互联网医院线上线下一体化门诊流程优化

doi:10.16381/j.cnki.issn1003-207x.2024.1571

摘要/Abstract

摘要：

本文针对人口老龄化背景下的慢病管理所面临的困境，结合我国患者在医生午休期间滞留的特殊情境，分析互联网医院线上线下一体化门诊流程。考虑到当前慢病患者对在线医疗接受度的提升及其对医疗服务的特殊需求，提出增加互联网医院线上门诊比重的优化流程，并通过建立Simulink仿真模型，对优化前后的流程进行对比。研究发现，优化后的流程能够以更低的成本达到更广泛的服务，显著提高医疗资源的利用效率。而进一步对优化后流程的敏感性分析表明，医院应根据患者需求量的变化灵活调整线上与线下服务的比例，当医疗资源利用尚未饱和时，应鼓励患者选择线上回诊，以提高效率并减轻实体门诊的运营压力；而在患者流量高峰期，应避免过度依赖线上服务，防止线上问诊系统过度拥堵。同时，尽管减少问诊时间有助于提高效率，但过度压缩问诊时间并不能持续提升医院收益，医院应在效率与质量之间找到平衡，优化诊疗流程而非单纯压缩每位患者的问诊时长。

关键词: 离散事件仿真, 互联网医院, 线上线下一体化门诊

Abstract:

As the aging population increases， the treatment demand from chronic disease patients is rising， posing unprecedented challenges to traditional healthcare systems. Chronic disease patients typically require long-term and regular consultations， significantly increasing the operational burden of healthcare systems. In this context， internet hospitals， as an emerging healthcare service model， provide a new solution to alleviate this issue. Particularly in the special situation in China， where patients often remain during doctors' lunch breaks， the integration of online and offline outpatient processes in internet hospitals shows great potential. By increasing the proportion of online consultations in the current process and optimizing the allocation of medical resources， overall efficiency can be improved， and costs can be reduced. An optimization plan based on a Simulink simulation model is proposed， aiming to explore the optimization of integrated online and offline outpatient processes in internet hospitals. Through the construction of a Simulink simulation model， detailed comparisons of the processes before and after optimization are conducted， analyzing the effects of the optimization plan on cost reduction and resource utilization. The research data is sourced from daily outpatient registration numbers from chronic disease departments in top-tier hospitals， statistical data from the National Health Commission， survey data on the willingness to use online medical services， and relevant literature， which provide reliable bases for model parameter allocation. Factors such as patient demand， the proportion of online and offline services， and doctors' workload are incorporated into the Simulink model to ensure that the optimization plan fits the actual situation. The results show that the optimized process can not only improve the efficiency of medical resource utilization but also effectively reduce the operational pressure on hospitals. Particularly when facing a large number of chronic disease patients， the proportion of online services plays a crucial role in enhancing efficiency. Additionally， sensitivity analysis of the optimization plan was conducted， and the results indicate that the proportion of online and offline services needs to be dynamically adjusted with changes in patient demand. When medical resources are not saturated， more patients should be encouraged to choose online follow-up consultations to improve overall efficiency. However， during peak patient flow periods， excessive reliance on online services should be avoided to prevent overcrowding of the online consultation system. It helps deepen the understanding of the relationship between online and offline services in internet hospitals is this study， further enriching the theoretical framework of integrated online and offline services. The research results not only provide practical optimization strategies for internet hospital managers， helping them flexibly adjust the proportion of online and offline services based on varying patient needs but also offer strong support for the allocation of hospital medical resources， aiming to improve overall service efficiency and the patient experience.

Key words: discrete event simulation, internet hospitals, integrated online-offline healthcare

中图分类号:

C931.2

翟运开,李正英,乔岩, 等. 互联网医院线上线下一体化门诊流程优化[J]. 中国管理科学, 2026, 34(5): 231-241.

Yunkai Zhai,Zhengying Li,Yan Qiao, et al. Optimization of Integrated Online and Offline Outpatient Services in Internet Hospitals[J]. Chinese Journal of Management Science, 2026, 34(5): 231-241.

图/表 15

表1

变量定义"

变量	定义	变量	定义
$w i$	第 $i$ 位患者的等待时间	$u i$	第 $i$ 位患者的空闲时间
$t i$	第 $i$ 位患者的到达时间	$x i$	第 $i$ 位患者的总服务时间
$x i c$	第 $i$ 位患者的问诊服务时间	$x i t$	第 $i$ 位患者的检查服务时间
$o$	当天医生的加班时间	$T$	医院总工作时间
$C w$	单位时间患者的等待成本	$C u$	单位时间患者的空闲成本
$C o$	单位时间患者的加班成本	$R 0$	单位患者的门诊收益
$N$	当天服务完患者总数	$N c$	运营期间完成服务离开的患者数
$k$	线上门诊的接受度	$β$	复诊率

表1

图1

图2

图3

图4

图5

表2

成本参数设置 (单位/元)"

参数	值	参数	值
$C w f$	2	$C w o$	1.6
$C u$	3	$C o$	4
$R 0$	252.2	—	—

表2

图6

图7

图8

表3

调整效益对比"

流程	惩罚成本 $C p$ /元	平均未完成服务人数/人	惩罚成本 $P$ /元	调整效益 $π *$ /元
BM	264.8	3	794.4	231.54
EOH	264.8	0	0	4651.64

表3

图9

图10

图11

图12

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