Informatics in Medicine Unlocked最新文献

{"title":"Systematic review of transformers, graph neural networks, and federated learning in oncology: Applications, challenges, and pathways to clinical translation","authors":"Sasan Shafiei ,&nbsp;Atousa Saleknezhad ,&nbsp;Erfan Hashemi ,&nbsp;Amin Sedokani ,&nbsp;Emad Gholami ,&nbsp;Ali Parouhan ,&nbsp;Omid Nikoo ,&nbsp;Nikta Taghipour ,&nbsp;Iman Fereydooni ,&nbsp;Elnaz Olama ,&nbsp;Hamid Reza Saeidnia","doi":"10.1016/j.imu.2026.101743","DOIUrl":"10.1016/j.imu.2026.101743","url":null,"abstract":"<div><h3>Background</h3><div>Advanced deep learning models, including transformers, graph neural networks (GNNs), and federated learning (FL), are reshaping oncology by enhancing the analysis of complex, multimodal cancer data for diagnostic and prognostic purposes. Despite their promise, challenges in explainability, generalizability, and clinical readiness hinder widespread adoption.</div></div><div><h3>Methods</h3><div>This systematic review aims to evaluate the applications of transformers, GNNs, and FL in cancer diagnosis and prognosis, identify key technical challenges, and outline strategies for clinical translation. A comprehensive search across PubMed, Scopus, IEEE Xplore, Google Scholar, Web of Science, arXiv, and medRxiv identified 1004 articles, with 16 systematic reviews meeting inclusion criteria after rigorous screening.</div></div><div><h3>Results</h3><div>Data were extracted on model applications, cancer types, data modalities, and technical challenges, and the quality was assessed using the PROBAST tool. Transformers excel in image-based diagnostics, such as skin cancer detection, and multimodal data integration. GNNs are effective for modeling biological networks, aiding in cancer subtype classification and driver gene prediction. FL enables privacy-preserving, multi-institutional model training for cancers like breast, lung, and prostate. Major challenges include limited explainability, poor generalizability across diverse cohorts, data heterogeneity, and insufficient external validation.</div></div><div><h3>Conclusions</h3><div>Transformers, GNNs, and FL offer transformative potential in oncology but face significant hurdles in interpretability and generalizability. Future progress requires interdisciplinary frameworks, transparent benchmarking, and robust validation to ensure clinical trust and impact.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"61 ","pages":"Article 101743"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146170875","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 of a neural network system for predicting in-hospital pressure ulcers in spinal trauma patients","authors":"Rezvan Raziee ,&nbsp;Mohsen Sadeghi-Naini ,&nbsp;Zahra Azadmanjir","doi":"10.1016/j.imu.2026.101742","DOIUrl":"10.1016/j.imu.2026.101742","url":null,"abstract":"<div><h3>Background and objectives</h3><div>This study aimed to developing and to deploying an optimal machine learning model to predict pressure ulcers (PUs) in hospitalized patients with spinal fractures, using data from the National Spinal Cord and Column Injury Registry of Iran (NSCIR-IR).</div></div><div><h3>Methods</h3><div>Data were from 4326 patients with traumatic spinal fractures. The preprocessing phase was included handling missing values, feature engineering, normalization, and addressing class imbalance (with a 3.4 % PU incidence) using the Synthetic Minority Oversampling Technique (SMOTE). Feature selection was carried out with univariate filtering methods such as ANOVA and chi-square tests, along with the random forest feature importance algorithm. Six traditional machine learning (ML) algorithms and six ensemble models were trained and evaluated using metrics such as the area under the receiver operating characteristic curve (AUC), recall, and Brier score.</div></div><div><h3>Results</h3><div>The multilayer perceptron neural network (MLP) emerged as the top-performing model, offers advantages for clinical use due to a higher AUC of 0.888 (0.85–0.92), a balanced accuracy, a good recall, calibration, and an acceptable net benefit on the decision curve. Key predictors identified included the ASIA Impairment Scale, the Glasgow Coma Scale score, SCI type, SaO2, and the number of damaged vertebrae. Shapley Additive Explanations (SHAP) analysis further highlighted the directional influence of these factors on PU risk.</div></div><div><h3>Conclusion</h3><div>The MLP model effectively predicts PU in patients with spinal fractures, outperforming other algorithms. Identified predictors align with clinical insights, are emphasizing the need for targeted preventive measures in hospitals. However, external validation with a larger multicenter cohort is recommended to confirm and to expand upon these findings.