Which patients are most likely to be readmitted — and where should a hospital focus its limited f...

Which patients are most likely to be readmitted — and where should a hospital focus its limited f...Which patients are most likely to be readmitted — and where should a hospital focus its limited f...

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Mwanahamisi Juma

• 2d

Which patients are most likely to be readmitted — and where should a hospital focus its limited follow-up resources?

I analyzed hospital readmission patterns using SQL, Power BI, and DAX, looking at diagnoses, prior readmissions, comorbidity burden, discharge disposition, insurance, demographics, and seasonal trends to identify where readmission risk was concentrated.

A few things stood out. Patients with no prior readmissions had a 27.79% readmission rate, compared with 69.50% after just one prior readmission. Patients with 4+ comorbidities reached 75.05%+, while high-risk conditions such as sepsis, COPD, heart failure, stroke, and kidney disease showed the highest readmission rates. Skilled nursing facility discharges were also particularly high at 92.80%.

The bigger opportunity was turning those patterns into action. I used the findings to define a practical high-risk flagging approach — prioritizing patients with previous readmissions, high comorbidity burden, high-risk diagnoses, or post-acute care needs for earlier follow-up, medication reconciliation, and stronger discharge coordination.

I built an interactive Power BI dashboard with executive, clinical, and risk-focused views, turning hospital readmission data into something healthcare teams could actually use to prioritize patients and target intervention where it matters most.

Tools: SQL · Power BI · DAX · Excel

Data Analysis Microsoft Excel Microsoft Power BI 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

Abdul Manan

• Sep 3

I work on Excel data analysis, data cleaning, interactive dashboards, Pivot Tables, and business reports. I turn raw data into clear, actionable insights that help businesses make smarter decisions.

Salako Ahmed

• Sep 4

One thing I’ve learned from working with data is that the numbers are only the beginning. The real value comes from understanding the story behind them and turning that into action.