This project focuses on analyzing healthcare data to uncover patterns, identify trends, and provide actionable insights to improve patient care and operational efficiency. The analysis is performed using Microsoft Excel, leveraging its powerful features for data cleaning, exploration, and visualization.
Data Cleaning and Preparation:
Handle missing values, duplicates, and inconsistencies in the dataset. Standardize data formats for uniformity and accuracy.
Descriptive Analytics:
Summarize patient demographics and health metrics. Analyze healthcare service utilization and outcomes.
Trend Analysis:
Identify seasonal trends in patient admissions and treatments. Highlight patterns in disease prevalence across different demographics.
Visualization and Reporting:
Create intuitive charts and dashboards for data-driven decision-making. Provide actionable recommendations based on the analysis.
Data Cleaning:
Used Excel functions (e.g., IFERROR, TRIM, CLEAN) to handle data quality issues. Applied filters and conditional formatting to spot anomalies.
Statistical Analysis:
Calculated averages, medians, and standard deviations for patient metrics. Performed correlation analysis to identify relationships between variables.
Pivot Tables:
Summarized patient data by age groups, genders, and diagnoses. Analyzed hospital resource utilization by department and time period.
Data Visualization:
Created charts (line, bar, pie, and scatter) to visualize trends and comparisons. Developed interactive dashboards using slicers and dynamic tables.