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Lookup NU author(s): Sonia Garcia Gonzalez-MoralORCiD, Dr Andrew Mkwashi, Fiona BeyerORCiD, Professor Dawn CraigORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© Sonia Garcia Gonzalez‑Moral, Andrew Mkwashi, Fiona R Beyer, Dawn Craig.Background: Strategic foresight relies on horizon scanning to detect weak signals of innovation and anticipate disruption in fast‑moving MedTech. While tools such as the Rumsfeld matrix and the Three Horizons model structure thinking about uncertainty and time, their application in MedTech is limited and insufficient for addressing the regulatory, technical, and evidential complexity of emerging or disruptive technologies. Regulatory frameworks provide stability through defined safety and performance pathways, but shifts and regional variability requirements complicate anticipatory assessment. These challenges expose methodological gaps in current practice: broad foresight frameworks lack the technical granularity, regulatory alignment, and systematic processes required for MedTech, where rapid innovation intensifies uncertainty. This paper presents a structured methodological framework to support systematic, reproducible, and decision‑focused horizon scanning in MedTech. Objective: The framework aims to (1) standardize methods for planning, execution, and reporting; (2) reduce uncertainty through systematic identification and interpretation of weak signals; and (3) improve transparency, quality, and comparability across policy, regulatory, clinical, and strategic contexts. Methods: The framework was developed following the 3‑step process by McMeekin et al. First, data were identified through a mapping review of MedTech futures and foresight methods, mapping UK health care decision‑makers, and participating in national and international horizon scanning initiatives (2021‐2025). Second, the framework was constructed by integrating the sources, methods, and insights with the principles of regulatory and technology readiness, supported by ongoing consultation with UK regulatory and health system bodies. Third, the framework is being applied and tested in MedTech horizon scanning projects. Formal validation and iterative revision are planned. Results: The MedTech Innovation Scanning Techniques (MIST) framework integrates foresight theory, horizon scanning methods, and the MedTech innovation pathway, enabling systematic weak signal detection across 3 time horizons. In the imminent horizon (technology readiness level [TRL] 8‐9), uncertainty is lowest, and weak signals are more visible, supported by rapid evidence synthesis and expert engagement. In the transitional horizon (TRL 4‐7), uncertainty increases due to trial outcomes, funding, and market dynamics, requiring broader evidence sources and complementary techniques such as scenario analysis and bibliometrics. In the emerging horizon (TRL 1‐3), weak signals are abundant but least predictable; early‑stage data (patents, preclinical research, and conference outputs) are analyzed using methods such as term‑similarity visualization to reveal nascent innovations. Across all horizons, MIST supports systematic signal identification, contextualization, and prioritization to inform anticipatory decision‑making. Conclusions: MedTech horizon scanning is challenging. MIST addresses these challenges by integrating the Three Horizons model, Rumsfeld matrix, technology pathways, regulatory considerations, and sources to guide weak signal detection. MIST provides a structured, transparent, and reproducible approach tailored to MedTech. While formal validation and integration of equity and ethical dimensions are ongoing, the framework fills a critical methodological gap in horizon scanning for MedTech.
Author(s): Garcia Gonzalez-Moral S, Mkwashi A, Beyer FR, Craig D
Publication type: Article
Publication status: Published
Journal: Journal of Medical Internet Research
Year: 2026
Volume: 28
Issue: 1
Print publication date: 21/05/2026
Online publication date: 09/02/2026
Acceptance date: 21/04/2026
Date deposited: 08/06/2026
ISSN (print): 1439-4456
ISSN (electronic): 1438-8871
Publisher: JMIR Publications Inc.
URL: https://doi.org/10.2196/93166
DOI: 10.2196/93166
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