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"61 ","pages":"Article 101742"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075003","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":"Epic overhaul at a Canadian hospital: Pre-Post evaluation insights from physicians and medical residents","authors":"Mirou Jaana ,&nbsp;Edward Riachy ,&nbsp;Erika MacPhee ,&nbsp;Heather Sherrard","doi":"10.1016/j.imu.2025.101725","DOIUrl":"10.1016/j.imu.2025.101725","url":null,"abstract":"<div><h3>Background</h3><div>The implementation of Electronic Medical Records (EMRs) in hospitals offers potential benefits but often disrupts clinician workflows, affecting care delivery and outcomes.</div></div><div><h3>Objective</h3><div>This study evaluates physicians' and medical residents’ perspectives on the impacts of introducing a new Epic system at a Canadian academic hospital.</div></div><div><h3>Methods</h3><div>A pre-post evaluation design was conducted using physician and resident surveys before (T0) and 4- and 9-months post-implementation (T1, T2) that assessed technology use, satisfaction with training and system use, and EMR's perceived impact on care delivery, work practices and quality.</div></div><div><h3>Results</h3><div>Satisfaction with training and system use declined for both groups in the first four months (more sharply for residents) but several measures improved at T2 as users readjusted to the system. There was a significant increase in physicians’ daily computer use (4 h at T0 to 6 h at T1; P &lt; .<em>001</em>). Limited early benefits of the Epic system were observed and a decline in perceived improvement in clinical documentation (P = .<em>006</em> and <em>.0012)</em>, order entry (P = .<em>018</em> and <em>.002)</em> and patient safety (P = .<em>044</em> and <em>.024)</em> were reported at T1 for physicians and residents, respectively. Although some medical practice/work indicators improved by 9 months for physicians, the changes were not statistically significant; these benefits were not observed for residents at T2. Medical training was not significantly affected by the new Epic system either immediately or later post implementation. At T1, 83% of physicians reported that the new system sometimes or often improved the quality of care, as opposed to only 33% of residents; no significant improvements were noted at 9 months post implementation by both groups.</div></div><div><h3>Conclusions</h3><div>Physicians and residents adapt differently to Epic and full system assimilation does not happen in one year. Early perceptions of Epic do not reflect its long-term potential, and meaningful benefits require prolonged stabilization periods for user satisfaction and efficiency gains. We caution hospital leaders not to rely heavily on a vendor-driven implementation, and recommend tailored training, rapid-cycle improvements, transparent communication, and monitoring of agreed-upon performance indicators to strengthen clinician engagement and support long-term success.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"61 ","pages":"Article 101725"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075004","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":"Clinical validation of artificial intelligence for gastrointestinal diseases","authors":"Marjan Talebi ,&nbsp;Negar Bozorgchami ,&nbsp;Gauransh Mishra ,&nbsp;Gaurav Mishra ,&nbsp;Rouzbeh Almasi Ghale","doi":"10.1016/j.imu.2026.101736","DOIUrl":"10.1016/j.imu.2026.101736","url":null,"abstract":"<div><div>Artificial intelligence (AI), particularly deep convolutional neural networks (DCNNs), has demonstrated significant potential for transforming the diagnosis and management of gastrointestinal (GI) diseases. This review critically examines the evolving landscape of AI applications in gastroenterology, moving beyond a simple catalog of tools to analyze their pathway to clinical integration. We synthesize current evidence across key functional domains including computer-aided detection (CADe), computer-aided diagnosis (CADx), and predictive outcome modeling highlighting performance metrics and early clinical adoption. Crucially, we identify a pronounced translational gap between technical validation and demonstrable improvement in patient-centered outcomes. The narrative underscores that while AI systems show high diagnostic accuracy in controlled studies, their ultimate clinical utility remains unproven. The conclusion distills core challenges including the need for rigorous multicenter randomized trials, solutions for algorithmic generalizability, and effective human-AI collaboration and emphasizes the urgent imperative for structured clinical validation frameworks to realize AI's promise in routine GI care. We further synthesize evidence by validation stage and study design, highlighting clinical endpoints such as ADR, APC, and complication rates, with GI Genius and ENDOANGEL exemplifying the gap between technical metrics and patient outcomes.