Dashboard

Real-time program statistics, patient alerts, and capita cycle distribution visualization

Overview

The DataMed dashboard provides a centralized view of program health with real-time statistics, patient engagement alerts, and capita cycle distribution analytics. It serves as the command center for clinical operations.

The dashboard focuses exclusively on active patients (estado=‘ACTIVO’) to provide actionable insights for current operations.

Accessing the Dashboard

1. Login

   Navigate to the DataMed application and log in with your credentials

2. Dashboard Landing

   The dashboard is typically the default landing page after login, or access it via Home → Dashboard in the navigation

3. Real-Time Data

   All dashboard statistics are calculated in real-time on page load, ensuring current information

Key Statistics

The dashboard displays three primary metrics at the top:

Total Active Patients

Count of patients with ACTIVO admission status

Updated in real-time from database query

Most Recent Admission

Latest patient to join the program

Shows name and admission date

Patients Needing Follow-Up

Count of patients without contact in 15+ days

Critical attention indicator

Statistics Calculation

# From dashboard/views.py:11-24
@login_required
def dashboard(request):
    hoy = timezone.now().date()
    limite = hoy - timezone.timedelta(days=15)
    
    # 1. Total active patients
    patients = Patient.objects.filter(
        ingresos__estado='ACTIVO'
    ).distinct().count()
    
    # 2. Most recent admission
    last_enter = Patient.objects.filter(
        ingresos__estado='ACTIVO'
    ).select_related().order_by(
        '-ingresos__fecha_inicio'
    ).first()
    
    # 3. Patients without recent follow-up
    total_sin_seguimiento = pacientes_queryset.count()

Why distinct() on Active Patients Count?

The distinct() call is critical because:

• A patient might theoretically have multiple ACTIVO admissions (edge case/data error)
• The query joins through the ingresos relationship
• Without distinct(), the same patient could be counted multiple times
• Ensures accurate patient count, not admission count

Follow-Up Alert System

The dashboard’s most critical feature is the follow-up alert list:

Alert Logic

1. Calculate 15-Day Threshold

   hoy = timezone.now().date()
   limite = hoy - timezone.timedelta(days=15)
   

   Patients without contact for 15+ days trigger an alert

2. Query Patients Needing Attention

   # From dashboard/views.py:26-36
   pacientes_queryset = Patient.objects.filter(
       ingresos__estado='ACTIVO'
   ).annotate(
       fecha_del_ingreso=Max('ingresos__fecha_inicio'),
       ultima_atencion=Max('ingresos__seguimientos__fecha_atencion')
   ).filter(
       Q(ultima_atencion__lt=limite) |  # Last contact > 15 days ago
       Q(ultima_atencion__isnull=True, fecha_del_ingreso__lt=limite)  # No contact and admitted > 15 days ago
   ).distinct().order_by('ultima_atencion', 'fecha_del_ingreso')
   
3. Display Alert List

   The query returns patients in two scenarios:

   • Lapsed Follow-Up: Had contact before, but > 15 days ago
   • No Initial Follow-Up: Admitted > 15 days ago, never contacted

   Sorted by urgency (oldest contact first)

Alert Scenarios

Scenario A: New Patient, No Follow-Up

• Patient admitted: February 1, 2026
• Today: February 20, 2026
• No Seguimiento records
• Alert Status: YES (19 days since admission)

Scenario B: Recent Follow-Up

• Patient admitted: January 1, 2026
• Last follow-up: February 28, 2026
• Today: March 5, 2026
• Alert Status: NO (only 5 days since contact)

Scenario C: Overdue Follow-Up

• Patient admitted: December 1, 2025
• Last follow-up: January 15, 2026
• Today: March 5, 2026
• Alert Status: YES (49 days since contact)

Annotation Explanation

The query uses two annotations:

fecha_del_ingreso=Max('ingresos__fecha_inicio')

• Gets the admission start date (should only be one ACTIVO admission)
• Used for “never contacted” scenario

ultima_atencion=Max('ingresos__seguimientos__fecha_atencion')

• Finds most recent follow-up date
• Null if no follow-ups recorded
• Used for “last contact” comparison

Pagination

# From dashboard/views.py:58-62
paginator = Paginator(pacientes_queryset, 10)
page_number = request.GET.get('page')
page_obj = paginator.get_page(page_number)

The alert list displays 10 patients per page to:

• Optimize load times
• Improve mobile usability
• Focus attention on manageable batches

The alert list is action-oriented - it should be reviewed daily and used to prioritize outreach efforts.

