The study, published in Frontiers in Stroke, offers the first real-world snapshot of quality monitoring practices across telestroke networks worldwide. An international team led by Ayush Agarwal of the All India Institute of Medical Sciences in New Delhi and Christine Tunkl of University Hospital Heidelberg combined two complementary data sources: a global survey of telestroke network leaders and a systematic review of the published literature. Of 254 networks identified worldwide, 88 responded to the survey, a response rate of 34 percent, with 71 percent of responses coming from networks in high-income countries. The systematic review analyzed 92 studies describing 64 acute telestroke networks across 17 countries, 92 percent of which were based in high-income settings.

The headline finding is deceptively simple: quality monitoring is far from universal. Only 64 of the 88 surveyed networks, or 72 percent, reported performing any quality monitoring at all, meaning roughly one in four networks operates without systematically measuring its own performance. The picture was brighter in the published literature, where every one of the 64 networks described in studies reported some quality metrics, but the authors caution that this likely reflects reporting choices rather than genuine differences in practice. Networks that fail to publish their data may still be monitoring it, and networks that responded to the survey may not have captured everything they do.

To make sense of the data, the researchers applied a framework first described by the health care quality pioneer Avedis Donabedian in 1988, which divides quality assessment into three interconnected constructs: structure, process, and outcomes. Structure refers to the settings where care occurs, such as standard operating procedures, the mode of teleconsultation, and the methods used to transfer brain images. Process refers to the mechanics of delivering care, including door-to-needle time, door-to-CT time, and thrombolysis rates. Outcomes refer to the effects of care, such as stroke severity scores after treatment, mortality, functional recovery, and safety measures like symptomatic intracranial hemorrhage.

The structural picture was relatively encouraging. In the survey, 87 percent of networks reported operating under standard operating procedures, 87 percent used real-time video consultation, and 92 percent documented their teleconsultations formally. For image transfer, 73 percent relied on Picture Archiving and Communication Systems, the standard hospital infrastructure for storing and sharing medical images, while 26 percent used specialized applications and 9 percent resorted to consumer messaging platforms such as WhatsApp or Viber. In the published literature, 95 percent of networks described real-time video examination, and the reliance on PACS was lower at 26 percent, though more than half of the published studies simply did not specify how images were transferred.

Process metrics told a more uneven story. Door-to-treatment time was the most frequently documented indicator, reported by 95 percent of survey networks and 87 percent of those in the literature. Thrombolysis rate, the proportion of eligible patients who receive clot-dissolving drugs, was the single most common performance indicator across both data sources, monitored by 93 percent of survey networks and 87 percent of published networks. But other recommended process measures lagged far behind. Door-to-consultation time was tracked by only 4 percent of survey networks, and inter-hospital transfer rates, which reveal how often telestroke consultations lead to patient transfers for advanced treatment, appeared in 50 percent of published studies but were not offered as a survey option and were reported by none of the responding networks.

The outcome metrics showed the starkest gaps, and these are the measures that matter most to patients. In-hospital mortality was documented by 78 percent of survey networks but only 31 percent of published networks. NIHSS scores at discharge, a standard measure of neurological deficit, were recorded by 65 percent of survey networks but a mere 1 percent of published networks. NIHSS at 24 hours was almost entirely absent from both sources, appearing in just 1 percent of survey networks and 4 percent of published ones. Functional outcome measured with the modified Rankin Scale at three or six months, the gold standard for judging whether stroke treatment restores independence, was reported by 50 percent of survey networks and 35 percent of published networks. Secondary prevention measures were tracked by 34 percent of survey networks and appeared in none of the published studies.

The authors argue that this lopsided focus on speed over results is not simply negligence but reflects systemic constraints. Responsibility for data monitoring is often shared ambiguously between the telestroke provider and the recipient hospital, and shared data infrastructures that would allow standardized collection are frequently lacking. The patient pathway in telestroke typically spans multiple hospitals, as patients are transferred for higher-level care, fragmenting the record and complicating the attribution of outcomes. And without clear incentives or regulatory requirements, data monitoring is easily deprioritized as time-consuming and resource-intensive. The result is a field that has matured technically while remaining surprisingly immature in how it measures its own success.