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"61 ","pages":"Article 101736"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025398","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":"Cross-procedure generalisation of an AI algorithm for surgical step prediction: from distal radius to fibula osteosynthesis","authors":"Camille Graëff ,&nbsp;Domenico Marrella ,&nbsp;Henri Favreau ,&nbsp;Nicolas Padoy ,&nbsp;Thomas Lampert ,&nbsp;Philippe Liverneaux","doi":"10.1016/j.imu.2026.101746","DOIUrl":"10.1016/j.imu.2026.101746","url":null,"abstract":"<div><div>Artificial intelligence (AI) methods for surgical step recognition have shown promising results, but their ability to generalise across different types of surgery remains a critical challenge. This study aimed to assess whether an AI algorithm designed for step prediction in videos of plate osteosynthesis for distal radius fractures could be directly applied to fibula osteosynthesis.</div><div>The algorithm was originally developed to recognise six steps of distal radius osteosynthesis: installation, approach, distal fixation, proximal fixation, verification, and closure. A fibula osteosynthesis video was recorded and annotated with six steps: installation, approach, fracture reduction, plate fixation, verification, and closure. As fracture reduction is a procedure-specific step not included in the original training set, algorithm predictions were analysed with and without this step. Predictions were also evaluated at different frame rates (5 and 1 fps).</div><div>The results showed that the algorithm achieved better performance on distal radius osteosynthesis videos, with accuracy, precision, and recall exceeding 80%, whereas performance remained below 80% for fibula osteosynthesis. The best predictions were obtained at 1 fps when the reduction step was excluded, highlighting both the influence of temporal resolution and the limitations of cross-procedure generalisation. Differences between the two surgeries studied are discussed to explain the observed prediction errors.</div><div>In conclusion, while the algorithm can be applied to fibula osteosynthesis as a proof of concept, its reduced performance indicates that procedure-specific fine-tuning is necessary. These findings suggest that the algorithm could serve as a foundation for the development of AI algorithms for other types of osteosynthesis.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"61 ","pages":"Article 101746"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146170877","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":"Overcoming technical barriers in healthcare with blockchain: A systematic review","authors":"Diana Costa ,&nbsp;Paulo Jorge Coelho ,&nbsp;Eftim Zdravevski ,&nbsp;Carlos Albuquerque ,&nbsp;Ivan Miguel Pires ,&nbsp;António Jorge Gouveia","doi":"10.1016/j.imu.2026.101741","DOIUrl":"10.1016/j.imu.2026.101741","url":null,"abstract":"<div><div>The rising digitalization of healthcare has increased reliance on complex information systems, creating the need for better integration and interoperability. Despite technology developments, healthcare organizations still confront technical challenges that restrict data transmission, scalability, and secure information sharing. This systematic review highlights important technological challenges to healthcare information system integration and examines the potential of blockchain technology to address them. Following PRISMA principles, a structured search of PubMed and Scopus revealed 24 peer-reviewed studies published from 2020 to 2024. The investigation suggests that interoperability restrictions, lack of data and language standardization, scalability challenges, cybersecurity hazards, and insufficient technical expertise are the most significant hurdles. Evidence suggests that blockchain technology can increase data integrity, security, and regulated interoperability through decentralized and permissioned systems. However, obstacles persist involving technical complexity, regulatory compliance, energy consumption, and organizational readiness. This paper highlights current knowledge on technical integration hurdles in healthcare and presents evidence-based insights on the potential role of blockchain in allowing interoperable, safe, and sustainable healthcare information systems.