医联网下医源性风险偏差感知扩散模型及干预
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Diffusion model of biased perceived iatrogenic risk and intervention in internet of healthcare systems
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    摘要:

    首先,本研究从多重网络理论视角出发,构建医联网环境下医源性风险偏差感知多渠道扩散模型.该模型考虑了医联网环境下不同信息渠道之间医源性风险偏差感知扩散的交互影响;其次,得出区分医联网下医源性风险偏差感知在公众中扩散开来与否的阈值;最后,利用实际数据对所建立的理论模型进行参数估计及案例分析.研究结论表明:1)与仅进行正确认知的宣传推广相比,在正确认知宣传的同时进行认知纠偏,医联网下医源性风险偏差感知的扩散效率将降低的更加明显;2)与仅对一种或两种渠道中医源性风险偏差感知的扩散进行深度管控相比,如果同时对所有渠道中医源性风险偏差感知的扩散均进行适度干预,医源性风险偏差感知的扩散效率将更显著地降低.

    Abstract:

    Firstly, from the perspective of multiplex network theory, a multi-channel diffusion model of the biased perceived iatrogenic risk (BPIR) in the context of the Internet of Healthcare Systems (IHS) is constructed in this paper. The model considers the interactive effects of perceived diffusion of BPIR among various information channels under IHS. Secondly, the threshold to distinguish whether BPIR spreads among the public under the IHS is obtained. Finally, the parameters of the established theoretical model are estimated and the case analysis is conducted using actual data. The results indicate that: 1) Compared with mere the publicity of correct cognition, the diffusion efficiency of BPIR under IHS would be significantly reduced when the correct cognition dissemination is combined with cognitive correction; 2) Compared with the in-depth control of the diffusion of BPIR in only one or two channels, moderate intervention across all channels results in a more significant reduction in the diffusion efficiency of BPIR.

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朱宏淼,齐佳音,靳祯.医联网下医源性风险偏差感知扩散模型及干预[J].管理科学学报,2024,(8):57~72

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  • 在线发布日期: 2024-10-16
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