Horizon Scans can be accelerated using novel information retrieval and artificial intelligence tools
Abstract
Introduction: Horizon scanning in healthcare assesses early signals of innovation, crucial for timely adoption. Current horizon scanning faces challenges in efficient information retrieval and analysis, especially in grey literature, presenting a need for innovative tools. Methodology: This article introduces SCANAR and AIDOC, open-source Python-based tools designed to improve horizon scanning. SCANAR automates the retrieval and processing of news articles, using de-duplication and unsupervised relevancy ranking. AIDOC aids filtration by leveraging large language models and text embeddings for calculation of semantic similarity, to reorder data based on relevancy. Results: Twelve internal datasets from horizon scans and four external benchmarking datasets were used. SCANAR improved retrieval efficiency by automating processes previously dependent on manual labour. AIDOC displayed work-saving potential, achieving around 65% theoretic reduction in manual review efforts at 95% recall or 20% reduction when adding CMH, an existing open-source algorithm to make data-driven decisions on stopping screening early. Comparative analysis with benchmarking data showed AIDOC's performance was similar to existing systematic review automation tools, though performance varied depending on dataset characteristics. Conclusion: The validation indicates that SCANAR and AIDOC show potential to enhance horizon scanning efficiency by streamlining data retrieval and prioritisation. These tools may alleviate methodological limitations and allow broader, swifter horizon scans. Further studies are suggested to optimize these models and to design new workflows and validation processes that integrate large language models.