There is evidence that monitoring pays off. A telestroke program in Catalonia, Spain, that tracked baseline and 24-hour NIHSS scores, thrombolysis rates, mortality, symptomatic hemorrhage, and three-month functional outcomes achieved a progressive drop in door-to-needle time between 2013 and 2015, an improvement the program’s researchers attributed directly to quality monitoring. A report from Michigan stroke hospitals between 2022 and 2023 found that telestroke evaluation increased the odds of thrombolysis by 61 percent among eligible patients but was associated with 44 percent lower odds of achieving door-to-needle times within 60 minutes, a gap that quality data helped identify and that innovations such as activating the teleconsultation before neuroimaging and storing the telestroke cart in the CT suite helped close. Registry data from Germany showed that telestroke implementation was associated with an absolute decrease of 14.4 percent in interhospital transfers across all stroke center levels, generating substantial cost savings.

To close the gap, the research team proposes a minimum dataset for acute telestroke services, organized along the Donabedian framework. On structure, it includes the teleconsultation mode and technical or service failures. On process, it includes door-to-needle time, intravenous thrombolysis rate, and the total number of stroke teleconsultations. On outcomes, it includes in-hospital mortality and symptomatic intracranial hemorrhage. The authors also recommend formal agreements that clearly define monitoring responsibilities between providers and recipient hospitals, regular joint data review, and eventually a national or global certification program with quality monitoring as a key requirement. Until the global stroke community agrees on a standardized core set of indicators, they warn, comparing outcomes across networks and driving meaningful improvement will remain an aspiration rather than a practice.

Subject of Research: Quality monitoring practices and recommended performance indicators in telestroke networks worldwide

Article Title: Quality monitoring in telestroke networks: real-world insights from survey response data and literature review

Article References: Quality monitoring in telestroke networks: real-world insights from survey response data and literature review. (n.d.). https://doi.org/10.3389/fstro.2026.1879025

Image Credits: AI Generated

DOI: 10.3389/fstro.2026.1879025

Keywords: telestroke, stroke care, quality monitoring, telemedicine, thrombolysis, quality indicators, NIHSS, door-to-needle time, patient outcomes, health care quality, stroke networks, Donabedian model

The study is notable not only for the size of the improvement but also for the methodological machinery behind it. Rather than relying on vague notions of good practice, the team built on an indicator system developed in earlier methodological work using the Delphi method, a structured consensus technique in which panels of experts iteratively rate and refine candidate quality measures until stable agreement is reached. The indicators were then weighted using the Analytic Hierarchy Process, a mathematical framework that converts expert judgments into consistent numerical priorities. This combination produced a set of twenty indicators spanning three categories: structural indicators, which measure whether the resources, protocols, and personnel needed for good pharmaceutical care are in place; process indicators, which measure whether recommended actions such as pain assessment and medication counseling are actually performed; and outcome indicators, which measure whether patients achieve adequate pain control in the end.

To turn those indicators into a single, interpretable score, the researchers employed TOPSIS, the Technique for Order Preference by Similarity to Ideal Solution. TOPSIS is a multi-criteria decision analysis method that positions each evaluated case in a multidimensional space defined by the indicator values, then calculates how close that case sits to an ideal solution, in which every indicator reaches its best possible value, and how far it sits from the worst possible solution. The resulting closeness coefficient, denoted C(i), ranges from zero to one and serves as a composite measure of pharmaceutical care quality. The team pre-specified a threshold of C(i) of at least 0.6 as the definition of overall adherence, giving the study an objective and reproducible yardstick rather than a subjective judgment call.

The evaluation took the form of a single-center before-and-after study with a historical control. First, the researchers retrospectively assessed a pre-intervention cohort of 100 patients who underwent elective orthopedic surgery between November 2024 and January 2025, establishing the baseline reference for pharmaceutical care quality. They then designed and introduced targeted interventions aimed at the weaknesses revealed by the indicator analysis. Finally, they prospectively evaluated a post-intervention cohort of 100 patients treated between April and June 2025. Each group comprised 200 patients in total across the study, and the two cohorts were compared on overall adherence and on the full-score attainment rates of the individual quality indicators, with a Bonferroni correction applied to guard against false positives arising from multiple statistical comparisons.

