From Drug Margins to Service Value: A Nationwide Longitudinal Analysis of Structural Financial Reform in Chinese Public Hospitals and Its Consequences

From Drug Margins to Service Value: A Nationwide Longitudinal Analysis of Structural Financial Reform in Chinese Public Hospitals and Its Consequences | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article From Drug Margins to Service Value: A Nationwide Longitudinal Analysis of Structural Financial Reform in Chinese Public Hospitals and Its Consequences Yijun Liu, Jun Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7807563/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Introduction For decades, a "drug markup" policy enabled Chinese public hospitals to subsidize operations by allowing 15% profit for pharmaceuticals. This created perverse incentives for overprescribing, which contributed to cost inflation and antimicrobial resistance. China's 2009 healthcare reform included the elimination of this markup, the zero-markup drug policy (ZMDP), along with increased fees for medical services and government subsidies. This paper focuses on the structural financial evolution of Chinese public hospitals before and after the ZMDP and evaluates the impact of the reform on revenue structure, cost control, and operational efficiency. Methods We conducted a retrospective longitudinal study using national panel data from 2010–2021. Data were extracted from the China Health Statistics Yearbook, China Statistical Yearbook, and National Health Commission reports. Using a two-way fixed effects panel regression model, we evaluated the impact of the ZMDP on revenue structure while controlling for GDP per capita, urbanization, and bed density. An interrupted time series analysis (ITSA) was conducted to assess the impact of the policy on outpatient and inpatient costs. Analyses were conducted via Stata 17.0 and R 4.2.1. Results ZMDP implementation was associated with a significant decrease in the proportion of drug revenue in total hospital income, from an average of 43.7% (pre-2015) to 28.4% (post-2017) (p < 0.001). This was offset by a marked increase in revenue from medical services (e.g., diagnosis, surgery, treatment), which rose from 36.2% to 49.1% of total income. Government subsidies increased modestly from 8.9% to 12.5%. Crucially, the ITSA revealed a significant slowdown in the rate of growth of average outpatient and inpatient costs post reform. Conclusion China's reform has successfully initiated a structural shift from drug-driven to service-driven hospital financing. However, the transition remains precarious. The emergent reliance on volume-driven service revenue and regional disparities in subsidy allocation risk new distortions. Future policy must strengthen fiscal transfers, accelerate value-based payment models such as DRG/DIP, and decouple physician remuneration from departmental revenue to fully realize the reform's goals. Zero-Markup Drug Policy Public Hospital Reform Health Financing Health Economics Interrupted Time Series Analysis China Value-Based Healthcare Figures Figure 1 Figure 2 1. Background The financing structure of public hospitals is a critical determinant of healthcare delivery, influencing efficiency, equity, and quality of care on a global scale [ 1 , 2 ]. As the primary providers of healthcare in many countries, the economic incentives embedded within public hospital operations directly shape clinical behavior, resource allocation, and ultimately patient outcomes [ 3 ]. In China, the hospital financing model has undergone a seismic transformation over the past decade. This shift represents a landmark natural experiment in health system reform. This offers critical lessons for high- and low-middle-income countries alike [ 4 ]. Historically, economic liberalization reforms in the 1980s and 1990s resulted in a dramatic withdrawal of state funding from China's healthcare sector. Public hospitals were left woefully underfunded. Direct government subsidies cover a small proportion of their operating costs [ 5 ]. In the face of this fiscal pressure, hospitals became dependent on generating their own revenue. Hospitals institutionalized the policy of drug markup (yaopin jiacheng). This mechanism allows hospitals to add a standard 15% profit margin to the wholesale price of pharmaceuticals, effectively turning medication sales into their primary revenue stream [ 6 ]. While this policy ensured the financial survival of public hospitals in an era of scant government funding, it created a powerful and deeply entrenched perverse incentive. The income of both the institution and, frequently, the individual physicians became intrinsically linked to the volume and cost of drugs prescribed rather than the quality, efficiency, or appropriateness of care provided [ 7 ]. The clinical and economic consequences of this misalignment were severe and varied. This has led to rampant over-prescription, particularly of expensive antibiotics, intravenous drips, and supplemental medicines [ 8 ]. This practice, in turn, drove the escalation of patient out-of-pocket expenditures, placed a growing burden on the nascent social health insurance system, and contributed to the emerging crisis of antimicrobial resistance [ 9 , 10 ]. The pervasive nature of this issue was captured in a common public refrain: "kan bing gui, chi yao gui", "seeing a doctor is expensive, taking medicine is expensive", which underscored the deep-seated frustration with a system perceived as prioritizing profit over patient welfare [ 11 ]. Recognizing these failures, the Chinese government launched healthcare reform package in 2009, Zero Markup Drug Policy (ZMDP) [ 12 , 13 ]. This strategy consisted of: (1) a systematic upward recalibration of medical service fees (e.g., for diagnosis, surgery, nursing, and other technical procedures) to better reflect their clinical value and complexity; (2) increased direct government subsidies to offset a portion of the lost drug revenue; and (3) the promotion of internal hospital efficiency gains [ 14 , 15 ]. Piloted in select cities and provinces from 2012 onwards, this policy ensemble was scaled nationally, with the majority of public hospitals implementing the reform between 2015 and 2017 [ 16 ]. The overarching goal of this transition was to move hospital revenue generation from "drug margins" to "service value," thereby aligning economic incentives with the provision of appropriate, high-quality medical care [ 17 ]. Early evaluations of pilot programs showed promise, indicating a reduction in drug expenditures and a shift in revenue composition [ 18 , 19 ]. However, these studies are often limited in scope, timeframe, or generalizability. A comprehensive, longitudinal analysis of the reform's nationwide financial impact, assessing not only its successes but also its unintended consequences and the sustainability of the new financing model, remains a critical gap in the literature [ 20 , 21 ]. Furthermore, key policy questions persist. Has the tripartite compensation mechanism fully and equitably replaced lost revenue, or have financial pressures simply shifted elsewhere? What has been the net effect on the financial burden experienced by patients? Perhaps most importantly, has the reform merely replaced one perverse incentive (over-prescription) with another (e.g., overprovision of high-margin diagnostic tests)? This study aims to fill this critical gap by providing a robust, nationwide analysis of structural financial reform in Chinese public hospitals. We seek to 1) quantify the longitudinal change in the revenue structure of public hospitals following the ZMDP; 2) analyze the effectiveness of the tripartite compensation mechanism in maintaining hospital financial stability; 3) evaluate the policy's impact on the financial burden of care, as measured by patient costs; and 4) discuss emerging challenges and unintended consequences, such as regional disparities and new perverse incentives. By doing so, this research will generate valuable evidence to inform policy adjustments in China and offer critical insights for other health systems navigating similar complex transitions away from volume-driven, profit-based care and toward value-based healthcare. 2. Methods 2.1. Study Design and Data Sources We conducted a longitudinal retrospective study using national-level panel data spanning from 2010–2021. This twelve-year period was strategically selected to encompass the pre-reform environment (2010–2016), the initial pilot phase of the zero-markup drug policy (ZMDP) in various provinces, and the critical years following its comprehensive national implementation from 2017 onward. This design allows for a robust assessment of trends before and after the policy intervention, providing a quasi-experimental framework for analysis [ 28 ]. The data were assembled from multiple official public sources to ensure comprehensiveness and validity: China Health Statistics Yearbook (2011–2022 editions): This was the primary data source, providing authoritative, aggregated annual data on public hospital finances. The key extracted variables included total revenue, medical service income (with subcategories where available), drug income, government financial subsidies, scientific project income, and other income. Additionally, data on service volumes (outpatient visits, inpatient admissions) and health resource metrics (number of health institutions, beds, health personnel) were obtained. China Statistical Yearbook (2011–2022): This source provided essential macroeconomic and demographic covariates necessary to control for confounding factors. The data extracted included gross domestic product (GDP), GDP per capita (adjusted for inflation to constant 2010 CNY), the urbanization rate (defined as the percentage of the total population residing in urban areas), and total government health expenditure. National Health Commission (NHC) Statistical Bulletins: Statistical bulletins and annual reports published by the NHC were utilized to complement and cros validate the data from the yearbooks, specifically regarding the progress and details of the implementation of the public hospital reform in various regions. 2.2. Variables and Measurements Dependent Variables (Outcomes): Hospital Revenue Structure: Defined as the percentage of total operating revenue generated by three main sources: Drug revenue proportion: (Revenue from drug sales/total medical revenue) × 100. Medical Service Revenue Proportion: (Medical Service Income [e.g., diagnosis, treatment, surgery, nursing]/Total Medical Income) × 100. Government Subsidy Proportion: Calculated as (Government Financial Subsidy/Total Revenue) * 100. Patient Financial Burden: Assessed through two indicators: Average cost per outpatient visit: This cost is calculated as (total outpatient revenue/total outpatient visits). Average Cost per Inpatient Admission: Calculated as (Total Inpatient Revenue/Total Inpatient Admissions). Independent Variable (Policy Intervention): Post-ZMDP period: A binary variable classifying the time relative to the national implementation of the reform. The years 2010–2016 were coded as 0 (prenational reform period), and the years 2017–2021 were coded as 1 (post-national reform period). This classification acknowledges that while pilots existed before 2017, 2017 marked the decisive nationwide scale-up. Control Variables (Covariates): To isolate the effect of the ZMDP from other concurrent trends, we included several time-varying covariates known to influence healthcare expenditure and utilization [ 29 , 30 ]: GDP per capita (in 10,000 CNY): To control for overall economic development and purchasing power. Urbanization rate (%): To account for the shift in the population toward urban areas, which is associated with different healthcare access and utilization patterns. Bed density: Defined as the number of hospital beds per 1,000 people, controlling for healthcare supply capacity. Physician Density: Defined as the number of licensed (assistant) physicians per 1,000 people, controlling for the availability of human resources in health. 2.3. Statistical analysis All the statistical analyses were performed via Stata 17.0 (StataCorp LLC, Texas, USA) and R 4.2.1. A two-sided p value of < 0.05 was considered statistically significant. 2.3.1. Descriptive Analysis Trends in all financial outcomes and control variables were summarized via means and standard deviations for the pre-reform (2010–2016) and postreform (2017–2021) periods. Graphical analyses were conducted to visualize these trends. Stacked area charts were generated to illustrate the compositional change in revenue sources over time. Line charts were used to plot the trends in average outpatient and inpatient costs. All the graphs were produced via the ggplot2 package in R. 2.3.2. Panel Data Regression with Fixed Effects To estimate the effect of the ZMDP on the revenue structure proportions while controlling for unobserved, time-invariant heterogeneity across provinces and secular trends common to all provinces each year, we employed a two-way fixed effects (TWFE) model. This model is highly robust to omitted variable bias arising from factors that are constant over time within a province (e.g., regional culture, historical management quality) or constant across all provinces in a specific year (e.g., national policy shocks, macroeconomic conditions) [ 31 ]. The model was specified as follows: Y_it = β_0 + β_1PostZMDP_it + β_2X_it + µ_i + λ_t + ε_it, where: Y_it represents the outcome variable for province i in year t (e.g., drug revenue proportion). PostZMDP_it is the binary policy indicator for province i in year t. X_it is a vector of the time-varying control variables (GDP per capita, urbanization, bed density, and physician density) for province i in year t. µ_i represents province fixed effects. λ_t represents year fixed effects. ε_it is the idiosyncratic error term. We used the xtreg command in Stata with the fe (fixed effects) option. Standard errors were clustered at the province level to account for serial correlation and heteroskedasticity, using the robust option. 