (current_month, quarter, year)
        include_alerts: Whether to include clinical alerts and notifications

    Returns:
        HACSResult with dashboard configuration and performance data

    Examples:
        generate_clinical_dashboard("Dr. Smith", "clinical", "cardiology", "current_month")
        generate_clinical_dashboard("Quality Director", "quality", "general", "quarter", True)
    """
    try:
        # Create actor instance for validation/context
        _ = Actor(name=actor_name, role="physician")

        # TODO: In a real implementation, this would:
        # 1. Configure dashboard based on user role and specialty
        # 2. Aggregate clinical and operational metrics
        # 3. Generate visualizations and trend analysis
        # 4. Include real-time alerts and notifications
        # 5. Create interactive dashboard components

        # Mock clinical dashboard
        dashboard_data = {
            "dashboard_type": dashboard_type,
            "clinical_specialty": clinical_specialty,
            "time_period": time_period,
            "generated_for": actor_name,
            "last_updated": datetime.now().isoformat(),
            "key_metrics": {
                "total_patients": 1247,
                "active_patients": 892,
                "quality_score": 87.3,
                "patient_satisfaction": 4.6,
                "clinical_outcomes_score": 82.1
            },
            "performance_indicators": [
                {"name": "Diabetes Control Rate", "value": 78.9, "target": 75.0, "trend": "improving"},
                {"name": "Blood Pressure Control", "value": 76.2, "target": 80.0, "trend": "stable"},
                {"name": "Preventive Care Completion", "value": 84.5, "target": 85.0, "trend": "improving"}
            ],
            "alerts": [
                {"level": "high", "message": "5 patients overdue for diabetes follow-up"},
                {"level": "medium", "message": "Lab results pending review for 12 patients"},
                {"level": "low", "message": "Quality measure reporting due in 7 days"}
            ] if include_alerts else [],
            "visualizations": [
                {"type": "trend_chart", "title": "Patient Volume Trends", "data_points": 30},
                {"type": "quality_gauge", "title": "Quality Score", "current_value": 87.3},
                {"type": "alert_summary", "title": "Clinical Alerts", "active_alerts": 3}
            ]
        }

        return HACSResult(
            success=True,
            message=f"Clinical dashboard generated for {clinical_specialty} specialty",
            data=dashboard_data,
            actor_id=actor_name
        )

    except Exception as e:
        return HACSResult(
            success=False,
            message=f"Failed to generate clinical dashboard: {str(e)}",
            error=str(e),
            actor_id=actor_name
        )

@hacs_tool(
    name="perform_risk_stratification",
    description="Perform patient risk stratification and predictive analytics for proactive care",
    category=ToolCategory.HEALTHCARE_ANALYTICS,
    healthcare_domains=['general_healthcare'],
    fhir_resources=['Patient', 'Observation', 'Encounter', 'Condition', 'MedicationRequest', 'Medication', 'Procedure', 'Goal']
)
def perform_risk_stratification(
    actor_name: str,
    patient_population: List[str],
    risk_model: str = "comprehensive",
    prediction_horizon: str = "12_months",
    include_interventions: bool = True
) -> HACSResult:
    """
    Perform patient risk stratification and predictive analytics for proactive care.

    This tool analyzes patient data to identify high-risk individuals, predict
    clinical outcomes, and recommend targeted interventions for care management
    and population health improvement.

    Args:
        actor_name: Name of the healthcare actor performing risk stratification
        patient_population: List of patient IDs for risk analysis
        risk_model: Risk prediction model (comprehensive, condition_specific, cost)
        prediction_horizon: Time horizon for predictions (6_months, 12_months, 24_months)
        include_interventions: Whether to include intervention recommendations

    Returns:
        HACSResult with risk stratification results and intervention recommendations

    Examples:
        perform_risk_stratification("Care Manager", patient_list, "comprehensive", "12_months")
        perform_risk_stratification("Dr. Smith", diabetes_patients, "condition_specific", "6_months", True)
    """
    try:
        # Create actor instance for validation/context
        _ = Actor(name=actor_name, role="physician")

        # TODO: In a real implementation, this would:
        # 1. Retrieve comprehensive patient data and clinical history
        # 2. Apply machine learning risk prediction models
        # 3. Calculate risk scores for various clinical outcomes
        # 4. Stratify patients into risk categories
        # 5. Generate targeted intervention recommendations

        # Mock risk stratification
        risk_results = {
            "risk_model": risk_model,
            "prediction_horizon": prediction_horizon,
            "population_analyzed": len(patient_population),
            "stratification_date": datetime.now().isoformat(),
            "risk_categories": {
                "high_risk": {"count": 89, "percentage": 15.2},
                "medium_risk": {"count": 156, "percentage": 26.7},
                "low_risk": {"count": 340, "percentage": 58.1}
            },
            "top_risk_factors": [
                {"factor": "Uncontrolled diabetes", "prevalence": 34.2},
                {"factor": "History of cardiovascular events", "prevalence": 28.7},
                {"factor": "Multiple chronic conditions", "prevalence": 45.3},
                {"factor": "Medication non-adherence", "prevalence": 22.1}
            ],
            "predicted_outcomes": {
                "hospitalization_risk": {"high": 89, "medium": 67, "low": 12},
                "emergency_visit_risk": {"high": 134, "medium": 89, "low": 23},
                "clinical_deterioration_risk": {"high": 76, "medium": 45, "low": 8}
            },
            "recommended_interventions": [
                {"risk_level": "high", "intervention": "Care management enrollment", "priority": 1},
                {"risk_level": "high", "intervention": "Monthly clinical outreach", "priority": 2},
                {"risk_level": "medium", "intervention": "Quarterly wellness checks", "priority": 1},
                {"risk_level": "medium", "intervention": "Medication adherence support", "priority": 2}
            ] if include_interventions else []
        }

        return HACSResult(
            success=True,
            message=f"Risk stratification completed for {len(patient_population)} patients",
            data=risk_results,
            actor_id=actor_name
        )

    except Exception as e:
        return HACSResult(
            success=False,
            message=f"Failed to perform risk stratification: {str(e)}",
            error=str(e),
            actor_id=actor_name
        )

__all__ = [
    "calculate_quality_measures",
    "analyze_population_health",
    "generate_clinical_dashboard",
    "perform_risk_stratification",
]