The headline result was striking. Overall adherence to pharmaceutical care standards, defined as a TOPSIS closeness coefficient of at least 0.6, increased from 41 percent in the pre-intervention cohort to 91 percent in the post-intervention cohort. That corresponds to a risk difference of 50 percentage points, with a 95 percent confidence interval of 37.83 to 60.05 and a P value below 0.001. The median difference in the composite score itself was 0.32, with a 95 percent confidence interval of 0.26 to 0.38. In practical terms, roughly nine out of ten surgical patients after the intervention received pharmaceutical care that met the expert-defined standard, compared with fewer than half before.

Because before-and-after studies with historical controls are vulnerable to bias, particularly when some documentation is missing from the earlier records, the team performed a sensitivity analysis to test how robust their conclusion was. The concern is straightforward: if documentation quality improved over time for reasons unrelated to actual care, some of the apparent improvement might be an artifact. The researchers therefore recalculated the comparison under the most optimistic assumption for the missing pre-intervention data, that is, the assumption most favorable to the baseline group. Even under that scenario, the post-intervention group remained significantly higher, at 91 percent versus 55 percent, with P below 0.001. The finding, in other words, survived a deliberately conservative stress test.

Dissecting the results by indicator type revealed a nuanced pattern. The four structural indicators showed descriptive improvements, with absolute increases ranging from 34 to 60 percentage points, suggesting that the intervention strengthened the underlying infrastructure of pharmaceutical care, such as the availability of standardized protocols and dedicated pharmacist involvement. Among the remaining sixteen indicators, multimodal analgesia, the practice of combining several analgesic mechanisms to achieve better pain relief with lower opioid doses, showed no statistically significant difference, sitting at 96 percent versus 99 percent. This ceiling effect is itself informative: the baseline performance was already near maximal, leaving little room for improvement on that particular measure.

The process and outcome indicators told a more varied story. Of the fifteen indicators in these categories other than multimodal analgesia, thirteen remained statistically significant after the stringent Bonferroni correction, which set the significance threshold at an adjusted alpha of 0.0031. Their risk differences ranged from 14 to 33 percentage points, indicating substantial and reliable gains in the day-to-day execution of pharmaceutical care and in intermediate clinical results. Two outcome indicators, however, did not clear the adjusted threshold: achievement of target resting pain control improved by 17 percentage points, and the combined indicator covering breakthrough pain incidence and its management improved by 10 percentage points, but neither reached the corrected level of significance. This suggests that while the machinery of care, the assessments, the documentation, and the medication management processes, improved dramatically, the ultimate clinical endpoints moved more modestly and require further work to confirm.

The authors are candid about the limitations of their design. A historical control cannot exclude secular trends, such as parallel improvements in staffing, awareness, or documentation practices that occurred between the two study windows. The reliance on a composite TOPSIS measure, while mathematically elegant, means that the headline adherence figure aggregates many distinct aspects of care, and the single-center setting raises questions about whether the same gains could be replicated in hospitals with different resources, case mixes, and pharmacy services. The two-month post-intervention window also leaves open the question of whether the improvements can be sustained over the long term, a common challenge for quality improvement programs of all kinds.

Even with those caveats, the study delivers something the perioperative pain field has lacked: empirical evidence that a rigorously constructed quality indicator system, combining Delphi consensus, hierarchical weighting, and TOPSIS scoring, can be implemented in routine clinical practice and can drive measurable, robust improvements in pharmaceutical care. The work provides a practical reference for hospitals seeking to formalize the pharmacist’s role in orthopedic pain management, and it demonstrates that quality in pharmaceutical care need not remain an abstraction. If the approach generalizes, the humble checklist of well-weighted indicators may prove to be one of the most effective tools yet for ensuring that patients waking up from orthopedic surgery receive the pain care that guidelines have long promised them.

