SQL [SEQUEL]
I have leveraged SQL extensively to extract, transform, and analyze healthcare data, supporting better decision‑making across clinical, financial, and operational domains.
These projects highlight my ability to design efficient queries, optimize workflows, and transform raw patient and claims data into meaningful insights. From building repositories that streamline reporting to integrating SQL outputs into dashboards, my work demonstrates how structured data can drive improved outcomes, cost savings, and regulatory compliance in the healthcare arena.
SQL Project: Analyzing Hospital Readmissions
Hospital readmissions are a critical metric for both patient outcomes and healthcare costs. Using SQL, this project demonstrates how to query hospital records to identify readmission rates, high‑risk patient groups, and trends over time.
The dataset includes:
Patients → demographics and unique IDs
Admissions → admission/discharge dates, length of stay, diagnoses
Diagnoses → condition codes linked to admissions
This analysis helps clinicians flag patient groups who may need extra support, while giving administrators the insights they need to plan staffing and allocate resources effectively.
For hospitals overall, it reduces costly penalties tied to high readmission rates and improves both financial and patient outcomes.
This project demonstrates my abilities with the use of complex SQL joins, conditional logic, and aggregation techniques to perform trend analysis, while translating raw data into healthcare‑specific KPIs such as readmissions, conditions, and patient outcomes.