Evolutionary logic and governance mechanisms of China's non-public hemodialysis centres (2009--2026): a mixed-methods policy analysis extending Kingdon's Multiple Streams Framework
This mixed-methods study analyzes the 2009–2026 evolution of China's non-public hemodialysis sector through a novel Regulation–Response–Feedback–Adjustment (RRFA) model, revealing how state-guided collaborative governance and iterative policy-market interactions have driven the industry's transition from experimental exploration to high-quality transformation.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
Every year, millions of people around the world rely on dialysis, a life-sustaining treatment that filters waste from the blood when kidneys fail. In many countries, this care is provided almost entirely by public hospitals, but in China, a massive shortage of beds in these facilities left countless patients without access. To solve this, the government began inviting private companies to build and run their own dialysis centers. This shift created a complex experiment: how does a government guide a private market to provide essential, life-saving care without letting profit motives compromise safety or fairness? The answer lies in the messy, ongoing tug-of-war between rules set from the top and the creative, sometimes chaotic, ways businesses respond on the ground.
Researchers have long studied how policies are made, often using a theory that suggests a policy window opens when a problem, a solution, and a political will happen to line up perfectly. Once that window closes, the theory assumes the policy is set and the process stops. However, a new study of China's non-public dialysis centers from 2009 to 2026 challenges this static view. The researchers, drawing on policy documents, interviews with industry leaders, and national health data, found that the process is not a single event but a continuous loop. They propose a model where the government sets rules, companies respond, the results feed back to the government, and the rules are adjusted, starting the cycle all over again. This dynamic interaction, they argue, is how China managed to expand the number of non-public dialysis centers to 903 nationwide by April 2026, contributing to a national dialysis patient population that reached 1.3 million by the end of 2025, while constantly refining its approach to quality and cost.
The story of China's private dialysis sector unfolds in three distinct chapters, each driven by a different set of pressures. The first chapter, from 2009 to 2015, was a period of cautious experimentation. At the time, the number of patients needing dialysis was rising sharply, but public hospitals were overwhelmed. The government, seeking to fill the gap, opened the door for private investment but offered vague guidelines and little oversight. Private companies were hesitant; only a few pioneers dared to enter, and growth was slow. The main issue was that without clear rules or insurance coverage, patients faced high costs, and the quality of care varied wildly. It was a time of uncertainty, where the government was testing the waters and the market was waiting to see what would happen.
By 2016, the second chapter began, marked by a shift to rapid, policy-driven expansion. The government had gathered enough experience from the early pilots to create clear national standards, officially legitimizing independent dialysis centers. This clarity acted like a green light for private capital. The number of centers exploded, growing from a handful to nearly 750 by 2020. Different types of companies rushed in: some built massive supply chains to control costs, others focused on serving remote rural areas, and some used aggressive financial strategies to buy up competitors. While this solved the problem of availability, it created new ones. Centers clustered in wealthy eastern cities, leaving the west behind, and the rush to expand sometimes led to safety issues, such as infections. The government had successfully encouraged growth, but the focus was still on quantity rather than quality.
The third chapter, starting in 2021, represents a turn toward high-quality transformation. The rapid expansion had exposed deep flaws, and the government began to tighten its grip, not by stopping the private sector, but by changing the incentives. A major reform in how medical insurance pays for treatment shifted the focus from "more procedures equals more money" to "better outcomes and lower costs." This forced companies to adapt. Large, efficient chains began to innovate and improve their management, while smaller, less efficient centers struggled to survive or exited the market entirely. The government used data from national registries and insurance audits to monitor quality closely, creating a feedback loop where poor performance triggered immediate adjustments in policy. This phase was not about stopping the private sector but about guiding it toward a more sustainable and equitable future.
The researchers identified three key mechanisms that drive this entire process. First, there is a constant "fit-and-bargain" between the rules and the market. Companies with different resources respond differently to the same rules; a large chain might easily meet strict staffing requirements, while a small rural clinic might struggle, leading to creative but sometimes risky workarounds. Second, there is a "distortion" in how information travels back to the government. Local officials might hide problems to protect their own interests, or companies might temporarily fix issues just to pass an inspection, meaning the data the government sees is not always the full picture. Third, the way insurance pays for care acts as the ultimate lever. When payments are tied to volume, companies cut corners to see more patients; when payments are tied to quality and cost control, they are forced to become more efficient.
This study suggests that the classic idea of a policy window closing forever is wrong. In China's system, the window never truly shuts. Instead, new problems and feedback from the ground constantly reopen the door for adjustment. The government does not just set a rule and walk away; it learns from the results, tweaks the incentives, and sets new rules, creating a cycle of continuous improvement. This "state-guided collaborative governance" offers a different path from the purely market-driven approach seen in the United States or the strictly government-led model of Japan. It relies on the government setting the goals and the baseline rules, while using market tools like insurance payments to steer private companies toward those goals.
The findings offer a blueprint for other countries facing similar challenges. The researchers argue that the key to success is not choosing between total government control or total free market, but finding a dynamic balance. They recommend that governments use tiered regulations, allowing smaller, rural centers to meet different standards than large urban chains, and use insurance payments to reward quality rather than just volume. By treating policy as a learning process rather than a one-time decision, countries can build healthcare systems that are both accessible and sustainable. The study concludes that this iterative, feedback-driven approach is a viable solution for transitional economies, proving that even in a complex, high-stakes field like life-saving medical care, the path forward is built on constant adjustment and mutual learning between the state and the market.
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