Subject of Research: Quality indicators for pharmaceutical care in orthopedic perioperative pain management

Article Title: Application and evaluation of a quality indicator system for pharmaceutical care in orthopedic perioperative pain management

Article References: Zhu, Q., Feng, P., Chen, X., Huang, J., & Chen, Y. (2026). Application and evaluation of a quality indicator system for pharmaceutical care in orthopedic perioperative pain management. PLOS One, 21(10), e0360057. https://doi.org/10.1371/journal.pone.0360057

Image Credits: AI Generated

DOI: 10.1371/journal.pone.0360057

Keywords: pharmaceutical care, perioperative pain management, orthopedic surgery, quality indicators, TOPSIS, Delphi method, Analytic Hierarchy Process, multimodal analgesia, patient safety, clinical pharmacy, quality improvement, pain control

The clinical stakes are considerable. Gastric cancer remains a leading cause of cancer death worldwide, and its prognosis depends overwhelmingly on the stage at which it is detected. Early gastric cancer, confined to the innermost layers of the stomach wall, can often be treated endoscopically with organ preservation and excellent survival, whereas advanced disease carries a far grimmer outlook. That is why screening and opportunistic upper endoscopy place such emphasis on meticulous inspection. Professional bodies such as the European Society of Gastrointestinal Endoscopy and the Japan Gastroenterological Endoscopy Society have long recognized that incomplete visualization is a major source of missed lesions, and they have promoted systematic inspection protocols that divide the stomach into standard anatomical regions. What has been missing is an automated, quantitative measure of exposure that does not depend on an observer’s subjective judgment of whether a photograph or video frame shows a well-distended, clean, fully visible patch of mucosa.

The research team, led by investigators at Renmin Hospital of Wuhan University together with collaborator Cesare Hassan of Humanitas University in Milan, set out to close that gap. Their approach rests on a deep convolutional neural network trained to perform semantic segmentation of endoscopic images, meaning the model learns to classify every pixel in a frame as belonging to visible gastric mucosa or not. Before training and validating the algorithm, a panel of expert endoscopists defined a reference standard covering 19 anatomical sites of the stomach, spanning the greater and lesser curvatures and the anterior and posterior walls of the gastric body and antrum, along with the cardia, fundus, and angularis. For each of these sites, the experts established a threshold of mucosal exposure that constitutes adequate visualization. When the algorithm’s measured exposure area for a given site exceeds that threshold, the site is classified as adequately exposed; when it falls short, the site is flagged as inadequately exposed.

Technical validation of the segmentation model was carried out using standard metrics for image segmentation performance, including the mean intersection over union, a measure of how closely the algorithm’s pixel-level delineation of mucosa matches expert annotation. But the true test of the OGMEA concept came from clinical data of unusual scale and rigor. Rather than relying on retrospectively collected endoscopy images, the team embedded their analysis within a randomized controlled trial involving 25,320 participants recruited across 24 centers. This multi-center dataset, gathered under a protocol approved by the ethics committee of Renmin Hospital of Wuhan University and the ethics boards of every participating site, with written informed consent from all participants, provided a large, prospectively collected, and heterogeneous body of examinations against which the association between mucosal exposure and lesion detection could be tested.

The results of that analysis are striking. When the researchers classified assessed anatomical sites by the algorithm’s verdict, they found that only 46.36 percent of sites were adequately exposed, while a slight majority, 53.64 percent, fell below the OGMEA threshold. In other words, even in the setting of a controlled trial, more than half of the stomach regions examined were not visualized to the standard defined by the expert panel. This single number is likely to provoke discussion in endoscopy units around the world, because it quantifies, for the first time at this scale, how much room for improvement exists in a procedure that many patients assume is exhaustive.

More important than the raw exposure figures is what they predicted. At the level of individual anatomical sites, the detection rates of early gastric cancer, gastric neoplasms, precancerous conditions, and focal lesions were all significantly higher in adequately exposed sites than in inadequately exposed ones, with differences reaching statistical significance at the level of P less than 0.001. When the analysis moved to the patient level, the pattern held with remarkable consistency: the number of adequately exposed sites per examination correlated positively with lesion detection across every lesion category, with correlation coefficients ranging from 0.830 to 1.000 and all comparisons significant at P less than 0.01. The message is intuitive but now empirically grounded: the more of the stomach lining an endoscopist shows the camera, the more likely the examination is to yield a clinically meaningful finding.

