基于患者就医行为选择的分级医疗物联网投资决策研究

doi:10.16381/j.cnki.issn1003-207x.2024.1199

Abstract

Abstract:

Since its implementation in 2009， China's hierarchical medical system has faced the dilemma of structural resource imbalance： Grade 3A hospitals （China’s highest-ranked medical facilities）， accounting for only 7.6% of the total number of hospitals， bear nearly 50% of the patient visits， operating under prolonged high load； while the utilization rate of primary medical institutions （such as community hospitals） remains below 60%. Due to information asymmetry and a lack of trust in community hospitals， patients with minor illnesses flock to Grade 3A hospitals， exacerbating the issues of “difficult and expensive access to healthcare”. Internet of Things （IoT） technology offers a new pathway to optimize medical resource allocation and enhance the efficiency of hierarchical diagnosis and treatment. However， its investment decisions require systematic analysis of the interactive effects of patient behavior choices， hospital competitive strategies， and policy constraints.The core problem addressed in this paper is， within a two-level referral system consisting of Grade 3A hospitals and community hospitals， how can IoT investment by community hospitals alter patient choice of healthcare providers， alleviate congestion in Grade 3A hospitals， and simultaneously optimize the pricing strategy of Grade 3A hospitals to achieve the goals of hierarchical medical care.Achieves Hierarchical Care　IoT supports information sharing and a two-way referral system， optimizing resource matching.The IoT investment level of community hospitals is a key lever for the success of hierarchical medical care. When investment is high， it creates a virtuous cycle of "patient diversion → Grade 3A hospital price reduction → decrease in both cost and waiting". Conversely， the hierarchical system fails. The government needs to constrain Grade 3A hospital pricing via guided prices and incentivize IoT construction in community hospitals. Future research could explore cross-hospital cost-sharing mechanisms and empirical validation.

Key words: medical behavior choice, tiered health care system, IoT, queuing theory, dynamic game

CLC Number:

C939

Shujun Xu. Tiered Health Care System IoT Investment Decision Based on Patient's Medical Behavior Choice[J]. Chinese Journal of Management Science, 2026, 34(3): 192-201.

Figures/Tables 4

模型参数	参数含义	参数假设及备注
$μ A, μ C$	三甲医院、社区医院服务能力	$λ A < μ A$
$λ A, λ C$	住院患者、门诊患者到达率	$λ A + λ C < μ A$
$Λ$	分级医疗系统总到达率	$Λ = λ A + λ C$
$C A$	三甲医院剩余服务能力	$C A = μ A - Λ$
$β$	物联网技术投资水平	$β ∈ 0,1$
$λ C E$	社区医院患者到达率	$λ C E = 1 - α λ C, 0 ≤ λ C E ≤ m i n μ C, λ C$
$U, U'$	患者到三甲医院、社区医院就医获得的总效用	$U > U', U' = U - θ U 1 + β$
$h$	患者单位排队时间成本	$h > 0$
$α$	门诊患者到三甲医院就诊率	$0 ≤ α ≤ 1$
$p ¯$	政府指导性定价	$p ¯ = U - h μ A - Λ$
c	三甲医院单位时间服务成本	$c > 0$
p	三甲医院统一诊疗费	$0 ≤ p ≤ p ¯$
θ	门诊患者的效用系数	$θ ∈ (0,1)$

社区医院服务能力	三甲医院期望利润函数极值点	三甲医院最优定价策略
$μ C < λ C$	——	$p * = p ¯$
$λ C < μ C ≤ λ C + M$ $p ¯ ≤ θ U 2 - z$	$p 0 > θ U 2 - z$	$p * = p ¯$
$λ C < μ C ≤ λ C + M$ $p ¯ ≤ θ U 2 - z$	$p 0 ∈ 0, θ U 2 - z$	$p * = m i n {p 0, p ¯}$
$μ C > λ C + M$ $p ¯ > θ U 2 - z$	$p 0 > θ U 2 - z$	$p * = p ¯$
$μ C > λ C + M$ $p ¯ > θ U 2 - z$	$p 0 ∈ 0, θ U 2 - z$	$p * = a r g m a x π (p)$

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Tiered Health Care System IoT Investment Decision Based on Patient's Medical Behavior Choice

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