Capita Cycle Distribution

The dashboard includes a visual chart showing patient distribution across the 18-month capita cycle:

Data Collection

# From dashboard/views.py:41-56
# 1. Get all active admissions
ingresos_activos = Ingreso.objects.filter(estado='ACTIVO')

# 2. Group by capita month manually (can't use DB aggregation on property)
conteos = {}
for ing in ingresos_activos:
    mes = ing.mes_capita  # Uses the @property mes_capita
    if mes:
        conteos[mes] = conteos.get(mes, 0) + 1

# 3. Sort for chart display
meses_ordenados = sorted(conteos.keys())
labels_grafica = [f"Capita {m}" for m in meses_ordenados]
datos_grafica = [conteos[m] for m in meses_ordenados]

Why Manual Aggregation?

The capita month (mes_capita) is a computed property, not a database field:

@property
def mes_capita(self):
    hoy = date.today()
    meses = (hoy.year - self.fecha_inicio.year) * 12 + \
            (hoy.month - self.fecha_inicio.month) + 1
    if meses < 1: return 1
    if meses > 18: return 18
    return meses

Since it’s calculated in Python (not SQL), we can’t use database aggregation functions like GROUP BY or COUNT(). The code must:

1. Fetch all active admissions
2. Calculate mes_capita for each in Python
3. Manually count occurrences in a dictionary

Chart Interpretation

Healthy Distribution

Ideal Pattern:

• Relatively even spread across months 1-18
• Slight decline toward month 18 (natural attrition)
• No dramatic spikes or valleys

Example:

Capita 1:  12 patients
Capita 2:  11 patients
Capita 3:  10 patients
...
Capita 18: 8 patients

Indicates steady enrollment and consistent cycle progression

Warning Patterns

Large Spike at Month 1:

Capita 1:  45 patients
Capita 2:  8 patients
Capita 3:  7 patients

Interpretation: Recent enrollment surge

• Action: Prepare for intensive diagnostic phase workload
• Risk: Resource strain in 2-3 months when titulation is needed

Cluster at Month 18:

Capita 16: 6 patients
Capita 17: 8 patients
Capita 18: 23 patients

Interpretation: Many cycles ending simultaneously

• Action: Plan closure assessments and cycle renewal decisions
• Risk: Potential patient churn if renewals not managed

Valley in Middle Months:

Capita 7:  2 patients
Capita 8:  1 patient
Capita 9:  3 patients

Interpretation: Historical enrollment gap or dropout

• Action: Review retention strategies
• Risk: May indicate quality or access issues

Capacity Planning

Use the distribution to predict workload:

Months 1-3 (Intensive Phase):

• Count patients in months 1-3
• Each needs: baseline PSG, titulation, multiple follow-ups
• Resource needs: High (2-3 visits per patient per month)

Months 4-12 (Active Monitoring):

• Count patients in months 4-12
• Each needs: Quarterly monitoring
• Resource needs: Medium (1 visit per patient per 3 months)

Months 13-18 (Maintenance):

• Count patients in months 13-18
• Each needs: Quarterly reviews, closure planning
• Resource needs: Medium-Low (1 visit per patient per 3 months)

Formula: Total visits/month ≈ (M1-3 × 2) + (M4-18 × 0.33)

Dashboard Layout

The dashboard typically displays information in this structure:

