The research, published in the International Journal for Equity in Health by a team from Guangxi Medical University led by Zijian Tang and Pinghua Zhu, tracked high-quality healthcare resource allocation, or HQHRA, from 2014 to 2023. Using a balanced panel of 310 province-year observations, the investigators built a composite index from four indicators: the stock of high-level hospital resources, hospital hierarchy, the structure of tertiary-hospital accreditation, and the scale structure of hospitals. Each indicator was combined with equal weights using the TOPSIS multi-criteria method, producing a single comparable score for every province in every year of the study period.

The headline finding is a story of gradual geographic rebalancing. The eastern region’s share of China’s high-quality hospital resources declined from 43 percent to 40 percent over the decade, while the western share climbed from 29 percent to 33 percent. The eastern–western gap, once a yawning 14 percentage points, narrowed to 7. Kernel density estimation confirmed that the overall national distribution shifted rightward, meaning more provinces moved into higher quality categories as the years passed. Yet the distributions also remained dispersed, signalling that broad improvement did not translate into uniform convergence across provinces. Some regions raced ahead while others advanced far more slowly, leaving substantial interprovincial differentiation intact.

Spatial statistics added a crucial layer to the picture. Global Moran’s I values stayed positive throughout the decade, ranging from 0.100 to 0.220, indicating that provinces with high HQHRA scores tended to cluster near other high-scoring neighbours, and low-scoring provinces clustered together too. The researchers then applied traditional and spatial Markov chain analysis to examine how provinces moved between quality states over time. Transitions occurred predominantly within the same state or between adjacent states, and the probabilities of moving up or down depended significantly on the quality of a province’s geographic neighbourhood. In other words, a province embedded among high-performing neighbours faced different transition odds than one surrounded by weak ones. The pattern revealed gradual evolution, strong path dependence, and spatially conditioned persistence—history and geography jointly shaped each province’s trajectory.

To uncover why some provinces achieved high-quality allocation while others did not, the team turned to the Technology–Organization–Environment (TOE) framework, organising eight conditions across technological, organisational, and environmental dimensions. They then deployed two complementary set-theoretic and constraint-based methods. Necessary condition analysis (NCA) tested whether any single factor acted as a hard bottleneck, while panel qualitative comparative analysis (panel QCA) searched for combinations of conditions that were jointly sufficient to produce high HQHRA.

The NCA results were striking. Three factors emerged as necessary but insufficient constraints: technological innovation capacity, clinical workforce capacity, and regional economic development. At the demanding 90 percent HQHRA target level, a province would need technological innovation capacity at a minimum of 59.9 percent of its observed range, clinical workforce capacity at 39.3 percent, and regional economic development at 53.9 percent of its observed range. Falling below these floors, the analysis suggests, makes high-level allocation effectively unreachable regardless of other efforts. Yet reaching these thresholds guarantees nothing on its own—each condition is a prerequisite, not a cause.

Panel QCA found no single set-theoretically necessary condition, but it identified four distinct sufficient configurations for achieving high HQHRA. The first is an innovation-led pattern, in which strong technological innovation capacity anchors the outcome. The second and third are variants of an innovation–demand pattern, one reinforced by economic strength and the other shaped by an ageing population context that raises demand for complex care. The fourth is a multi-capacity–demand synergy pattern, combining organisational capability with demand-side pressures. The overall solution achieved a consistency of 0.864 and coverage of 0.686, indicating the configurations reliably account for a substantial share of high-HQHRA cases. The researchers describe this structure as constrained equifinality: multiple different routes lead to the same destination, but only for provinces that have cleared the minimum floors on innovation, workforce, and economic capacity.

The policy implications are pointed. The authors argue that blanket institutional expansion—simply building more hospitals everywhere—is not enough. Instead, they call for prioritising low-HQHRA provinces that are embedded in disadvantaged neighbourhood contexts, where spatial spillovers work against improvement. Regional medical-centre and counterpart-assistance programmes, they suggest, should couple infrastructure investment with technology transfer, specialist rotations, workforce development and retention, referral coordination, and local capacity building. Rather than uniform prescriptions, provincial interventions should be tailored to local bottlenecks: innovation absorption in some regions, demand-responsive specialty services in others, ageing-oriented integrated care where demographic pressure dominates, or regional capability diffusion where neighbourhood effects hold sway.

