Using artificial intelligence to improve pain assessment and pain management: a scoping review

Meina Zhang; Linzee Zhu; Shih-Yin Lin; Keela Herr; Chih-Lin Chi; Ibrahim Demir; Karen Dunn Lopez; Nai-Ching Chi

doi:10.1093/jamia/ocac231

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Using artificial intelligence to improve pain assessment and pain management: a scoping review

Journal article

Open access

Peer reviewed

Using artificial intelligence to improve pain assessment and pain management: a scoping review

Meina Zhang, Linzee Zhu, Shih-Yin Lin, Keela Herr, Chih-Lin Chi, Ibrahim Demir, Karen Dunn Lopez and Nai-Ching Chi

Journal of the American Medical Informatics Association : JAMIA, Vol.30(3), pp.570-587

03/2023

DOI: 10.1093/jamia/ocac231

PMCID: PMC9933069

PMID: 36458955

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Abstract

CONTEXTOver 20% of US adults report they experience pain on most days or every day. Uncontrolled pain has led to increased healthcare utilization, hospitalization, emergency visits, and financial burden. Recognizing, assessing, understanding, and treating pain using artificial intelligence (AI) approaches may improve patient outcomes and healthcare resource utilization. A comprehensive synthesis of the current use and outcomes of AI-based interventions focused on pain assessment and management will guide the development of future research. OBJECTIVESThis review aims to investigate the state of the research on AI-based interventions designed to improve pain assessment and management for adult patients. We also ascertain the actual outcomes of Al-based interventions for adult patients. METHODSThe electronic databases searched include Web of Science, CINAHL, PsycINFO, Cochrane CENTRAL, Scopus, IEEE Xplore, and ACM Digital Library. The search initially identified 6946 studies. After screening, 30 studies met the inclusion criteria. The Critical Appraisals Skills Programme was used to assess study quality. RESULTSThis review provides evidence that machine learning, data mining, and natural language processing were used to improve efficient pain recognition and pain assessment, analyze self-reported pain data, predict pain, and help clinicians and patients to manage chronic pain more effectively. CONCLUSIONSFindings from this review suggest that using AI-based interventions has a positive effect on pain recognition, pain prediction, and pain self-management; however, most reports are only pilot studies. More pilot studies with physiological pain measures are required before these approaches are ready for large clinical trial.

Details

Title: Subtitle: Using artificial intelligence to improve pain assessment and pain management: a scoping review
Creators: Meina Zhang - University of Iowa
Linzee Zhu - University of Iowa
Shih-Yin Lin - Rory Meyers College of Nursing, New York University, New York, New York, USA.
Keela Herr - University of Iowa
Chih-Lin Chi - University of Minnesota
Ibrahim Demir - University of Iowa
Karen Dunn Lopez
Nai-Ching Chi - University of Iowa
Resource Type: Journal article
Publication Details: Journal of the American Medical Informatics Association : JAMIA, Vol.30(3), pp.570-587
DOI: 10.1093/jamia/ocac231
PMID: 36458955
PMCID: PMC9933069
NLM abbreviation: J Am Med Inform Assoc
ISSN: 1067-5027
eISSN: 1527-974X
Grant note: DOI: 10.13039/100000056, name: NINR, award: 2T32NR011147-06A1
Language: English
Electronic publication date: 12/02/2022
Date published: 03/2023
Academic Unit: Electrical and Computer Engineering; Civil and Environmental Engineering; Iowa Neuroscience Institute; Nursing; Injury Prevention Research Center
Record Identifier: 9984321879202771

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