Analysis of Clinic Operation Efficiency and its Changing Trend in Beijing

Xinyue Sun¹, Xingmiao Feng¹, Yu Wang¹, Bo Lü¹, Ziyan Zhai¹, Shuyu Liang¹, Kai Meng¹,²*

¹School of Public Health, Capital Medical University, Beijing 100069, China
²Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China

Corresponding author: Kai Meng, Professor; E-mail: mengkai@ccmu.edu.cn

Abstract

Background The number of clinics in Beijing is growing rapidly, but clinics suffer from serious problems such as a lack of physicians, lower quality of service, and weak supervision. Objective To explore the clinic operation efficiency and its change trend in Beijing, and to provide reference for rational allocation of clinic medical resources. Methods Data Envelopment Analysis (DEA) and Malmquist index model were used to estimate the static and dynamic efficiency of clinics in Beijing from 2013 to 2020. Input indicators included building area, number of working staff, and total cost, while output indicators included number of patient visits and total income. Results From 2013 to 2020, the comprehensive technical efficiency and pure technical efficiency showed a fluctuating downward trend, while scale efficiency showed a fluctuating upward trend. Medical aesthetic clinics had the lowest comprehensive technical efficiency, and urban clinics had higher comprehensive technical efficiency than suburban clinics. Clinic efficiency improved from 2013 to 2015, but decreased from 2015 to 2020. Conclusion The overall efficiency of clinics in Beijing is not high, with low technical efficiency and certain shortages in clinic input resources. There are differences in clinic efficiency across different categories and regions. It is suggested to strengthen personnel training, encourage doctors from large hospitals to establish clinics at the grassroots level, and improve clinic diagnosis and treatment levels. Additionally, internal and external supervision capacity should be strengthened, and more detailed supporting measures for establishing clinics should be formulated.

Keywords Clinic; Operation efficiency; DEA; Malmquist index model; Beijing

Introduction

Clinics are an important component of the primary healthcare service system, primarily providing outpatient diagnosis and treatment services for common and frequently occurring diseases, as well as some family doctor contract services \cite{1}. In 2015, Beijing proposed encouraging qualified medical personnel to open clinics or practice individually in communities \cite{2}, aiming to promote the rational flow of health technicians, divert outpatient volume from large hospitals, and ultimately alleviate the problem of "difficulty in seeing a doctor" for residents. In 2019, the state promulgated the "Guiding Opinions on Carrying Out Pilots to Promote the Development of Clinics" \cite{3} and the "Notice on Issuing Opinions on Promoting the Continuous, Healthy and Standardized Development of Social Medical Institutions" \cite{4}, providing new development opportunities for clinics as an indispensable part of social medical institutions. From 2009 to 2019, the number of clinics in Beijing increased from 1,757 to 2,934, and clinic service volume grew from 3.553 million to 4.132 million visits. Although the number of clinics in Beijing is growing rapidly, clinics face serious problems such as a shortage of physicians, lower service quality, and weak supervision \cite{5}. Currently, domestic research mostly uses qualitative case analysis and quantitative descriptive analysis to study the current situation of clinic quantity and scale allocation in different regions and categories \cite{6}, but no studies have evaluated clinic efficiency.

Methods

Study Design and Data Sources

This study examined clinics in Beijing from 2013 to 2020. Data were obtained from Beijing Health Statistical Reports from 2013 to 2021, including basic clinic information, personnel information, financial status, and medical service provision. Inclusion criteria: clinics operating in Beijing from 2013 to 2020 that reported complete data for one year or more. Exclusion criteria: clinics with missing values and illogical data, such as sudden surges and subsequent drops in clinic inputs. When analyzing annual static efficiency, clinics constituted an unbalanced panel dataset with a total of 3,945 decision-making units (DMUs). When analyzing the trend of clinic resource allocation efficiency from 2013 to 2020, Beijing's 16 administrative districts were used as DMUs.

