Academic Life Sciences University Streamlines Data and Clinical Research with Generative AI
A leading academic life sciences university faced fragmentation of its data sources, impacting operational efficiency and patient care. To address this, the institution turned to a sophisticated generative AI platform architected on Microsoft Azure technology, supported by Lantern, to centralize data and build a secure, unified knowledge base for researchers and clinicians. The system integrated Azure OpenAI, Azure AI Document Intelligence (OCR), AI Search, Azure Functions, and Azure SQL Database, allowing real-time ingestion and processing of millions of data points from diverse sources (websites, files, APIs). New AI assistants under development are tailored for specialized clinical needs, including clinical trial onboarding, application of clinical guidelines, and department-specific knowledge support. HIPAA compliance and robust governance ensured data security and ethical use, while customizable large language model interactions allowed safe, task-driven access to sensitive data. Results included over 1,000 daily users, 1 million+ endpoints ingested, enhanced research output, and a framework for ongoing AI agent and assistant expansion in healthcare.
- Organization
- Academic Life Sciences University
- Industry
- Healthcare
- Location
- United States
- Published
- July 2024
Reported outcomes
Strategic outcomes
Primary read
Use case focus
Showing 3 of 3
- 1Centralized AI-powered knowledge management in academic healthcare
- 2Automated clinical data extraction and research assistant
- 3Specialized generative AI assistants for medical departments
- Fragmented and siloed data across departments and systems hindered efficiency and patient care.
- Needed scalable, secure knowledge base to support multiple clinical and research workflows.
- Demand for HIPAA-compliant, governed AI access to integrate sensitive patient data safely and at scale.
- Required tools for writing, coding assistance, decision support, and knowledge management in academic medicine.
- Implemented a generative AI-enabled data management platform with Azure OpenAI for secure large language model access.
- Used Azure Functions for data ingestion and orchestration, and Azure SQL Database for centralized and scalable storage.
- Applied Azure AI Document Intelligence (OCR) and AI Search to index, organize, and provide actionable insights from disparate sources.
- Customizable AI assistants designed for clinical guideline support, research, and department-specific needs.
- Established strong AI oversight and compliance with HIPAA and institutional data governance policies.
- Unified knowledge base serving 2,300+ users, 1,000+ daily active users, and 1M+ ingested endpoints.
- Reduced administrative burden for clinicians and researchers.
- Enabled secure, advanced LLM-powered interactions for better patient care and research innovation.
- Generated over a dozen peer-reviewed research papers using AI-powered tools.
- Launched a foundation for the continued rollout of AI-powered assistants in healthcare.
Architecture
A data management platform ingests data from diverse sources using Azure Functions, stores it in Azure SQL Database, applies Azure AI Document Intelligence (OCR) and AI Search for indexing and retrieval, and exposes capabilities via Azure OpenAI-powered large language models. AI assistants are developed for domain-specific tasks, governed by strict HIPAA-compliant access controls.
Implementation partners1
Sources & evidence1
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