Predictive Analytics

February 2026 · 5 min

Overview

The Predictive Analytics Dashboard uses statistical analysis and machine learning on your hospital's historical data to forecast demand, identify high-risk patients, and optimize resource allocation. It is accessible at /dashboard/bi-analytics/predictive and provides three main analysis modules: bed demand forecasting, department load prediction, and patient readmission risk scoring.

Bed Demand Forecast

The bed demand module predicts hospital occupancy over the next 7 days:

• Input Data -- Historical admission/discharge patterns, seasonal trends, day-of-week patterns, current census
• Output -- Daily predicted admissions, discharges, and net bed demand with confidence intervals
• Visualization -- Area chart showing predicted vs. actual capacity utilization
• Alerts -- Automated warnings when predicted occupancy exceeds 85% (configurable threshold)

This helps operations teams plan staffing, bed allocation, and elective surgery scheduling.

Department Load Prediction

Predicts patient volume by department for the upcoming week:

• OPD Departments -- Expected consultation volume based on appointment bookings, walk-in patterns, and seasonal illness trends
• Emergency -- Predicted ED visits based on day-of-week, weather, and local event data
• Laboratory -- Expected test volume based on scheduled admissions and outpatient trends
• Radiology -- Predicted imaging volume by modality

Each department shows a heat map of expected load with peak hours highlighted for staffing optimization.

Readmission Risk

The readmission risk module scores recently discharged patients on their 30-day readmission likelihood:

• Risk Factors -- Age, comorbidities, length of stay, number of prior admissions, diagnosis complexity, medication count, lab values at discharge
• Scoring -- 0-100 scale: Low (0-39), Medium (40-69), High (70-100)
• Action Triggers -- High-risk patients are automatically flagged for follow-up calls, early clinic appointments, and care coordinator review
• Dashboard -- Table of at-risk patients sorted by risk score with one-click access to patient profile

The model improves over time as more discharge and readmission data accumulates.

Data Requirements

For accurate predictions, the system needs:

• At least 3 months of historical admission/discharge data (6+ months recommended)
• Complete diagnosis coding (ICD-10) for admitted patients
• Lab results linked to patient encounters
• Accurate census data (bed management module active)

Predictions are recalculated nightly and cached for fast dashboard loading.

Notes

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