Search Filters to Identify Automation in HEOR: An Umbrella Review of Performance and Overlap

Abstract

Background: Search strategies used to identify evidence on automation in Health Economics and Outcomes Research (HEOR) often lack sensitivity and specificity, resulting in information overload or missed studies. This umbrella review evaluated and compared the performance and overlap of search filters commonly used to retrieve automation-related HEOR evidence. Methods: Systematic literature reviews (SLRs) and search filters focusing on any form of automation in HEOR were included. Searches (January 01, 2023–July 03, 2024) were conducted in EMBASE, ISSG Resource, and Google Scholar. Subject headings, search terms, and performance metrics were extracted. Reference lists were cross-checked. Screening was performed by one reviewer, with 20% verified by a second reviewer. The PRESS checklist was used to assess search strategy quality. The protocol was registered with the Open Science Framework (OSF). Results: Seven SLRs and one standalone filter, reporting 11 search strategies, met the inclusion criteria. HEOR relevance was defined by studies applying search filters in contexts of SLRs, indirect treatment comparisons, and economic modeling. Included SLRs retrieved between 5-273 studies. PubMed was the most frequently searched database. Commonly used subject headings included “artificial intelligence,” “deep learning,” “machine learning,” and “natural language processing,” with “artificial intelligence” the most frequent free-text term. Inclusion rates varied: title/abstract (1%–8%), full-text (27%–86%), and final inclusion (0.13%–2.31%). Time to include one study ranged from 0.6-8 hours. Conclusions: Considerable variability in search filter performance was observed, causing lower specificity and inefficient evidence retrieval. Standardized, high-performing search strategies are needed to enhance efficiency and reliability in identifying automation-related HEOR evidence.

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