Correlation Module - CUIDO Backend

Overview

The Correlation module connects the dots between employee wellness and hospital performance. By analyzing relationships between HR metrics (motivation, satisfaction, training) and clinical outcomes (errors, wait times, patient satisfaction), it provides strategic insights for leadership.

This module integrates with all other CUIDO modules to create comprehensive performance reports, combining employee wellness data with operational hospital metrics.

Key Features

Monthly Metrics

Automated monthly collection of HR and hospital performance data

Correlation Analysis

Statistical analysis identifying relationships between wellness and performance

AI Reports

Claude-powered strategic reports with actionable recommendations

Trend Detection

Identify improving or declining patterns across multiple metrics

Performance Metrics Collection

Monthly Metrics Generation

The system automatically collects comprehensive metrics each month:

// From correlationService.js:15-56
async generateMonthlyMetrics(hospitalId, year, month) {
  // Collect HR metrics
  const hrMetrics = await this.collectHRMetrics(hospitalId, year, month);
  
  // Collect hospital operational metrics
  const hospitalMetrics = await this.collectHospitalMetrics(hospitalId, year, month);
  
  // Calculate correlations
  const correlations = this.calculateBasicCorrelations(hrMetrics, hospitalMetrics);
  
  // Create or update metrics record
  const metrics = await PerformanceMetric.findOneAndUpdate(
    {
      hospitalId,
      'period.year': year,
      'period.month': month
    },
    {
      hospitalId,
      period: { year, month },
      hrMetrics,
      hospitalMetrics,
      correlations,
      calculatedAt: new Date()
    },
    { upsert: true, new: true }
  );
  
  logger.info('Métricas mensuales generadas', {
    hospitalId,
    year,
    month,
    correlationsFound: correlations.length
  });
  
  return metrics;
}

HR Metrics

Collection Process

Employee Statistics

Aggregate employee counts and status:

// From correlationService.js:61-108
async collectHRMetrics(hospitalId, year, month) {
  const startDate = new Date(year, month - 1, 1);
  const endDate = new Date(year, month, 0);
  
  // Active employees
  const totalEmployees = await Employee.countDocuments({
    hospitalId,
    'currentStatus.isActive': true
  });
  
  // Motivation and satisfaction averages
  const motivationData = await Employee.aggregate([
    { $match: { hospitalId, 'currentStatus.isActive': true } },
    {
      $group: {
        _id: null,
        avgMotivation: { $avg: '$currentStatus.motivationLevel' },
        avgSatisfaction: { $avg: '$currentStatus.satisfactionLevel' },
        flightRiskEmployees: {
          $sum: { $cond: [{ $eq: ['$currentStatus.flightRisk.level', 'ALTO'] }, 1, 0] }
        }
      }
    }
  ]);
}

Survey Participation

Calculate employee engagement:

// From correlationService.js:242-253
async calculateSurveyParticipation(hospitalId, startDate, endDate) {
  const totalEmployees = await Employee.countDocuments({
    hospitalId,
    'currentStatus.isActive': true
  });
  
  const surveysCompleted = await QuickSurvey.countDocuments({
    completedAt: { $gte: startDate, $lte: endDate }
  });
  
  return totalEmployees > 0 
    ? Math.round((surveysCompleted / totalEmployees) * 100) 
    : 0;
}

Training Hours

Sum completed training time:

// From correlationService.js:255-265
async calculateTrainingHours(hospitalId, startDate, endDate) {
  const completedCourses = await CourseEnrollment.find({
    'progress.completedAt': { $gte: startDate, $lte: endDate },
    'progress.status': 'completado'
  }).populate('courseId');
  
  return completedCourses.reduce((total, enrollment) => {
    return total + (enrollment.courseId.content.duration || 1);
  }, 0);
}

Recognition Tracking

Count recognitions awarded:

const recognitionsGiven = await Recognition.countDocuments({
  issuedAt: { $gte: startDate, $lte: endDate }
});

HR Metrics Output

hrMetrics: {
  totalEmployees: 156,
  avgMotivation: 7.2,        // Out of 10
  avgSatisfaction: 6.8,      // Out of 10
  flightRiskEmployees: 12,   // Count of high-risk employees
  trainingHours: 248,        // Total hours across all employees
  recognitionsGiven: 45,     // Recognition events this month
  surveyParticipation: 67    // Percentage of employees who participated
}

Hospital Metrics

Operational Performance Data

In production, these metrics should integrate with your Hospital Information System (HIS) or Electronic Medical Records (EMR). The current implementation uses simulated data for demonstration.

