Intelligent Public Health Platform: A Multi-Stage Operational Process for Emergency Prevention and Health Management

Lanzhen Chen; Rudan Lin; Xiaopeng Li; Xiaopan Ding

doi:10.62051/ijepes.v3n3.04

Authors

Lanzhen Chen
Rudan Lin
Xiaopeng Li
Xiaopan Ding

DOI:

https://doi.org/10.62051/ijepes.v3n3.04

Keywords:

Intelligent Public Health, IoT(Internet of Things), Regional Collaborative Treatment, Personalized Health Management

Abstract

The deep integration of IoT technology and 5G communication is reshaping the public health emergency response system. This paper centers on the construction of an intelligent public health prevention and control platform, focusing on its operational process and expected results. The platform covers three stages: pre-hospital emergency response, in-hospital treatment and post-hospital rehabilitation. It constructs a multi-level collaborative intelligent medical system, integrating core modules such as 5G emergency vehicle dynamic dispatching, real-time transmission of multimodal vital signs, and intelligent allocation of regional resources. The platform realizes precise positioning of pre-hospital patients and ambulance path optimization, dynamic allocation of ICU and emergency beds within the hospital, and post-hospital remote health monitoring and personalized rehabilitation management. The platform not only supports highly effective response to public health emergencies, but also promotes the intelligence and refinement of medical processes by optimizing the allocation of medical resources and improving the level of health management.

References

[1] Y. Wang, “Research on integration of remote sensing (RS), geographic information (GIS), and global navigation satellite system (GNSS),” Digital Communication World, vol. 20, no. 5, pp. 1–3, 2022. In Chinese.

[2] R. Katsuma and S. Yoshida, “Dynamic routing for emergency vehicle by collecting real-time road conditions,” International Journal of Communications, Network and System Sciences, vol. 11, no. 2, 2018.

[3] R. Mahajan and P. Kaur, “5G-enabled communication for real-time patient monitoring: Opportunities and challenges,” in 2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI), vol. 2, no. 1, pp. 1–6, Nov. 2024.

[4] M. Miller, D. Bootland, L. Jorm, and B. Gallego, “Improving ambulance dispatch triage to trauma: a scoping review using the framework of development and evaluation of clinical prediction rules,” Injury, vol. 53, no. 6, pp. 1746–1755, 2022.

[5] S. Shyam, S. E. Juliet, and K. Ezra, “A UWB system model for patient tracking and monitoring for smart healthcare,” 2022 6th International Conference on Devices, Circuits and Systems (ICDCS), vol. 6, pp. 32–37, 2022.

[6] S. Walczak, W. E. Pofahl, and R. J. Scorpio, “A decision support tool for allocating hospital bed resources and determining required acuity of care,” Decision Support Systems, vol. 34, no. 4, pp. 445–456, 2003.

[7] A. Parihar, J. B. Prajapati, B. G. Prajapati, B. Trambadiya, A. Thakkar, and P. Engineer, “Role of IoT in healthcare: Applications, security & privacy concerns,” Intelligent Pharmacy, vol. 2, no. 5, pp. 707–714, 2024.

[8] X. Chen, H. Xie, and X. Tao, “Artificial intelligence and multimodal data fusion for smart healthcare: topic modeling and bibliometrics,” Artif. Intell. Rev., vol. 57, p. 91, 2024.

[9] G. Matsumura, S. Honda, T. Kikuchi, Y. Mizuno, H. Hara, Y. Kondo, H. Nakamura, S. Watanabe, K. Hayakawa, K. Nakajima, and K. Takei, “Real-time personal healthcare data analysis using edge computing for multimodal wearable sensors,” Device, vol. 3, no. 2, p. 100597, 2025.

[10] U. B. Khalid, M. Naeem, F. Stasolla, M. H. Syed, M. Abbas, and A. Coronato, “Impact of AI-powered solutions in rehabilitation process: Recent improvements and future trends,” International Journal of General Medicine, vol. 943, pp. 943–969, 2024.

[11] M. Tilala, S. Pamulaparthyvenkata, A. D. Chawda, and A. P. Benke, “Explore the technologies and architectures enabling real-time data processing within healthcare data lakes, and how they facilitate immediate clinical decision-making and patient care interventions,” European Chemical Bulletin, vol. 11, pp. 4537–4542, 2024.

[12] A. Tahernejad, A. Sahebi, A. S. S. Abadi, et al., “Application of artificial intelligence in triage in emergencies and disasters: a systematic review,” BMC Public Health, vol. 24, p. 3203, 2024.

[13] B. M. Porto, “Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review,” BMC Emergency Medicine, vol. 24, p. 219, 2024.