Leveraging artificial intelligence and decision support systems in hospital-acquired pressure injuries prediction: A comprehensive review

Khaled M Toffaha; Mecit Can Emre Simsekler; Mohammed Atif Omar

doi:10.1016/j.artmed.2023.102560

Leveraging artificial intelligence and decision support systems in hospital-acquired pressure injuries prediction: A comprehensive review

Artif Intell Med. 2023 Jul:141:102560. doi: 10.1016/j.artmed.2023.102560. Epub 2023 Apr 25.

Authors

Khaled M Toffaha¹, Mecit Can Emre Simsekler², Mohammed Atif Omar¹

Affiliations

¹ Department of Industrial and Systems Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
² Department of Industrial and Systems Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates. Electronic address: emre.simsekler@ku.ac.ae.

PMID: 37295900
DOI: 10.1016/j.artmed.2023.102560

Abstract

Background: Hospital-acquired pressure injuries (HAPIs) constitute a significant challenge harming thousands of people worldwide yearly. While various tools and methods are used to identify pressure injuries, artificial intelligence (AI) and decision support systems (DSS) can help to reduce HAPIs risks by proactively identifying patients at risk and preventing them before harming patients.

Objective: This paper comprehensively reviews AI and DSS applications for HAPIs prediction using Electronic Health Records (EHR), including a systematic literature review and bibliometric analysis.

Methods: A systematic literature review was conducted through PRISMA and bibliometric analysis. In February 2023, the search was performed using four electronic databases: SCOPIS, PubMed, EBSCO, and PMCID. Articles on using AI and DSS in the management of PIs were included.

Results: The search approach yielded 319 articles, 39 of which have been included and classified into 27 AI-related and 12 DSS-related categories. The years of publication varied from 2006 to 2023, with 40% of the studies taking place in the US. Most studies focused on using AI algorithms or DSS for HAPIs prediction in inpatient units using various types of data such as electronic health records, PI assessment scales, and expert knowledge-based and environmental data to identify the risk factors associated with HAPIs development.

Conclusions: There is insufficient evidence in the existing literature concerning the real impact of AI or DSS on making decisions for HAPIs treatment or prevention. Most studies reviewed are solely hypothetical and retrospective prediction models, with no actual application in healthcare settings. The accuracy rates, prediction results, and intervention procedures suggested based on the prediction, on the other hand, should inspire researchers to combine both approaches with larger-scale data to bring a new venue for HAPIs prevention and to investigate and adopt the suggested solutions to the existing gaps in AI and DSS prediction methods.

Keywords: Decision support systems; HAPIs prediction; Hospital-acquired pressure injuries; Machine learning; Patient safety; Systematic review.

Publication types

Systematic Review
Review
Research Support, Non-U.S. Gov't

MeSH terms

Artificial Intelligence*
Hospitals
Humans
Pressure Ulcer* / diagnosis
Pressure Ulcer* / epidemiology
Pressure Ulcer* / prevention & control
Retrospective Studies
Risk Factors