Horizon Scanning Methods for Health Care Technology Innovation Identification: Rapid Scoping Review of Patent Research Studies
Bibliographic record
Abstract
Background: Patents are an early sign of innovation, yet their role in horizon scanning for health care remains unclear. Objective: This study investigates the role of, and methods for, patent analysis in advancing health care technology innovation in a sector that is characterized by diverse health care 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 health care strategies. Methods: A rapid scoping review was conducted following Cochrane Rapid Review Methods recommendations and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, with a preregistered protocol on the Open Science Framework. Searches in Embase, IEEE Xplore, and Web of Science targeted records published 2020 onward to capture the most recent sources, methods, and tools. Three independent reviewers screened studies using Rayyan (Qatar Computing Research Institute). We included any study type published since 2020 that provided patent sources data, methods, and tools applied to the study of health care technologies. 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 used to synthesize findings. Results: Our searches identified 1741 studies, of which 124 were included after title, abstract, and full-text screening, with 54% being original research, 43.5% reviews, and the remainder being conference abstracts (2.5%). Most studies (68%) relied solely on patent databases, while others searched the gray and published literature. Research objectives of the included studies were grouped into 10 themes, with trend analysis (50%) and the provision of recommendations for future research, policy, and strategy development (20%) being the most common. Our review identified up to 47 patent databases, with 27% of studies using multiple sources. Whenever time limits were reported, the mean time horizon for patent searches was 24.6 years, ranging from 1900 to 2019. Automated approaches, used in 33% (n=43) of studies, frequently used tools such as Gephi (Gephi Consortium) for network visualization. Disease mapping based on National Institute for Health and Care Excellence classification indicated that cancer (19%) and respiratory conditions (16%), particularly COVID-19, were key areas. Conclusions: Patent data are valuable for identifying technological trends and informing policy and research strategies. While patents provide crucial insights into emerging technologies, inconsistent deduplication practices across studies pose the risk of data inflation, accentuating the need for transparency and rigor. 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.057 | 0.141 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".