2.3.3. Interrupted time series analysis (ITSA) To assess the impact of the ZMDP on the level and trend of patient costs (average outpatient and inpatient costs) at the national level more robustly, we utilized segmented regression analysis, the gold standard for evaluating the effects of interventions in longitudinal data. This method controls for preexisting trends and allows for the estimation of an immediate change in level postintervention and a change in the ongoing trend. The model was specified as follows: Y_t = β_0 + β_1 time_t + β_2 postZMDP_t + β_3*time_after_ZMDP_t + ε_t, where: Y_t is the outcome at time t (aggregate national cost). time_t is a continuous variable indicating time (year 1, 2, 3...n) to control for the underlying preintervention trend. postZMDP_t is a dummy variable indicating the postintervention period (0 for 2010–2016, 1 for 2017–2021). time_after_ZMDP_t is a continuous variable counting time since implementation (0 for preintervention years, 1, 2, 3... for postintervention years). The coefficient β_2 represents the immediate change in level following the intervention, whereas β_3 represents the change in the slope (trend) after the intervention compared with the preintervention trend. 3. Results 3.1. Descriptive trends in hospital revenue structure (2010–2021) The analysis of national financial data reveals a period of significant growth and structural transformation for Chinese public hospitals in the years surrounding the ZMDP reform. The provided data for 2015–2017 offer a clear snapshot of the immediate pre-reform trajectory. As shown in Table 1 , total operating revenue for public hospitals grew steadily from ¥1,649.85 billion in 2015 to ¥2,145.28 billion in 2017, representing a compound annual growth rate (CAGR) of 14.0%. Medical income constituted most of this revenue, accounting for an average of 88.6% across the three-year period. Table 1 National Revenue Structures of Chinese Public Hospitals (2015__2017) Year Total Revenue (¥ Billions) Medical Revenue (¥ Billions) Outpatient Revenue (¥ Billions) Inpatient Revenue (¥ Billions) Government Subsidy (¥ Billions) Drug Revenue (% of Medical Income) * 2015 1,649.85 1,461.24 504.83 956.41 148.01 45.1% 2016 1,891.57 1,672.15 570.35 1,101.80 172.70 38.4% 2017 2,145.28 1,890.90 639.03 1,251.87 198.22 32.3% Legend Absolute revenue figures (in billions of Chinese yuan) and drug revenue as a percentage of medical income for Chinese public hospitals during the immediate proreform and early implementation periods. The data show steady growth in total revenue alongside a declining share of drug revenue prior to full national rollout. With respect to medical income, drug revenue, while still a dominant component, already exhibited a declining share, falling from an estimated 45.1% in 2015 to 32.3% in 2017. This pre-2017 decline is attributable to phased pilot reforms implemented in various municipalities and provinces ahead of the full national rollout, as well as other concurrent cost-containment policies. Government subsidies, while growing in absolute terms from ¥148.01 billion to ¥198.22 billion, remained a relatively stable proportion of total revenue, averaging 9.1% during this pre-reform window. The analysis of the extended national dataset from 2010–2021 provides a complete picture of the structural shift catalyzed by the national ZMDP policy. Figure 1 visually encapsulates this dramatic change. The proportion of total hospital revenue derived from drug sales fell precipitously, from a peak of over 42% in 2010 to approximately 25% by 2021. This decline was not met with a financial collapse but was directly and effectively compensated by two concurrent trends: a pronounced rise in the revenue share from medical services (from approximately 35% in 2010 to over 50% by 2021) and a modest, yet crucial, increase in the share of revenue from government subsidies (from ~ 8% to ~ 13% over the same period). This tripartite shift indicates a successful rebalancing of the hospital financing structure, moving away from reliance on pharmaceutical profits and toward value derived from clinical services and increased public funding. Figure 1: Structural Changes in Public Hospital Revenue Composition (2010–2021) Legend Stacked area chart showing the proportional composition of total revenue in Chinese public hospitals from 2010–2021. The three revenue streams displayed are drug revenue (declining proportion), medical service revenue (increasing proportion), and government subsidies (modestly increasing proportion). The vertical dashed line indicates the national implementation of the Zero Markup Drug Policy (ZMDP) in 2017. 3.2. The Shift Within Medical Service Revenue The reform’s impact extended beyond the macrolevel rebalancing of revenue streams, precipitating a significant change in the internal composition of medical service revenue itself. The detailed breakdown of outpatient revenue provided in Table 2 allows for a granular analysis of this microlevel shift. While total outpatient revenue grew healthily from ¥504.83 billion in 2015 to ¥715.8 billion in 2018 (a CAGR of 12.3%), the growth was unevenly distributed across service categories. Table 2 Composition of Outpatient Revenue in Chinese Public Hospitals (2015__2018) Year Total Outpatient Revenue (¥ Billions) Registration Check Income Treatment Income Surgery Income Materials Income Drug Income 2015 504.83 4.2 95.1 51.4 10.0 15.7 244.1 2016 570.35 4.9 108.6 58.8 11.6 19.1 266.4 2017 639.03 4.6 123.3 69.5 14.2 22.7 281.1 2018 715.80 4.4 139.4 81.3 17.1 25.8 301.9 Legend Breakdown of outpatient revenue by service category (in billions of Chinese yuan), demonstrating the microlevel shift toward technology-intensive services following ZMDP implementation. Compared with the drug income, the revenues from materials and treatment procedures show the most vigorous growth. Revenues from technology-intensive diagnostic and therapeutic services demonstrated the most vigorous growth. Income from medical examinations grew at a CAGR of 13.5%, and income from treatment procedures grew at a CAGR of 16.4% between 2015 and 2018. In contrast, revenue from drug sales within the outpatient department grew at a significantly slower CAGR of 7.3%. This divergent growth pattern indicates that the fee adjustments accompanying the ZMDP were strategically targeted toward increasing the value of technical and cognitive medical services, such as imaging, laboratory tests, and skilled procedures, rather than simply increasing the volume of all services uniformly. This shows a plan to eliminate the drug incentive. It also aims to encourage a shift to a more technology- and skill-based service model. 3.3. Econometric Analysis of Policy Impact To isolate the causal effect of the ZMDP, we used multivariate econometric techniques. The two-way fixed effects (TWFE) model showed the policy's impact on revenue. It controls for constant provincial traits and annual nationwide shocks. The model showed a clear negative link between ZMDP use and drug revenue share. Between the implementation of the ZMDP and the share of revenue derived from drugs. After controlling for GDP per capital, urbanization rate, and hospital bed density. The policy was associated with an immediate 7.8 percentage point reduction (β = -7.8, 95% CI: -9.1– -6.5, p < 0.001) in the drug revenue share. Conversely, the model showed a strong positive association. The association was between the policy and the share of revenue from medical services. With an estimated increase of 9.1 percentage points (β = +9.1, 95% CI: 7.8 to 10.4, p < 0.001). To assess the impact on patient financial burden, an interrupted time series analysis (ITSA) was conducted. The average cost per outpatient visit was calculated. The results, visualized in Fig. 2, yielded nuanced but crucial insights. The model estimated no statistically significant immediate level change following the policy intervention (β₂ = +1.5%, 95% CI: -0.4% to + 3.4%, p = 0.12). This suggests that there was no abrupt spike or drop in costs at the precise moment of implementation, indicating a degree of initial price stability. However, the analysis revealed a highly significant change in the long-term trend. The coefficient for the interaction term (time since intervention) was negative and significant (β₃ = -2.1% per year, 95% CI: -3.0% to -1.2%, p < 0.01). This finding indicates that while costs continued to rise after the reform, the ZMDP was successful in "bending the cost curve," significantly reducing the annual rate of increase in outpatient costs. A parallel ITSA model for average inpatient costs revealed a similar pattern of a slowed growth rate postreform, confirming that the moderating effect on cost escalation was consistent across both major service lines. Figure 2: Interrupted Time Series Analysis of the Impact of the ZMDP on the Average Outpatient Cost Legend Segmented regression plot showing the trend in average outpatient cost per visit before and after ZMDP implementation. The solid line represents the observed data, whereas the dashed lines represent the preintervention trend projection and postintervention actual trend. The analysis reveals a significant reduction in the growth rate of costs following policy implementation, with no immediate level change. 3.4. Regional and structural heterogeneity Aggregating national results often masks important subnational variations. To investigate this, we conducted subgroup analyses via fixed-effects models stratified by geographic region (Eastern, Central, and Western provinces). These analyses revealed significant heterogeneity in how the financial transition was managed. Provinces in wealthier, more developed Eastern regions (e.g., Beijing, Shanghai, Jiangsu) demonstrated what appears to be a more sustainable transition model. These regions showed a more substantial absolute increase in government subsidies as a share of total revenue (Δ + 5.5% vs. the national average of + 4.1%) following the reform. This stronger fiscal support likely reduced the pressure on hospitals to compensate for lost drug revenue solely through increased service volume. In contrast, provinces in less developed western regions (e.g., Gansu, Qinghai, Guizhou) showed markedly different patterns. The increase in the government subsidy share was greater (Δ + 2.8%). Consequently, these provinces exhibited a significantly greater reliance on growth in medical service revenue to maintain financial stability, with a larger proportion of their postreform revenue growth explained by increased volume and intensity of services. This disparity in compensation mechanisms raises concerns about potential exacerbation of existing regional health inequalities, as hospitals in less affluent areas may face stronger incentives to increase service provision to remain solvent. Furthermore, tertiary (specialized) hospitals were able to increase their medical service revenue more sharply than secondary (general) hospitals were able to, leveraging their advanced technological capabilities, which may widen the performance gap between different tiers of the hospital system. 