Laboratory Data Analysis with SQL

Objective

Analyze laboratory exam data to evaluate operational efficiency, focusing on turnaround time and workload distribution across units.

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Business Context

Clinical laboratories often face delays in delivering exam results, which can impact patient care and medical decision-making.

This project simulates a laboratory environment to explore how data analysis can identify inefficiencies and support process optimization.

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Approach

A relational database was created using PostgreSQL, including patients, exams, and laboratory units.

SQL queries were used to extract insights related to performance, volume, and delays.

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Key Insights

• Average turnaround time varies by exam type

• Some laboratory units perform faster than others

• High exam volume per patient may indicate potential bottlenecks

• Delayed exams can be identified and quantified for further investigation

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Technologies

• PostgreSQL

• SQL

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Project Structure

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Example Analysis

• Join between patients and exams to track exam history

• Average turnaround time calculation (result\_date - order\_date)

• Identification of delayed exams

• Aggregation of exam volume per patient and per laboratory unit

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Future Improvements

• Integration with Python for advanced analysis

• Data visualization (Power BI or Matplotlib)

• Real-world dataset application

• Performance optimization with indexes