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. 2024 Jan 23;8(2):313-352.
doi: 10.1007/s41666-024-00159-4. eCollection 2024 Jun.

Clinical Information Retrieval: A Literature Review

Sonish Sivarajkumar  1 , Haneef Ahamed Mohammad  2 , David Oniani  3 , Kirk Roberts  4 , William Hersh  5 , Hongfang Liu  4 , Daqing He  2 , Shyam Visweswaran  1   6   7 , Yanshan Wang  1   3   6   7
Affiliations

Clinical Information Retrieval: A Literature Review

Sonish Sivarajkumar et al. J Healthc Inform Res. .

Abstract

Clinical information retrieval (IR) plays a vital role in modern healthcare by facilitating efficient access and analysis of medical literature for clinicians and researchers. This scoping review aims to offer a comprehensive overview of the current state of clinical IR research and identify gaps and potential opportunities for future studies in this field. The main objective was to assess and analyze the existing literature on clinical IR, focusing on the methods, techniques, and tools employed for effective retrieval and analysis of medical information. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted an extensive search across databases such as Ovid Embase, Ovid Medline, Scopus, ACM Digital Library, IEEE Xplore, and Web of Science, covering publications from January 1, 2010, to January 4, 2023. The rigorous screening process led to the inclusion of 184 papers in our review. Our findings provide a detailed analysis of the clinical IR research landscape, covering aspects like publication trends, data sources, methodologies, evaluation metrics, and applications. The review identifies key research gaps in clinical IR methods such as indexing, ranking, and query expansion, offering insights and opportunities for future studies in clinical IR, thus serving as a guiding framework for upcoming research efforts in this rapidly evolving field. The study also underscores an imperative for innovative research on advanced clinical IR systems capable of fast semantic vector search and adoption of neural IR techniques for effective retrieval of information from unstructured electronic health records (EHRs).

Supplementary information: The online version contains supplementary material available at 10.1007/s41666-024-00159-4.

Keywords: Applications; Electronic health records; Information retrieval; Natural language processing; Tools.

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Conflict of interest statement

Competing InterestsThe authors declare no competing interests.

Figures

Fig. 1
Fig. 1
A basic IR process diagram
Fig. 2
Fig. 2
Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram of the article screening and identification process
Fig. 3
Fig. 3
Distribution of clinical IR publications per year
Fig. 4
Fig. 4
Categorization of publication types
Fig. 5
Fig. 5
Top 13 publication venues for clinical IR articles
Fig. 6
Fig. 6
Categorization of clinical IR article types
Fig. 7
Fig. 7
Categorization of clinical IR applications
See this image and copyright information in PMC

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