Application of artificial intelligence in emergency nursing of patients with chronic obstructive pulmonary disease
Open accessAbstract
The research achievements of artificial intelligence technology in the development of chronic obstructive pulmonary disease were explored, and the advantages and problems encountered in the development of intelligent nursing were analyzed. This paper presents the application of artificial intelligence in the emergency care of patients with chronic obstructive pulmonary disease. The method included 447 COPD patients in a randomized controlled trial to observe the improvement of quality of life at 4 and 12 months after artificial intelligence medical intervention. A prospective randomized controlled trial included 101 patients with COPD who underwent a 9-month web-based knowledge exercise on the prevention of acute exacerbation of COPD through artificial intelligence medicine and were randomly divided into two groups: the experimental group and the control group. The results show that, in the experimental group and the control group, after 4 months, the quality of life does not change; after 12 months, compared with controls, the quality of life and emotional and psychological conditions have improved obviously. 29 patients who participated in the experiment and were randomly divided into the experimental group and the control group showed satisfactory results. COPD hospitalized rate and length of hospital stay were decreased in the experimental group than in the control group. For single-factor analysis, artificial intelligence medical intervention has not achieved significant significance, and the experimental results have preliminarily confirmed the effectiveness of artificial intelligence medical treatment.
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An RCT evaluating AI in emergency nursing for COPD patients failed to provide sufficient details on randomization procedures and generalizability, meeting only three MMAT criteria.
“Similarly, an RCT evaluating the application of AI in emergency nursing for COPD patients failed to provide sufficient details on randomization procedures and generalizability, meeting only three criteria (32).”
From Artificial intelligence in nursing: an integrative review of clinical and operational impacts (2025) · cites this paper as [32]
The 18 included studies were rated as having a moderate risk of bias.
“Overall, the 18 studies were rated as having a moderate risk of bias (29–48).”
From Artificial intelligence in nursing: an integrative review of clinical and operational impacts (2025) · cites this paper as [32]
Hong et al. (32) found that AI-driven telemedicine platforms facilitated real-time patient monitoring and early intervention, which reduced hospitalization rates and enhanced patients' quality of life.
“In emergency nursing for COPD patients, Hong et al. (32) found that AI-driven telemedicine platforms facilitated real-time patient monitoring and early intervention, which reduced hospitalization rates and enhanced patients’ quality of life.”
From Artificial intelligence in nursing: an integrative review of clinical and operational impacts (2025) · cites this paper as [32]
Studies by Hong et al. and Rony et al. (32, 38) highlighted significant apprehensions regarding the extensive collection and use of personal health data by AI systems.
“Studies by Hong et al. and Rony et al. (32, 38) highlighted significant apprehensions regarding the extensive collection and use of personal health data by AI systems.”
From Artificial intelligence in nursing: an integrative review of clinical and operational impacts (2025) · cites this paper as [32]
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Artificial intelligence in nursing: an integrative review of clinical and operational impacts
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- Authors
- Hong, L., Cheng, X., & Zheng, D.
- Affiliations
- Department of Emergency, The First People’s Hospital of Chun’an County, Hangzhou, Zhejiang 311700, China
- Department of Nursing, The First People’s Hospital of Chun’an County, Hangzhou, Zhejiang 311700, China
- Department of Infection, The First People’s Hospital of Chun’an County, Hangzhou, Zhejiang 311700, China
- Corresponding author
- Zheng, Deming
- Venue
- Contrast Media & Molecular Imaging
- Publisher
- Wiley
- Published
- 2021-11-24
- Volume
- 2021
- Pages
- 1-6
- ISSN
- 1555-4309 (ISSN-L)
- 1555-4317
- PMC
- PMC8635944
- OpenAlex
- W3215106906
- Cited by
- 30
- References
- 21
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 6 · 2023: 14 · 2022: 2
- FWCI
- 3.56
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Contrast Media & Molecular Imaging (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Chronic Obstructive Pulmonary Disease (COPD) Research
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Telemedicine and Telehealth Implementation
- Keywords
- artificial intelligence, emergency nursing, chronic obstructive pulmonary disease, acute exacerbation prevention, quality of life, hospitalization rates, length of hospital stay
- MeSH terms
- Artificial Intelligence, Humans, Prospective Studies, Quality of Life, Emergency Nursing, Pulmonary Disease, Chronic Obstructive
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