Studies in health technology and informatics最新文献

{"title":"Systematic Evaluation of Manufacturer Disclosure Statements for Medical Device Security (MDS2) to Strengthen Hospital OT Security Measures - Lessons Learned.","authors":"Stefan Stein, Michael Pilgermann, Martin Sedlmayr","doi":"10.3233/SHTI251404","DOIUrl":"10.3233/SHTI251404","url":null,"abstract":"<p><strong>Introduction: </strong>The growing number of connected medical devices in hospitals poses serious operational technology (OT) security challenges. Effective countermeasures require a structured analysis of the communication interfaces and security configurations of individual devices.</p><p><strong>State of the art: </strong>Although Manufacturer Disclosure Statements for Medical Device Security (MDS2, Version 2019) offer relevant information, they are rarely integrated into cybersecurity workflows. Existing studies are limited in scope and lack scalable methodologies for systematic evaluation.</p><p><strong>Concept: </strong>This study analyzed 209 MDS2 documents and 161 security white papers to extract structured information on ports, protocols, and protective measures. Over 52,000 question-answer pairs were converted into a machine-readable format using customized parsing and validation routines. The aim was to establish whether this dataset could inform risk assessments and future applications involving Large Language Models (LLMs).</p><p><strong>Implementation: </strong>The analysis revealed 367 distinct ports, including common protocols such as HTTPS (443), DICOM (104), and RDP (3389), as well as vendor-specific proprietary ports. Approximately 40% of the devices used over 20 ports, indicating a broad attack surface. OCR errors and inconsistent formatting required manual corrections. A consolidated dataset was developed to support clustering, comparison across vendors and versions, and preparation for downstream LLM use, particularly via structured SBOM and configuration data.</p><p><strong>Lessons learned: </strong>Although no model training was conducted, the structured dataset can support AI-based OT security workflows. The findings highlight the critical need for up-to-date, machine-readable manufacturer data in standardized formats and schemas. Such information could greatly enhance the automation, comparability, and scalability of hospital cybersecurity measures.</p>","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"331 ","pages":"256-264"},"PeriodicalIF":0.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144984928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance of Contradiction Rule Implementations in Health Data for Efficient Data Quality Assessments.","authors":"Khalid O Yusuf, Irina Chaplinskaya-Sobol, Anne Schoneberg, Sabine Hanss, Sabine Blaschke, Jörg J Vehreschild, Isabel Bröhl, Karin Fiedler, Margarete Scherer, Shimita Sikdar, Patricia Wagner, Ramsia Geisler, Olga Miljukov, Milena Milovanovic, Jens-Peter Reese, Dagmar Krefting","doi":"10.3233/SHTI251410","DOIUrl":"10.3233/SHTI251410","url":null,"abstract":"<p><strong>Introduction: </strong>Boolean rules are the building blocks for rule-based data quality assessment (DQA) in health research. While some DQA rules are generic, contradiction rules are guided by established facts supported by domain knowledge. A recent study reported performance degradation in infrastructure as DQA rules scale. Different implementation approaches can be used for DQA rules. In this study, we examine the performance of varied DQA rule implementations for contradictory dependencies in data items for cardiovascular disease assessment and propose an optimization method that integrates the strengths of different approaches.</p><p><strong>Methods: </strong>We implemented three Boolean rule implementations considered for contradiction assessment of 12 cardiovascular disease items used in the cross-sectoral platform of the national COVID-19 cohort: 1) raw domain rule-set joined using the Boolean-OR operator; 2) two minimal Boolean rules derived from the twelve raw domain rule-set through rule reduction; and 3) atomic Boolean rules representing each rule in the raw domain rule-set. The implementations are examined on speed of execution and memory utilization on the original dataset of about 2000 subjects amplified by factors of 2.5, 5, 10, 50, and 100. A two-step approach is adopted to integrate the implementation of the fastest and atomic contradiction rules.