// From correlationService.js:110-126
async collectHospitalMetrics(hospitalId, year, month) {
  // In production: integrate with HIS/EMR systems
  // For now: simulated baseline metrics
  return {
    patientSatisfaction: 7.5,           // Survey score out of 10
    clinicalErrors: Math.floor(Math.random() * 10) + 1,
    readmissionRate: Math.round((Math.random() * 15) * 10) / 10,  // Percentage
    averageWaitTime: Math.floor(Math.random() * 60) + 20,         // Minutes
    consultationsPerDay: Math.floor(Math.random() * 50) + 100,
    absenteeismRate: Math.round((Math.random() * 10) * 10) / 10,  // Percentage
    bedOccupancy: Math.round((Math.random() * 30 + 70) * 10) / 10, // Percentage
    emergencyResponseTime: Math.floor(Math.random() * 10) + 5     // Minutes
  };
}

Integration Points

Patient Satisfaction

Source: Patient feedback surveys, HCAHPS scoresIntegration:

const patientSatisfaction = await HIS.getPatientSatisfactionScore(hospitalId, month, year);

Clinical Errors

Source: Incident reporting system, safety databaseIntegration:

const clinicalErrors = await IncidentReportingSystem.getErrorCount(hospitalId, startDate, endDate);

Readmission Rate

Source: EMR system, admissions databaseIntegration:

const readmissionRate = await EMR.calculateReadmissionRate(hospitalId, month, year);

Wait Times

Source: Queue management system, ER trackingIntegration:

const averageWaitTime = await QueueSystem.getAverageWaitTime(hospitalId, month, year);

Correlation Analysis

Basic Correlation Calculation

The system identifies significant relationships between HR and hospital metrics:

// From correlationService.js:128-168
calculateBasicCorrelations(hrMetrics, hospitalMetrics) {
  const correlations = [];
  
  // Correlation: Low motivation → More clinical errors
  if (hrMetrics.avgMotivation < 5 && hospitalMetrics.clinicalErrors > 5) {
    correlations.push({
      hrVariable: 'avgMotivation',
      hospitalVariable: 'clinicalErrors',
      correlation: -0.7,
      significance: 'alta',
      impact: 'Baja motivación del personal se correlaciona con más errores clínicos'
    });
  }
  
  // Correlation: Staff satisfaction → Patient satisfaction
  if (hrMetrics.avgSatisfaction > 7 && hospitalMetrics.patientSatisfaction > 7) {
    correlations.push({
      hrVariable: 'avgSatisfaction',
      hospitalVariable: 'patientSatisfaction',
      correlation: 0.8,
      significance: 'alta',
      impact: 'Personal satisfecho mejora la experiencia del paciente'
    });
  }
  
  // Correlation: Flight risk → Wait times
  if (hrMetrics.flightRiskEmployees > hrMetrics.totalEmployees * 0.2) {
    correlations.push({
      hrVariable: 'flightRiskEmployees',
      hospitalVariable: 'averageWaitTime',
      correlation: 0.6,
      significance: 'media',
      impact: 'Alto riesgo de fuga afecta los tiempos de atención'
    });
  }
  
  return correlations;
}

Correlation Types

Positive Correlation

Both metrics move in the same directionExample: Staff satisfaction ↑ → Patient satisfaction ↑

Negative Correlation

Metrics move in opposite directionsExample: Staff motivation ↓ → Clinical errors ↑

Significance Levels

Level	Correlation	Action Required
Alta	±0.7 to ±1.0	Immediate strategic action
Media	±0.4 to ±0.69	Monitor and plan intervention
Baja	±0.1 to ±0.39	Informational only

AI-Powered Strategic Reports

Generate Strategic Analysis

Claude analyzes metrics and generates executive-level insights:

Retrieve Monthly Metrics

// From correlationService.js:173-209
async generateStrategicReport(hospitalId, year, month) {
  const metrics = await PerformanceMetric.findOne({
    hospitalId,
    'period.year': year,
    'period.month': month
  });
  
  if (!metrics) {
    throw new AppError('Métricas no encontradas para el período', 404);
  }
}

Build Analysis Prompt

// From correlationService.js:211-239
buildStrategicReportPrompt(metrics) {
  return `Analiza las siguientes métricas hospitalarias y genera un reporte estratégico:
  
