Hospitals generate large volumes of patient, clinical and financial data
This project analyzes admissions, discharges, department workload, treatment costs, and patient outcomes to identify operational bottlenecks and improve decision-making.
- Objectives
- Analyze patient admissions and discharges
- Measure average length of stay
- Track diagnosis trends
- Evaluate departmental workload
- Monitor financial performance
- Assess readmission rates
- Dataset Overview
Patients ........ 20,000 Admissions ........ 50,000 Billing ....... 50,000 Treatment Events ----- 200,000 Diagnoses ---- 250 Departments ---- 10 5. Tech Stack
- Python
- Pandas
- NumPy
- re
- seaborn
- matplotlibpyplot
Analysis Insight
- The age group of young adults (19-350) was the highest admitted age group
- Infection had the highest cost of treatment
- The average length of stay by department was 10.5 hours
- 9972 patients were currently readmitted
- While the department of Oncology experiences the highest mortality
- The highest workload by hour is 20 hours
- Oncology experiences the highest admission rate by diagnosis
- The majority of the inbound patients were through emergency, seconded by elective
- Finally, the rate of mortality remains low compared to the patient that were discharged