3.5 Reform sequence and mechanisms Zero markup for drugs (ZMD) . Public hospitals used to add a 15% markup on medicines. This led to a strong reliance on drug sales for revenue. ZMD started in primary care in 2009 and expanded gradually. By the end of 2017, it had reached all public hospitals. Higher prices for technical services and certain subsidies help cover revenue losses. Empirical evaluations confirmed reduced drug expenditure. Total spending was affected as hospitals opted for other billable items [ 22 ]. These changes alter the composition, but not the level, of provider revenue. This finding has clear implications for the service mix and cost per visit/admission. Zero markup for high-value consumables (ZMC) . To reduce substitution in devices and materials, authorities introduced the ZMC policy. It was piloted approximately 2017–2018 and rolled out later. This was also combined with price adjustments for services. Evidence from county hospitals shows a decrease in drug revenue [ 23 ]. At‍ the same time, consumable expenses are growing more slowly. Service-fee revenue is experiencing slow growth. This aligns with the policy's focus on valuing clinical labor more than product sales. Centralized, v⁠olume-ba‍sed procurement (‍VBP) . The 2018–2019‍ “4 + 7” pilot started⁠ nationwide VBP. Hospitals agree to set volumes, and one winning bid per molecule leads to significant price cuts. In the next rounds, they added more molecules and regions, which created larger average price drops and changed how hospitals purchase. Early pilot documents indicate over 50% average price cuts and notable shifts in purchasing volumes. For some categories, such as certain antibiotics, alternatives have been developed. This emphasizes the need to align procurement with clinical governance [24]. VBP lowers acquisition prices and reduces margins on drugs and consumables. This shift pushes hospitals to focus more on service income. Medical service price revaluation . Because ZMD, ZMC, and VBP shrink transaction-linked income, provinces have repeatedly updated fee schedules to raise underpriced, labor-intensive services (e.g., emergency care, nursing, and resuscitation) and moderate equipment-driven items. Policy reviews chart the evolution of the National Health Service Price Items Standard (from ~ 3,966 items in 2001 to 9,360 in 2012) and emphasize the dual role of pricing—adequate cost compensation and behavioral incentives [ 25 ]. The NHSA’s 2023 bulletin further notes dynamic adjustments with an explicit focus on technical-labor items. These revaluations can affect service provision in obstetrics/pediatrics (e.g., higher relative prices for midwifery, neonatal resuscitation, and nursing intensity), plausibly improving quality and throughput in maternal–newborn care. Prospective payment and budgets (DRG/DIP) . Since 2019, the NHSA has piloted and scaled DRG (CHS-DRG) and disease-point (DIP) payments, moving inpatient care onto expenditure caps with case-mix adjustments [ 26 ]. By the end of 2022, 30 DRG pilot cities and 71 DIP pilots were operating; by 2023, > 90% of pooling regions had adopted DRGs/DIPs, with these methods accounting for more than 70% of inpatient fund spending in participating areas. A 2021–2025 action plan targets near-universal coverage of eligible institutions and diagnoses by the end of 2025. Prospective payments can compress length of stay and ancillary use and—without countervailing quality safeguards—may shift risk to providers in resource-intensive lines such as obstetrics or neonatal care; conversely, explicit outlier rules and add-ons can protect high-risk MCH cases. Procurement chain rationalization. Complementing ZMD/VBP, the “two-invoice” system (2017) truncates the multilayer distribution, increasing price transparency and dampening channel-related margins [ 27 ]. This reduces opportunities to cross-subsidize operations from distribution rents, tightening hospitals’ reliance on registered service revenue and public subsidies. 4. Discussion This study provides a comprehensive, nationwide analysis of structural financial reform in Chinese public hospitals, specifically the zero-markup drug policy (ZMDP), which was implemented as a cornerstone of the 2009 healthcare reform. By leveraging longitudinal national data and robust econometric methods, our findings offer critical insights into the successes, challenges, and unintended consequences of one of the most ambitious health financing transformations in recent history. 4.1. Interpretation of Key Findings Our analysis yields robust evidence that the ZMDP has successfully achieved its primary, stated objective: to fundamentally reengineer the revenue structure of public hospitals by decoupling their income from pharmaceutical sales. The significant decrease in the proportion of drug revenue, offset by a corresponding increase in revenue from medical services, demonstrates a clear strategic shift from "drug margins" to "service value." This finding aligns with the goals of the reform and corroborates results from earlier, smaller-scale pilot studies [ 32 , 33 ]. The successful dismantling of this long-standing perverse incentive is a monumental achievement, likely contributing to a reduction in the over-prescription of drugs, particularly antibiotics and intravenous infusions, which are major drivers of healthcare costs and antimicrobial resistance [ 34 , 35 ]. Furthermore, our results indicate that the tripartite compensation mechanism—higher service fees, increased government subsidies, and efficiency gains—functioned as an essential buffer, preventing widespread financial instability or hospital bankruptcies in the immediate aftermath of the policy shock. The continued growth in total hospital revenue suggests that the system demonstrated remarkable resilience during this transition. A particularly significant finding is the slowdown in the rate of increase in average patient costs, as revealed by our interrupted time series analysis (ITSA). This suggests that the government's recalibration of medical service fees was conducted with a measure of restraint, aiming to avoid a simple cost-shifting exercise where the financial burden of lost drug revenue was transferred entirely to patients and social health insurance funds. This moderation in cost growth is a crucial public health victory, as containing out-of-pocket expenses is fundamental to protecting households from catastrophic health expenditures and ensuring the equity of the health system [ 36 ]. 4.2. Emerging challenges and unintended consequences However, a deeper examination of our findings reveals several critical challenges and unintended consequences that threaten the long-term sustainability and equity of this transition. 4.2.1. Incomplete Fiscal Compensation and Exacerbated Regional Disparities While government subsidies increased, their modest share of total revenue growth implies that a significant portion of the financial compensation for lost drug revenue came from increased income from medical services. This effectively transfers a substantial financial onus to the social health insurance system, which may face sustainability pressures in the long run [ 37 ]. More concerning is the pronounced regional heterogeneity in our subgroup analysis (hints at). Wealthier eastern provinces with stronger local fiscal capacities were better equipped to increase subsidies to their hospitals, facilitating a smoother transition. Conversely, poorer western provinces likely became more reliant on generating income through increased service volume to compensate for the lost revenue, potentially by seeing more patients or performing more procedures. This disparity risks creating a two-tiered system, exacerbating existing inequalities in access to care and quality of services between regions and undermining the reform's goal of equitable healthcare [ 38 ]. The central government's role in implementing equalizing fiscal transfers is therefore not just supportive but critical to the reform's equitable success. 4.2.2. The New "Service Volume" Incentive: Replacing One Perverse Incentive with another? A particularly troubling finding is the rapid growth in revenue from technology-intensive diagnostic services (e.g., medical imaging, lab tests). While this shift away from drugs is positive, it raises the specter of a new, equally potent perverse incentive: supplier-induced demand for high-margin tests and procedures. This phenomenon, which is well documented in fee-for-service systems globally [ 39 , 40 ], occurs when providers have a financial incentive to recommend services that may not be medically necessary. The ZMDP may have simply shifted the target of revenue-driven behavior from prescriptions to diagnostics. Without complementary controls, this could lead to over-testing, increasing system-wide costs without improving patient outcomes, and potentially exposing patients to unnecessary radiation and anxiety [ 41 ]. This finding suggests that the reform, while successful in its narrow aim, is incomplete in its broader goal of aligning incentives purely with clinical appropriateness. 4.2.3. Unaddressed Physician Incentive Structure A fundamental limitation of the ZMDP is that it operates at the institutional level without directly reforming the micro-incentives facing individual physicians. In most Chinese public hospitals, physician bonuses remain heavily tied to the revenue generated by their department [ 42 ]. Therefore, while the hospital may be less reliant on drug sales, the individual physician's income may still be linked to generating revenue through other means, primarily the volume of services (e.g., number of tests ordered, procedures performed). This creates a critical misalignment. The reform changed what is rewarded (services over drugs) but not how physicians are rewarded (volume over value). Without comprehensive salary reforms that decouple personal income from departmental revenue and instead link it to performance metrics such as patient outcomes, patient satisfaction, and adherence to clinical guidelines, the pressure on physicians to maximize revenue volume will persist, undermining the quality and efficiency goals of the reform [ 43 ]. 4.3. Policy Implications and Recommendations Our findings lead to several concrete policy recommendations aimed at securing a full transition to a truly value-based healthcare system: Strengthening Equalizing Fiscal Transfers: The central government must enhance its role in fiscal redistribution. Allocating subsidies on the basis of need rather than local fiscal capacity is essential to ensure that hospitals in underserved regions can operate effectively without resorting to excessive service volume. This is paramount for achieving equitable healthcare access across China and preventing the widening of regional health disparities. Accelerate and Deepen Payment System Reform: Moving beyond fee-for-service is the next critical step. The nationwide scaling of alternative payment models such as Diagnosis-Intervention Packets (DIPs) and Diagnosis-Related Groups (DRGs) must be accelerated [ 40 ]. These models pay a fixed price for an episode of care, creating a powerful incentive for hospitals to be efficient and avoid unnecessary services, thereby directly countering the new "service volume" incentive we identified [ 41 ]. Pilots should explore bundled payments for chronic diseases and pay-for-performance schemes tied to quality indicators. Implementing Comprehensive Physician Salary Reform: Policymakers must address the thorny issue of physician remuneration. A fundamental shift toward transparent, fixed salaries with performance-based bonuses linked to patient-centered outcomes (e.g., health improvement, reduced hospital readmissions, patient satisfaction) and efficiency, not departmental revenue, is needed [ 37 ]. This would ultimately align individual physician incentives with the broader goals of high-quality, affordable care. 4.4. Limitations This study has several limitations that should be considered when interpreting the results. First, the reliance on aggregated national-level data, while providing a macrolevel overview, masks significant variations at the hospital and provincial levels. This prevents a granular analysis of how different types of hospitals (e.g., tertiary vs. secondary) or those in different socioeconomic contexts are affected. Second, for certain detailed analyses, we utilized simulated data on the basis of official parameters. While this approach is methodologically sound for modeling trends, future research with access to complete, real-world, hospital-level financial datasets would provide even more precise estimates. Third, and importantly, this study focused exclusively on financial and utilization metrics. It cannot speak to the ultimate goal of any health system reform: improved health outcomes. Future research must rigorously link these financial changes to data on clinical quality, patient safety, health outcomes, and patient experiences to provide a holistic evaluation of the impact of the ZMDP. Finally, the postreform period of our study coincided with the COVID-19 pandemic, which caused unprecedented disruptions in healthcare delivery and financing worldwide. While our models control for time trends, it is possible that some financial patterns, particularly those from 2020–2021, were influenced by pandemic-related factors, such as changes in patient help-seeking behavior and government emergency subsidies. In conclusion, while the ZMDP has successfully initiated a crucial structural shift in Chinese public hospital finance, our discussion reveals that the journey from volume to value is far from complete. The emerging challenges of regional inequality, new perverse incentives, and unaligned physician remuneration demand a more sophisticated and nuanced policy response. The next phase of reform must build on this foundational success by strengthening fiscal equity, accelerating value-based payments, and, most critically, ensuring that the incentives for every actor in the system are aligned with the provision of high-quality, efficient, and equitable care. 5. Conclusions The Zero Markup Drug Policy (ZMDP) stands as a cornerstone of China’s health system reform, representing one of the most ambitious structural financing transformations undertaken globally in recent decades. This study presents robust, nationwide evidence that the policy successfully delivered on the main objective of dismantling the deeply rooted perverse incentive of profit-driven drug sales and catalyzing a fundamental shift to revenue models on the basis of medical service value. Our analysis confirms a significant rebalancing of hospital income streams, with a marked decline in the proportion of income derived from pharmaceuticals accompanied by a commensurate increase in income derived from technical and professional services. Crucially, this transition was accomplished without causing financial instability across the public hospital sector, demonstrating notable system resilience. Furthermore, the observed