Combining Implementation and Data Sciences to Accelerate Evidence Integration into Healthcare – ImpleMATE
Abstract
Background The translation of research evidence into routine healthcare practice is often slow and inconsistent, even timely implementation can significantly improve patient outcomes. While implementation science offers strategies to close this gap, current approaches are frequently manual, fragmented, and poorly integrated within healthcare systems. To address these challenges, we propose ImpleMATE – an AI-assisted implementation science platform designed to streamline implementation efforts. Grounded in the Learning Health System (LHS) model, ImpleMATE aims to establish a continuous, data-driven cycle of learning and improvement in implementation practice. Methods ImpleMATE will be developed through a co-design and co-production approach rooted in human-centred design principles. Development will proceed through four key activities: (1) establishing a data processing pipeline and building an implementation-focused ontology; (2) creating and validating an AI system to extract implementation knowledge, structure the ontology, and support implementation solution delivery; (3) designing an interactive web application to deliver AI-assisted decision support and streamline implementation processes; and (4) developing an evaluation framework to assess platform’s effectiveness and plan for national integration. These activities align with three core components of the LHS model: converting data into knowledge, translating knowledge into practice, and feeding implementation process and outcome data back into the system for continuous learning. The platform will be underpinned by strong ethical and governance frameworks to ensure data privacy, transparency, and responsible AI use. Discussion ImpleMATE aims to transform the adoption of evidence-based innovations in healthcare by embedding trustworthy AI into the core of implementation practice. Through the integration of structured ontologies, real-time AI reasoning, and an interactive user interface, the platform will provide tailored solutions to support implementation efforts. Designed as a dynamic learning system, ImpleMATE will evolve with user input and real-world data, offering a scalable, ethically grounded solution to accelerate and enhance implementation across healthcare settings.
// Source
Authors: J. Chan, Janna Hastings, Gabriella Tiernan, Jeannie Paterson, Nigel Lovell, Luc Betbeder-Matibet, Patrick Kin Man Tung, Sarah Pink, Débora Lanzeni, Thomasina Donovan, Andrew Milat, Louisa Jorm, Carolyn Mazariego, Chi Zhang, Guillaume Fontaine, Stephanie Best, Georgina Kennedy, Susan Michie, Frank Lin, The ImpleMATE Network, Natalie Taylor
Institutions: The University of Sydney, The University of Melbourne, UNSW Sydney, University College London, Monash University, University of Technology Sydney, Ottawa Hospital, Queensland University of Technology, McGill University, New South Wales Department of Health, Victoria University, University of St.Gallen, Garvan Institute of Medical Research, Melbourne Health, Prince of Wales Hospital, Institute for Medical Research, ZHAW Zurich University of Applied Sciences, Melbourne Genomics Health Alliance, SIB Swiss Institute of Bioinformatics, Innovative Clinical Research, University of Applied Sciences St. Gallen, Ingham Institute, State Academic University of Humanities