01 — Healthcare Overview
Overview page showing total records, average age, average billing, average length of stay, total billing, hospital count, gender distribution, age groups, top medical conditions, admission types and test-result analysis.
DATA ANALYTICS PROJECT
Hospital & Healthcare Analytics
Interactive healthcare analytics project using Excel, Python, PostgreSQL, SQL and Power BI to analyze patient records, hospital activity, admissions, billing and healthcare patterns.
Project Overview
This project analyzes healthcare records to understand patient demographics, medical conditions, admission patterns, hospital activity, billing, insurance and length of stay. Excel was used for initial data inspection and preparation, Python and Jupyter Notebook for data cleaning and analysis, PostgreSQL and SQL for structured business analysis, and Power BI for the final interactive dashboard.
Key KPIs
10KTotal Records
51.45Average Age
25.52KAverage Billing
15.56Avg Length of Stay
255.17MTotal Billing
10KTotal Hospitals
Dashboard Views
02 — Admission & Hospital Analysis
Analysis page covering admission trends over time, medical condition by gender, total billing by hospital, a hospital-level summary table and test-results distribution.
Interactive Filtering
03 — Filtered Healthcare Overview
Demonstrates dynamic filtering using Gender, Medical Condition and Admission Type. In the shown example, selecting Female, Cancer and Elective updates the KPIs and visuals to the selected patient segment.
04 — Filtered Hospital Analysis
Demonstrates the second dashboard page responding to filters, with changes reflected in admission trends, medical-condition analysis, hospital billing, summary data and test-result distribution.
Key Insights
- The dashboard contains approximately 10K healthcare records.
- Average patient age is 51.45 years.
- Average billing amount is approximately 25.52K, with total billing of about 255.17M.
- Average length of stay is 15.56 days.
- The 56+ age group is the largest visible age segment in the overview.
- Medical-condition, admission-type, gender and hospital filters allow focused analysis of patient segments.
Future Scope
- Add patient readmission analysis and readmission-rate KPIs.
- Extend the dashboard with department-level operational analysis where data is available.
- Add more detailed time-series analysis for admissions and billing.
- Develop predictive analysis for demand, length of stay or readmission using Python.
- Add drill-through views for hospitals, medical conditions and patient segments.
Tools & Technologies
Project Outcome
Built an interactive healthcare analytics solution that combines data preparation, SQL analysis and Power BI visualization to turn patient and hospital records into understandable KPIs, trends and filterable insights.
View GitHub ↗