Enhancing Systematic Reviews in Health Science Using Integrated Search Interfaces, Discovery Layers and Federated Searching

Abstract

ABSTRACT This article highlights the importance of integrated search interfaces (ISI) in systematic reviews and examines the current guidelines on their use. ISI can help researchers determine the scope of their review topic and identify relevant search terms and subject headings. They can also assist in developing and documenting search strategies, as well as rapidly expanding searches to additional databases, registers, and gray literature. ISI can also be used for ongoing literature surveillance and the identification of retractions and errata. Limitations and challenges associated with the use of ISI in systematic reviews include; difficulties in translating search syntax between databases, limitations in the extensibility of controlled subject classifications, the need to select relevant databases, the commercialization of ISI hindering scientific reproducibility, challenges in maintaining the reproducibility of search strategies over time, and skills and knowledge deficits in the workforce. This article also discusses opportunities for vendors to enhance ISI to better support systematic review workflows. Features to improve ISI may include duplicate citation identification, cross-platform integration with citation management tools and screening platforms, transparency of search interface configuration, integration of bibliometrics and semantic mapping, and the use of artificial intelligence (AI) to enhance search strategies. Advancements in Large Language Models in the realm of AI, along with APIs designed to incorporate these models into various software tools, have the capability to significantly enhance the effectiveness and comprehensiveness of systematic reviews.