Utilizing standardized nursing terminologies in implementing an AI-powered fall-prevention tool to improve patient outcomes: a multihospital study
Open accessAbstract
Objectives: Standardized nursing terminologies (SNTs) are necessary to ensure consistent knowledge expression and compare the effectiveness of nursing practice across settings. This study investigated whether SNTs can support semantic interoperability and outcoming tracking over time by implementing an AI-powered CDS tool for fall prevention across multiple EMR systems. Materials and Methods: The study involved 3 tertiary academic hospitals and 1 public hospital with different EMR systems and nursing terms, and employed an AI-powered CDS tool that determines the fall risk within the next hour (prediction model) and recommends tailored care plans (CDS functions; represented by SNTs). The prediction model was mapped to local data elements and optimized using local data sets. The local nursing statements in CDS functions were mapped using an ICNP-based inpatient fall-prevention catalog. Four implementation models were compared, and patient outcomes and nursing activities were observed longitudinally at one site. Results: The postimplementation approach was practical for disseminating the AI-powered CDS tool for nursing. The 4 hospitals successfully implemented prediction models with little performance variation; the AUROCs were 0.8051–0.9581. The nursing process data contributed markedly to fall-risk predictions. The local nursing statements on preventing falls covered 48.0%–86.7% of statements. There was no significant longitudinal decrease in the fall rate (P = .160, 95% CI = −1.21 to 0.21 per 1000 hospital days), but rates of interventions provided by nurses were notably increased. Conclusion: SNTs contributed to achieving semantic interoperability among multiple EMR systems to disseminate AI-powered CDS tools and automatically track nursing and patient outcomes.
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Current research focuses on AI solutions for patient monitoring and clinical decision support.
“Current research, including works like Cho et al. [41], focuses on AI solutions for patient monitoring and clinical decision support.”
From Nurses’ perceptions, experience and knowledge regarding artificial intelligence: results from a cross-sectional online survey in Germany (2024) · cites this paper as [41]
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- Authors
- Cho, I., Cho, J., Hong, J. H., Choe, W. S., & Shin, H.
- Affiliations
- Brigham and Women's Hospital (US)
- National Health Insurance Service Ilsan Hospital (KR)
- Samsung Medical Center (KR)
- Inha University (KR)
- Corresponding author
- Cho, Insook
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2023-07-28
- Volume
- 30
- Issue
- 11
- Pages
- 1826-1836
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC10586045
- OpenAlex
- W4385332268
- Cited by
- 26
- References
- 40
- Citations by year
- 2026: 10 · 2025: 5 · 2024: 7 · 2023: 4
- FWCI
- 17.11
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of the American Medical Informatics Association (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Research Foundation of Korea (NRF-2019R1A2C2007583)
- Topics
- Health Sciences › Nursing › Issues, ethics and legal aspects › Nursing Diagnosis and Documentation
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Keywords
- standardized nursing terminology, fall prevention, clinical decision support, artificial intelligence, semantic interoperability, electronic health records, fall risk prediction, nursing process, International Classification for Nursing Practice, implementation model, patient outcomes, nursing interventions
- MeSH terms
- Standardized Nursing Terminology, Artificial Intelligence, Humans, Inpatients, Nursing Process
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