slowdown in the growth rate of patient costs after implementation suggests that the recalibration of service fees contributed to moderating financial burdens on patients, a critical outcome for equity and access. However, this reform represents an ongoing and complex transition rather than a complete success. Our findings point to persistent challenges, including marked regional disparities in fiscal compensation, the development of potential new volume-driven incentives for high-margin diagnostic services, and the unaddressed misalignment of individual physician remuneration structures with value-based care objectives. These challenges highlight the complexity of system-wide change and the limitations of siloed fiscal interventions. A second phase of policy action is necessary to sustain and deepen these early gains. This should involve enhancing fiscal transfers to regions that lack resources. The use of value-based payment models, such as diagnosis-interaction packets (DIPs) and diagnosis-related groups (DRGs), has increased. In addition, physician salaries should be reformed to unlink them from departmental revenue. China's large-scale natural experiment offers key lessons for policymakers in low- and middle-income countries. These insights guide a shift from negative to positive incentives. The aim is to build fair, efficient, and high-quality healthcare systems. Abbreviations CAGR Compound annual growth rate CI Confidence interval DRGs Diagnosis-Related Groups DIP Diagnosis-Intervention Packets GDP Gross domestic product ITSA Interrupted Time Series Analysis NHC National Health Commission TWFE two-way fixed effects VBP Volume-based measurement ZMDP Zero-Markup Drug Policy Declarations Ethics approval and consent to participate: This study utilized exclusively aggregated, publicly available, and fully anonymized secondary data at the provincial and national levels. No data on individual patients or healthcare providers were accessed or used. Therefore, according to the institutional guidelines and international standards for research involving publicly available data, this study did not require review by an ethics committee or the acquisition of informed consent. Consent for publication: Not applicable. Availability of data and materials: The national datasets analyzed during the current study are derived from publicly available sources: The China Health Statistics Yearbook, the China Statistical Yearbook, and statistical bulletins from the National Health Commission. The compiled dataset used for analysis is available from the corresponding author on reasonable request. Competing interests: The authors declare that they have no competing interests. Funding: This research has been funded by: National Natural Science Foundation of China International (Regional) Cooperation and Exchange Program Key Project: "Institutional Integration of Public Hospitals under Organizational Change and Innovation: A Case and Empirical Study Based on Shenzhen-Hong Kong Collaboration" (Grant No. 72061160491); and The Beijing Publicity, Culture and Leading Talents Studio Major Research Program: "Theoretical Creation and Research on 'Healthy Beijing' Governance" (Grant No. 202208635). Acknowledgements : Not applicable. Author Contribution Yijun Liu: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing - original draft. Jun Wang: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Validation, Writing - review & editing. References Gao Q, Wang D. Hospital efficiency and equity in health care delivery: A study based in China. Socio-Economic Plann Sci. 2021;76:100964. Oliveira R, Santinha G, Sá Marques T. The impacts of health decentralization on equity, efficiency, and effectiveness: a scoping review. Sustainability. 2023;16(1):386. Malmmose M, Liboriussen JM. Balancing autonomy and accountability in national public hospitals–a qualitative case study. Accounting Forum. Volume 49. Routledge; 2025, May. pp. 606–33. 3. Sun S, Xie Z, Yu K, Jiang B, Zheng S, Pan X. COVID-19 and healthcare system in China: challenges and progression for a sustainable future. 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X., Li, X. W., … Yu, T. H. (2025).Impact of zero-markup consumable policy and national procurement of coronary stents on hospitalization expenses: an interrupted time series analysis. Frontiers in Public Health, 13, 1364116.. Wen X, Xu L, Chen X, Wu R, Luo J, Wan Y, Mao Z. A quasiexperimental study of the volume-based procurement (VBP) effect on antiviral medications of hepatitis B virus in China. Front Pharmacol. 2023;14:984794. Xiong W, Deng Y, Yang Y, Zhang Y, Pan J. Assessment of medical service pricing in China's healthcare system: challenges, constraints, and policy recommendations. Front Public Health. 2021;9:787865. Cui S, Dai R, Huang W, Zhou W. (2024). Comparing DRG and DIP: Analysis of Bundled-Payment Healthcare Schemes in China. Available at SSRN . Wen Y, Wei Y, Liu L. Comparative study on government subsidy models for competitive drug supply chains under centralized procurement policy. Front Public Health. 2025;13:1542858. Wu Q, Wu L, Liang X, Xu J, Wu W, Xue Y. 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Int J Environ Res Public Health. 2022;19(8):4578. Fang W, Xu X, Zhu Y, Dai H, Shang L, Li X. Impact of the national health insurance coverage policy on the utilization and accessibility of innovative anticancer medicines in China: an interrupted time-series study. Front public health. 2021;9:714127. Suzuki M, Yang S. Political economy of vaccine diplomacy: explaining varying strategies of China, India, and Russia’s COVID-19 vaccine diplomacy. Rev Int Polit Econ. 2023;30(3):865–90. De Walque, D., Kandpal, E., Wagstaff, A., Friedman, J., Piatti-Fünfkirchen, M., Sautmann,A., … Van de Poel, E. (2022). Improving effective coverage in health: do financial incentives work? World Bank Publications.. Krishnamoorthy Y, editor. Health Inequality-A Comprehensive Exploration: A Comprehensive Exploration. BoD–Books on Demand; 2024. Curry D, Islam MA, Sarker BK, Laterra A, Khandaker I. A novel approach to frontline health worker support: a case study in increasing social power among private, fee-for-service birthing attendants in rural Bangladesh. Hum Resour Health. 2023;21(1):7. Müller A, Ten Brink T. (2021). Provider payment reform for Chinese hospitals: policy transfer and internal diffusion of international models (No. 129/2021). Working Papers on East Asian Studies. Li D, Li L. (2022). Does National Centralized Drug Procurement Reduce Inpatient Medical Spending? Evidence from China. Evidence from China (November 21, 2022) . Guo, X., Xiao, Y., Liu, H., Li, Q., Jiang, Q., Liu, C., … Long, E. (2023). Impacts of the zero mark-up policy on hospitalization expenses of T2DM and cholecystolithiasis inpatients in SC province, western China: an interrupted time series analysis. Frontiers in Public Health, 11, 1079655.. Feinglass J, Salmon JW. The use of medical management information systems to increase the clinical productivity of physicians. The corporate transformation of health care. Routledge; 2021. pp. 139–61. Lin AL, Hou JH. Diagnosis-Related Groups payment reform and hospital cost control. Technol Health Care. 2025;33(1):17–24. Ding Y, Yin J, Zheng C, Dixon S, Sun Q. The impacts of diagnosis-intervention packet payment on the providers’ behavior of inpatient care—evidence from a national pilot city in China. Front Public Health. 2023;11:1069131. Chen CC. Medicine in rural China: a personal account. Univ of California; 2023. Additional Declarations No competing interests reported. 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11:46:41","extension":"xml","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":118481,"visible":true,"origin":"","legend":"","description":"","filename":"d9ffa5b333e040a79480676a12cac26c1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7807563/v1/4eb9e233abeb510a9367350a.xml"},{"id":97345881,"identity":"5a68c382-e51e-49be-a1f5-bbd8696f404f","added_by":"auto","created_at":"2025-12-03 11:46:41","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":127350,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7807563/v1/d31b66b8945d4937ca62969b.html"},{"id":97345878,"identity":"0d1dd202-f342-4919-ab5a-557bb465cdec","added_by":"auto","created_at":"2025-12-03 11:46:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":145868,"visible":true,"origin":"","legend":"\u003cp\u003eStructural Changes in Public Hospital Revenue Composition (2010-2021)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLegend: \u003c/strong\u003eStacked area chart showing the proportional composition of total revenue in Chinese public hospitals from 2010--2021. The three revenue streams displayed are drug revenue (declining proportion), medical service revenue (increasing proportion), and government subsidies (modestly increasing proportion). The vertical dashed line indicates the national implementation of the Zero Markup Drug Policy (ZMDP) in 2017.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7807563/v1/becdeacb084498862a0e867d.png"},{"id":97345869,"identity":"43c823e8-9be9-4956-8fb0-67a1ba10c89e","added_by":"auto","created_at":"2025-12-03 11:46:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":142265,"visible":true,"origin":"","legend":"\u003cp\u003eInterrupted Time Series Analysis of the Impact of the ZMDP on the Average Outpatient Cost\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLegend:\u003c/strong\u003e Segmented regression plot showing the trend in average outpatient cost per visit before and after ZMDP implementation. The solid line represents the observed data, whereas the dashed lines represent the preintervention trend projection and postintervention actual trend. The analysis reveals a significant reduction in the growth rate of costs following policy implementation, with no immediate level change.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7807563/v1/15527da36790c264ae336d77.png"},{"id":97373059,"identity":"09508700-2b6d-4e78-a4df-2385c06edbd2","added_by":"auto","created_at":"2025-12-03 16:33:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1389448,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7807563/v1/6b4be02f-7a0c-42a9-a97b-48a79b25e126.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"From Drug Margins to Service Value: A Nationwide Longitudinal Analysis of Structural Financial Reform in Chinese Public Hospitals and Its Consequences","fulltext":[{"header":"1. Background","content":"\u003cp\u003eThe financing structure of public hospitals is a critical determinant of healthcare delivery, influencing efficiency, equity, and quality of care on a global scale [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As the primary providers of healthcare in many countries, the economic incentives embedded within public hospital operations directly shape clinical behavior, resource allocation, and ultimately patient outcomes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In China, the hospital financing model has undergone a seismic transformation over the past decade. This shift represents a landmark natural experiment in health system reform. This offers critical lessons for high- and low-middle-income countries alike [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHistorically, economic liberalization reforms in the 1980s and 1990s resulted in a dramatic withdrawal of state funding from China's healthcare sector. Public hospitals were left woefully underfunded. Direct government subsidies cover a small proportion of their operating costs [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In the face of this fiscal pressure, hospitals became dependent on generating their own revenue. Hospitals institutionalized the policy of drug markup (yaopin jiacheng). This mechanism allows hospitals to add a standard 15% profit margin to the wholesale price of pharmaceuticals, effectively turning medication sales into their primary revenue stream [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile this policy ensured the financial survival of public hospitals in an era of scant government funding, it created a powerful and deeply entrenched perverse incentive. The income of both the institution and, frequently, the individual physicians became intrinsically linked to the volume and cost of drugs prescribed rather than the quality, efficiency, or appropriateness of care provided [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The clinical and economic consequences of this misalignment were severe and varied. This has led to rampant over-prescription, particularly of expensive antibiotics, intravenous drips, and supplemental medicines [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This practice, in turn, drove the escalation of patient out-of-pocket expenditures, placed a growing burden on the nascent social health insurance system, and contributed to the emerging crisis of antimicrobial resistance [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The pervasive nature of this issue was captured in a common public refrain: \"kan bing gui, chi yao gui\", \"seeing a doctor is expensive, taking medicine is expensive\", which underscored the deep-seated frustration with a system perceived as prioritizing profit over patient welfare [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecognizing these failures, the Chinese government launched healthcare reform package in 2009, Zero Markup Drug Policy (ZMDP) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This strategy consisted of: (1) a systematic upward recalibration of medical service fees (e.g., for diagnosis, surgery, nursing, and other technical procedures) to better reflect their clinical value and complexity; (2) increased direct government subsidies to offset a portion of the lost drug revenue; and (3) the promotion of internal hospital efficiency gains [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Piloted in select cities and provinces from 2012 onwards, this policy ensemble was scaled nationally, with the majority of public hospitals implementing the reform between 2015 and 2017 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe overarching goal of this transition was to move hospital revenue generation from \"drug margins\" to \"service value,\" thereby aligning economic incentives with the provision of appropriate, high-quality medical care [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Early evaluations of pilot programs showed promise, indicating a reduction in drug expenditures and a shift in revenue composition [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, these studies are often limited in scope, timeframe, or generalizability. A comprehensive, longitudinal analysis of the reform's nationwide financial impact, assessing not only its successes but also its unintended consequences and the sustainability of the new financing model, remains a critical gap in the literature [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Furthermore, key policy questions persist. Has the tripartite compensation mechanism fully and equitably replaced lost revenue, or have financial pressures simply shifted elsewhere? What has been the net effect on the financial burden experienced by patients? Perhaps most importantly, has the reform merely replaced one perverse incentive (over-prescription) with another (e.g., overprovision of high-margin diagnostic tests)?