Notably, the study also proposes new yardsticks for measuring success. Rather than counting beds or buildings, policy performance should be assessed through reductions in avoidable patient outflow, increases in locally managed complex cases, workforce retention, and the adoption of transferred technologies—metrics that capture whether capacity is genuinely taking root rather than merely being distributed on paper.

For a nation of 1.4 billion people, the stakes of these findings extend well beyond academic debate. China’s hospital hierarchy system concentrates tertiary accreditation and specialist expertise in a relatively small number of elite institutions, and patients routinely travel across provincial borders for complex care, a phenomenon known as patient outflow. If high-quality resources remain locked into advantaged regions and advantaged neighbourhoods, health equity suffers in ways that ordinary expansion cannot fix. This study’s central contribution is to show that the routes out of that trap are real but conditional: a province must first build its innovation, workforce, and economic foundations, and then choose the configuration that matches its own technological, organisational, and environmental circumstances. The decade from 2014 to 2023 demonstrated that convergence is possible—the eastern–western gap nearly halved—but the persistence of spatially conditioned path dependence means the next decade’s progress will depend less on where resources are poured and more on whether the underlying capacities exist to absorb them.

Subject of Research: Spatiotemporal distribution, dynamic evolution, and configurational mechanisms of high-quality healthcare resource allocation across 31 mainland Chinese provincial regions from 2014 to 2023, analysed with panel QCA and necessary condition analysis within the TOE framework

Article Title: Spatiotemporal distribution, dynamic evolution, and configurational mechanisms of high-quality healthcare resource allocation in China—evidence from panel QCA and NCA within the TOE framework

Article References: Tang, Z., Zeng, P., Chen, H., Chen, Y., Yang, S., Zhang, W., Wang, Q., & Zhu, P. (2026). Spatiotemporal distribution, dynamic evolution, and configurational mechanisms of high-quality healthcare resource allocation in China—evidence from panel QCA and NCA within the TOE framework. International Journal for Equity in Health. https://doi.org/10.1186/s12939-026-03041-4

Image Credits: AI Generated

DOI: 10.1186/s12939-026-03041-4

Keywords: healthcare resource allocation, China, health equity, panel QCA, necessary condition analysis, TOE framework, hospital hierarchy, spatial Markov chains, regional inequality, health policy, constrained equifinality, TOPSIS

The study, led by Balázs Kertesz of the Doctoral School of Business and Management at the University of Debrecen, together with Klára Bíró of the Institute of Health Economics and Management and Péter Balogh of the Institute of Methodology and Business Digitalization, was designed to fill a stubborn evidence gap. Hungary’s public system has faced well-documented underfunding, leading to long waiting lists and constrained access to specialists. Those frictions have become the engine of private-sector growth, as households increasingly pay directly for consultations, diagnostics and elective procedures. Yet until now, policymakers have had little rigorous information about which attributes of private services actually matter to the general adult population, rather than to existing private clinic customers. Understanding those preferences matters for anyone planning service networks, setting prices, or designing policies aimed at narrowing regional inequalities in access.

Methodologically, the researchers deployed a two-stage approach that is considered state of the art in stated-preference research. Before constructing the main choice experiment, they conducted a best-worst scaling (BWS) exercise to identify which candidate attributes of private healthcare were most relevant to respondents. BWS asks participants to repeatedly identify the best and worst options in small sets of attributes, producing a ranking of relative importance that is less vulnerable to the scale distortions of simple rating scales. From this preparatory work, five attributes survived into the final experimental design: the cost of the service, the distance to the provider, the availability of appointments, the reputation of the provider, and the type of clinic or provider.

The main survey then confronted each of the 1,014 respondents with a series of realistic decision scenarios. In each of eight choice situations, participants saw three hypothetical private healthcare offers described by varying combinations of the five attributes, alongside a fourth option: to decline all of them and remain in public healthcare. This opt-out alternative is methodologically crucial. Because Hungarian adults can always default to the public system, a well-designed experiment must allow them to express that preference rather than forcing them to choose among private options they might reject in real life. Including a no-choice option anchors the experiment in the genuine decision architecture of Hungary’s dual system and prevents the overstatement of demand that plagues forced-choice designs.

