Streamlining Clinical Trials for a German Pharma Company with Azure and AI Solutions

The first step was to migrate the client’s massive dataset from Excel to an Azure PostgreSQL Database. This provided the scalability they needed to handle future data growth and enabled more efficient querying and analysis.

Understanding the importance of efficient and accurate data collection, we built an Azure Bot Service-powered chatbot. This chatbot interacted directly with researchers to gather critical data points—such as patient demographics, drug type, and trial phase—while integrating with Azure Functions to validate and store data automatically in Azure SQL Database. This eliminated manual entry errors and saved valuable research time.

To help the research team predict patient outcomes with greater confidence, we developed a machine learning model using Azure Machine Learning. This model was trained on historical trial data and deployed on Azure Kubernetes Service (AKS), enabling real-time predictions. When researchers entered patient data, the model would provide predictions instantly, guiding them to make data-driven decisions before starting new trials.

To further empower the team, we developed a Power BI dashboard that displayed real-time insights from the trial data. The dashboard visualized key metrics, trends, and predicted outcomes, giving researchers a clear picture of trial performance and enabling them to adjust strategies as needed.