LLM-Based Agents for Healthcare Vendor Intelligence Extraction

Healthcare organizations in the United States face a complex and time-consuming process when selecting new technology vendors. Important information about healthcare software—such as which hospitals already use it, what electronic health record systems it integrates with, and whether it meets security standards—is scattered across many sources online. This project will explore how advanced artificial intelligence systems can automatically gather and organize this information to make it easier to evaluate healthcare technology companies. Working with Toboggan Labs and its client partner, the research will focus on improving AI tools that extract reliable vendor data from public sources and on designing practical methods to measure the accuracy and cost of these systems at scale. The results will help our client partner maintain a comprehensive and up-to-date database of healthcare technology vendors, strengthening its marketplace that connects hospitals with appropriate solutions. Ultimately, this work aims to reduce the research burden for healthcare organizations and support faster, more informed technology purchasing decisions.

Faculty Supervisor:

Martin Vallières;Shirin Abbasinejad Enger

Student:

Partner:

Toboggan Labs

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Research Institute of the McGill University Health Centre

Program:

Accelerate

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Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.