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"61 ","pages":"Article 101741"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025321","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":"When classical encryption fails: A non-invasive post-quantum security layer for medical image transfers","authors":"Nino Ricchizzi ,&nbsp;Pascal Alig ,&nbsp;Niklas Schmitz ,&nbsp;Jan Pelzl","doi":"10.1016/j.imu.2026.101747","DOIUrl":"10.1016/j.imu.2026.101747","url":null,"abstract":"<div><div>Quantum computing threatens the long-term security of widely deployed public-key cryptography used to protect medical image transfer. The Digital Imaging and Communications in Medicine (DICOM) standard is the dominant format and protocol suite for medical imaging workflows and is commonly transported over HTTPS.</div><div>Conventional DICOM-over-HTTPS deployments rely on classical cryptographic primitives that are not resistant to quantum adversaries. This work evaluates the feasibility of integrating post-quantum cryptography (PQC) into existing DICOM transfer infrastructures without modifying DICOM applications or their cryptographic libraries.</div><div>We present an empirical case study of xPIPE and RSNA MIRC-CTP as a widely deployed DICOM-over-HTTPS toolchain used in hospital environments. We propose a non-invasive proxy-based design that requires no modifications to the xPIPE client, the MIRC-CTP server, or their cryptographic libraries. The design encapsulates TLS-protected DICOM traffic within an additional PQC-secured tunnel, preserving the existing TLS channel while adding a quantum-resistant outer layer. We implement a proof of concept that preserves the existing Java TLS stack (<span>javax.net.ssl</span>) used by MIRC-CTP for the inner channel and adds a PQC-secured outer tunnel based on stunnel. We further compare alternative PQC integration strategies and discuss their security and operational trade-offs.</div><div>Our results show that current PQC deployments impose an computational overhead and introduce integration constraints in heterogeneous environments. Nevertheless, the proposed approach enables incremental migration by maintaining compatibility with existing systems while providing an additional post-quantum security layer.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"61 ","pages":"Article 101747"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147422383","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":"Integrating knowledge graph embeddings with clinical data: A case study on Acute Kidney Injury prediction","authors":"Samuele Buosi ,&nbsp;Mohan Timilsina ,&nbsp;Conor Judge ,&nbsp;Edward Curry","doi":"10.1016/j.imu.2026.101734","DOIUrl":"10.1016/j.imu.2026.101734","url":null,"abstract":"<div><div>The integration of artificial intelligence (AI) and machine learning (ML) in healthcare has significantly advanced predictive analytics. Acute Kidney Injury (AKI), a condition marked by sudden kidney function loss, necessitates early intervention to improve patient outcomes. This study introduces a novel approach that leverages knowledge graph embeddings (KGE) to enhance the predictive accuracy of ML models for AKI detection. Knowledge graphs (KGs) model entities and their interrelations in a graph format, integrating heterogeneous data sources to provide a comprehensive view of complex biological systems. The AI contribution lies in the application of embedding techniques that transform these graphs into continuous vector spaces, improving the ability to capture semantic similarities and infer new relationships within the data.</div><div>On the engineering side, we applied this AI-driven approach to healthcare by leveraging the Medical Information Mart for Intensive Care (MIMIC) III dataset to construct a KG, generate embeddings, and incorporate them into ML models for AKI prediction. This engineering application aims to demonstrate the utility of KGEs in clinical settings. Our approach involved extracting patient features, generating KGE, and training various models, such as those utilizing complex, translational, holographic, and multiplicative embeddings. The models were evaluated through ranking-based and binary classification protocols. The transparency of the AI models enhances their trustworthiness in clinical practice, and the findings underscore the need for continued collaboration with clinicians to refine these techniques and ensure successful deployment in healthcare.