End-to-End BI Solution: Clinical Trial Analytics in Power BI

1. Project Overview

This project showcases an end-to-end business intelligence solution I developed from the ground up to tackle critical inefficiencies in the pharmaceutical research pipeline. The project's focus was to create a robust Power BI dashboard for monitoring patient recruitment and tracking trial adherence in real-time.

The process began with a crucial data engineering phase, where I programmatically generated a complex, multi-table relational dataset using Python. This script was designed to simulate the disparate and complex data sources found in a real-world clinical trial (e.g., patient records, lab results, site information). This synthetic data was then ingested, modeled, and transformed within Power BI to build a powerful, interactive analytics suite.

The final solution replaces static, error-prone spreadsheets with a dynamic BI tool. It provides stakeholders with actionable insights into recruitment velocity, site performance, and patient adherence, empowering managers to forecast trial completion dates, mitigate risks proactively, and ultimately, help bring new treatments to market faster.

2. The Business Problem

Clinical trial management is frequently hampered by a lack of centralized, real-time insight into the operational pipeline. This leads to costly delays and risks the integrity of the trial. The key business problems this project was designed to solve are:

• Fragmented Data: Crucial information is often locked in disconnected systems (EHR, CTMS, lab portals). This fragmentation makes it nearly impossible for trial managers to get a holistic view of performance without slow, manual data consolidation.
• Lack of Real-Time Visibility: Reliance on static, weekly reports means that critical issues—such as a sudden drop in patient screening at a key site—are often identified too late, leading to reactive rather than proactive management.
• Inefficient Site Performance Management: Stakeholders lacked the ability to objectively compare recruitment and adherence metrics across different clinical sites, preventing the timely implementation of support for underperforming locations.
• High Risk of Poor Patient Adherence: Manually tracking patient compliance is labor-intensive and fails to provide an immediate view of individuals at risk of dropping out, which can jeopardize the statistical power of the trial.

3. My Solution: An End-to-End Approach

I addressed these challenges by architecting a complete BI solution, mirroring a real-world workflow from data engineering to final dashboard delivery.

Step 1: Data Engineering & Synthesis (Python)