Where is the money going — and what is actually driving healthcare claim costs? I analyzed 4,500 ...

Where is the money going — and what is actually driving healthcare claim costs? I analyzed 4,500 ...Where is the money going — and what is actually driving healthcare claim costs? I analyzed 4,500 ...

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Mwanahamisi Juma

• 2d

Where is the money going — and what is actually driving healthcare claim costs?

I analyzed 4,500 healthcare insurance claims worth 22.56M using SQL, Python, and Power BI to identify cost drivers, provider performance patterns, and inefficiencies in claims processing.

A few things stood out. Pediatrics accounted for 4.9M in claims, making it the largest expenditure driver among provider specialties. Outpatient services had the highest claim volume, while a small number of diagnosis codes accounted for a disproportionate share of total costs. Claim spending also peaked in November 2023.

I then tested whether patient demographics, provider specialty, and claim type were actually driving claim amounts or claim outcomes. The statistical tests found no significant associations, suggesting that clinical and operational factors may matter more than demographics alone.

Based on the findings, I recommended monitoring high-cost diagnoses and specialties, investigating the November spending spike, improving the handling of pending and denied claims, increasing electronic claim submission, and incorporating clinical and operational variables into future predictive models.

I built interactive Power BI dashboards covering executive KPIs, patient demographics, provider performance, and claims activity — giving healthcare teams a clearer view of where costs are concentrated and where operational problems need investigation.

Tools: SQL · Python · Power BI · Excel

Data Analysis Microsoft Power BI Python SQL

Sagar Sheoran

• Sep 4

Too many businesses try to build complex predictive AI models before their basic data pipelines are even functioning. You cannot skip the fundamentals.

The Data Hierarchy of Needs for scaling SMBs: Level 1: Stable Collection (SQL) — Your database health and data pipelines. If your raw data is broken, everything built on top of it will fail. Level 2: Data Governance (Python) — Enforcing uniform definitions, cleaning messy data, and ensuring total integrity. Level 3: Automated BI (Power BI) — Moving away from manual spreadsheets to automated executive views and daily KPI tracking. Level 4: Predictive Insights (AI / ML) — Churn models and forecasting. This only generates ROI if levels 1 through 3 are rock solid.

Stop wasting time on manual data entry and broken reports. If you are stuck at Level 1 or 2, I can help you scale up. Check out my Contra services to get your SQL pipelines automated and your Power BI dashboards built right the first time.

Data Analysis Microsoft Power BI Python

Ramesh Balami

• Sep 5

Amazing Information

Alessandro Alpago

pro

• Sep 3

I just wrapped up a new project. Spendback uses AI agents to continuously audit advertising, AI, and vendor spend, identifying billing issues, recovering overcharges, and protecting future budgets. This new AI era keeps opening doors for me, one after another. Very grateful.