Operational Analytics, Predictive Intelligence & Revenue Recovery
This project explores how predictive analytics and operational intelligence can reduce healthcare appointment no-shows and improve scheduling efficiency.
Using a public medical appointment no-show dataset containing over 110,000 records, the project models operational leakage, provider idle capacity, scheduling risk patterns, and financial recovery opportunities.
#Dashbord Overview
- Specialty visits showed the highest no-show risk
- Lead times greater than 14 days significantly increased missed appointments
- Morning appointments demonstrated lower no-show behavior
- Friday afternoon scheduling showed the highest patient absence patterns
| Metric | Value |
|---|---|
| No-Show Rate | 20.2% |
| Revenue Leakage | $3.35M |
| Recovery Opportunity | $1.17M |
| Modeled Idle Capacity | 847 hrs |
- Predictive risk scoring
- Scheduling optimization
- Patient outreach workflows
- Waitlist auto-fill modeling
- Executive operational dashboards
- Python
- Pandas
- Tableau
- HTML/CSS
- Predictive Analytics
- Operational Intelligence
Prototype project built using public medical appointment no-show datasets and modeled operational assumptions. Intended for educational and portfolio demonstration purposes only.