Because simple correlations can be confounded, the investigators went further, applying multivariable logistic regression using generalized estimating equation models that account for clustering of patients within centers. These adjusted models controlled for whether the participant had been allocated to the intervention or control arm of the original trial, the number of standard anatomical sites screened, and the total inspection time. Even after these adjustments, each additional adequately exposed site was associated with higher odds of detecting early gastric cancer, with an adjusted odds ratio of 1.081 and a 95 percent confidence interval of 1.021 to 1.146, statistically significant at P equal to 0.008. The same held for precancerous conditions, with an adjusted odds ratio of 1.056, and for focal lesions, with an adjusted odds ratio of 1.059. For neoplastic lesions overall, the association pointed in the same direction but narrowly missed conventional statistical significance, with an adjusted odds ratio of 1.038 and a P value of 0.060. Sensitivity analyses clustered by individual endoscopist produced materially consistent estimates, strengthening the case that the effect is not an artifact of a few unusually thorough or unusually lax operators.

What makes the OGMEA system potentially transformative is its fit with the existing trajectory of endoscopy quality assurance. Quality indicators have already reshaped colonoscopy, where measures such as adenoma detection rates and cecal intubation rates are now standard benchmarks for training, credentialing, and pay-for-performance programs. Upper gastrointestinal endoscopy has lagged behind, partly because the stomach’s complex, cavernous anatomy makes exposure harder to standardize and partly because no practical tool existed to measure it in real time or in retrospect. The abbreviations that pepper the study, from CCMEA, the cumulative colorectal mucosal exposure area, to GRACE, the gastroscopy rate of cleanliness evaluation, trace the field’s growing effort to borrow quantitative rigor from its lower-gastrointestinal counterpart. An automated measure of gastric exposure, computed by a neural network from the very video stream the endoscope already produces, could be integrated into endoscopy reporting software without adding any burden to the procedure itself, giving departments a continuous, objective feed of quality data.

The study also illustrates a broader shift in how artificial intelligence is being deployed in medicine. Much of the public conversation about AI in endoscopy has centered on computer-aided detection, in which algorithms highlight polyps or suspicious lesions directly. The OGMEA work takes a complementary path: rather than finding the lesion, the algorithm measures whether the human operator created the conditions under which a lesion could be found. This framing sidesteps some of the regulatory and medicolegal complexities of autonomous diagnosis while still harnessing deep learning to improve outcomes. It also generates actionable feedback for trainees, who can see precisely which of the 19 anatomical sites they tend to under-visualize, whether that is the posterior wall of the fundus or the lesser curvature of the antrum, and adjust their technique accordingly.

Cautions remain, as they should with any new quality metric. The exposure thresholds were defined by expert consensus, and different panels might draw the line between adequate and inadequate visualization somewhat differently. The association between exposure and detection, even in adjusted models, is observational in nature; a randomized trial in which endoscopists are compelled to reach OGMEA targets would be needed to prove that raising exposure causally reduces missed cancers. The modest magnitude of the per-site effect, an odds ratio just above 1, accumulates meaningfully only across many sites, which is precisely how endoscopy works in practice but means the metric is best viewed as one component of a broader quality framework rather than a standalone guarantee. Still, with data from more than 25,000 participants across two dozen centers, consistent results at both site and patient levels, and robustness in sensitivity analyses, the OGMEA study makes a compelling case that the stomach’s hidden surfaces deserve to be counted, and that a neural network watching the monitor may soon help ensure that no corner of the gastric mucosa goes unseen.

