The research team, led by Sabine Vogler of the Austrian National Public Health Institute, took an unusually broad approach. Previous studies of biosimilar policy typically examined a single country or a handful of biological substances. This analysis covered 27 European countries, including 24 European Union member states, Norway and Switzerland from the European Free Trade Association, and the United Kingdom, and spanned 27 biological active substances comprising roughly 200 individual products. The investigators drew on IQVIA MIDAS sales data covering the period from 2008 through the second quarter of 2024, capturing both hospital and community pharmacy settings for most countries. Cyprus, Denmark, Malta, Iceland and Liechtenstein had to be excluded because no data were recorded for them in the commercial database, a limitation the authors acknowledge, particularly given Denmark’s reputation as a biosimilar success story.

Quantitative analysis was only half the design. The team combined the sales data with a systematic mapping of national policies, compiled through standardised country fiches completed using information from the Pharmaceutical Pricing and Reimbursement Information network, a long-standing collaboration of public authorities responsible for medicine pricing across Europe. Twenty-one of the country fiches were validated by national officials, lending the policy mapping a degree of official confirmation that most cross-country comparisons lack. The researchers then layered on four detailed case studies, tracking market shares before and after specific interventions: switching guidance issued by Italy’s medicines agency, prescribing quotas for insulin glargine in France, and the introduction of pharmacy-level biosimilar substitution in Norway and France.

The policy taxonomy the researchers developed distinguishes supply-side measures, which shape prices and procurement, from demand-side measures, which target the behaviour of prescribers, pharmacists and patients. Supply-side tools include the biosimilar price link, in which a biosimilar’s price is set at a defined percentage below the originator’s; external price referencing, which borrows price benchmarks from other countries; competitive tendering, ubiquitous in hospital procurement; and horizon scanning to prepare payers for upcoming patent expiries. Demand-side tools range from prescribing by International Non-Proprietary Name and formal switching programmes to prescribing quotas, pharmacy substitution and gain-sharing models that return a portion of the savings generated by biosimilar use to hospitals or clinicians. Educational and awareness-raising activities, though present in every country studied, varied so widely in scope that the team excluded them from the comparative analysis.

The mapping revealed how saturated some policy instruments have become while others remain rare. Tendering is universal, applied in all 27 countries, particularly for hospital medicines. The biosimilar price link operates in 21 countries, and 18 countries include biologicals in their reference price systems for reimbursement. On the demand side, 24 countries now oblige, encourage or support prescribers to switch patients from originator biologics to biosimilars, or between biosimilars. Seventeen countries permit INN prescribing for biologicals and 15 impose prescribing quotas. By contrast, pharmacy-level substitution, long a staple of generic medicine policy, exists in only 11 countries, though momentum is building: Norway introduced it in 2021, France in 2022, and Finland, Germany and Switzerland all followed in 2024, typically starting with a narrow list of substances. Gain-sharing models, the study’s standout performer, remain confined to just ten countries.

When the researchers cross-referenced policies with market outcomes, a clear pattern emerged. Countries that had introduced gain-sharing models, encouraged switching, applied a biosimilar price link or imposed prescribing quotas showed, on average, the highest biosimilar market shares. Tendering and tendering-like procedures run by public payers in the outpatient sector were also associated with stronger uptake. The association with gain-sharing is particularly striking because the policy is so uncommon, implemented mainly in higher-income countries with large markets such as France, Germany and the United Kingdom. The authors are careful to stress that their cross-sectional comparison is exploratory and does not establish causation, but the case studies and the wider literature point in the same direction. In Italy, biosimilar market shares rose after the medicines agency published a position paper in March 2018 endorsing the interchangeability of biological medicines and supporting prescriber-initiated switches. In France, where quotas of 20 percent were set for insulin glargine in 2017 and raised to 40 percent in 2021, uptake of the targeted biosimilar increased. In Norway and France, the substances newly subject to pharmacy substitution, insulin glargine and teriparatide in the former and filgrastim and pegfilgrastim in the latter, saw rapid uptake gains after implementation.

The study’s systematic review of 16 empirical evaluations reinforced these findings. Prescribing quotas in Germany and Italy, gain-sharing experiments in France and Ireland, switching programmes in the United Kingdom and Denmark, and tendering systems in Hungary all increased biosimilar uptake in published analyses, with several also documenting substantial savings. One notable exception involved the biosimilar price link in Hungary, which reduced prices without lifting uptake, a reminder that lowering prices alone does not automatically change prescribing behaviour. The literature also emphasised the importance of policy design: gain-sharing works best when the clinical units that generate the savings are the ones that receive them, and price links must create a gap large enough to influence prescriber decisions.

