Clinical Decision Support and Natural Language Processing in Medicine: Systematic Literature Review (Preprint)

Abstract

<sec> <title>BACKGROUND</title> Ensuring access to accurate and verified information is essential for effective patient treatment and diagnosis. Although health workers rely on the internet for clinical data, there is a need for a more streamlined approach. </sec> <sec> <title>OBJECTIVE</title> This systematic review aims to assess the current state of artificial intelligence (AI) and natural language processing (NLP) techniques in health care to identify their potential use in electronic health records and automated information searches. </sec> <sec> <title>METHODS</title> A search was conducted in the PubMed, Embase, ScienceDirect, Scopus, and Web of Science online databases for articles published between January 2000 and April 2023. The only inclusion criteria were (1) original research articles and studies on the application of AI-based medical clinical decision support using NLP techniques and (2) publications in English. A Critical Appraisal Skills Programme tool was used to assess the quality of the studies. </sec> <sec> <title>RESULTS</title> The search yielded 707 articles, from which 26 studies were included (24 original articles and 2 systematic reviews). Of the evaluated articles, 21 (81%) explained the use of NLP as a source of data collection, 18 (69%) used electronic health records as a data source, and a further 8 (31%) were based on clinical data. Only 5 (19%) of the articles showed the use of combined strategies for NLP to obtain clinical data. In total, 16 (62%) articles presented stand-alone data review algorithms. Other studies (n=9, 35%) showed that the clinical decision support system alternative was also a way of displaying the information obtained for immediate clinical use. </sec> <sec> <title>CONCLUSIONS</title> The use of NLP engines can effectively improve clinical decision systems’ accuracy, while biphasic tools combining AI algorithms and human criteria may optimize clinical diagnosis and treatment flows. </sec> <sec> <title>CLINICALTRIAL</title> PROSPERO CRD42022373386; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=373386 </sec>

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