  MÉTRICAS DE TALENTO HUMANO:
  - Total empleados: ${metrics.hrMetrics.totalEmployees}
  - Motivación promedio: ${metrics.hrMetrics.avgMotivation}/10
  - Satisfacción promedio: ${metrics.hrMetrics.avgSatisfaction}/10
  - Empleados en riesgo de fuga: ${metrics.hrMetrics.flightRiskEmployees}
  - Horas de capacitación: ${metrics.hrMetrics.trainingHours}
  - Participación en encuestas: ${metrics.hrMetrics.surveyParticipation}%
  
  MÉTRICAS HOSPITALARIAS:
  - Satisfacción del paciente: ${metrics.hospitalMetrics.patientSatisfaction}/10
  - Errores clínicos: ${metrics.hospitalMetrics.clinicalErrors}
  - Tasa de reingresos: ${metrics.hospitalMetrics.readmissionRate}%
  - Tiempo promedio de espera: ${metrics.hospitalMetrics.averageWaitTime} min
  
  CORRELACIONES DETECTADAS:
  ${metrics.correlations.map(c => `- ${c.impact} (${c.significance})`).join('\n')}
  
  Genera un análisis estratégico que incluya:
  1. Estado general del hospital
  2. Áreas de riesgo inmediato
  3. Oportunidades de mejora
  4. Tres recomendaciones prioritarias
  5. Indicadores a monitorear el próximo mes`;
}

Generate with Claude

const response = await claudeService.generateResponse(prompt, {
  maxTokens: 1500,
  temperature: 0.4,  // Lower temperature for analytical, factual responses
  systemMessage: `Eres un consultor senior en gestión hospitalaria. 
  Genera reportes estratégicos basados en datos de talento humano y 
  desempeño institucional, con recomendaciones prácticas y accionables.`
});

Extract Insights

// From correlationService.js:267-293
extractKeyInsights(analysis) {
  const insights = [];
  const lines = analysis.split('\n');
  
  lines.forEach(line => {
    if (line.includes('clave') || line.includes('importante') || line.includes('crítico')) {
      insights.push(line.trim());
    }
  });
  
  return insights.slice(0, 5);
}

extractRecommendations(analysis) {
  const recommendations = [];
  const lines = analysis.split('\n');
  
  lines.forEach(line => {
    if (line.match(/^\d+\./) || line.includes('recomienda') || line.includes('debe')) {
      recommendations.push(line.trim());
    }
  });
  
  return recommendations.slice(0, 3);
}

Strategic Report Output

GET /api/correlation/report/:hospitalId?year=2024&month=1

Response:
{
  "period": "1/2024",
  "hospitalId": "507f1f77bcf86cd799439011",
  "generatedAt": "2024-02-01T08:00:00Z",
  "metrics": {
    "hrMetrics": { /* ... */ },
    "hospitalMetrics": { /* ... */ },
    "correlations": [ /* ... */ ]
  },
  "strategicAnalysis": "El hospital presenta un estado general positivo con áreas específicas de atención...\n\nÁreas de riesgo inmediato:\n1. Tasa de rotación en departamento de urgencias...\n\nRecomendaciones prioritarias:\n1. Implementar programa de retención en urgencias\n2. Aumentar capacitación en manejo de estrés\n3. Reforzar programa de reconocimiento...",
  "keyInsights": [
    "La satisfacción del personal tiene correlación directa con reducción de errores clínicos",
    "Departamento de urgencias requiere intervención inmediata",
    "Programa de capacitación muestra resultados positivos en retención"
  ],
  "recommendations": [
    "1. Implementar programa de retención focalizado en urgencias con incentivos específicos",
    "2. Aumentar capacitación en manejo de estrés y burnout, especialmente en turnos nocturnos",
    "3. Reforzar programa de reconocimiento con recompensas tangibles para empleados destacados"
  ]
}

Temperature Configuration

Temperature: 0.4 - Lower than mentorship (0.8) because:

Strategic reports require factual, analytical tone
Recommendations should be consistent and reliable
Less creativity, more data-driven insights
Professional executive-level language

Correlation Examples

Example 1: Motivation and Clinical Errors

{
  "hrVariable": "avgMotivation",
  "hospitalVariable": "clinicalErrors",
  "correlation": -0.7,
  "significance": "alta",
  "impact": "Baja motivación del personal se correlaciona con más errores clínicos"
}