</p><p><strong>Results: </strong>The raw domain rule-set (1) was more than 100 times faster than the atomic rules (3) and 9 times faster than the minimal Boolean rules (2) with the largest employed dataset. It requires about 3 times more memory than the other implementations. All implementations show linear dependency on the dataset size, except for minimal Boolean rules (2) with a slower slope in memory utilization. Two-step rule processing reduced the speed gap between raw rule-set (1) and atomic rules (3) from 100 times faster to just 3 times in the unified implementation.</p><p><strong>Discussion: </strong>Only atomic rules (3) support detailed and traceable results for DQA, required for further inspection of the contradictions. A combined rule processing can bridge the speed gap between raw rule-set and atomic rules by executing the fastest rules on entire dataset and atomic rules only on the fraction of data with contradictions, allowing for fast but detailed DQA.</p>","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"331 ","pages":"318-326"},"PeriodicalIF":0.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144985014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Designing Supportive Dashboards for Crisis Response to Protect Vulnerable Populations: A Qualitative Study.","authors":"Jan Carlo Schmid, Sophie Anne Inès Klopfenstein, Lina Katharina Mosch, Pauline Reiss, Sylvia Thun, Malek Bajbouj, Marie von Lilienfeldt-Toal, Arndt David Bialobrzeski","doi":"10.3233/SHTI251417","DOIUrl":"10.3233/SHTI251417","url":null,"abstract":"<p><strong>Introduction: </strong>The COVID-19 pandemic exposed both direct and collateral health impacts especially on vulnerable populations, underscoring the need for more targeted and equitable crisis response strategies. Health-related dashboards could support better information sharing, research, and care delivery, but current dashboards often fail to address the needs of vulnerable groups. This study aimed to assess expert perspectives on key aspects of a new crisis response health dashboard to protect vulnerable populations intended to be used by medical professionals and affected persons.</p><p><strong>Methods: </strong>A prospective, participatory workshop was conducted with a multidisciplinary group of researchers from the COLLPAN consortium (n = 20). The workshop employed the 6-3-5 method developed by Bernd Rohrbach. Data were collected through semi-structured textual responses, transcribed, and analyzed using a thematic analysis with MAXQDA (version 24.5.0).</p><p><strong>Results: </strong>The envisioned dashboard targets a wide range of users-including patients, healthcare professionals, researchers, policymakers-with particular attention to those with limited digital literacy. Core functionalities include data visualization, management, analysis, networking, and administrative support, enhanced by multilingual, app-based, and artificial intelligence assisted features. The proposed content encompasses resource availability, epidemiological indicators, disease burden with regional and international comparisons, and the inclusion of individual risk profiling. Data sources include health, administrative, socioeconomic, and demographic datasets. The limitations identified relate to technical, regulatory, user-centered, definitional, and resource-based challenges.</p><p><strong>Discussion and conclusion: </strong>The study highlights the importance of inclusive, user-centered design in the development of health-related dashboards, particularly to address the needs of vulnerable populations. By involving diverse stakeholders at an early stage and strengthening the technical foundations, digital solutions have the potential to reduce health inequalities rather than reinforcing them.</p>","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"331 ","pages":"369-377"},"PeriodicalIF":0.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144985022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ECG Synthesis and Utility Analysis - A Diffusion Model Based Approach.","authors":"Sanketa Hegde, Merten Prüser, Nikola Cenic, Anatol Bollinger, Marie Arens, Jan Köhlen, Eimo Martens, Christoph Dieterich","doi":"10.3233/SHTI251414","DOIUrl":"10.3233/SHTI251414","url":null,"abstract":"<p><strong>Introduction: </strong>With the growing demand for privacy-preserving healthcare solutions, the generation of synthetic electrocardiograms (ECGs) offers a valuable alternative to using real patient data.</p><p><strong>Methods: </strong>In this study, we present the adaptation of the SSSD-ECG diffusion model to generate high-quality synthetic 12-lead ECGs for Sinus Rhythm/Normal and Atrial Fibrillation (AF) conditions using 10-second recordings from the 12-lead MIMIC-IV ECG dataset.