Data-driven recommendations to reduce administrative burden and optimize shift scheduling
Key Operational Insight: Emergency Department experiences a 40% surge on Tuesday mornings.
Action Required: Reallocate 2 additional staff to ED on Tuesday 7am–3pm shift to reduce wait times by estimated 22 minutes.
Department Wait Times
High Variance in ED
Peak Arrival Heatmap
Tue 10am Overload
Readmission by Age
Seniors 66+ High Risk
Occupancy Forecast
+15% Demand Next Week
24-Hour Staffing Needs by Department
Hourly Recommendations
Staff Directory & Assignments
30-Day Occupancy + 7-Day Forecast
+15% Demand Forecast
Bed Utilization
100 Beds Total
—
Occupied Beds
—
Available Beds
—
Utilization Rate
+15%
7-Day Forecast Change
01
Reallocate Staff to ED
Heatmap shows 40% patient volume spike on Tuesdays 8am–12pm.
Shift two nurses/one physician to cover Tuesday 7am–3pm.
02
Senior Follow-up Protocol
66+ age group shows 27% readmission rate vs 12% hospital average.
Nurse-led phone check-in within 48h of discharge.
03
Predictive Radiology Scheduling
Bed occupancy forecast +15% next week; Radiology wait time variability is high.
Pre-schedule additional MRI/CT tech hours on forecasted peak days.
Patient Records & History
Patient ID
Admit Date
Department
Diagnosis
Attending Doctor
Risk
Project Datasets Dictionary
ETL Processed JSON
The dashboard is powered by a set of static JSON files generated through our Python ETL pipeline. These files represent the cleaned, transformed, and aggregated state of our raw hospital operations data.
Dataset Name
Description
Format
Action
detailed_patients.json
Comprehensive patient journey records including admission, discharge, diagnosis, length of stay, and readmission risk markers. Used for the Reports table.
JSON
bed_occupancy_daily.json
Historical daily aggregation of bed occupancy rates. Used for utilization metrics and trend charting.
JSON
bed_forecast.json
Predictive linear regression output mapping expected bed demand over the next 7 days based on seasonal and historical trends.
JSON
peak_load_heatmap.json
Aggregated patient arrival counts bucketed by hour of day and day of week to identify emergency department bottlenecks.
JSON
dept_wait_metrics.json
Time-series data tracking the average wait time from admission to first physician contact across different hospital departments.
JSON
staff_details.json
Directory of attending physicians and nurses, including current assignments and departmental mapping.
JSON
staffing_needs_by_dept_hour.json
Calculated staffing recommendations based on patient load volume and acuity levels across a 24-hour cycle.
JSON
readmission_rates_by_age.json
Aggregated percentages of patient 30-day readmissions segmented into primary age brackets.