Why do demand-side measures outperform? The authors argue that the principal barriers to biosimilar uptake are behavioural rather than economic. Physicians remain hesitant to switch clinically stable patients, citing lingering concerns about immunogenicity and equivalence, while patients harbour their own fears about safety. Policies that directly address these reservations, through clear regulatory guidance, financial incentives that reward prescribers, and credible communication, appear to provide the leverage that price mechanisms alone cannot. The Norwegian experience with the NOR-SWITCH trial is instructive: prescribing of infliximab biosimilars increased even before the landmark switching study was officially published, once draft findings confirming the safety of the switch circulated informally. The authors suggest that, given the accumulated real-world evidence on switching safety, improving communication should now take priority over commissioning yet more switching studies.

The findings arrive at a critical moment. A next wave of biosimilars is expected to enter European markets in the coming years, challenging some of the highest-priced originator biologics, and the untapped potential identified in low-uptake countries represents billions in possible savings. The study’s message to policymakers is twofold. First, effective interventions usually combine measures, ideally pairing demand-side drivers with supply-side tools such as strategic procurement that considers more than price alone and guards against markets becoming unattractive to suppliers. Second, design and context matter: the same policy instrument can succeed or fail depending on how it is calibrated, how stakeholders are engaged, and how it interacts with other measures already in place. The authors call for continuous monitoring and evaluation as countries implement and refine their policies, noting that even pharmacy substitution, too recent in several countries to show measurable population-level effects, showed promising early signals in the case studies. For a continent united by a single medicines regulator but divided by a fourfold gap in biosimilar uptake, the lesson is that approval was never the hard part; policy is.

Subject of Research: Policy analysis of biosimilar uptake and market shares across 27 European countries

Article Title: Biosimilar uptake policies in Europe: a cross-country mixed-methods policy analysis

Article References: Vogler, S., Habimana, K., Walter, M., Knoll, V., Kuriata, E., Leoszkiewicz, K., Mucha, K., Pelczarska, A., Skassa, E., Petrova, G., Milushewa, P., Mitkova, Z., Tachkov, K., Perelman, J., Alves, J., Santos, C., & Vasconcelos, H. (2026). Biosimilar uptake policies in Europe: a cross-country mixed-methods policy analysis. The Lancet Regional Health – Europe, 70, Article 101868. https://doi.org/10.1016/j.lanepe.2026.101868

Image Credits: AI Generated

DOI: 10.1016/j.lanepe.2026.101868

Keywords: biosimilars, pharmaceutical policy, drug pricing, Europe, market share, tendering, prescribing quotas, gain-sharing, switching, pharmacy substitution, health economics, The Lancet Regional Health

The debate centers on a study by Backes and colleagues, published earlier in the same journal, which asked whether the choice of hospital information system influences digital maturity scores. Digital maturity, in this context, is typically measured through structured national assessment frameworks that grade hospitals on the sophistication of their clinical, administrative, and technical digital capabilities, from electronic documentation and data exchange to advanced analytics and telehealth integration. Because hospitals overwhelmingly rely on commercial vendors for their core information systems, the intuition that vendor characteristics, such as market presence, product breadth, or implementation experience, might correlate with maturity outcomes is compelling. Policymakers increasingly want to know whether choosing the right vendor can accelerate digital transformation, and vendor selection has become a strategic decision with multi-million-dollar consequences.

Lyu and colleagues do not dispute that the question matters. What they dispute is whether the analytical approach used to answer it can support the conclusions drawn. Their commentary organizes its critique around three technical pillars: provider-level inference, self-inclusion, and market-size dependence. Each of these describes a distinct way that the statistical machinery of the original analysis could produce misleading estimates, and together they form a checklist that the authors believe should be applied to any study attempting to connect vendor characteristics to hospital outcomes.

The first pillar concerns what the authors call provider-level inference. When researchers examine whether vendor characteristics are associated with hospital digital maturity, the vendor characteristics themselves vary at the level of the provider, not the hospital. Multiple hospitals in a dataset may share the same vendor, meaning their values on vendor-level explanatory variables are identical. Ignoring this clustering and treating each hospital as an independent observation inflates the effective sample size for vendor-level effects and can dramatically overstate statistical confidence. The correspondence points to the established literature on cluster-robust inference, including widely cited methodological guidance by MacKinnon, Nielsen, and Webb, which emphasizes that standard errors must account for the structure of the data. Recent work by Huang on the failure of cluster-robust methods in small samples adds a further caution: when the number of clusters, in this case the number of distinct vendors, is limited, even cluster-robust corrections can perform poorly, producing confidence intervals that are too narrow and p-values that are too optimistic.