</p><p><strong>Results: </strong>We validate the utility of the generated ECGs through downstream classification tasks, with models trained on synthetic ECG features achieving an F1-score of 0.80 when tested on real data, and 0.91 when trained on real data and tested on synthetic data. Additionally, blind tests conducted by physicians at two university hospital sites demonstrated that the synthetic signals effectively mimic real ECGs in both morphology and key features.</p><p><strong>Conclusion: </strong>This work establishes diffusion-based models as an effective tool for generating realistic synthetic ECGs, providing valuable resources for model development, supporting testing of clinical decision- making solutions, and enabling research in contexts where real data is scarce or not shareable.</p>","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"331 ","pages":"346-356"},"PeriodicalIF":0.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144985051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GeMTeX's De-Identification in Action: Lessons Learned & Devil's Details.","authors":"Christina Lohr, Jakob Faller, Andrea Riedel, Hung Manh Nguyen, Markus Wolfien, Justin Hofenbitzer, Luise Modersohn, Jutta Romberg, Fabian Prasser, Jazia Omeirat, Yutong Wen, Oksana Galusch, Udo Hahn, Marvin Seiferling, Christoph Dieterich, Peter Klügl, Franz Matthies, Janina Kind, Martin Boeker, Markus Löffler, Frank Meineke","doi":"10.3233/SHTI251406","DOIUrl":"10.3233/SHTI251406","url":null,"abstract":"<p><strong>Introduction: </strong>In 2024, the GeMTeX project launched the largest ever de-identification campaign for German-language clinical reports, and, as a pilot study, published GraSCCoPHI, the first de-identified German-language gold standard corpus of synthetic discharge summaries.</p><p><strong>Methods: </strong>GeMTeX's de-identification workflow is described here - including annotation tool management and, pre-annotation experience, such as assembling and training annotation groups and the evolution of guidelines.</p><p><strong>Results: </strong>We present the project's progress in the first year with respect to de-identification efforts and the challenges we faced during the rollout at six hospital sites in four German states. The refinement of the annotation guidelines became an ongoing process, often with unforeseen hurdles to overcome as we moved from testing to production. From our current internal interim corpus (9,000 documents with about 20 million tokens), we are publishing the first quantitative insights, such as the average amount of identifiable information per document, a list of confounding factors we did not anticipate at the beginning of the project, and three key lessons learned.</p><p><strong>Conclusion: </strong>We note that the unforeseen hurdles behave like the Pareto principle and fall into the set of less than 20% of the annotations.</p>","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"331 ","pages":"274-282"},"PeriodicalIF":0.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144985052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Catnip for MedCAT: Optimizing the Input for Automated SNOMED CT Mapping of Clinical Variables.","authors":"Julia Gehrmann, Asme Dogan, Lea Hagelschuer, Lars Quakulinski, Anne Koy, Oya Beyan","doi":"10.3233/SHTI251390","DOIUrl":"10.3233/SHTI251390","url":null,"abstract":"<p><strong>Introduction: </strong>Mapping local medical data assets to international data standards such as medical ontology SNOMED CT fosters data harmonization and, thereby, global progress in medical research. Since its intense resource requirements often hinder manual SNOMED CT mapping, automated mapping tools such as MedCAT have been developed. We investigated how the formulation of study variable names (VNs) influences the efficacy and accuracy of the SNOMED CT concepts identified by MedCAT.</p><p><strong>Methods: </strong>We extracted 763 VNs from the GEPESTIM database hosted locally in REDCap and created three VNs using different REDCap metadata items for MedCAT-based SNOMED CT mapping. A fourth VN version was created manually. The mapping was evaluated based on the number and quality of identified SNOMED CT concepts, using manual scoring to assess concept accuracy while ensuring a blind evaluation process.</p><p><strong>Results: </strong>Increasing the expressiveness of VNs by adding more metadata items led to more SNOMED CT concepts being mapped, but also introduced mismatches, particularly when additionally included metadata contained misleading terms. The best overall mapping performance was achieved on the manually specified VNs while a basic VN version with minimal extra information from the metadata resulted in similarly good results.