%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 26
%N 
%P e58380
%T Enhancing Patient Safety Through an Integrated Internet of Things Patient Care System: Large Quasi-Experimental Study on Fall Prevention
%A Wen,Ming-Huan
%A Chen,Po-Yin
%A Lin,Shirling
%A Lien,Ching-Wen
%A Tu,Sheng-Hsiang
%A Chueh,Ching-Yi
%A Wu,Ying-Fang
%A Tan Cheng Kian,Kelvin
%A Hsu,Yeh-Liang
%A Bai,Dorothy
%+ School of Gerontology and Long-Term Care, College of Nursing, Taipei Medical University, No.250, Wuxing Street, Xinyi District, Taipei, 110, Taiwan, 886 227361661 ext 6332, dbai@tmu.edu.tw
%K patient safety
%K falls
%K fall prevention
%K fall risk
%K sensors
%K Internet of Things
%K bed-exit alert
%K motion-sensing mattress system
%K care quality
%K quality improvement
%K ubiquitous health
%K mHealth
%D 2024
%7 3.10.2024
%9 Original Paper
%J J Med Internet Res
%G English
%X Background: The challenge of preventing in-patient falls remains one of the most critical concerns in health care. Objective: This study aims to investigate the effect of an integrated Internet of Things (IoT) smart patient care system on fall prevention. Methods: A quasi-experimental study design is used. The smart patient care system is an integrated IoT system combining a motion-sensing mattress for bed-exit detection, specifying different types of patient calls, integrating a health care staff scheduling system, and allowing health care staff to receive and respond to alarms via mobile devices. Unadjusted and adjusted logistic regression models were used to investigate the relationship between the use of the IoT system and bedside falls compared with a traditional patient care system. Results: In total, 1300 patients were recruited from a medical center in Taiwan. The IoT patient care system detected an average of 13.5 potential falls per day without any false alarms, whereas the traditional system issued about 11 bed-exit alarms daily, with approximately 4 being false, effectively identifying 7 potential falls. The bedside fall incidence during hospitalization was 1.2% (n=8) in the traditional patient care system ward and 0.1% (n=1) in the smart ward. We found that the likelihood of bedside falls in wards with the IoT system was reduced by 88% (odds ratio 0.12, 95% CI 0.01-0.97; P=.047). Conclusions: The integrated IoT smart patient care system might prevent falls by assisting health care staff with efficient and resilient responses to bed-exit detection. Future product development and research are recommended to introduce IoT into patient care systems combining bed-exit alerts to prevent inpatient falls and address challenges in patient safety. 
%M 39361417
%R 10.2196/58380
%U https://www.jmir.org/2024/1/e58380
%U https://doi.org/10.2196/58380
%U http://www.ncbi.nlm.nih.gov/pubmed/39361417