┌─────────────────────────────────────────────────────────┐
│  Key Statistics Row                                     │
│  [Active Patients] [Latest Admission] [Need Follow-Up] │
└─────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────┐
│  Capita Distribution Chart                              │
│  Bar chart showing patient count by capita month        │
└─────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────┐
│  Patients Needing Follow-Up (Detailed List)             │
│  ┌───────────────────────────────────────────────────┐  │
│  │ Patient 1 - Last contact: 23 days ago   [Contact]│  │
│  │ Patient 2 - Last contact: 19 days ago   [Contact]│  │
│  │ Patient 3 - No contact since admission  [Contact]│  │
│  │ ...                                               │  │
│  └───────────────────────────────────────────────────┘  │
│  [Page 1] [2] [3] [Next]                                │
└─────────────────────────────────────────────────────────┘

Real-Time Updates

All dashboard data is calculated on each page load:

No Caching

Statistics reflect current database state

Refresh page to see latest data

Performance Considerations

For large patient databases (>1000 patients), consider:

• Caching capita distribution (recalculate hourly)
• Indexing estado and fecha_inicio fields
• Optimizing follow-up query with database indexes

Optimization Opportunities

For production deployments at scale:

# Add database indexes
class Ingreso(models.Model):
    estado = models.CharField(
        max_length=20,
        choices=ESTADO_CHOICES,
        default='ACTIVO',
        db_index=True  # Index for faster filtering
    )
    fecha_inicio = models.DateField(db_index=True)

# Cache capita distribution for 1 hour
from django.core.cache import cache

def get_capita_distribution():
    cached = cache.get('capita_distribution')
    if cached:
        return cached
    
    # Calculate distribution...
    distribution = {'labels': labels_grafica, 'data': datos_grafica}
    cache.set('capita_distribution', distribution, 3600)  # 1 hour
    return distribution

Dashboard Context Data

The view passes this context to the template:

# From dashboard/views.py:64-72
return render(request, 'dashboard/dashboard.html', {
    'patients': patients,  # Total active count
    'last_enter': last_enter,  # Most recent patient object
    'page_obj': page_obj,  # Paginated alert list
    'total_sin_seguimiento': total_sin_seguimiento,  # Alert count
    'hoy': hoy,  # Today's date
    'labels_grafica': labels_grafica,  # Chart x-axis labels
    'datos_grafica': datos_grafica,  # Chart y-axis data
})

User Workflows

1. Morning Check-In

   Daily Routine:

   1. Log in to DataMed
   2. Review dashboard statistics
   3. Check “Patients Needing Follow-Up” count
   4. Click through alert list pages
2. Triage Alerts

   For each patient in alert list:

   • Check capita month (prioritize early months)
   • Review days since last contact
   • Click patient name to access full record
3. Take Action

   From patient record:

   • Call patient to schedule follow-up
   • Register new appointment/exam
   • Document contact in clinical notes
   • Patient automatically clears from alert list
4. Capacity Planning

   Weekly Review:

   • Analyze capita distribution chart
   • Identify upcoming workload spikes
   • Plan staff scheduling accordingly

Best Practices

Review dashboard daily

Make the dashboard your first task each day:

• Identify urgent patient needs
• Track program growth trends
• Monitor alert list size

Goal: Zero patients in alert list (or minimal count)

Act on alerts within 24 hours

When a patient appears in the alert list:

• Contact within 24 hours of alert appearance
• Don’t let patients exceed 20 days without contact
• Document all contact attempts (even if unsuccessful)

Monitor capita distribution trends

Review the distribution chart weekly:

• Screenshot for weekly team meetings
• Compare month-over-month changes
• Adjust enrollment pace based on capacity

Use statistics for program reporting

Dashboard metrics support:

• Monthly reports to administration
• Payer audits (total active patients, engagement)
• Quality improvement initiatives
• Staff workload assessment

Mobile Responsiveness

The dashboard is optimized for desktop use, but pagination ensures mobile usability. Statistics cards stack vertically on small screens.

Future Enhancements

Potential dashboard improvements:

Adherence Metrics

Average CPAP usage hours across active patients

Exam Completion Rates

Percentage of patients with required exams by capita month

Outcomes Tracking

Average IAH reduction, patient-reported outcomes

Financial Dashboard

Capita revenue, cost per patient, profit margins

Next Steps

Follow-Up System

Deep dive into follow-up tracking and state management

Patient Management

Learn about patient lifecycle and capita cycles