Subject of Research: Artificial intelligence-based assessment of gastric mucosal exposure during endoscopy and its association with gastric lesion detection

Article Title: Artificial intelligence-based assessment of gastric mucosal exposure and its association with lesion detection: a multicenter study

Article References: Du, H., Chen, Z., Li, J., Zhang, X., Zu, Q., Zeng, Z., Dong, Z., Hassan, C., Yu, H., & Wan, X. (2026). Artificial intelligence-based assessment of gastric mucosal exposure and its association with lesion detection: a multicenter study. BMC Medicine. https://doi.org/10.1186/s12916-026-05288-8

Image Credits: AI Generated

DOI: 10.1186/s12916-026-05288-8

Keywords: artificial intelligence, endoscopy, gastric cancer, mucosal exposure, quality indicators, deep learning, early detection, gastroenterology, precancerous lesions, multicenter study, screening, Artificial

The research, led by Michael Lorenz and Stefan Listl of Heidelberg University together with Ziade Sarroukh of Radboud University Medical Center, was conducted as part of the European Union-funded DELIVER project. Its starting point is deceptively simple: you cannot systematically improve something you have not collectively defined. Quality improvement interventions, the authors contend, require a shared understanding of what quality means and a normative direction that tells stakeholders where improvement efforts should be headed. Without that foundation, initiatives launched by different actors pull in different directions, resources are wasted, and vulnerable populations who already struggle to access care continue to be left behind.

The burden that motivated the work is substantial. Oral diseases impose a heavy toll on societies, both in human and economic terms, and access to oral healthcare remains unevenly distributed, with disadvantaged groups facing the greatest barriers. There is, the researchers note, renewed political will at the level of the United Nations to extend Universal Health Coverage to oral health, a development that could bring care to millions who currently go without. But expanding coverage without addressing quality, they warn, risks scaling up services that are inequitable, unsafe, or simply ineffective. Solving these intertwined problems, the study argues, requires coherent efforts that span multiple stakeholders and sectors, and that coherence begins with language.

To build that shared language, the team designed a structured five-step co-development process. First, they carried out a literature screening to identify the themes that scientific studies associate with the quality of oral healthcare. Second, they compiled those themes into a starting list that could anchor subsequent discussions. Third, they convened World Café sessions, a deliberative format in which participants rotate through small-group conversations, allowing ideas to be debated, refined, and built upon in a way that large formal meetings rarely permit. These sessions brought together oral healthcare providers, policymakers, and patients from across Europe, ensuring that the definition would reflect not only technical expertise but also lived experience and administrative reality.

The fourth step applied thematic analysis to the rich information generated in the World Café sessions, systematically coding and synthesizing what participants had said. Finally, the process closed with voting and completion, allowing the consortium to converge on a formulation that every stakeholder group could endorse. The researchers reported the study in accordance with the Consolidated criteria for reporting qualitative studies, known as COREQ-32, a transparency standard designed to make qualitative research methods visible and assessable. The work received ethics approval from the Medical Faculty of Heidelberg University, and all participants provided informed consent, underscoring the methodological rigor behind what might otherwise appear to be a purely discursive exercise.

The definition that emerged is both ambitious and precise. Quality oral healthcare, the consortium agreed, refers to the contribution of healthcare to optimal oral health. That contribution, in turn, entails that care alleviates oral health inequity, is comprehensive, empowers patients, mitigates harm and encourages learning systematically, improves well-being, minimizes the waste of resources, and is provided at the right time. To maximize quality, the definition continues, care should be equitable, accessible, patient-centered, safe, effective, efficient, and timely. Each clause condenses an enormous body of health services research and stakeholder deliberation into actionable language, and the seven closing attributes deliberately echo the widely used Institute of Medicine framework for healthcare quality while extending it with explicitly equity-oriented and patient-empowering dimensions.

What makes the definition scientifically significant is not only its content but its provenance. According to the authors, it is the first definition of oral healthcare quality that was co-developed together with providers, policymakers, and patients from various European countries. That participatory pedigree matters for a practical reason: definitions imposed from above tend to stall at the implementation stage, because the people expected to act on them were never invested in their creation. A definition forged through deliberation, by contrast, carries what the researchers call meaningful normative directionality. It tells practitioners, system managers, and patient advocates not merely what quality is but in what direction improvement should move, giving divergent actors a common compass for collective problem-solving.