</p><p><strong>Conclusion: </strong>Our study identified key challenges in using MedCAT for automatically mapping study variables to SNOMED CT concepts. To improve accuracy, we recommend refining VNs reducing misleading terms and iteratively improving VN phrasing for optimal mapping outcome. Furthermore, it appears reasonable to always conduct a final manual review of the mapping outcome especially for critical variables and for those VNs containing negations or abbreviations.</p>","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"331 ","pages":"142-152"},"PeriodicalIF":0.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144985064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards Fairness in Synthetic Healthcare Data: A Framework for the Evaluation of Synthetization Algorithms.","authors":"Yannik Warnecke, Martin Kuhn, Felix Diederichs, Tobias J Brix, Lena Clever, Ralph Bergmann, Dominik Heider, Michael Storck","doi":"10.3233/SHTI251376","DOIUrl":"10.3233/SHTI251376","url":null,"abstract":"<p><strong>Introduction: </strong>Synthetic data generation is a rapidly evolving field, with significant potential for improving data privacy. However, evaluating the performance of synthetic data generation methods, especially the tradeoff between fairness and utility of the generated data, remains a challenge.</p><p><strong>Methodology: </strong>In this work, we present our comprehensive framework, which evaluates fair synthetic data generation methods, benchmarking them against state-of-the-art synthesizers.</p><p><strong>Results: </strong>The proposed framework consists of selection, evaluation, and application components that assess fairness, utility, and resemblance in real-world scenarios. The framework was applied to state-of-the-art data synthesizers, including TabFairGAN, DECAF, TVAE, and CTGAN, using a publicly available medical dataset.</p><p><strong>Discussion: </strong>The results reveal the strengths and limitations of each synthesizer, including their bias mitigation strategies and trade-offs between fairness and utility, thereby showing the framework's effectiveness. The proposed framework offers valuable insights into the fairness-utility tradeoff and evaluation of synthetic data generation methods, with far-reaching implications for various applications in the medical domain and beyond.</p><p><strong>Conclusion: </strong>The findings demonstrate the importance of considering fairness in synthetic data generation and the need for fairness focused evaluation frameworks, highlighting the significance of continued research in this area.</p>","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"331 ","pages":"25-34"},"PeriodicalIF":0.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144985076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Embedding FHIR in Medical PDF: A Migration Path for Interoperable Documentation.","authors":"Jonas Bienzeisler, Alexander Kombeiz, Hauke Heidemeyer, Miriam Hertwig, Bernadett Erdmann, Marco Pegoraro, Saskia Ehrentreich, Patrick A Eder, Jasmin Mosebach, Asarnusch Rashid, Raphael W Majeed","doi":"10.3233/SHTI251395","DOIUrl":"10.3233/SHTI251395","url":null,"abstract":"<p><strong>Introduction: </strong>Medical services routinely transmit patient data using PDF, even as FHIR emerges as the standard for structured healthcare interoperability. This mismatch reflects a broader fragmentation in digital documentation, where pragmatic workflows often outpace technical ideals.</p><p><strong>Methods: </strong>We propose embedding FHIR bundles into PDF to enable structured data reuse without disrupting established processes. These hybrid documents can be processed via FHIR Binary endpoints, allowing downstream systems to extract, validate, and map the embedded data to interoperable resources.</p><p><strong>Results: </strong>A proof-of-concept using German emergency medical services records demonstrates that vital parameters and timestamps can be transmitted as PDF while preserving machine-readable structure.</p><p><strong>Conclusion: </strong>This approach respects existing transport mechanisms and accommodates heterogeneous IT landscapes. By bridging legacy formats with modern standards, the method offers a scalable migration path toward interoperability-ready to deploy wherever PDF are already in use. Thus, our approach provides a migration pathway for integrating routine data into interoperable research infrastructures, enabling structured reuse without altering clinical workflows.</p>","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"331 ","pages":"186-194"},"PeriodicalIF":0.