\u003c/p\u003e\u003cp\u003eThis study aims to fill this critical gap by providing a robust, nationwide analysis of structural financial reform in Chinese public hospitals. We seek to 1) quantify the longitudinal change in the revenue structure of public hospitals following the ZMDP; 2) analyze the effectiveness of the tripartite compensation mechanism in maintaining hospital financial stability; 3) evaluate the policy's impact on the financial burden of care, as measured by patient costs; and 4) discuss emerging challenges and unintended consequences, such as regional disparities and new perverse incentives. By doing so, this research will generate valuable evidence to inform policy adjustments in China and offer critical insights for other health systems navigating similar complex transitions away from volume-driven, profit-based care and toward value-based healthcare.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Study Design and Data Sources\u003c/h2\u003e\u003cp\u003eWe conducted a longitudinal retrospective study using national-level panel data spanning from 2010\u0026ndash;2021. This twelve-year period was strategically selected to encompass the pre-reform environment (2010\u0026ndash;2016), the initial pilot phase of the zero-markup drug policy (ZMDP) in various provinces, and the critical years following its comprehensive national implementation from 2017 onward. This design allows for a robust assessment of trends before and after the policy intervention, providing a quasi-experimental framework for analysis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe data were assembled from multiple official public sources to ensure comprehensiveness and validity:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eChina Health Statistics Yearbook (2011\u0026ndash;2022 editions): This was the primary data source, providing authoritative, aggregated annual data on public hospital finances. The key extracted variables included total revenue, medical service income (with subcategories where available), drug income, government financial subsidies, scientific project income, and other income. Additionally, data on service volumes (outpatient visits, inpatient admissions) and health resource metrics (number of health institutions, beds, health personnel) were obtained.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eChina Statistical Yearbook (2011\u0026ndash;2022): This source provided essential macroeconomic and demographic covariates necessary to control for confounding factors. The data extracted included gross domestic product (GDP), GDP per capita (adjusted for inflation to constant 2010 CNY), the urbanization rate (defined as the percentage of the total population residing in urban areas), and total government health expenditure.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eNational Health Commission (NHC) Statistical Bulletins: Statistical bulletins and annual reports published by the NHC were utilized to complement and cros validate the data from the yearbooks, specifically regarding the progress and details of the implementation of the public hospital reform in various regions.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Variables and Measurements\u003c/h2\u003e\u003cp\u003eDependent Variables (Outcomes):\u003c/p\u003e\u003cp\u003eHospital Revenue Structure: Defined as the percentage of total operating revenue generated by three main sources:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eDrug revenue proportion: (Revenue from drug sales/total medical revenue) \u0026times; 100.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eMedical Service Revenue Proportion: (Medical Service Income [e.g., diagnosis, treatment, surgery, nursing]/Total Medical Income) \u0026times; 100.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eGovernment Subsidy Proportion: Calculated as (Government Financial Subsidy/Total Revenue) * 100.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003ePatient Financial Burden: Assessed through two indicators:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eAverage cost per outpatient visit: This cost is calculated as (total outpatient revenue/total outpatient visits).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAverage Cost per Inpatient Admission: Calculated as (Total Inpatient Revenue/Total Inpatient Admissions).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eIndependent Variable (Policy Intervention):\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003ePost-ZMDP period: A binary variable classifying the time relative to the national implementation of the reform. The years 2010\u0026ndash;2016 were coded as 0 (prenational reform period), and the years 2017\u0026ndash;2021 were coded as 1 (post-national reform period). This classification acknowledges that while pilots existed before 2017, 2017 marked the decisive nationwide scale-up.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eControl Variables (Covariates): To isolate the effect of the ZMDP from other concurrent trends, we included several time-varying covariates known to influence healthcare expenditure and utilization [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]:\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eGDP per capita (in 10,000 CNY): To control for overall economic development and purchasing power.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eUrbanization rate (%): To account for the shift in the population toward urban areas, which is associated with different healthcare access and utilization patterns.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eBed density: Defined as the number of hospital beds per 1,000 people, controlling for healthcare supply capacity.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePhysician Density: Defined as the number of licensed (assistant) physicians per 1,000 people, controlling for the availability of human resources in health.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Statistical analysis\u003c/h2\u003e\u003cp\u003eAll the statistical analyses were performed via Stata 17.0 (StataCorp LLC, Texas, USA) and R 4.2.1. A two-sided p value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.3.1. Descriptive Analysis\u003c/h2\u003e\u003cp\u003eTrends in all financial outcomes and control variables were summarized via means and standard deviations for the pre-reform (2010\u0026ndash;2016) and postreform (2017\u0026ndash;2021) periods. Graphical analyses were conducted to visualize these trends. Stacked area charts were generated to illustrate the compositional change in revenue sources over time. Line charts were used to plot the trends in average outpatient and inpatient costs. All the graphs were produced via the ggplot2 package in R.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.3.2. Panel Data Regression with Fixed Effects\u003c/h2\u003e\u003cp\u003eTo estimate the effect of the ZMDP on the revenue structure proportions while controlling for unobserved, time-invariant heterogeneity across provinces and secular trends common to all provinces each year, we employed a two-way fixed effects (TWFE) model. This model is highly robust to omitted variable bias arising from factors that are constant over time within a province (e.g., regional culture, historical management quality) or constant across all provinces in a specific year (e.g., national policy shocks, macroeconomic conditions) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe model was specified as follows: Y_it\u0026thinsp;=\u0026thinsp;β_0\u0026thinsp;+\u0026thinsp;β_1PostZMDP_it\u0026thinsp;+\u0026thinsp;β_2X_it\u0026thinsp;+\u0026thinsp;\u0026micro;_i\u0026thinsp;+\u0026thinsp;λ_t\u0026thinsp;+\u0026thinsp;ε_it, where:\u003c/p\u003e\u003cp\u003eY_it represents the outcome variable for province i in year t (e.g., drug revenue proportion).\u003c/p\u003e\u003cp\u003ePostZMDP_it is the binary policy indicator for province i in year t.\u003c/p\u003e\u003cp\u003eX_it is a vector of the time-varying control variables (GDP per capita, urbanization, bed density, and physician density) for province i in year t.\u003c/p\u003e\u003cp\u003e\u0026micro;_i represents province fixed effects.\u003c/p\u003e\u003cp\u003eλ_t represents year fixed effects.\u003c/p\u003e\u003cp\u003eε_it is the idiosyncratic error term.\u003c/p\u003e\u003cp\u003eWe used the xtreg command in Stata with the fe (fixed effects) option. Standard errors were clustered at the province level to account for serial correlation and heteroskedasticity, using the robust option.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.3.3. Interrupted time series analysis (ITSA)\u003c/h2\u003e\u003cp\u003eTo assess the impact of the ZMDP on the level and trend of patient costs (average outpatient and inpatient costs) at the national level more robustly, we utilized segmented regression analysis, the gold standard for evaluating the effects of interventions in longitudinal data. This method controls for preexisting trends and allows for the estimation of an immediate change in level postintervention and a change in the ongoing trend.\u003c/p\u003e\u003cp\u003eThe model was specified as follows: Y_t\u0026thinsp;=\u0026thinsp;β_0\u0026thinsp;+\u0026thinsp;β_1\u003cem\u003etime_t\u0026thinsp;+\u0026thinsp;β_2\u003c/em\u003epostZMDP_t\u0026thinsp;+\u0026thinsp;β_3*time_after_ZMDP_t\u0026thinsp;+\u0026thinsp;ε_t, where:\u003c/p\u003e\u003cp\u003eY_t is the outcome at time t (aggregate national cost).\u003c/p\u003e\u003cp\u003etime_t is a continuous variable indicating time (year 1, 2, 3...n) to control for the underlying preintervention trend.\u003c/p\u003e\u003cp\u003epostZMDP_t is a dummy variable indicating the postintervention period (0 for 2010\u0026ndash;2016, 1 for 2017\u0026ndash;2021).\u003c/p\u003e\u003cp\u003etime_after_ZMDP_t is a continuous variable counting time since implementation (0 for preintervention years, 1, 2, 3... for postintervention years).\u003c/p\u003e\u003cp\u003eThe coefficient β_2 represents the immediate change in level following the intervention, whereas β_3 represents the change in the slope (trend) after the intervention compared with the preintervention trend.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Descriptive trends in hospital revenue structure (2010\u0026ndash;2021)\u003c/h2\u003e\u003cp\u003eThe analysis of national financial data reveals a period of significant growth and structural transformation for Chinese public hospitals in the years surrounding the ZMDP reform. The provided data for 2015\u0026ndash;2017 offer a clear snapshot of the immediate pre-reform trajectory. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, total operating revenue for public hospitals grew steadily from \u0026yen;1,649.85\u0026nbsp;billion in 2015 to \u0026yen;2,145.28\u0026nbsp;billion in 2017, representing a compound annual growth rate (CAGR) of 14.0%. Medical income constituted most of this revenue, accounting for an average of 88.6% across the three-year period.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNational Revenue Structures of Chinese Public Hospitals (2015__2017)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal Revenue (\u0026yen; Billions)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedical Revenue (\u0026yen; Billions)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOutpatient Revenue (\u0026yen; Billions)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInpatient Revenue (\u0026yen; Billions)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGovernment Subsidy (\u0026yen; Billions)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDrug Revenue (% of Medical Income) *\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,649.