Total patients
—
Across all departments
HOSPITAL OPERATIONS / 01
Overview
A clear view of patient care and hospital performance.
Average patient age
—
Years across all admissions
Total revenue
—
Total patient billing
Average stay
—
Days per admission
Pending payments
—
Outstanding balance
Most occupied
—
By number of admissions
ADMISSIONS
Patient distribution
PATIENT MIX
Gender distribution
REVENUE CYCLE
Financial overview
Total billed—
Paid —Pending —
LATEST ACTIVITY
Recent admissions
| Patient | Department | Admission date | Bill | Status |
|---|
RECORDS / 02
Patient Records
Browse and filter the synthetic patient registry.
Showing 0 records
1 / 1
SERVICE LINES / 03
Department Analytics
Compare admissions, patient profiles, length of stay, and billing.
VOLUME
Admissions by department
HIGHEST ADMISSIONS
—
—
patients in this department
DEPARTMENT PERFORMANCE
Service line comparison
| Department | Patients | Avg. age | Avg. stay | Total billing |
|---|
REVENUE CYCLE / 04
Financial Analytics
A department-level view of billing and payment status.
Total revenue
—
Billed across all records
Average bill
—
Per patient record
Payments received
—
— payments marked paid
Outstanding
—
— payments pending
DEPARTMENT REVENUE
Billing by service line
PAYMENT STATUS
Collection summary
MATPLOTLIB / 05
Visualizations
Four charts rendered in Python and delivered as PNG images.
01 / DEPARTMENT
Patient admissions
02 / GENDER
Patient mix
03 / ADMISSIONS
Monthly trend
04 / REVENUE
Department billing
IN-BROWSER PYTHON / 06
Python Code Lab
Explore and run the Python source powering this dashboard.
Editable source · runs in this browserPython 3 · UTF-8
EXECUTION OUTPUT
ReadyRun the source to see actual Python output.
ACADEMIC PROJECT / 07
About MediMetrics
A browser-based hospital analytics project for Informatics Practices.
PROJECT OVERVIEW
Hospital Analytics System
MediMetrics explores how Python data tools can turn a hospital's patient records into useful operational and financial insights.
- Project name
- MediMetrics
- Subject
- Informatics Practices (065)
- Class
- XII
- Academic session
- 2026–2027
PROJECT DOCUMENTATION
Objectives
- Generate a reproducible synthetic hospital dataset with Python.
- Use Pandas to clean records, describe data, and compare departments.
- Visualize patient admissions and revenue using Matplotlib.
- Make the analysis accessible in an interactive browser dashboard.
METHODOLOGY
From records to insights
01
Generate fictional patient records with a fixed Python random seed.
02
Prepare the Pandas DataFrame and handle basic data cleaning.
03
Analyze departments, payments, and descriptive statistics.
04
Visualize findings as Matplotlib charts rendered in the browser.
TECHNOLOGY
Project stack
PythonPandasMatplotlibHTMLCSSJavaScriptPyodideVercel
CONCLUSION
What this project demonstrates
MediMetrics demonstrates an end-to-end analytics workflow using familiar Class XII IP concepts. All patient records are fictional, and Python, Pandas, and Matplotlib execute locally in the browser without a backend.
STUDENT DETAILS