Two years ago I was working as a Clinical Officer, not a data analyst. I saw hospital numbers every day — appointments, billing, readmissions — but I didn't have the tools to actually dig into them.

Now I do. And here's what I've learned: a lot of hospitals and insurers are sitting on numbers that could save them real money, if someone actually analyzed them properly.

Here's a sample of what turned up across three recent projects:

📊 Hospital operations — I analyzed appointment and billing data and found that 51.5% of appointments ended in a no-show or cancellation, and 33% of payments failed outright. Chemotherapy and MRI turned out to be the top revenue-generating services — which changes where you'd prioritize fixing the leaks.

📊 Patient readmissions — I found that a single prior readmission more than doubled a patient's risk, from 27.79% to 69.50%. Patients with four or more comorbidities crossed 75%. Numbers like that can actually change how discharge planning gets done.

📊 Insurance claims — I analyzed 4,500 claims worth $22.5M and found something unexpected: patient age, income, and gender weren't significantly associated with claim cost. Pediatrics was the real cost driver at $4.9M — the specialty mattered, not the demographics.

I do this work with SQL, Python, Excel, Power BI, and Tableau — but the tools alone aren't the point. My clinical background is what helps me understand what's actually happening behind each number, not just what's sitting in a spreadsheet column.

If you've got operations, claims, or readmission data you haven't fully dug into — send me a sample. I'll tell you honestly what it can (and can't) show before you commit to anything.