The definition is explicitly designed as a foundation for operationalization rather than an end in itself. The authors describe it as the basis for developing targeted quality indicators and improvement interventions at three distinct levels: the practice level, where individual dental teams design and deliver care; the community level, where prevention programs and local services shape oral health outcomes; and the health systems level, where financing, regulation, and policy determine who gets care and how good that care can be. In other words, the definition is meant to cascade downward into measurable indicators, which can then be used to benchmark performance, identify gaps, and evaluate whether interventions actually make care more equitable, safer, and more efficient.

That cascading logic is where the study’s implications become potentially far-reaching. Oral health has long been the poor relation of health systems research, often excluded from universal coverage schemes and quality frameworks that focus on general medical care. By anchoring oral healthcare quality in a definition co-owned by multiple stakeholder groups, the DELIVER consortium has created a conceptual tool that could inform national quality strategies, professional guidelines, and patient-facing standards across Europe and beyond. The timing is pointed: as UN member states debate extending Universal Health Coverage to oral health, governments will need exactly the kind of agreed-upon quality framework this definition provides to ensure that newly covered services meet a meaningful bar.

For the researchers, the deeper lesson is methodological. Complex, systemic problems in health care are rarely solved by technical fixes alone; they require the alignment of values, incentives, and understandings across the many actors who constitute a system. The five-step process piloted here, combining systematic literature screening with deliberative World Café workshops and structured consensus voting, offers a replicable template for other fields grappling with similarly contested concepts, from mental health service quality to long-term care standards. The study’s conclusion is understated but consequential: a shared, actionable definition of quality is a key enabler of quality improvement itself. In a field where millions of people suffer preventable pain, inequity, and ineffective treatment, agreeing on what good care means may prove to be the most quietly radical intervention of all.

The study’s transparency apparatus extends beyond the COREQ-32 checklist itself. The published supplementary material documents the full audit trail of the consensus process, including an overview of the literature review with its search strategy and results, the initial topic list that served as supporting material in the workshops, sensitivity checks on the voting results, and a detailed breakdown of non-researcher participants by country and stakeholder group. This level of documentation allows readers to trace how each element of the final definition was anchored in evidence and deliberation, and to assess whether particular stakeholder groups or countries were over- or under-represented in shaping the outcome.

Institutionally, the work sits at the intersection of two research environments: the Section for Oral Health at the Heidelberg Institute of Global Health, part of Heidelberg University Hospital, and the Department of Dentistry, Quality and Safety of Oral Health Care at Radboud University Medical Center in the Netherlands. The DELIVER project, within which the study was embedded, received funding from the European Union’s Horizon Europe Research and Innovation Program under grant agreement 101057077, and the open access publication fee was supported through Projekt DEAL with additional acknowledgment of Heidelberg University. The article was published as open access under a Creative Commons Attribution 4.0 International License, meaning the definition and its supporting materials can be freely reused, adapted, and distributed with appropriate credit, a licensing choice that lowers the barrier for health systems and professional bodies seeking to adopt the framework.

The publication record also reflects the compressed timeline typical of modern rapid-access publishing. The manuscript was received in February 2026 and accepted in September of the same year, with the version shared online carrying a permanent digital object identifier that makes it citable even before the final Version of Record replaces it. For a field in which contested concepts often stall in committee for years, the combination of a structured deliberative method, documented consensus checks, and immediate open availability positions this definition to be tested, criticized, and refined by the very communities it was designed to serve.

Subject of Research: Co-development of an actionable definition of oral healthcare quality with providers, policymakers, and patients in Europe

Article Title: Reconsidering how we define quality: insights from the co-development of an actionable definition of oral healthcare quality,mntion

Article References: Lorenz, M., Sarroukh, Z., & Listl, S. (2026). Reconsidering how we define quality: insights from the co-development of an actionable definition of oral healthcare quality,mntion. Health Research Policy and Systems. https://doi.org/10.1186/s12961-026-01536-8

Image Credits: AI Generated

DOI: 10.1186/s12961-026-01536-8

Keywords: oral health, quality of care, co-development, definition, World Café, deliberative methods, Universal Health Coverage, health equity, quality indicators, Europe, patient-centered care, health systems