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144985107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Implementation of an Open, Modular, and Participatory Toolchain for Distributed IT Development in Healthcare Research - Lessons Learned.","authors":"Daniel Neumann, Richard Gebler, Jana Kiederle, Jördis Beck, Fabio Aubele, Alexander Struebing, Florian Schmidt, Matthias Reusche, Helene Koester, Markus Loeffler, Sebastian Staeubert","doi":"10.3233/SHTI251418","DOIUrl":"10.3233/SHTI251418","url":null,"abstract":"<p><strong>Introduction: </strong>Distributed healthcare research infrastructures face significant challenges when translating routine clinical data into harmonized, research-ready formats using HL7 FHIR standards.</p><p><strong>State of the art: </strong>Existing FHIR-based pipelines such as the SMART/HL7 FHIR Bulk Data Access API, FHIR-to-OMOP mappings, and analytical services like Pathling demonstrate technical feasibility. However, most assume semantically valid FHIR data, operate within single-institution settings, and lack practical guidance for deployment across heterogeneous, regulated environments. Technical Framework and Deployment: Within the German Medical Informatics Initiative (MII) and the INTERPOLAR project, we developed an open, modular, and participatory toolchain for decentralized FHIR-based data transformation and export across multiple Data Integration Centers (DICs). The toolchain supports FHIR extraction, profile-based transformation, REDCap integration, and OMOP-compatible export. Deployment required adapting to local infrastructures, regulatory boundaries (e.g., de-identified FHIR stores, restricted network access), and clinical domain needs. Configurable modules, proxy support, and site-specific adaptations were essential for integration into operational hospital workflows.</p><p><strong>Lessons learned: </strong>Key lessons include the necessity of early access to real data, the limitations of synthetic test data, the value of joint workshops for profile interpretation, and the need for adaptable validation tooling. Organizational knowledge gaps, inconsistent FHIR implementations, and performance issues in resource flattening were addressed through co-design and iterative rollout strategies. Validator modules are essential across technical, content, and cross-site consistency levels.</p><p><strong>Conclusion: </strong>Centralized development paired with decentralized, participatory deployment enables scalable, GDPR-compliant infrastructures for embedded clinical research. This approach offers a replicable framework for future multi-site initiatives aiming to leverage real-world data across diverse environments.</p>","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"331 ","pages":"378-385"},"PeriodicalIF":0.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144985110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Calculator for the Maturity Model for Medical Patient Registries - Lessons Learned.","authors":"Tobias C von Brevern, Duc Bui Tien, Marie Habermann, Bogdan Noskov, Lucas Pilz, Jonas Bienzeisler, Anna Niemeyer, Rainer Röhrig, Raphael W Majeed","doi":"10.3233/SHTI251385","DOIUrl":"10.3233/SHTI251385","url":null,"abstract":"<p><strong>Introduction: </strong>Patient registries are an important resource for medical research and decision making. However, registry quality is difficult to assess due to their vast differences in structure and function. The Registry Maturity Model offers a way to self-assess the quality.</p><p><strong>State-of-the-art: </strong>The Registry Maturity Model is an established approach to assessing registry maturity. However, its current implementation in Excel poses practical challenges in terms of scalability and accessibility for widespread use within the registry community.</p><p><strong>Concept: </strong>To improve accessibility and scalability, we developed a publicly available and user-friendly web application.</p><p><strong>Implementation: </strong>The user interface of our application guides users through the quality attributes of the model via structured questions with predefined answer options. Inputs are instantly processed to calculate and visualize the maturity level across the various utility dimensions in real time. For continuity and reuse, the results can be saved and reused.</p><p><strong>Lessons learned: </strong>Our application aims to improve the model's usability, scalability and maintainability and makes it easier for stakeholders to evaluate a registries quality for their use purpose.</p>","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"331 ","pages":"102-111"},"PeriodicalIF":0.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144984988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}