The statistical engine behind the analysis was a conditional logit (CL) model, the workhorse specification for discrete choice data. Conditional logit assumes that the utility an individual derives from a given healthcare option is a linear function of its attributes plus a random error term, and that the probability of choosing any one option is proportional to its estimated utility relative to the full choice set. From the estimated coefficients, the team could compute both the relative importance of each attribute and—critically for health economics—willingness-to-pay (WTP) figures. WTP calculations translate abstract utility weights into monetary terms: they answer the question of how much more a patient would be willing to pay for, say, a provider located half as far away, or an appointment available a week sooner.

The results are unambiguous in their hierarchy. Provider distance emerged as the single most important attribute, accounting for 40.01% of the explained preference structure. Cost followed at 32.31%, and appointment availability took third place at 19.13%. Together, these three practical attributes dominated roughly nine-tenths of the preference weight in the model. Institutional reputation and provider type—the attributes often emphasized in private clinics’ own marketing, from glossy branding to physician celebrity—were far less influential. In other words, Hungarian patients are not primarily buying prestige when they enter the private market; they are buying proximity, affordability and speed. The willingness-to-pay estimates reinforce this picture, quantifying exactly how much shorter travel times and faster appointments are worth relative to each other and to price.

These findings carry a counterintuitive edge that helps explain the trajectory of Hungary’s private healthcare market. Many observers might assume that private care functions as a status good, with patients choosing flagship downtown clinics and renowned specialists. The data suggest something more mundane and, arguably, more consequential: demand behaves like a market for convenience. A private clinic that opens closer to where people live or work, prices its services within reach of median households, and offers appointments without long delays will capture preference share even if its reputation is unremarkable. Conversely, a prestigious institution located far from patients, or charging fees well beyond their comfort zone, leaves much of the market on the table—or sends it back to the public system entirely, where the no-choice option remains a real and frequently exercised alternative.

For policymakers, the study’s implications extend beyond marketing strategy into questions of equity and system design. Distance as the dominant attribute is a red flag for regional inequality: patients in smaller towns and rural areas face systematically longer travel to private providers, meaning the convenience premium of private care is distributed unevenly across the country. If accessibility, rather than quality perception, is what patients weigh most heavily, then the geography of private investment decisions will shape health access patterns for years to come. The findings can inform service-planning discussions about where new capacity should be located, how pricing structures affect uptake among lower-income groups, and how the public system’s waiting times—the very frictions that push patients private—might be reduced to rebalance the two sectors. The authors frame their results as input for exactly these policy conversations on access, affordability and regional disparities in Hungary’s mixed public-private healthcare system.

The research also exemplifies a broader methodological trend in health services research: the pairing of best-worst scaling with discrete choice experiments to build more realistic, behaviorally grounded preference models. By using BWS to prune the attribute set before the DCE, the researchers reduced cognitive burden on respondents and improved the interpretability of the final estimates. The inclusion of the public-care opt-out, the quota-representative sampling frame, and the translation of utilities into willingness-to-pay values together produce evidence that speaks directly to both economics and policy. The study received ethical approval from the Research Ethics Committee of the Faculty of Economics at the University of Debrecen and was conducted under the ethical principles of the Declaration of Helsinki, with all participants providing informed consent. Open access funding was provided by the University of Debrecen, and the authors thank Dr. Péter Czine for verifying the analytical methods. As Hungary’s private healthcare sector continues to expand against the backdrop of public-sector strain, this experiment provides a rare quantitative answer to a deceptively simple question: what makes a Hungarian adult choose to pay for care? The answer—closer, cheaper, sooner—may disappoint brand strategists, but it should energize anyone committed to making healthcare genuinely accessible.

Subject of Research: Patient preferences for private healthcare services among the Hungarian adult population, measured with a discrete choice experiment

Article Title: Understanding patient preferences for private healthcare services among the general Hungarian adult population using discrete choice experiment

Article References: Kertesz, B., Bíró, K., & Balogh, P. (2026). Understanding patient preferences for private healthcare services among the general Hungarian adult population using discrete choice experiment. Discover Social Science and Health. https://doi.org/10.1007/s44155-026-00482-8

Image Credits: AI Generated

DOI: 10.1007/s44155-026-00482-8

Keywords: discrete choice experiment, private healthcare, Hungary, patient preferences, willingness to pay, best-worst scaling, conditional logit, healthcare access, regional inequality, health policy, University of Debrecen, out-of-pocket spending