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"61 ","pages":"Article 101734"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145969415","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":"Diabetes risk modeling through tabular-to-image transformations and ensemble learning","authors":"Shenghao Wang ,&nbsp;Darshana Upadhyay ,&nbsp;Marzia Zaman ,&nbsp;Kshirasagar Naik ,&nbsp;Shesh Nath Rai","doi":"10.1016/j.imu.2026.101748","DOIUrl":"10.1016/j.imu.2026.101748","url":null,"abstract":"<div><div>Diabetes is a rapidly growing global health concern, and early detection and personalized risk assessment are critical for preventing severe complications. However, current predictive approaches often struggle with robustness, scalability, and class imbalance in clinical datasets. To address these challenges, this study proposes a dual-pipeline diabetes risk prediction framework that processes (i) raw tabular clinical data using CatBoost and multilayer perceptrons (MLPs), and (ii) tabular-to-image representations using Convolutional Neural Networks (CNNs) trained on images generated through the Novel Algorithm for Convolving Tabular Data (NCTD) and Image Generator for Tabular Data (IGTD). The two pipelines are integrated through multiple ensemble strategies, which combine complementary feature representa-tions to enhance predictive stability and generalization. Furthermore, class imbalance is systematically mitigated through resampling and class-weighting techniques, further contributing to improved model robustness. Our results indicate that the stacking ensemble achieved the most balanced performance (Accuracy = 0.8234, Macro-F1 = 0.6875). In contrast, a five-model stacking ensemble achieved a slightly higher accuracy (0.8327) at the cost of reduced macro-F1, indicating lower sensitivity to minority-class patterns. Overall, results show that although tabular-to-image transformation offers advantages in privacy preservation and feature abstraction, tabular tree-based models and deep learning models remain more reliable for equitable prediction. These findings highlight the value of integrating both pipelines to achieve a scalable and robust framework for diabetes risk assessment.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"61 ","pages":"Article 101748"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147422382","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":"Time-critical quality assessment of a digital hub for acute stroke care: Implementing key performance indicators within the CAEHR (CArdiovascular diseases – Enhancing Healthcare through cross-sectoral Routine data integration) project","authors":"Anna-Lena Hofmann ,&nbsp;Julia Wendel ,&nbsp;Udo Selig ,&nbsp;Peter Cidlinsky ,&nbsp;Christine Vogl ,&nbsp;Sabrina Scriba ,&nbsp;Christian Zech ,&nbsp;Thomas Wurmb ,&nbsp;Maximilian Ertl ,&nbsp;Muntazir Mehdi ,&nbsp;Udo Bavendiek ,&nbsp;Dagmar Krefting ,&nbsp;Jens Volkmann ,&nbsp;Peter Kraft ,&nbsp;Christian Hametner ,&nbsp;Karl Georg Haeusler ,&nbsp;Jens-Peter Reese ,&nbsp;Peter Heuschmann","doi":"10.1016/j.imu.2026.101745","DOIUrl":"10.1016/j.imu.2026.101745","url":null,"abstract":"<div><h3>Purpose</h3><div>Timely treatment for acute stroke requires rapid admission to specialized centers, which is challenging in rural areas. Hereby we report: the conceptualization of implementing digital interfaces to improve time-critical communication between Emergency Medical Services (EMS), regional hospitals, and stroke centers; the development of key performance indicators (KPIs) for pre-hospital stroke care; and the experience of a proof-of-concept pilot study.</div></div><div><h3>Method</h3><div>Within the CAEHR (CArdiovascular diseases – Enhancing Healthcare through cross-sectoral Routine data integration) project, three HL7/FHIR compliant interfaces were conceptualized to enhance communication between EMS, regional hospitals, and the stroke unit at the University Hospital of Würzburg based on experiences of a telemedical network in rural Northwest-Bavaria. To prepare the evaluation of the impact of these interfaces, a standardized set of KPIs for pre-hospital stroke care was developed and piloted regarding feasibility within a proof-of-concept pilot study.</div></div><div><h3>Results</h3><div>An interface enabling real-time exchange of EMS pre-notification and protocols was developed to improve EMS-to-stroke center communication. Another interface linked PACS systems of regional hospitals with the stroke unit to facilitate faster image transmission. 27 KPIs for measuring acute stroke care quality were defined by a multidisciplinary group using a standardized evidence-based approach. In a proof-of-concept pilot study comprising 25 patients, KPIs demonstrated feasibility regarding implementation and patient follow-up.</div></div><div><h3>Conclusion</h3><div>Digital interfaces to improve time-critical communication in acute stroke care were successfully conceptualized. A larger validation study is required to assess their impact on pre-defined quality measures in pre-hospital stroke care.</div></div><div><h3>Study registration</h3><div>The study was registered on the German Clinical Trials Register (DRKS00029103) in July 2022.</div></div><div><h3>Drks id</h3><div>DRKS00029103.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"61 ","pages":"Article 101745"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189678","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}