SQL-Based Live project

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Project Background

As a data analyst within a healthcare organization, your role involves analyzing patient data to enhance hospital efficiency and improve patient care. Your primary objectives include:

1. Identifying the busiest days and times within the hospital.

2. Determining the most common medical procedures performed.

3. Highlighting patients with frequent visits to facilitate better health management.

Step 1: Define Objectives

Primary Goals:

• Optimize staffing schedules based on peak hospital traffic.

• Refine inventory management for frequently used supplies.

• Deliver targeted care to patients with recurrent medical needs.

Step 2: Understand Your Data

The healthcare database comprises the following tables:

• patients: Contains patient details (e.g., patient_id, patient_name, age, gender).

• appointments: Records of hospital visits (e.g., appointment_id, patient_id, doctor_id, appointment_date, reason).

• procedures: Information pertaining to medical procedures (e.g., procedure_id, procedure_name, procedure_date, patient_id, doctor_id).

Step 3: Extract Data

Utilize SQL to collect relevant data for analysis.

Query 1: Retrieve All Appointments

This query retrieves appointment records for the first quarter of 2025.

Query 2: Join Data for Comprehensive Overview

This query combines patient names, appointment details, and procedures for a holistic view.

Step 4: Perform Analysis

Analysis 1: Identify Busiest Days

SELECT EXTRACT(DOW FROM appointment_date) AS day_of_week, COUNT(*) AS total_appointments
FROM appointments
GROUP BY day_of_week
ORDER BY total_appointments DESC;

This query identifies which days of the week experience the highest patient traffic.

Analysis 2: Common Medical Procedures

This analysis reveals the top five most frequently performed procedures.

Analysis 3: Frequent Visitors

This identifies patients with more than three visits, enabling tailored care plans.

Step 5: Data Cleaning

Ensure the accuracy of results through data cleansing:

• Remove Duplicate Appointments:

Step 6: Present Insights

Data Visualization Tools: Tools like Tableau, Power BI, or Excel can connect directly to your SQL queries or database to turn raw data into intuitive visuals like bar charts, heatmaps, and tables.

Programming Libraries:

If you’re comfortable with Python, libraries like Matplotlib, Seaborn, or Plotly allow you to create custom visualizations from SQL-extracted datasets.

• R (for statisticians) offers visualization packages like ggplot2.

• Develop a heatmap to visualize peak hospital traffic by time and day.

• Utilize a bar chart to represent the top five procedures.

• Create a table or dashboard outlining frequent patients along with their visit counts.

Step 7: Actionable Recommendations

• Increase staffing or extend operational hours during peak days.

• Ensure adequate inventory for the most common procedures.

• Reach out to frequent patients to provide proactive care.

Conclusion

SQL serves as the foundational tool that drives each step of this healthcare project, transforming raw data into actionable insights. If you would like to explore specific components of this workflow further, please feel free to reach out.