A clinical comparative study of an intelligent IoT-based HFNC device to provide respiratory support for patients with acute respiratory failure：a multi-center prospective controlled study (Preprint)
Authors: Zhang Yixiang , Wang Yinfeng , Chen Qingquan , Zhuang Jiajing , Zheng Huaxian , Lu Haibin , Zhang Chunfa , Ding Jinzhen , Shi Liyong , Lin Xiaoping , Wu Weijing , Chen Xiaoyang 
Published: May 2023

UNSTRUCTURED

Background: The prevalence of respiratory diseases has increased significantly, resulting in a rise in the use of high-flow nasal cannula (HFNC) therapy. However, certain limitations still exist in the clinical practice of HFNC therapy, such as prolonged equipment training and limited healthcare resources, which can lead to unforeseen emergencies. Fortunately, with the advent of Internet of Things (IoT) technology, great potential has emerged for developing novel solutions for medical equipment management to overcome these challenges. By integrating technology, information, and services, the IoT-based HFNC equipment and remote management platform can provide real-time patient monitoring, timely adjustments, and early warnings for respiratory failure, leading to improved clinical outcomes and economic benefits. Therefore, our study explored the use of these innovative technologies in enhancing clinical effectiveness and resource use.
Methods: In this study, we developed a remote management platform for respiratory equipment using the latest Internet of Things (IoT) and big data analysis technologies. Data on patients treated with high-flow nasal cannulas (HFNCs) were collected from 12 medical institutions in Fujian Province from December 2020 to December 2022. Patients were randomly allocated to either the ordinary HFNC group or the intelligent HFNC group. Basic patient information, medical history, laboratory indicators, total hospitalization costs and duration, comfort level, dryness score, and cough disorder scores were all recorded. The two groups were compared using the t tests and chi-square tests, and a P value of less than .05 was considered statistically significant. Advanced statistical methods were employed to ensure that the data were accurately analyzed and that the results were valid and reliable. Overall, we applied the latest scientific approaches and technologies to ensure the highest-quality data and analysis.
Results: A total of 619 patients were enrolled in this study, with no statistically significant differences between the ordinary HFNC group and the intelligent HFNC group in terms of general information. However, the use of intelligent HFNCs was associated with a significantly reduced hospitalization cost and duration compared to the ordinary HFNC group. Furthermore, patients using intelligent HFNCs reported consistently lower levels of dryness and rated higher in terms of comfort compared to those using ordinary HFNCs. There were no statistically significant differences in blood oxygen level, complications, clinical outcome, impaired coughing, dyspnea, or assisted respiratory muscle mobilization between the two groups. Overall, our findings suggested that the use of intelligent HFNCs may be a promising and effective solution for the management of respiratory diseases, with potential benefits for both patients and healthcare systems.
Conclusions: We successfully developed a remote management platform for respiratory equipment using Internet of Things (IoT) technology and big data analysis, with the intelligent high-flow nasal cannula (HFNC) as the core. Our findings indicated that patients who received treatment with the intelligent HFNC experienced improved comfort, shorter hospital stays, and reduced hospitalization costs compared to those who received traditional HFNC treatment. This platform not only provided precise oxygen therapy to patients with respiratory failure, but also supported physicians in analyzing conditions, enacting parameter settings, and issuing early sickness warnings. Consequently, the integration of IoT technology with intelligent HFNCs holds great potential in terms of cost and resource management and may present advantages compared to traditional HFNC methods.