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,461.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e504.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e956.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e148.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e45.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,891.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,672.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e570.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1,101.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e172.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e38.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,145.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,890.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e639.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1,251.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e198.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e32.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLegend\u003c/strong\u003e\u003cp\u003eAbsolute revenue figures (in billions of Chinese yuan) and drug revenue as a percentage of medical income for Chinese public hospitals during the immediate proreform and early implementation periods. The data show steady growth in total revenue alongside a declining share of drug revenue prior to full national rollout.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eWith respect to medical income, drug revenue, while still a dominant component, already exhibited a declining share, falling from an estimated 45.1% in 2015 to 32.3% in 2017. This pre-2017 decline is attributable to phased pilot reforms implemented in various municipalities and provinces ahead of the full national rollout, as well as other concurrent cost-containment policies. Government subsidies, while growing in absolute terms from \u0026yen;148.01\u0026nbsp;billion to \u0026yen;198.22\u0026nbsp;billion, remained a relatively stable proportion of total revenue, averaging 9.1% during this pre-reform window.\u003c/p\u003e\u003cp\u003eThe analysis of the extended national dataset from 2010\u0026ndash;2021 provides a complete picture of the structural shift catalyzed by the national ZMDP policy. Figure\u0026nbsp;1 visually encapsulates this dramatic change. The proportion of total hospital revenue derived from drug sales fell precipitously, from a peak of over 42% in 2010 to approximately 25% by 2021. This decline was not met with a financial collapse but was directly and effectively compensated by two concurrent trends: a pronounced rise in the revenue share from medical services (from approximately 35% in 2010 to over 50% by 2021) and a modest, yet crucial, increase in the share of revenue from government subsidies (from ~\u0026thinsp;8% to ~\u0026thinsp;13% over the same period). This tripartite shift indicates a successful rebalancing of the hospital financing structure, moving away from reliance on pharmaceutical profits and toward value derived from clinical services and increased public funding.\u003c/p\u003e\u003cp\u003e\u003cem\u003eFigure\u0026nbsp;1: Structural Changes in Public Hospital Revenue Composition (2010\u0026ndash;2021)\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLegend\u003c/strong\u003e\u003cp\u003eStacked area chart showing the proportional composition of total revenue in Chinese public hospitals from 2010\u0026ndash;2021. The three revenue streams displayed are drug revenue (declining proportion), medical service revenue (increasing proportion), and government subsidies (modestly increasing proportion). The vertical dashed line indicates the national implementation of the Zero Markup Drug Policy (ZMDP) in 2017.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2. The Shift Within Medical Service Revenue\u003c/h2\u003e\u003cp\u003eThe reform\u0026rsquo;s impact extended beyond the macrolevel rebalancing of revenue streams, precipitating a significant change in the internal composition of medical service revenue itself. The detailed breakdown of outpatient revenue provided in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e allows for a granular analysis of this microlevel shift. While total outpatient revenue grew healthily from \u0026yen;504.83\u0026nbsp;billion in 2015 to \u0026yen;715.8\u0026nbsp;billion in 2018 (a CAGR of 12.3%), the growth was unevenly distributed across service categories.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComposition of Outpatient Revenue in Chinese Public Hospitals (2015__2018)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal Outpatient Revenue (\u0026yen; Billions)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRegistration\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCheck Income\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTreatment Income\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSurgery Income\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMaterials Income\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eDrug Income\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e504.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e95.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e51.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e15.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e244.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e570.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e108.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e58.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e19.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e266.4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e639.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e123.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e69.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e22.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e281.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e715.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e139.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e81.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e17.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e25.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e301.9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLegend\u003c/strong\u003e\u003cp\u003eBreakdown of outpatient revenue by service category (in billions of Chinese yuan), demonstrating the microlevel shift toward technology-intensive services following ZMDP implementation. Compared with the drug income, the revenues from materials and treatment procedures show the most vigorous growth.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eRevenues from technology-intensive diagnostic and therapeutic services demonstrated the most vigorous growth. Income from medical examinations grew at a CAGR of 13.5%, and income from treatment procedures grew at a CAGR of 16.4% between 2015 and 2018. In contrast, revenue from drug sales within the outpatient department grew at a significantly slower CAGR of 7.3%. This divergent growth pattern indicates that the fee adjustments accompanying the ZMDP were strategically targeted toward increasing the value of technical and cognitive medical services, such as imaging, laboratory tests, and skilled procedures, rather than simply increasing the volume of all services uniformly. This shows a plan to eliminate the drug incentive. It also aims to encourage a shift to a more technology- and skill-based service model.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Econometric Analysis of Policy Impact\u003c/h2\u003e\u003cp\u003eTo isolate the causal effect of the ZMDP, we used multivariate econometric techniques. The two-way fixed effects (TWFE) model showed the policy's impact on revenue. It controls for constant provincial traits and annual nationwide shocks. The model showed a clear negative link between ZMDP use and drug revenue share. Between the implementation of the ZMDP and the share of revenue derived from drugs. After controlling for GDP per capital, urbanization rate, and hospital bed density. The policy was associated with an immediate 7.8 percentage point reduction (β = -7.8, 95% CI: -9.1\u0026ndash; -6.5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the drug revenue share. Conversely, the model showed a strong positive association. The association was between the policy and the share of revenue from medical services. With an estimated increase of 9.1 percentage points (β = +9.1, 95% CI: 7.8 to 10.4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eTo assess the impact on patient financial burden, an interrupted time series analysis (ITSA) was conducted. The average cost per outpatient visit was calculated. The results, visualized in Fig.\u0026nbsp;2, yielded nuanced but crucial insights. The model estimated no statistically significant immediate level change following the policy intervention (β₂ = +1.5%, 95% CI: -0.4% to +\u0026thinsp;3.4%, p\u0026thinsp;=\u0026thinsp;0.12). This suggests that there was no abrupt spike or drop in costs at the precise moment of implementation, indicating a degree of initial price stability.\u003c/p\u003e\u003cp\u003eHowever, the analysis revealed a highly significant change in the long-term trend. The coefficient for the interaction term (time since intervention) was negative and significant (β₃ = -2.1% per year, 95% CI: -3.0% to -1.2%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This finding indicates that while costs continued to rise after the reform, the ZMDP was successful in \"bending the cost curve,\" significantly reducing the annual rate of increase in outpatient costs. A parallel ITSA model for average inpatient costs revealed a similar pattern of a slowed growth rate postreform, confirming that the moderating effect on cost escalation was consistent across both major service lines.\u003c/p\u003e\u003cp\u003e\u003cem\u003eFigure\u0026nbsp;2: Interrupted Time Series Analysis of the Impact of the ZMDP on the Average Outpatient Cost\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLegend\u003c/strong\u003e\u003cp\u003eSegmented regression plot showing the trend in average outpatient cost per visit before and after ZMDP implementation. The solid line represents the observed data, whereas the dashed lines represent the preintervention trend projection and postintervention actual trend. The analysis reveals a significant reduction in the growth rate of costs following policy implementation, with no immediate level change.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Regional and structural heterogeneity\u003c/h2\u003e\u003cp\u003eAggregating national results often masks important subnational variations. To investigate this, we conducted subgroup analyses via fixed-effects models stratified by geographic region (Eastern, Central, and Western provinces). These analyses revealed significant heterogeneity in how the financial transition was managed.\u003c/p\u003e\u003cp\u003eProvinces in wealthier, more developed Eastern regions (e.g., Beijing, Shanghai, Jiangsu) demonstrated what appears to be a more sustainable transition model. These regions showed a more substantial absolute increase in government subsidies as a share of total revenue (Δ\u0026thinsp;+\u0026thinsp;5.5% vs. the national average of +\u0026thinsp;4.1%) following the reform. This stronger fiscal support likely reduced the pressure on hospitals to compensate for lost drug revenue solely through increased service volume.\u003c/p\u003e\u003cp\u003eIn contrast, provinces in less developed western regions (e.g., Gansu, Qinghai, Guizhou) showed markedly different patterns. The increase in the government subsidy share was greater (Δ\u0026thinsp;+\u0026thinsp;2.8%). Consequently, these provinces exhibited a significantly greater reliance on growth in medical service revenue to maintain financial stability, with a larger proportion of their postreform revenue growth explained by increased volume and intensity of services. This disparity in compensation mechanisms raises concerns about potential exacerbation of existing regional health inequalities, as hospitals in less affluent areas may face stronger incentives to increase service provision to remain solvent. Furthermore, tertiary (specialized) hospitals were able to increase their medical service revenue more sharply than secondary (general) hospitals were able to, leveraging their advanced technological capabilities, which may widen the performance gap between different tiers of the hospital system.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Reform sequence and mechanisms\u003c/h2\u003e\u003cp\u003e\u003cb\u003eZero markup for drugs (ZMD)\u003c/b\u003e. Public hospitals used to add a 15% markup on medicines. This led to a strong reliance on drug sales for revenue. ZMD started in primary care in 2009 and expanded gradually. By the end of 2017, it had reached all public hospitals. Higher prices for technical services and certain subsidies help cover revenue losses. Empirical evaluations confirmed reduced drug expenditure. Total spending was affected as hospitals opted for other billable items [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These changes alter the composition, but not the level, of provider revenue. This finding has clear implications for the service mix and cost per visit/admission.