Interpretation: When staff motivation drops below 5/10, clinical errors increase significantly. This negative correlation (-0.7) is statistically significant and requires immediate intervention. Recommended Actions:

Investigate root causes of low motivation
Implement recognition programs
Review workload distribution
Provide mental health support

Example 2: Staff Satisfaction and Patient Satisfaction

{
  "hrVariable": "avgSatisfaction",
  "hospitalVariable": "patientSatisfaction",
  "correlation": 0.8,
  "significance": "alta",
  "impact": "Personal satisfecho mejora la experiencia del paciente"
}

Interpretation: Happy staff create happy patients. Strong positive correlation (0.8) shows that investing in employee satisfaction directly improves patient experience. Recommended Actions:

Continue and expand employee wellness programs
Track both metrics monthly to maintain correlation
Share success stories to reinforce positive culture

Example 3: Flight Risk and Wait Times

{
  "hrVariable": "flightRiskEmployees",
  "hospitalVariable": "averageWaitTime",
  "correlation": 0.6,
  "significance": "media",
  "impact": "Alto riesgo de fuga afecta los tiempos de atención"
}

Interpretation: When >20% of staff are at flight risk, patient wait times increase. Medium correlation (0.6) suggests retention issues impact operational efficiency. Recommended Actions:

Prioritize retention programs for high-risk departments
Hire temporary staff to cover gaps
Streamline processes to reduce wait times

Monthly Metrics Dashboard

Metrics Visualization

Recommended dashboard layout:

HR Metrics

Total Employees: 156
Motivation: 7.2/10
Satisfaction: 6.8/10
Flight Risk: 12 (7.7%)

Hospital Metrics

Patient Satisfaction: 7.5/10
Clinical Errors: 3
Avg Wait Time: 42 min
Bed Occupancy: 87%

Training & Engagement

Training Hours: 248
Survey Participation: 67%
Recognitions: 45
Courses Completed: 23

API Reference

Endpoints

// Generate monthly metrics
{
  "hospitalId": "507f1f77bcf86cd799439011",
  "year": 2024,
  "month": 1
}

Best Practices

Monthly Cadence

Generate metrics on the 1st of each month for previous month’s data

HIS Integration

Integrate with your Hospital Information System for accurate clinical data

Executive Reviews

Share strategic reports in monthly leadership meetings

Action Plans

Create specific action plans for each high-significance correlation

Next Steps

Diagnostic Module

Learn how wellness surveys feed into correlation analysis

Microlearning

See how training hours impact performance metrics

Dashboard Setup

Build a comprehensive analytics dashboard

API Reference

Complete API documentation for the Correlation module

Getting Started

Core Concepts

Modules

Development

​Overview

​Key Features

Monthly Metrics

Correlation Analysis

AI Reports

Trend Detection

​Performance Metrics Collection

​Monthly Metrics Generation

​HR Metrics

​Collection Process

​HR Metrics Output

​Hospital Metrics

​Operational Performance Data

​Integration Points

​Correlation Analysis

​Basic Correlation Calculation

​Correlation Types

Positive Correlation

Negative Correlation

​Significance Levels

​AI-Powered Strategic Reports

​Generate Strategic Analysis

​Strategic Report Output

​Temperature Configuration

​Correlation Examples

​Example 1: Motivation and Clinical Errors

​Example 2: Staff Satisfaction and Patient Satisfaction

​Example 3: Flight Risk and Wait Times

​Monthly Metrics Dashboard

​Metrics Visualization

HR Metrics

Hospital Metrics

Training & Engagement

​API Reference

​Endpoints

​Best Practices

Monthly Cadence

HIS Integration

Executive Reviews

Action Plans

​Next Steps

Diagnostic Module

Microlearning

Dashboard Setup

API Reference

Build docs developers (and LLMs) love

Overview

Key Features

Performance Metrics Collection

Monthly Metrics Generation

HR Metrics

Collection Process

HR Metrics Output

Hospital Metrics

Operational Performance Data

Integration Points

Correlation Analysis

Basic Correlation Calculation

Correlation Types

Significance Levels

AI-Powered Strategic Reports

Generate Strategic Analysis

Strategic Report Output

Temperature Configuration

Correlation Examples

Example 1: Motivation and Clinical Errors

Example 2: Staff Satisfaction and Patient Satisfaction

Example 3: Flight Risk and Wait Times

Monthly Metrics Dashboard

Metrics Visualization

API Reference

Endpoints

Best Practices

Next Steps