https://catalogartifact.azureedge.net/publicartifacts/ltim.ltimindtree_dataandai_interimoptimizer-448fc1ed-472a-4453-8f80-5c9e96c78ad1/image1_LTM216X216px.png

LTM Data And AI InterimOptimizer

LTIMindtree Limited

Hinnakiri Tasuta

Outcreate interim review delays with GenAI-powered analysis, reporting, and decision support for clinical trials.

InterimOptimizer by LTM is a GenAI platform on Microsoft Azure that automates interim analysis and reporting for clinical trial teams. In an 8-week proof-of-concept, organizations can validate how it improves trial efficiency, data visibility, and decision-making within their Azure environment.

Using Azure OpenAI and Azure Machine Learning, InterimOptimizer analyzes siloed trial data, surfaces real-time insights, automates report creation, and supports faster query resolution through a contextual chatbot. It also supports synthetic data generation and trend analysis, helping teams reduce manual effort and move critical trial decisions forward with confidence.

Problem Statement

Interim analysis helps study teams review trial data before completion and decide whether a study should continue, change, or stop. Common challenges include:

  • Data Integrity Issues: Inaccurate or inconsistent data can weaken trust in interim findings.
  • Incomplete Data: Missing values and limited datasets can reduce confidence and increase manual work.
  • Misinterpretation Risk: Incorrect statistical or trend interpretation can increase scientific, regulatory, and operational risk.
  • Bias and Confounding Factors: Hidden distortions can affect outcomes and lead to flawed decisions.

    • Solution Overview

    Business Group: Clinical Trials

    Overview: Clinical trials evaluate new medicines or treatments across four phases, from safety and dosage to effectiveness, side effects, and long-term outcomes after approval.

    InterimOptimizer addresses these challenges through Microsoft Azure:

  • Connected Data Relationships: Maps interdependencies across clinical data elements so teams can identify meaningful patterns faster.
  • Synthetic Data and Trend Analysis: Uses GenAI to address data gaps and support stronger interim assessments.
  • Automated Report Summaries: Creates interim analysis reports with charts, summaries, and downloadable narrative text across demographic, efficacy, and safety data.
  • Contextual Chatbot: Provides real-time responses on interim outcomes and likely trends, helping study teams resolve questions faster.

    • Key Benefits

  • Improved Accuracy: Strengthens the quality and consistency of interim analysis outputs.
  • Better Data Insight: Surfaces clearer patterns across trial data for stronger study decisions.
  • Faster Decision-Making: Shortens the time between analysis and action.
  • Greater Consistency: Standardizes reporting and interpretation across teams, studies, and timepoints.
  • Higher Productivity: Reduces manual effort in reporting and query handling.
  • Stronger Azure Alignment: Works within Azure environments, supporting familiar cloud services and governance controls.

    • Target Personas

  • Clinical researchers
  • Regulatory teams
  • Data managers
  • Sponsors

    • Technology Stack

  • Microsoft Azure: Provides the cloud foundation, including Azure OpenAI and Azure Machine Learning, for secure AI-enabled analysis and reporting.
  • Vector database: Stores computed clinical metrics and embeddings for faster, more relevant responses.
  • Python, HTML, CSS, JavaScript, and Flask: Support the application and user interface.

    • Professional Services

    Getting Started: Azure Adoption

  • Azure Environment Setup: Establishes the deployment foundation so teams can start quickly.
  • GenAI Solution Configuration: Connects Azure AI, machine learning, and analytics services for interim analysis use cases.
  • Clinical Trial Alignment: Adapts the solution to study requirements, compliance needs, and operating realities.

    • Extending Usage

  • Secure Data Deployment: Uses Azure SQL Database and Blob Storage to support reliable performance and growing data volumes.
  • Architecture Tuning: Uses Azure Kubernetes Service and Azure Monitor to support application health and performance.
  • Ongoing Support: Uses Azure DevOps and Azure Security Center to strengthen release management, security oversight, and continuity.

    • Engagement Scope: 8-Week Proof of Concept

    During this 8-week engagement, LTM will:

  • Assess Readiness: Review the client data landscape and Azure readiness.
  • Deploy Securely: Set up InterimOptimizer in a secure Azure environment.
  • Configure Core Features: Integrate sample datasets and configure reporting, chatbot, and visualization capabilities.
  • Validate Performance: Strengthen performance and support growing workloads using Azure-native tools.
  • Deliver the Roadmap: Provide outcomes, recommendations, and a roadmap for wider Azure deployment.

    • Lühiülevaade

    https://catalogartifact.azureedge.net/publicartifacts/ltim.ltimindtree_dataandai_interimoptimizer-448fc1ed-472a-4453-8f80-5c9e96c78ad1/image3_InterimOptimizer.png