DOI: 10.12688/f1000research.169999.2 ISSN: 2046-1402

Combining implementation and data sciences to accelerate evidence integration into healthcare – ImpleMATE

Jeffery Chan, Janna Hastings, Gabriella Tiernan, Jeannie Paterson, Nigel Lovell, Luc Betbeder-Matibet, Patrick Kin Man Tung, Sarah Pink, Debora Lanzeni, Thomasina Donovan, Andrew Milat, Louisa Jorm, Carolyn Mazariego, Chi Zhang, Guillaume Fontaine, Stephanie Best, Georgina Kennedy, Susan Michie, Frank Lin, , Natalie Taylor
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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.

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