This Excel assignment helped me use data storytelling to turn raw data into meaningful insights about healthcare quality and outcomes. I applied advanced skills in informatics and systems thinking to organize, analyze, and visualize data in a clear way. Working through the project showed me how technology supports evidence-based decisions and helps communicate results that can guide real-world improvements in care. It also reminded me of the importance of accuracy and integrity when working with health information.

Competencies developed:
4.1h – Apply and critically evaluate advanced knowledge in a defined area of nursing practice.
5.1i / 5.1k – Establish data-driven benchmarks and integrate outcome metrics to inform practice and policy recommendations.
7.2g – Analyze internal and external factors that drive healthcare costs and outcomes (readmissions tie directly to cost and reimbursement).
8.2f – Generate information and knowledge from health information databases.

Explanation:
This assignment takes raw patient data and turns it into readmission rate insights by condition, age group, and discharge planning status, so it's really a quality and data analysis exercise. Building the readmission rate calculations and dashboard shows the ability to turn raw health data into decision-useful information (8.2f). Comparing readmission rates across categories and using that to spot patterns, like discharge planning's effect on readmissions, reflects setting up benchmarks to monitor outcomes (5.1i/5.1k). Since readmissions drive cost and reimbursement, this also touches the cost-effectiveness side of care delivery (7.2g). And pulling it all together into a meaningful clinical story demonstrates applying advanced knowledge to a real practice problem (4.1h).

Please click the link to view document.