InterimOptimizer by LTIMindtree is a Gen-AI platform on Microsoft Azure that accelerates clinical trials by automating interim analysis and enabling interactive reporting. This 8-week POC lets customers validate its impact on trial efficiency, data visibility, and decision-making within their Azure environment. Using Azure OpenAI and Azure ML, it delivers real-time insights from siloed data, automates reporting, generates reports with graphical visualizations, and provides chatbot assistance for accurate query resolution. It supports synthetic data, trend analysis, and improves visibility across functional and regulatory variables—saving time, ensuring accuracy, and empowering researchers with interactive insights.
Problem Statement
Interim analysis is a statistical evaluation of trial data conducted before a study’s planned completion, which guides decisions on study modifications. However, faces below challenges:
Data Integrity Issues – Inaccurate/inconsistent data
Statistical Misinterpretation – Mislead conclusions
Inadequate Data – Weaken findings
Missing Values – Hinder analysis
Bias & Confounding Factors – May distort results
Solution Overview
Business Group: Clinical Trials
Overview: Clinical trials test new medicines or treatments across phases:
Phase I: Safety and dosage
Phase II & III: Effectiveness and side effects
Phase IV: Long-term effects post-approval
InterimOptimizer addresses key challenges via Microsoft Azure:
Decoding Data Relationships: Understand interdependencies among data elements to uncover insights.
Synthetic Data and Trend Analysis: Gen-AI enabled synthetic data generation and trend analysis to address inadequate data and missing values
Automated Report Summary Generation: Generates comprehensive interim analysis reports with bar graphs and charts. Includes Gen-AI classification and summarization of demography, efficacy, and safety data across groups (Active/Placebo/Comparator) and timepoints. Produces human-like downloadable text reports.
Contextual Chatbot for Database Queries: Real-time query resolution for interim data outcomes and future trends.
Key Benefits
Improved Accuracy
Data-Driven Insights
Faster Decision-Making
Consistency and Standardization
Enhanced Productivity
Cost Efficiency
Scalable
R&D Acceleration
Azure Integration
Target Personas
Clinical researchers
Regulatory teams
Data managers
Sponsors
Technology Stack
Microsoft Azure – Core infrastructure, AI services (Azure OpenAI, Azure Machine Learning)
Vector Database – Stores computed clinical metrics and embeddings
Python, HTML, CSS, JavaScript, Flask – Web application and UI development
Professional Services:
Getting Started: Azure Adoption
Azure environment setup and onboarding
Gen-AI powered solution with Azure AI, ML, and analytics
Custom configuration aligned with clinical trial and compliance needs
Extending Usage:
Secure, scalable deployment using Azure SQL Database and Blob Storage
Architecture optimization via Azure Kubernetes Service and Azure Monitor
Ongoing support with Azure DevOps and Azure Security Center
Engagement Scope: 8-Week Proof of Concept
During this 8-week engagement, LTIMindtree will:
Assess customer data landscape and Azure readiness
Deploy InterimOptimizer in a secure Azure environment
Integrate sample datasets and configure core features
Demonstrate key capabilities including automated report generation, chatbot assistance for query resolution, and graphical visualizations
Optimize performance and scalability using Azure-native tools
Deliver a final report with outcomes, recommendations, and roadmap for full-scale deployment on Azure