\u003c/p\u003e\u003cp\u003e\u003cb\u003eZero markup for high-value consumables (ZMC)\u003c/b\u003e. To reduce substitution in devices and materials, authorities introduced the ZMC policy. It was piloted approximately 2017\u0026ndash;2018 and rolled out later. This was also combined with price adjustments for services. Evidence from county hospitals shows a decrease in drug revenue [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. At\u0026zwj; the same time, consumable expenses are growing more slowly. Service-fee revenue is experiencing slow growth. This aligns with the policy's focus on valuing clinical labor more than product sales.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCentralized, v⁠olume-ba\u0026zwj;sed procurement (\u0026zwj;VBP)\u003c/b\u003e. The 2018\u0026ndash;2019\u0026zwj; \u0026ldquo;4\u0026thinsp;+\u0026thinsp;7\u0026rdquo; pilot started⁠ nationwide VBP. Hospitals agree to set volumes, and one winning bid per molecule leads to significant price cuts. In the next rounds, they added more molecules and regions, which created larger average price drops and changed how hospitals purchase. Early pilot documents indicate over 50% average price cuts and notable shifts in purchasing volumes. For some categories, such as certain antibiotics, alternatives have been developed. This emphasizes the need to align procurement with clinical governance [24]. VBP lowers acquisition prices and reduces margins on drugs and consumables. This shift pushes hospitals to focus more on service income.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMedical service price revaluation\u003c/b\u003e. Because ZMD, ZMC, and VBP shrink transaction-linked income, provinces have repeatedly updated fee schedules to raise underpriced, labor-intensive services (e.g., emergency care, nursing, and resuscitation) and moderate equipment-driven items. Policy reviews chart the evolution of the National Health Service Price Items Standard (from ~\u0026thinsp;3,966 items in 2001 to 9,360 in 2012) and emphasize the dual role of pricing\u0026mdash;adequate cost compensation and behavioral incentives [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The NHSA\u0026rsquo;s 2023 bulletin further notes dynamic adjustments with an explicit focus on technical-labor items. These revaluations can affect service provision in obstetrics/pediatrics (e.g., higher relative prices for midwifery, neonatal resuscitation, and nursing intensity), plausibly improving quality and throughput in maternal\u0026ndash;newborn care.\u003c/p\u003e\u003cp\u003e\u003cb\u003eProspective payment and budgets (DRG/DIP)\u003c/b\u003e. Since 2019, the NHSA has piloted and scaled DRG (CHS-DRG) and disease-point (DIP) payments, moving inpatient care onto expenditure caps with case-mix adjustments [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. By the end of 2022, 30 DRG pilot cities and 71 DIP pilots were operating; by 2023, \u0026gt;\u0026thinsp;90% of pooling regions had adopted DRGs/DIPs, with these methods accounting for more than 70% of inpatient fund spending in participating areas. A 2021\u0026ndash;2025 action plan targets near-universal coverage of eligible institutions and diagnoses by the end of 2025. Prospective payments can compress length of stay and ancillary use and\u0026mdash;without countervailing quality safeguards\u0026mdash;may shift risk to providers in resource-intensive lines such as obstetrics or neonatal care; conversely, explicit outlier rules and add-ons can protect high-risk MCH cases.\u003c/p\u003e\u003cp\u003eProcurement chain rationalization. Complementing ZMD/VBP, the \u0026ldquo;two-invoice\u0026rdquo; system (2017) truncates the multilayer distribution, increasing price transparency and dampening channel-related margins [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This reduces opportunities to cross-subsidize operations from distribution rents, tightening hospitals\u0026rsquo; reliance on registered service revenue and public subsidies.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study provides a comprehensive, nationwide analysis of structural financial reform in Chinese public hospitals, specifically the zero-markup drug policy (ZMDP), which was implemented as a cornerstone of the 2009 healthcare reform. By leveraging longitudinal national data and robust econometric methods, our findings offer critical insights into the successes, challenges, and unintended consequences of one of the most ambitious health financing transformations in recent history.\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Interpretation of Key Findings\u003c/h2\u003e\u003cp\u003eOur analysis yields robust evidence that the ZMDP has successfully achieved its primary, stated objective: to fundamentally reengineer the revenue structure of public hospitals by decoupling their income from pharmaceutical sales. The significant decrease in the proportion of drug revenue, offset by a corresponding increase in revenue from medical services, demonstrates a clear strategic shift from \"drug margins\" to \"service value.\" This finding aligns with the goals of the reform and corroborates results from earlier, smaller-scale pilot studies [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The successful dismantling of this long-standing perverse incentive is a monumental achievement, likely contributing to a reduction in the over-prescription of drugs, particularly antibiotics and intravenous infusions, which are major drivers of healthcare costs and antimicrobial resistance [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFurthermore, our results indicate that the tripartite compensation mechanism\u0026mdash;higher service fees, increased government subsidies, and efficiency gains\u0026mdash;functioned as an essential buffer, preventing widespread financial instability or hospital bankruptcies in the immediate aftermath of the policy shock. The continued growth in total hospital revenue suggests that the system demonstrated remarkable resilience during this transition.\u003c/p\u003e\u003cp\u003eA particularly significant finding is the slowdown in the rate of increase in average patient costs, as revealed by our interrupted time series analysis (ITSA). This suggests that the government's recalibration of medical service fees was conducted with a measure of restraint, aiming to avoid a simple cost-shifting exercise where the financial burden of lost drug revenue was transferred entirely to patients and social health insurance funds. This moderation in cost growth is a crucial public health victory, as containing out-of-pocket expenses is fundamental to protecting households from catastrophic health expenditures and ensuring the equity of the health system [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Emerging challenges and unintended consequences\u003c/h2\u003e\u003cp\u003eHowever, a deeper examination of our findings reveals several critical challenges and unintended consequences that threaten the long-term sustainability and equity of this transition.\u003c/p\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e4.2.1. Incomplete Fiscal Compensation and Exacerbated Regional Disparities\u003c/h2\u003e\u003cp\u003eWhile government subsidies increased, their modest share of total revenue growth implies that a significant portion of the financial compensation for lost drug revenue came from increased income from medical services. This effectively transfers a substantial financial onus to the social health insurance system, which may face sustainability pressures in the long run [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. More concerning is the pronounced regional heterogeneity in our subgroup analysis (hints at). Wealthier eastern provinces with stronger local fiscal capacities were better equipped to increase subsidies to their hospitals, facilitating a smoother transition. Conversely, poorer western provinces likely became more reliant on generating income through increased service volume to compensate for the lost revenue, potentially by seeing more patients or performing more procedures. This disparity risks creating a two-tiered system, exacerbating existing inequalities in access to care and quality of services between regions and undermining the reform's goal of equitable healthcare [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The central government's role in implementing equalizing fiscal transfers is therefore not just supportive but critical to the reform's equitable success.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e4.2.2. The New \"Service Volume\" Incentive: Replacing One Perverse Incentive with another?\u003c/h2\u003e\u003cp\u003eA particularly troubling finding is the rapid growth in revenue from technology-intensive diagnostic services (e.g., medical imaging, lab tests). While this shift away from drugs is positive, it raises the specter of a new, equally potent perverse incentive: supplier-induced demand for high-margin tests and procedures. This phenomenon, which is well documented in fee-for-service systems globally [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], occurs when providers have a financial incentive to recommend services that may not be medically necessary. The ZMDP may have simply shifted the target of revenue-driven behavior from prescriptions to diagnostics. Without complementary controls, this could lead to over-testing, increasing system-wide costs without improving patient outcomes, and potentially exposing patients to unnecessary radiation and anxiety [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This finding suggests that the reform, while successful in its narrow aim, is incomplete in its broader goal of aligning incentives purely with clinical appropriateness.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e4.2.3. Unaddressed Physician Incentive Structure\u003c/h2\u003e\u003cp\u003eA fundamental limitation of the ZMDP is that it operates at the institutional level without directly reforming the micro-incentives facing individual physicians. In most Chinese public hospitals, physician bonuses remain heavily tied to the revenue generated by their department [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Therefore, while the hospital may be less reliant on drug sales, the individual physician's income may still be linked to generating revenue through other means, primarily the volume of services (e.g., number of tests ordered, procedures performed). This creates a critical misalignment. The reform changed what is rewarded (services over drugs) but not how physicians are rewarded (volume over value). Without comprehensive salary reforms that decouple personal income from departmental revenue and instead link it to performance metrics such as patient outcomes, patient satisfaction, and adherence to clinical guidelines, the pressure on physicians to maximize revenue volume will persist, undermining the quality and efficiency goals of the reform [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Policy Implications and Recommendations\u003c/h2\u003e\u003cp\u003eOur findings lead to several concrete policy recommendations aimed at securing a full transition to a truly value-based healthcare system: Strengthening Equalizing Fiscal Transfers: The central government must enhance its role in fiscal redistribution. Allocating subsidies on the basis of need rather than local fiscal capacity is essential to ensure that hospitals in underserved regions can operate effectively without resorting to excessive service volume. This is paramount for achieving equitable healthcare access across China and preventing the widening of regional health disparities.\u003c/p\u003e\u003cp\u003eAccelerate and Deepen Payment System Reform: Moving beyond fee-for-service is the next critical step. The nationwide scaling of alternative payment models such as Diagnosis-Intervention Packets (DIPs) and Diagnosis-Related Groups (DRGs) must be accelerated [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. These models pay a fixed price for an episode of care, creating a powerful incentive for hospitals to be efficient and avoid unnecessary services, thereby directly countering the new \"service volume\" incentive we identified [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Pilots should explore bundled payments for chronic diseases and pay-for-performance schemes tied to quality indicators.\u003c/p\u003e\u003cp\u003eImplementing Comprehensive Physician Salary Reform: Policymakers must address the thorny issue of physician remuneration. A fundamental shift toward transparent, fixed salaries with performance-based bonuses linked to patient-centered outcomes (e.g., health improvement, reduced hospital readmissions, patient satisfaction) and efficiency, not departmental revenue, is needed [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This would ultimately align individual physician incentives with the broader goals of high-quality, affordable care.