ArticleBMJ open2025
Application of artificial intelligence to electronic health record data in long-term care facilities: a scoping review protocol.
Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- How Digital Stress and eHealth Literacy Relate to Missed Nursing Care and Willingness to Use AI Decision Support.Healthcare (Basel, Switzerland) · 2026Article
- Spotlighting the need for integrated health information technologies and databases to combat infections and sepsis in long-term care facilities.Innovation in aging · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionAlthough 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.