Research Methods

Data Envelopment Analysis (DEA) is a non-parametric method that uses linear programming to evaluate the relative efficiency of decision-making units with multiple inputs and outputs. The comprehensive technical efficiency measured can be decomposed into pure technical efficiency and scale efficiency. Pure technical efficiency reflects the management capability and technical level of a decision-making unit, while scale efficiency reflects its resource allocation level \cite{7}. Based on literature review and considering the availability of indicators, this study selected three input indicators: building area (square meters), number of staff on duty (persons), and total cost (thousand yuan) \cite{8-10}. Since most clinics are for-profit institutions, output indicators were selected as: number of patient visits (person-times) and total income (thousand yuan) \cite{11-12}. When using DEA, the number of DMUs should be at least three times the sum of input and output variables \cite{13}, a condition satisfied by this study. To further understand efficiency trends, the Malmquist index model was used to calculate the dynamic changes in total factor productivity (TFP) between years. TFP represents the additional production efficiency achieved under given input levels, which can be decomposed into technical progress and technical efficiency change. Technical progress refers to producing more output with the same input combination through technological improvement, while technical efficiency change refers to releasing the potential of existing technology to a greater extent by improving coordination among various resource elements \cite{14}. Technical efficiency change can be further decomposed into pure technical efficiency change and scale efficiency change. Based on policy encouragement, Beijing's clinics are experiencing rapid growth in number and internal scale expansion. Therefore, an input-oriented variable returns to scale model was selected for efficiency calculation.

Statistical Methods

This study collected clinic-related data from Beijing Health Statistical Reports from 2013 to 2021. Data were organized using Excel, and basic clinic characteristics were described using frequency, mean, and percentage. MaxDEA 7 statistical software was used to evaluate clinic operation efficiency.

Results

Clinic Distribution and Scale

In 2020, there were 1,298 clinics in Beijing's suburban districts. Chaoyang, Changping, and Shunyi districts ranked top three with 477, 401, and 275 clinics respectively, while Fengtai, Mentougou, and Tongzhou districts ranked lowest with fewer than 30 clinics each (see Table 1 [TABLE:1]).

From 2013 to 2020, general clinics were the most numerous category in Beijing's medical market, followed by traditional Chinese medicine (TCM) (general) clinics and dental clinics. In 2015, the number of dental clinics surpassed TCM (general) clinics to rank second. In terms of trends, the numbers of general clinics, TCM (general) clinics, dental clinics, and medical aesthetic clinics increased annually, with medical aesthetic clinics showing the highest average annual growth rate of 23.70%. The number of other clinics decreased annually with an average growth rate of -2.85% (see Table 2 [TABLE:2]).

Input-Output Indicators

From 2013 to 2019, Beijing's clinics showed increasing trends in building area, number of staff on duty, and total cost. However, in 2020, although input indicators continued to grow, clinic outputs decreased significantly compared to 2019 due to the COVID-19 pandemic. By category, general clinics had the highest input-output indicators, followed by dental clinics. By region, urban clinics had higher inputs and outputs than suburban clinics (see Table 3 [TABLE:3]).

Static Efficiency Analysis

From 2013 to 2020, Beijing's clinics showed a fluctuating downward trend in comprehensive technical efficiency and pure technical efficiency, while scale efficiency showed a fluctuating upward trend. Efficiency values peaked in 2014, with comprehensive technical efficiency at 0.314, pure technical efficiency at 0.533, and scale efficiency at 0.654. Due to the pandemic, 2020 had the lowest comprehensive technical efficiency at only 0.142. From 2013 to 2020, over 90% of clinics exhibited increasing returns to scale each year, indicating that output growth rates exceeded input growth rates for most clinics \cite{15}.

By category, medical aesthetic clinics had the lowest mean comprehensive technical efficiency at 0.156, while other clinics had the highest at 0.197. For pure technical efficiency, TCM (general) clinics had the highest mean, while medical aesthetic clinics had the lowest. For scale efficiency, medical aesthetic clinics had the highest, while TCM (general) clinics had the lowest. By region, urban clinics had higher comprehensive technical efficiency and scale efficiency than suburban clinics, but lower pure technical efficiency (see Table 4 [TABLE:4]).

Dynamic Efficiency Analysis

Analysis of dynamic efficiency from 2013 to 2020 showed that only 2013-2014 and 2014-2015 had total factor productivity (TFP) indices greater than 1, indicating efficiency improvement. The 2013-2014 period had a technical efficiency change index greater than 1, while 2014-2015 had a technical progress index greater than 1. Subsequent years showed varying degrees of efficiency decline, with the most severe regression in 2019-2020, likely due to pandemic-related operational difficulties.

Comparing efficiency changes across categories, only other clinics had a TFP index greater than 1, indicating progress from 2013-2020. All other categories had TFP indices less than 1, indicating efficiency decline. Decomposition of clinics with TFP indices less than 1 revealed that all categories experienced regression in both technical efficiency and technical progress, except for TCM (general) and dental clinics which showed no change in technical efficiency. Urban clinics had a TFP index greater than 1, indicating progress, while suburban clinics experienced efficiency decline (see Table 5 [TABLE:5]).