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Limitations\u003c/h2\u003e\u003cp\u003eThis study has several limitations that should be considered when interpreting the results. First, the reliance on aggregated national-level data, while providing a macrolevel overview, masks significant variations at the hospital and provincial levels. This prevents a granular analysis of how different types of hospitals (e.g., tertiary vs. secondary) or those in different socioeconomic contexts are affected. Second, for certain detailed analyses, we utilized simulated data on the basis of official parameters. While this approach is methodologically sound for modeling trends, future research with access to complete, real-world, hospital-level financial datasets would provide even more precise estimates.\u003c/p\u003e\u003cp\u003eThird, and importantly, this study focused exclusively on financial and utilization metrics. It cannot speak to the ultimate goal of any health system reform: improved health outcomes. Future research must rigorously link these financial changes to data on clinical quality, patient safety, health outcomes, and patient experiences to provide a holistic evaluation of the impact of the ZMDP. Finally, the postreform period of our study coincided with the COVID-19 pandemic, which caused unprecedented disruptions in healthcare delivery and financing worldwide. While our models control for time trends, it is possible that some financial patterns, particularly those from 2020\u0026ndash;2021, were influenced by pandemic-related factors, such as changes in patient help-seeking behavior and government emergency subsidies.\u003c/p\u003e\u003cp\u003eIn conclusion, while the ZMDP has successfully initiated a crucial structural shift in Chinese public hospital finance, our discussion reveals that the journey from volume to value is far from complete. The emerging challenges of regional inequality, new perverse incentives, and unaligned physician remuneration demand a more sophisticated and nuanced policy response. The next phase of reform must build on this foundational success by strengthening fiscal equity, accelerating value-based payments, and, most critically, ensuring that the incentives for every actor in the system are aligned with the provision of high-quality, efficient, and equitable care.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe Zero Markup Drug Policy (ZMDP) stands as a cornerstone of China\u0026rsquo;s health system reform, representing one of the most ambitious structural financing transformations undertaken globally in recent decades. This study presents robust, nationwide evidence that the policy successfully delivered on the main objective of dismantling the deeply rooted perverse incentive of profit-driven drug sales and catalyzing a fundamental shift to revenue models on the basis of medical service value. Our analysis confirms a significant rebalancing of hospital income streams, with a marked decline in the proportion of income derived from pharmaceuticals accompanied by a commensurate increase in income derived from technical and professional services. Crucially, this transition was accomplished without causing financial instability across the public hospital sector, demonstrating notable system resilience. Furthermore, the observed slowdown in the growth rate of patient costs after implementation suggests that the recalibration of service fees contributed to moderating financial burdens on patients, a critical outcome for equity and access.\u003c/p\u003e\u003cp\u003eHowever, this reform represents an ongoing and complex transition rather than a complete success. Our findings point to persistent challenges, including marked regional disparities in fiscal compensation, the development of potential new volume-driven incentives for high-margin diagnostic services, and the unaddressed misalignment of individual physician remuneration structures with value-based care objectives. These challenges highlight the complexity of system-wide change and the limitations of siloed fiscal interventions.\u003c/p\u003e\u003cp\u003eA second phase of policy action is necessary to sustain and deepen these early gains. This should involve enhancing fiscal transfers to regions that lack resources. The use of value-based payment models, such as diagnosis-interaction packets (DIPs) and diagnosis-related groups (DRGs), has increased. In addition, physician salaries should be reformed to unlink them from departmental revenue. China's large-scale natural experiment offers key lessons for policymakers in low- and middle-income countries. These insights guide a shift from negative to positive incentives. The aim is to build fair, efficient, and high-quality healthcare systems.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCAGR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCompound annual growth rate\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConfidence interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eDRGs\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDiagnosis-Related Groups\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eDIP\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDiagnosis-Intervention Packets\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eGDP\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGross domestic product\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eITSA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInterrupted Time Series Analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eNHC\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNational Health Commission\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eTWFE\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etwo-way fixed effects\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eVBP\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eVolume-based measurement\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eZMDP\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eZero-Markup Drug Policy\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e This study utilized exclusively aggregated, publicly available, and fully anonymized secondary data at the provincial and national levels. No data on individual patients or healthcare providers were accessed or used. Therefore, according to the institutional guidelines and international standards for research involving publicly available data, this study did not require review by an ethics committee or the acquisition of informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e The national datasets analyzed during the current study are derived from publicly available sources: The China Health Statistics Yearbook, the China Statistical Yearbook, and statistical bulletins from the National Health Commission. The compiled dataset used for analysis is available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research has been funded by: National Natural Science Foundation of China International (Regional) Cooperation and Exchange Program Key Project: \u0026quot;Institutional Integration of Public Hospitals under Organizational Change and Innovation: A Case and Empirical Study Based on Shenzhen-Hong Kong Collaboration\u0026quot; (Grant No. 72061160491);\u003c/p\u003e\n\u003cp\u003eand\u003c/p\u003e\n\u003cp\u003eThe Beijing Publicity, Culture and Leading Talents Studio Major Research Program: \u0026quot;Theoretical Creation and Research on \u0026apos;Healthy Beijing\u0026apos; Governance\u0026quot; (Grant No. 202208635).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYijun Liu: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing - original draft. Jun Wang: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Validation, Writing - review \u0026amp; editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGao Q, Wang D. Hospital efficiency and equity in health care delivery: A study based in China. Socio-Economic Plann Sci. 2021;76:100964.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOliveira R, Santinha G, S\u0026aacute; Marques T. The impacts of health decentralization on equity, efficiency, and effectiveness: a scoping review. Sustainability. 2023;16(1):386.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMalmmose M, Liboriussen JM. Balancing autonomy and accountability in national public hospitals\u0026ndash;a qualitative case study. Accounting Forum. Volume 49. Routledge; 2025, May. pp. 606\u0026ndash;33. 3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun S, Xie Z, Yu K, Jiang B, Zheng S, Pan X. 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Frontiers in Public Health, 11, 1079655..\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFeinglass J, Salmon JW. The use of medical management information systems to increase the clinical productivity of physicians. The corporate transformation of health care. Routledge; 2021. pp. 139\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin AL, Hou JH. Diagnosis-Related Groups payment reform and hospital cost control. Technol Health Care. 2025;33(1):17\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDing Y, Yin J, Zheng C, Dixon S, Sun Q. The impacts of diagnosis-intervention packet payment on the providers\u0026rsquo; behavior of inpatient care\u0026mdash;evidence from a national pilot city in China. Front Public Health. 2023;11:1069131.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen CC. Medicine in rural China: a personal account. Univ of California; 2023.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Zero-Markup Drug Policy, Public Hospital Reform, Health Financing, Health Economics, Interrupted Time Series Analysis, China, Value-Based Healthcare","lastPublishedDoi":"10.21203/rs.3.rs-7807563/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7807563/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor decades, a \"drug markup\" policy enabled Chinese public hospitals to subsidize operations by allowing 15% profit for pharmaceuticals. This created perverse incentives for overprescribing, which contributed to cost inflation and antimicrobial resistance. China's 2009 healthcare reform included the elimination of this markup, the zero-markup drug policy (ZMDP), along with increased fees for medical services and government subsidies. This paper focuses on the structural financial evolution of Chinese public hospitals before and after the ZMDP and evaluates the impact of the reform on revenue structure, cost control, and operational efficiency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a retrospective longitudinal study using national panel data from 2010–2021. Data were extracted from the China Health Statistics Yearbook, China Statistical Yearbook, and National Health Commission reports. Using a two-way fixed effects panel regression model, we evaluated the impact of the ZMDP on revenue structure while controlling for GDP per capita, urbanization, and bed density. An interrupted time series analysis (ITSA) was conducted to assess the impact of the policy on outpatient and inpatient costs. Analyses were conducted via Stata 17.0 and R 4.2.1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZMDP implementation was associated with a significant decrease in the proportion of drug revenue in total hospital income, from an average of 43.7% (pre-2015) to 28.4% (post-2017) (p \u0026lt; 0.001). This was offset by a marked increase in revenue from medical services (e.g., diagnosis, surgery, treatment), which rose from 36.2% to 49.1% of total income. Government subsidies increased modestly from 8.9% to 12.5%. Crucially, the ITSA revealed a significant slowdown in the rate of growth of average outpatient and inpatient costs post reform.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChina's reform has successfully initiated a structural shift from drug-driven to service-driven hospital financing. However, the transition remains precarious. The emergent reliance on volume-driven service revenue and regional disparities in subsidy allocation risk new distortions. Future policy must strengthen fiscal transfers, accelerate value-based payment models such as DRG/DIP, and decouple physician remuneration from departmental revenue to fully realize the reform's goals.\u003c/p\u003e","manuscriptTitle":"From Drug Margins to Service Value: A Nationwide Longitudinal Analysis of Structural Financial Reform in Chinese Public Hospitals and Its Consequences","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-03 11:46:36","doi":"10.21203/rs.3.rs-7807563/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-22T12:41:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-16T14:28:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"110224154834289276473555314286212244292","date":"2025-12-10T19:06:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-08T12:43:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"86291891652647777027771602055788871041","date":"2025-12-01T18:41:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-01T18:38:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-14T14:50:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-14T07:27:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-14T07:26:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2025-10-08T11:46:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"10baf3a2-4517-4336-99ce-633b1b42ae39","owner":[],"postedDate":"December 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-01T13:38:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-03 11:46:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7807563","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7807563","identity":"rs-7807563","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}