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

Abstract

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

CLC Number:

R197

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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References 30

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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

Influence of telemedicine on the operation of public hospitals in China

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