远程医疗对我国公立医院运营的影响

doi:10.19723/j.issn.1671-167X.2024.03.014

摘要/Abstract

摘要：

目的: 分析远程医疗功能对公立医院运营的影响，特别关注其对不同专业技术等级医院的异质性作用，以深入理解远程医疗在弥补医疗资源配置不均衡方面的现实意义。方法: 利用《2022卫生健康信息化统计调查数据报告》调查数据，对8 944家公立医院进行横断面研究，使用多元线性模型、倾向性评分匹配(propensity score matching, PSM)、分组回归等方法，评估远程医疗对医院收入、诊疗人次、住院人次的影响。结果: 有35.51%的公立医院开通了远程医疗，医院等级、医院类别、所在区域、隶属关系、床位数都对医院运营状况有显著影响。开通远程医疗对公立医院收入、诊疗人次和出院人次均有显著提升作用。PSM结果显示，其对医院收入、诊疗人次、出院人次的净效应分别为0.191 (P < 0.01)、0.216 (P < 0.01)、0.353 (P < 0.01)。异质性检验显示，远程医疗对二级医院的收入和诊疗人次具有显著正向影响，系数分别为0.088 (P < 0.05)和0.127 (P < 0.01)；对一级及以下医院的出院人次影响更大，系数为1.203 (P < 0.01)；但对三级医院整体收入和业务能力并无显著影响。结论: 远程医疗的开通对公立医院的收入和业务能力增加均有显著促进作用，但在不同等级的医院之间存在差异性，对基层医疗机构的影响更为显著，对改善基层公立医院的运营状况起到积极作用。

关键词: 远程医疗, 公立医院, 医院收入, 会诊

Abstract:

Objective: Telemedicine, as an information-based tool, is widely recognized as an effective solution for compensating for the imbalanced allocation of medical resources in China. This study specifi-cally aimed to analyze the impact of telemedicine functions on the operational efficiency of public hospitals, with a particular focus on their heterogeneous effects on hospitals of different levels. Methods: A cross-sectional research design was used based on the 2022 Health Informatization Statistical Survey data, and 8 944 public hospitals were used as research objects to analyze the impact of telemedicine on hospital revenues and business capacity. Multivariate linear model, propensity score matching (PSM), and grouped regression methods were employed to evaluate the impact of telemedicine on hospital revenues, number of consultations, and the number of discharges. Results: The descriptive results showed that telemedicine was available in 35.51% of public hospitals. The analysis also demonstrated that various factors, such as hospital level, academic category, area of the hospital, administrational level and number of beds all had a significant influence on the operation of the hospital. Moreover, the regression results showed that opening telemedicine could increase hospital revenues by 0.140 (P < 0.01), hospital consultations by 0.136 (P < 0.01), and the number of discharges by 0.316 (P < 0.01). After correcting for endogeneity using the propensity score matching, the results showed that the effect of opening telemedicine on hospital revenues, consultations, and the number of discharges was 0.191 (P < 0.01), 0.216 (P < 0.01), and 0.353 (P < 0.01), respectively. Further heterogeneity analysis was conducted to explore the differential effects of telemedicine on hospitals of different levels. Grouped regression showed that telemedicine had a positive impact on the income of secondary hospitals, with a coefficient of 0.088 (P < 0.05), and it had a more significant positive impact on hospital consultations in secondary hospitals, with a coefficient of 0.127 (P < 0.01). An even greater impact on the number of discharges in primary hospitals, with a coefficient of 1.203 (P < 0.01). Telemedicine, on the other hand, did not have a significant positive impact on the overall revenue and operational capacity of tertiary hospitals. Conclusion: Telemedicine had a significant promoting effect on hospital revenues, hospital consultations and the number of discharges, and this effect was differentiated between hospitals of different levels. Through the construction of telemedicine, primary hospitals were able to significantly improve their business capacity and revenue, which played a positive role in improving the operation of primary public hospitals.

Key words: Telemedicine, Public hospitals, Hospital revenue, Consultation

中图分类号:

R197

冉珂欣,李与涵,冯文. 远程医疗对我国公立医院运营的影响[J]. 北京大学学报（医学版）, 2024, 56(3): 471-478.

Kexin RAN,Yuhan LI,Wen FENG. Influence of telemedicine on the operation of public hospitals in China[J]. Journal of Peking University (Health Sciences), 2024, 56(3): 471-478.