The implications are substantial. If a study includes thousands of hospitals served by only a handful of major vendors, the effective information about vendor-level effects is bounded by the number of vendors, not the number of hospitals. Any claim that a particular vendor attribute, such as market share or product portfolio breadth, is significantly associated with maturity outcomes must survive inference procedures that respect this clustering structure. The commentary argues that without such corrections, the reported associations may reflect statistical artifacts rather than genuine market dynamics, and the field risks building policy recommendations on findings that would not replicate.

The second pillar, self-inclusion, addresses a subtler but equally consequential problem. In many studies of this type, the vendors being evaluated as potential drivers of digital maturity are themselves embedded in the market being studied, and in some analytical framings, entities can effectively appear on both sides of the regression equation. When a provider characteristic is derived from data that includes the very hospitals whose outcomes it is meant to predict, the explanatory variable and the outcome variable become mechanically entangled. This can induce spurious correlation: the predictor partially contains information about the outcome by construction, rather than by any real-world causal pathway. Lyu and colleagues argue that the original analysis did not adequately separate the measurement of provider characteristics from the hospital populations used to compute them, leaving open the possibility that at least part of the observed association is an artifact of this circularity rather than evidence that vendor choice shapes maturity.

The third pillar, market-size dependence, concerns how vendor characteristics are defined and scaled. Characteristics such as vendor market share are inherently relative quantities that depend on the size and composition of the market being measured. A vendor serving a large fraction of hospitals in one region may serve a tiny fraction in another, and the same vendor may occupy different market positions in different hospital segments. If the analysis pools heterogeneous markets or computes vendor characteristics over an ill-defined population, the resulting measures can conflate vendor quality or strategy with simple market structure. An association between market share and digital maturity might then reflect regional differences in healthcare infrastructure, funding, or policy environments, rather than any property of the vendors themselves. The commentary suggests that without careful attention to how the relevant market is delimited and how provider characteristics are normalized, the estimated relationships remain open to confounding by market size.

The correspondence also situates the debate within a broader international context. Assessing hospital digital maturity has become a priority across health systems, and a recent viewpoint in the Journal of Medical Internet Res compared national assessment approaches in five countries, revealing just how differently countries operationalize the concept. Meanwhile, surveys of digital health companies’ experiences with electronic health record interfaces, including work published in the Journal of the American Medical Informatics Association, highlight how deeply vendor capabilities and interoperability practices shape what hospitals can actually achieve with their systems. Taken together, this literature underscores that vendor-hospital relationships are real and consequential, which makes it all the more important, the authors contend, that the statistical evidence linking them be rigorous.

For hospital leaders and procurement officials, the practical message is one of caution. If the association between vendor characteristics and digital maturity is weaker or less certain than early studies suggest, then decisions driven by the assumption that a particular vendor guarantees maturity gains may be misplaced. Investments in organizational readiness, staff training, workflow redesign, and governance may matter as much as or more than vendor selection, a conclusion consistent with decades of health informatics research showing that technology adoption succeeds or fails on sociotechnical grounds. The commentary does not claim that vendors are irrelevant; rather, it insists that the field currently lacks the inferential rigor needed to quantify exactly how much vendor characteristics contribute to maturity outcomes.

The authors of the correspondence, who report no funding and no competing interests, frame their intervention as constructive: a set of analytical safeguards, provider-level clustering with appropriate robust or hierarchical standard errors, careful exclusion of self-referential constructs, and explicit attention to market definitions, that future studies should adopt before drawing policy-relevant conclusions. As health systems worldwide pour resources into digital transformation and vendors compete to position their platforms as engines of maturity, the message from Hangzhou is clear: before declaring that system choice drives digital maturity, researchers must first ensure their statistics can legitimately make that claim. Until then, the true drivers of hospital digitalization remain an open and urgently important question.

Subject of Research: Methodological critique of statistical associations between health information system provider characteristics and hospital digital maturity

Article Title: Clarifying Associations Between HIS Provider Characteristics and Hospital Digital Maturity: Provider-Level Inference, Self-Inclusion, and Market-Size Dependence

Article References: Lyu, H., Hu, Y., & Fan, S. (2026). Clarifying Associations Between HIS Provider Characteristics and Hospital Digital Maturity: Provider-Level Inference, Self-Inclusion, and Market-Size Dependence. Journal of Medical Systems, 50(1), Article 129. https://doi.org/10.1007/s10916-026-02456-4

Image Credits: AI Generated

DOI: 10.1007/s10916-026-02456-4

Keywords: hospital information systems, digital maturity, health IT vendors, cluster-robust inference, provider-level analysis, market share, electronic health records, health informatics, statistical methodology, hospital digitalization, vendor selection, Journal of Medical Systems