Transforming rapid response team through artificial intelligence

Kwangha Lee

doi:10.4266/acc.000425

Articles

Page Path: HOME > Acute Crit Care > Volume 40(1); 2025 > Article

Editorial Rapid response system Transforming rapid response team through artificial intelligence: Acute and Critical Care 2025;40(1):136-137.
DOI: https://doi.org/10.4266/acc.000425
Published online: February 19, 2025

Kwangha Lee^1,2,3

¹Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Pusan National University Hospital, Busan, Korea

²Department of Internal Medicine, Pusan National University School of Medicine, Busan, Korea

³Biomedical Research Institute, Pusan National University Hospital, Busan, Korea

Address for correspondence: Kwangha Lee Department of Internal Medicine, Pusan National University School of Medicine, 179 Gudeok-ro, Seo-gu, Busan 49241, Korea Tel: +82-51-240-7743 Fax: +82-52-245-3127 Email: jubilate@pusan.ac.kr

• Received: February 4, 2025 • Accepted: February 5, 2025

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

2,473 Views
93 Download
1 Web of Science
1 Crossref

prev next

Full Article

Download PDF

NOTES
REFERENCES

The rapid response team (RRT) plays a crucial role in reducing the incidence of cardiopulmonary resuscitation among hospitalized patients in general wards. RRTs contribute to the prevention of cardiopulmonary arrest and help lower overall and unexpected hospital mortality rates. However, to make a tangible impact, RRTs must be effectively and frequently utilized. Despite clear activation criteria, many clinicians hesitate to call RRTs, delaying or entirely omitting activation even when patients meet the necessary conditions. Such delays or failures to respond in time significantly increase patient morbidity and mortality [1].

In this issue of Acute and Critical Care, Lim et al. [2] examined the perspectives of nurses and residents regarding RRTs in a 1,400-bed tertiary hospital in South Korea. Their findings suggest that while the majority acknowledge the importance of RRTs in preventing patient deterioration, several barriers hinder their optimal use. One of the main challenges is a lack of awareness and confidence in activation protocols, with only 22% of respondents reporting familiarity with RRT criteria. Additionally, cultural dynamics—such as strong hierarchical structures—caused 85.7% of respondents to seek approval from senior physicians before initiating RRT activation. The reluctance to activate RRTs due to fear of criticism for unnecessary alerts further exacerbates the issue. The authors emphasize that addressing these barriers through targeted educational initiatives, modifications in institutional culture, and structural enhancements is crucial to improving RRT efficiency and ensuring better patient outcomes.

Artificial intelligence (AI) and machine learning offer promising avenues to address these challenges. These advanced technologies enable more precise early warning systems, surpassing traditional detection methods for patient deterioration. AI-powered systems can eliminate human hesitations and inefficiencies by autonomously recognizing warning signs and triggering RRT activation, leading to improved patient care. Various AI-driven solutions have been designed to facilitate timely RRT activation and reduce delays. One such innovation is the Deep Learning–Based Cardiac Arrest Risk Management System (DeepCARS), which has demonstrated superior accuracy in predicting in-hospital cardiac arrest and unplanned intensive care unit admissions compared to conventional early warning score systems. DeepCARS integrates vast amounts of electronic health record data to provide real-time alerts, enabling proactive clinical interventions and improving patient survival rates [3]. Moreover, a recent study demonstrated the effectiveness of another AI-based deterioration detection model, which significantly reduced the likelihood of care escalation events—including RRT activations, intensive care unit transfers, and cardiopulmonary arrests. This provides strong evidence supporting the role of AI-driven innovations in enhancing RRT effectiveness and optimizing patient management [4].

The integration of AI and machine learning into RRT frameworks holds transformative potential for patient care by resolving long-standing barriers to RRT activation. By refining early warning systems, enhancing predictive accuracy, and enabling swift interventions, these technologies can substantially reduce the risks associated with delayed or missed RRT activations. Furthermore, the widespread adoption of AI-powered critical care platforms reflects a global trend toward leveraging digital innovations to promote equitable healthcare access and improve outcomes. Moving forward, continued investment in clinician education, cultural shifts, and technological advancements will be essential to ensuring that RRTs function optimally, safeguarding patient well-being, and elevating the standard of hospital care.

NOTES

CONFLICT OF INTEREST

Kwangha Lee is an editorial board member of the journal but was not involved in the peer reviewer selection, evaluation, or decision process of this article. No other potential conflicts of interest relevant to this article were reported.

FUNDING

None.

ACKNOWLEDGMENTS

None.

AUTHOR CONTRIBUTIONS

All the work was done by KL.

REFERENCES

1. Winters BD. Rapid response systems. Crit Care Clin 2024;40:583-98.Article PubMed
2. Lim SY, Woo HG, Park JS, Cho YJ, Lee JH, Lee YJ. Resident and nurse attitudes toward a rapid response team in a tertiary hospital in South Korea. Acute and Crit Care 2025;40:18-36.
3. Cho KJ, Kim JS, Lee DH, Lee SM, Song MJ, Lim SY, et al. Prospective, multicenter validation of the deep learning-based cardiac arrest risk management system for predicting in-hospital cardiac arrest or unplanned intensive care unit transfer in patients admitted to general wards. Crit Care 2023;27:346.Article PubMed PMC PDF
4. Gallo RJ, Shieh L, Smith M, Marafino BJ, Geldsetzer P, Asch SM, et al. Effectiveness of an artificial intelligence-enabled intervention for detecting clinical deterioration. JAMA Intern Med 2024;184:557-62.Article PubMed PMC

Figure & Data

References

Citations

Citations to this article as recorded by

DeepCARS-Identified High-Risk Patients: Clinical Interventions and Outcomes During the Korean Healthcare Crisis
Hyojin Jang, Wanho Yoo, Sora Hwang, Kwangha Lee
Medicina.2025; 61(11): 1896. CrossRef

PubReader
ePub Link

Cite

CITE: export Copy Format; Close

Download Citation

Download a citation file in RIS format that can be imported by all major citation management software, including EndNote, ProCite, RefWorks, and Reference Manager.

Format:

RIS — For EndNote, ProCite, RefWorks, and most other reference management software
BibTeX — For JabRef, BibDesk, and other BibTeX-specific software

Include:

Citation for the content below
Citation and abstract for the content below

Transforming rapid response team through artificial intelligence

Acute Crit Care. 2025;40(1):136-137. Published online February 28, 2025

DOI: https://doi.org/10.4266/acc.000425

XML Download

Transforming rapid response team through artificial intelligence