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Variables	n (%) or $\bar x \pm s$	Dependent variable
Variables	n (%) or $\bar x \pm s$	Hospital revenue (F/t/β)	Hospital consultations (F/t/β)	Number of discharges (F/t/β)
Telemedicine
Yes	3 176 (35.51)	19.048^***	17.542^***	20.430^***
No	5 768 (64.49)
Hospital level		2 643.946^***	1 663.037^***	1 300.762^***
Primary hospitals	1 576 (17.62)
Secondary hospitals	4 825 (53.95)
Other tertiary hospitals	1 028 (11.49)
Tertiary first-class hospital	1 515 (16.94)
Academic category		27.140^***	172.278^***	103.792^***
Traditional Chinese medicine hospital	2 272 (25.40)
Specialized hospitals	1 389 (15.53)
General hospital	5 283 (59.07)
Area		29.750^***	36.153^***	14.700^***
Western regions	2 858 (31.95)
Central regions	2 616 (29.25)
Eastern regions	3 470 (38.80)
Administrational level		486.539^***	274.839^***	206.224^***
Other hospitals	1 446 (16.17)
County hospitals	2 729 (30.51)
County-level city hospitals	2 231 (24.94)
Prefecture-level city hospitals	1 939 (21.68)
Provincial hospitals	555 (6.21)
National hospitals	44 (0.49)
Beds	526.275±602.097	0.002^***	0.002^***	0.003^***
Provinces	8 944 (100.00)	24.167^***	27.619^***	11.654^***

Variable	Dependent variable
Variable	ln (hospital revenue)	ln (hospital consultations)	ln (number of discharges)
Telemedicine (Ref: no)
Yes	0.140^***(5.406)	0.136^***(4.828)	0.316^*** (7.134)
Hospital level (Ref: primary hospitals)
Secondary hospitals	1.968^*** (33.289)	1.628^*** (25.176)	3.544^*** (28.681)
Other tertiary hospitals	2.686^*** (36.447)	2.339^*** (30.361)	4.349^*** (32.468)
Tertiary first-class hospital	2.983^*** (35.345)	2.695^*** (31.376)	4.447^*** (29.185)
Academic category (Ref: traditional Chinese medicine hospital)
Specialized hospitals	-0.016 (-0.359)	-0.654^*** (-12.043)	-0.613^*** (-6.274)
General hospital	0.343^*** (11.388)	0.369^*** (11.293)	0.308^*** (5.826)
Area (Ref: Western region)
Central regions	0.765^*** (5.488)	1.079^*** (9.455)	1.106^*** (3.531)
Eastern regions	2.007^*** (13.899)	2.039^*** (13.584)	1.332^*** (4.093)
Administrational level (Ref: other hospitals)
County hospitals	0.995^*** (18.242)	0.851^*** (14.256)	1.722^*** (15.181)
County-level city hospitals	0.918^*** (14.641)	0.777^*** (11.309)	1.282^*** (10.681)
Prefecture-level city hospitals	0.795^*** (12.911)	0.510^*** (7.339)	0.902^*** (7.410)
Provincial hospitals	0.755^*** (7.495)	0.339^*** (3.466)	0.601^*** (3.863)
National hospitals	0.955^*** (4.803)	0.668^*** (3.280)	0.662^* (1.949)
Beds	0.001^*** (24.641)	0.001^*** (20.910)	0.002^*** (20.338)
Constant	7.146^*** (49.985)	7.816^*** (64.493)	1.774^*** (5.734)
Provinces fixed effect	Yes	Yes	Yes
n	8 941	8 943	8 917
R²	0.662	0.546	0.514

Variable	Matching	Mean		Bias/%	Reduct bias/%	t test
Variable	Matching	Treated	Control	Bias/%	Reduct bias/%	t	P>\|t\|
Hospital level	Unmatched	2.482	2.165	34.0	99.7	15.36	< 0.001
	Matched	2.482	2.483	-0.1		-0.04	0.968
Academic category	Unmatched	2.340	2.335	0.5	-164.7	0.23	0.816
	Matched	2.340	2.328	1.4		0.54	0.590
Area	Unmatched	2.012	2.099	-10.3	89.8	-4.70	< 0.001
	Matched	2.012	2.004	1.0		0.41	0.679
Affiliation	Unmatched	2.855	2.657	16.9	87.4	7.62	< 0.001
	Matched	2.855	2.830	2.1		0.85	0.395
Beds	Unmatched	636.410	465.740	28.1	83.1	12.95	< 0.001
	Matched	636.410	607.610	4.7		1.76	0.078
Province	Unmatched	38.946	36.788	14.4	78.7	6.51	< 0.001
	Matched	38.946	39.406	-3.1		-1.24	0.214

Method	Dependent variable
	ln (hospital revenue)		ln (hospital consultations)		ln (number of discharges)
	ATT	t	ATT	t	ATT	t
Nearest neighbor matching 1 ∶1	0.168^***	2.91	0.194^***	3.56	0.388^***	4.51
Nearest neighbor matching 1 ∶3	0.154^***	3.32	0.208^***	4.33	0.285^***	4.06
Radius matching	0.271^***	6.27	0.271^***	6.52	0.463^***	6.98
Kernel matching	0.169^***	2.83	0.191^***	3.17	0.274^***	3.16
Mean	0.191		0.216		0.353

Items	ln (hospital revenue)
Items	Primary hospitals	Secondary hospitals	Other tertiary hospitals	Tertiary first-class hospital
Telemedicine (Ref: no)
Yes	0.258^* (1.834)	0.088^*** (3.503)	-0.003 (-0.046)	0.028 (0.752)
Control variable	Yes	Yes	Yes	Yes
n	1 575	4 825	1 027	1 514
R²	0.244	0.517	0.381	0.657