Evidence map›Paper›PMID 39446419›Full record

ArticleJournal of medical Internet research2024

Construction and Application of a Private 5G Standalone Medical Network in a Smart Health Environment: Exploratory Practice From China.

Baozhan Chen, Xiaobing Shi, Tianyi Feng, Shuai Jiang, Yunkai Zhai, Mingxing Ren, Dongqing Liu, Chengzeng Wang, Jinghong Gao

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Baozhan ChenThe First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.ORCID 0000-0002-3199-5472
Xiaobing ShiThe First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.ORCID 0009-0008-8646-475X
Tianyi FengChina Academy of Information and Communications Technology, Beijing, China.ORCID 0009-0002-8045-0863
Shuai JiangThe First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.ORCID 0000-0003-0788-880X
Yunkai ZhaiSchool of Management Engineering, Zhengzhou University, Zhengzhou, China.ORCID 0000-0002-1608-4953
Mingxing RenThe First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.ORCID 0009-0009-3849-2083
Dongqing LiuThe First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.ORCID 0000-0002-8669-2946
Chengzeng Wang *The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.ORCID 0000-0002-2044-8115
Jinghong Gao *The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.ORCID 0000-0003-0011-4983

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo date, the differentiated requirements for network performance in various health care service scenarios-within, outside, and between hospitals-remain a key challenge that restricts the development and implementation of digital medical services.

objectiveThis study aims to construct and implement a private 5G (the 5th generation mobile communication technology) standalone (SA) medical network in a smart health environment to meet the diverse needs of various medical services.

methodsBased on an analysis of network differentiation requirements in medical applications, the system architecture and functional positioning of the proposed private 5G SA medical network are designed and implemented. The system architecture includes the development of exclusive and preferential channels for medical use, as well as an ordinary user channel. A 3-layer network function architecture is designed, encompassing resource, control, and intelligent operation layers to facilitate management arrangements and provide network open services. Core technologies, including edge cloud collaboration; service awareness; and slicing of access, bearer, and core networks, are employed in the construction and application of the 5G SA network.

resultsThe construction of the private 5G SA medical network primarily involves system architecture, standards, and security measures. The system, featuring exclusive, preferential, and common channels, supports a variety of medical applications. Relevant standards are adhered to in order to ensure the interaction and sharing of medical service information. Security is achieved through mechanisms such as authentication, abnormal behavior analysis, and dynamic access control. Three typical medical applications that rely on the 5G network in intrahospital, interhospital, and out-of-hospital scenarios-namely, mobile ward rounds, remote first aid, and remote ultrasound-were conducted. Testing of the 5G-enabled mobile ward rounds showed an average download rate of 790 Mbps and an average upload rate of 91 Mbps. Compared with 4G, the 5G network more effectively meets the diverse requirements of various business applications in prehospital emergency scenarios. For remote ultrasound, the average downlink rate of the 5G network is 4.82 Mbps, and the average uplink rate is 2 Mbps, with an average fluctuation of approximately 8 ms. The bandwidth, performance, and delay of the 5G SA network were also examined and confirmed to be effective.

conclusionsThe proposed 5G SA medical network demonstrates strong performance in typical medical applications. Its construction and application could lead to the development of new medical service models and provide valuable references for the further advancement and implementation of 5G networks in other industries, both in China and globally.

Indexed as

TelemedicineChinaComputer Communication NetworksHumansWireless Technology5Gapplicationconstructionmedical private networkperformance test

Identifiers

PMID39446419
PMCPMC11544345

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.