Application of artificial intelligence to electronic health record data in long-term care facilities: a scoping review protocol

Abstract

INTRODUCTION: Although artificial intelligence (AI) has been widely applied to electronic health record (EHR) data in hospital environments, its use in long-term care (LTC) facilities remains unexplored. Limited information technology infrastructure and unique challenges in LTC settings require a comprehensive examination of AI's potential to enhance care quality and operational efficiency. With the aim of examining the application of AI to EHR data in LTC facilities, this scoping review will identify current AI applications for EHR in LTC, informing future research and potential care improvements in LTC settings. METHODS AND ANALYSIS: This review will follow the scoping review methodological guidelines. The protocol of this scoping review has been registered on the Open Science Framework. The inclusion criteria are EHR (participants), AI (concept) and LTC facilities (context), with no date restrictions, but limited to articles published in English. Studies of any design focusing on AI applications for EHR in LTC settings will be considered. A systematic search will be performed on MEDLINE (Ovid), CINAHL (EBSCOhost), the Cochrane Central Register of Controlled Trials (Ovid), the Cochrane Database of Systematic Reviews (Ovid) and SCOPUS (Elsevier) by an information specialist. Two reviewers will independently screen titles and abstracts for inclusion based on predefined criteria. The same process will be repeated for full-text screening. Discrepancies will be resolved through team meetings with the third, fourth and fifth reviewers. All reasons for exclusion at the full-text stage will be documented and reported, with any discrepancies resolved by a review team. ETHICS AND DISSEMINATION: As the data will be collected from existing literature, ethical approval is not required. The findings will be disseminated through conference presentations and publication in a peer-reviewed journal. The results will map current knowledge on AI applications in LTC facilities, thereby providing a foundation for future research aimed at enhancing the implementation and effectiveness of AI technologies in such settings.

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