Objectives: This study investigates the role of and methods for patent analysis in advancing medical technology (MedTech) innovation, a sector characterized by diverse, non-pharmacological or non-immunological healthcare technologies and significant research investment. Patents are critical early indicators of innovation, supporting horizon scanning and weak signal detection. The study aimed to identify intellectual property sources, evaluate methods for patent retrieval and analysis, and outline objectives for using patent data to anticipate trends and inform healthcare strategies. It also offered a methodological framework to support stakeholders in adopting innovative MedTech solutions. Methods: A rapid review (RR) was conducted using Cochrane Rapid Review Methods and PRISMA guidelines, with a pre-registered protocol on the Open Science Framework. Searches in Embase, IEEE Xplore, and Web of Science targeted records from 2020 onwards. Three independent reviewers screened studies using Rayyan. We included any study type, published since 2020 that provided sufficient data on patent sources, methods and tools applied to the study of MedTech. Our data extraction included bibliographic details, study characteristics, and methodological information. Risk of bias assessments were not undertaken. Narrative and tabular methods, supplemented by visual charts, were employed to synthesise findings. Results: Our searches identified 1,741 studies, of which 124 were included after title, abstract, and full-text screening, with 54% being original research, 44% reviews, and the remainder being conference abstracts. Most studies (68%) relied solely on patent databases, while others searched the grey literature. Research objectives of the included studies were grouped into nine themes, with trend analysis (50%) and policy recommendations (20%) being the most common. The review analysed 199 patent databases, with 27% of studies using multiple sources. Time horizons for patent searches averaged 24.6 years, ranging from 1900 to 2019. Automated approaches, employed in 33% of studies, frequently utilised tools like Gephi for network visualization. Disease mapping, based on NICE classification, indicated that cancer (19%) and respiratory conditions (16%), particularly COVID-19, were key areas, while digital health dominated the "health and social care delivery" category. Conclusions: The review highlights the value of patent data in trend analysis and its broader role in shaping policies and research strategies. While patents provide crucial insights into emerging technologies, inconsistent de-duplication practices across studies pose a risk of data inflation, accentuating the need for transparency and rigour. Finally, this review emphasized the importance of data transformation and visualization in detecting emerging trends with Python and R being the most commonly used programming languages for developing custom tools.

The authors have declared no competing interest.

This study/project is funded by the National Institute for Health and Care Research (NIHR) (NIHRIO/project reference HSRIC-2016-10009).

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