Each sheet represents a different table from the hospital management schema, and includes realistic data with intentional nulls, blanks, and NA values for simulation.
Relational Database Management System (RDBMS) schema for a Hospital Management System involves defining structured tables and relationships to manage patients, doctors, appointments, treatments, billing, etc.
🛢Core Required Schemas
Here’s a complete schema with primary keys, foreign keys, and relationships:
📄Patients
📄Doctors
📄Appointments
📄Medical_Records
📄 Billing
📄 Rooms
📄 Staff
📄 Lab reports
📄 Medications (Pharmacy)
📄 Insurance
📄 User authentication/roles
📄Departments
📄 Emergency contacts
📄 Discharge summary
📄 Schedules for staff
Relationships Summary:
- Patients, Doctors, Staff are the main entities.
- Appointments connects patients and doctors.
- edical_Records and Lab_Reports track health history.
- Billing, Prescriptions, Discharge_Summary cover financial and medical discharge.
- Users table handles authentication.
- Departments organize the hospital structure.
- Insurance and Emergency_Contacts enrich patient data.
Great question! Based on the hospital management dataset provided, here are Key Performance Indicators (KPIs) and visualization ideas you can use in Power BI for insightful dashboards and reporting:
- Patient KPIs
– Total Patients Registered
– New Patients This Month
– Patients by City/Country
– Patient Gender Distribution
– Average Age of Patients
📊 Visuals: KPI cards, Pie charts, Bar charts, Maps
- Appointment KPIs
– Total Appointments
– Completed vs Cancelled Appointments
– Appointments by Doctor
– Appointments per Department
– Peak Appointment Times (Heatmap by Date & Time)
📊 Visuals: Line charts, Stacked bars, Calendar heatmap
- Doctor KPIs
– Active Doctors
– Appointments per Doctor
– Specialization-wise Doctor Count
– Patient Load per Doctor
📊 Visuals: Bar chart, Matrix, Tree Map
- Billing & Revenue KPIs
– Total Revenue
– Pending Payments
– Revenue by Department / Doctor
– Monthly Billing Trends
– Average Billing per Patient
📊 Visuals: KPI cards, Area chart, Waterfall chart
- Admission KPIs
Admissions vs Discharges
Average Length of Stay
Bed Occupancy Rate
Admissions by Reason
Room Utilization Rate
📊 Visuals: Gauge, Funnel chart, Donut chart
- Test & Prescription KPIs
Top Prescribed Medications
Tests Conducted per Department
Pending Test Results
Abnormal Test Rate
📊 Visuals: Column charts, Pie chart, Word cloud (for medicines)
- Staff & Nurse KPIs
– Staff by Role
– New Hires
– Department Staffing Levels
– Average Tenure (Hire Date)
📊 Visuals: Bar charts, Cards, Stacked bars
- Department Performance
– Department-wise Patient Count
– Revenue by Department
– Tests & Prescriptions per Department
📊 Visuals: Matrix, Grouped bars, Slicers for filtering
🔍 General Data Analysis Questions
Below are realistic data analysis questions you might face in a Data Analyst role, based on the hospital management dataset:
- What is the patient gender distribution across different cities or states?
- Which departments have the highest patient inflow and why?
- Which doctors are handling the most appointments, and is there an overload?
- What is the average appointment duration and success rate (Completed vs Cancelled)?
- Which time slots are most preferred for appointments?
- How does the number of appointments vary over the week or month?
- What is the average billing amount per patient or per department?
- Which patients have outstanding or pending payments?
- What percentage of patients are readmitted within 30 days of discharge?
💉 Medical Record / Treatment Analysis
- What are the most common diagnoses across all departments?
- How effective are certain treatments (tracked by repeated records for similar diagnoses)?
- Which medications are most prescribed, and in which departments?
- Are there any patterns in patient recovery time by diagnosis or department?
- How often are abnormal test results observed, and in which test types?
🧾 Finance & Billing Insights
- What is the trend in revenue over the past 12 months?
- Which departments contribute the most to total revenue?
- How does billing vary by patient demographics (e.g., age group, gender, location)?
- What is the distribution of payment status (Paid, Pending, Partial)?
- Are there duplicate or suspicious billing records (e.g., repeated entries with the same amount and date)?
🏥 Operations & Capacity Planning
- What is the average length of stay per patient?
- What is the room occupancy rate across departments?
- Are there rooms frequently marked as unavailable?
- Which departments or rooms have the highest admission frequency?
- Which times of year see the highest admission volumes?
👩⚕️ Human Resource Analytics
- How has staff headcount changed over the years?
- What is the average tenure of nurses and staff members?
- Is staff availability aligned with appointment and admission demand?
- Which departments have a higher concentration of nurse assignments?
- Are there departments understaffed or overstaffed based on patient volumes?