Discussion

Growth in Clinic Numbers and Scale but Low Overall Efficiency

Driven by national and Beijing municipal policies \cite{16}, the number of social medical institutions in Beijing has grown rapidly. As a component of social medical institutions, clinics in Beijing showed annual increases in both number and input scale from 2013 to 2020. As a first-tier city, Beijing residents have higher consumption levels and greater acceptance of the comfortable and private services offered by clinics, which may have also contributed to clinic development \cite{17}. However, overall clinic efficiency remains low. From 2013 to 2020, the mean comprehensive technical efficiency of clinics was less than 0.2. Decomposing comprehensive technical efficiency into pure technical efficiency and scale efficiency, both operated at less than 50% of optimal levels, with pure technical efficiency lower than scale efficiency. This indicates more serious deficiencies in technical level and internal management, possibly because clinic talent and technology lag far behind hospitals \cite{18} and clinic management methods are outdated \cite{19}, failing to meet the rapid development needs of clinics. This study recommends strengthening personnel training and improving clinic diagnosis and treatment levels. Doctors from large hospitals should be encouraged to establish clinics at the grassroots level \cite{2}, with supporting policies to ensure high-quality resources flow to clinics. Additionally, more general practitioners should be trained and deployed to clinics, incorporating clinic personnel into national education and training plans \cite{17} and creating clear career development pathways to improve overall clinic service quality.

Efficiency Variations Across Clinic Types and Regions

Results show efficiency differences across clinic categories and regions. Other clinics had the highest comprehensive technical efficiency, while medical aesthetic clinics had the lowest at only 0.156. From 2013 to 2020, medical aesthetic clinics experienced the largest growth, likely because medical aesthetics have become popular services with quick profitability \cite{20}. Driven by substantial profits and lacking sufficient regulatory basis or means for economic oversight \cite{21}, the medical aesthetic clinic market has seen widespread unlicensed practice, cross-specialty practice, and false advertising, with inadequate internal management measures. Similar phenomena exist in popular clinic markets such as TCM and dentistry \cite{22}. Therefore, we recommend strengthening internal and external regulatory capacity for these clinic markets. Internally, clinics should actively explore "modern management systems," hire professional managers for operations, and provide regular standardized training for medical staff. Externally, the clinic filing system should be improved by establishing a unified clinic personnel information system, perfecting monitoring and early warning mechanisms for physician mobility, and creating a clinic "blacklist" system to achieve standardized management.

By region, urban clinics in Beijing had higher comprehensive technical efficiency than suburban clinics, and urban clinic efficiency improved over time, likely because urban residents have higher consumption levels and medical resources are concentrated in urban areas, making residents more willing to seek care at clinics \cite{23}. Based on this, suburban clinics could explore effective group development to achieve scale and avoid individual operation, thereby improving resource utilization efficiency \cite{16}.

Insufficient Operational Inputs for Beijing Clinics

Results show that although scale efficiency showed an upward trend from 2013 to 2020, it remained at a low level overall. However, over 90% of clinics exhibited increasing returns to scale each year, indicating that current inputs in Beijing's clinics are being utilized relatively fully. The cause of low scale efficiency is the small scale of clinics and insufficient inputs in human, financial, and material resources. The reasons for the generally small clinic scale may include both external objective environments and subjective choices of clinic operators. Regarding the external environment, Beijing has many large hospitals that concentrate high-quality medical resources, making clinics relatively marginal in the entire health system \cite{24}. Studies have found that TCM clinics suffer from shortages of young physicians, technical personnel gaps, and uneven quality \cite{25,26}. For clinic operators, smaller clinics have lower risks, require less management and operational investment, and are less likely to face investment failure \cite{27}. For clinics with low scale efficiency but increasing returns to scale, forming "clinic alliances" or "clinic groups" could expand scale \cite{28}, integrate quality resources, reduce operational risks, and ultimately enhance competitiveness.

Policy Recommendations for Sustainable Clinic Development

With low overall efficiency, insufficient health resource inputs, and significant efficiency differences across clinic categories, current clinic policies focus primarily on encouraging clinic establishment. This study recommends developing more detailed supporting and regulatory policies for opening clinics, with category-specific and region-specific measures. The study fills the research gap in evaluating Beijing clinic efficiency using DEA methods and provides empirical evidence for optimizing resource allocation and improving operational efficiency.

Limitations

In terms of DEA evaluation indicator selection, domestic scholars use various healthcare resource indicators. Due to data availability, this study could not further decompose the input indicator of staff on duty into medical and non-medical personnel, nor could it decompose the output indicator of patient visits into outpatient and inpatient services. Future research should further explore the data to refine input-output indicators for analysis.

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