OP57 Introducing The New MedTech Innovation Scanning Techniques Framework - A Tool For Guiding Horizon Scanning Of MedTech Innovations

Sep 29, 2026

Uploaded by Rowan

Horizon scanning (HS) identifies future trends and weak signals for timely healthcare decisions, but currently lacks a formal framework. The Innovation Observatory’s MedTech Horizon Scanning Techniques (MIST) framework integrates evidence synthesis, HS methods, and foresight approaches, aligning scanning techniques with foresight stages and the technology development pipeline. MIST was built iteratively by combining HS expertise with regulatory knowledge and mapping development stages to European regulatory milestones.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages2

Reading Time5 min

AccessFree to download

Introduction

Methods

Results

Conclusions

Oral Presentations S27 (development process, purpose, and policy context) were extracted. Thematic analysis of both reviews informed the development of an online Delphi survey, distributed via several HTA stakeholder groups and societies, including HTAi. Results: We identified 8,651 unique records from four databases (PubMed, the Cochrane Library, Embase, EconLit) and extracted 328 definitions. Analysis revealed three key components of a definition for DHT: intended purpose, users, and underlying technology. The targeted review identified nine taxonomies classifying DHTs by maturity stages, intended purpose, digital health architecture, illness severity, personalization capacity, and levels of human intervention. The first round of the Delphi survey, with 118 complete responses, achieved consensus on the three key components, using intended purpose as a classification criterion. The results of the second round will be available in January 2025 . Conclusions: This study provided a unified definition for DHTs, incorporating three key components: intended purpose, users, and underlying technology. The Delphi process validated intended purpose as a primary classification criterion. This standardized definition and taxonomy streamlines assessment, enhances cross-border HTA collaboration, and enables stakeholders to compare and evaluate the impact of DHTs appropriately, thus fostering informed decision-making. OP56 Comparing Large Language Models For Horizon Scanning: A Feature Assessment Hannah O’Keefe ([nho11@newcastle.ac.uk](nho11@newcastle.ac.uk)) and Chris Marshall Introduction: Integrating large language models (LLMs) into horizon scanning workflows requires understanding of baseline features, like the ability to extract data and handle noisy data, and contextual understanding to inform considerations for LLM use. We evaluated 25 LLMs to assess their applicability for horizon scanning methods in general and to inform the design and integration strategy ofour unit’s advanced horizon scanning system. Methods: We developed a comprehensive framework detailing 32features across10categories for25LLMs. To build this framework, we used ChatGPT-4 to generate a preliminary list of categories, features, and LLMs relevant to HS. We supplemented this with parameters from the 2024 LeewayHertz assessment and finalized it through team consensus. Next, we employed a human-in-the-loop approach utilizing a recursive prover-verifier-chain: Microsoft Copilot>Claude 3 .5>ChatGPT-4 . Each LLM was assessed for variations in baseline features impacting their applicability in horizon scanning methods and potential integration into our horizon scanning system. Results: We identified six variable features (19%) across five categories. Nineteen ofthe LLMs support on-premises or self-hosted deployment. Regarding integration flexibility, only seven LLMs were opensource and four lacked strong vendor support. Eighteen models offered a usage-based pricing system, allowing budget tailoring. Five LLMs excelled in handling noisy data, beneficial for horizon scanning methods dealing with diverse information sources. Seventeen models had multimodal capabilities. Conclusions: Variations in key features among the 25 candidate LLMs affected their suitability for integration into horizon scanning workflows. Units must consider the trade-offs between deployment options, open-source availability, vendor support, pricing models, data handling capabilities, and multimodal features. This extensive framework supports assessment and selection of appropriate LLMs for horizon scanning workflows by filtering models according to these key features. OP57 Introducing The New MedTech Innovation Scanning Techniques Framework: A Tool For Guiding Horizon Scanning Of MedTech Innovations Sonia Garcia Gonzalez-Moral (sonia.garcia-gonzalez[moral@io.nihr.ac.uk](moral@io.nihr.ac.uk)) and Andrew Mkwashi Introduction: Horizon scanning (HS) identifies future trends and w

FAQs

What problem does the MIST framework address in horizon scanning?

It addresses the lack of a formal framework for horizon scanning in medtech, where HS is used to identify future trends and weak signals but lacks structured guidance.

How was the MIST framework developed?

It was developed iteratively: first by establishing an international MedTech development pathway, then mapping it to regulatory milestones under recent European legislation, and finally overlaying the Three Horizons Model to connect weak-signal sources to the most relevant horizons.

What horizons does the MIST framework include and what do they provide?

It presents three horizons—emerging, transitional, and imminent—each mapped to technology readiness levels, life-cycle stages, time periods, and regulatory milestones, with recommended HS, information retrieval, analysis methods, and sources for weak signal detection.

Sep 29, 2026

Uploaded by Rowan