The McMaster Health Information Research Unit: Over a Quarter-Century of Health Informatics Supporting Evidence-Based Medicine

Lokker, Cynthia and McKibbon, K Ann and Afzal, Muhammad and Navarro, Tamara and Linkins, Lori-Ann and Haynes, R Brian and Iorio, Alfonso (2024) The McMaster Health Information Research Unit: Over a Quarter-Century of Health Informatics Supporting Evidence-Based Medicine. Journal of Medical Internet Research, 26. e58764. ISSN 1438-8871

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Abstract

Evidence-based medicine (EBM) emerged from McMaster University in the 1980-1990s, which emphasizes the integration of the best research evidence with clinical expertise and patient values. The Health Information Research Unit (HiRU) was created at McMaster University in 1985 to support EBM. Early on, digital health informatics took the form of teaching clinicians how to search MEDLINE with modems and phone lines. Searching and retrieval of published articles were transformed as electronic platforms provided greater access to clinically relevant studies, systematic reviews, and clinical practice guidelines, with PubMed playing a pivotal role. In the early 2000s, the HiRU introduced Clinical Queries—validated search filters derived from the curated, gold-standard, human-appraised Hedges dataset—to enhance the precision of searches, allowing clinicians to hone their queries based on study design, population, and outcomes. Currently, almost 1 million articles are added to PubMed annually. To filter through this volume of heterogenous publications for clinically important articles, the HiRU team and other researchers have been applying classical machine learning, deep learning, and, increasingly, large language models (LLMs). These approaches are built upon the foundation of gold-standard annotated datasets and humans in the loop for active machine learning. In this viewpoint, we explore the evolution of health informatics in supporting evidence search and retrieval processes over the past 25+ years within the HiRU, including the evolving roles of LLMs and responsible artificial intelligence, as we continue to facilitate the dissemination of knowledge, enabling clinicians to integrate the best available evidence into their clinical practice.

Item Type: Article
Identification Number: 10.2196/58764
Dates:
Date
Event
16 July 2024
Accepted
31 July 2024
Published Online
Uncontrolled Keywords: health informatics, evidence-based medicine, information retrieval, evidence-based, health information, Boolean, natural language processing, NLP, journal, article, Health Information Research Unit, HiRU
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions: Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User: Gemma Tonks
Date Deposited: 12 Sep 2024 13:45
Last Modified: 12 Sep 2024 13:45
URI: https://www.open-access.bcu.ac.uk/id/eprint/15829

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