Stud Health Technol Inform. 2025 Apr 24;324:234-239. doi: 10.3233/SHTI250194.

ABSTRACT

BACKGROUND: Standardizing laboratory data is essential for interoperability and secondary use in clinical research and healthcare. However, many laboratory information systems (LIS) still rely on internal codes rather than internationally recognized terminologies, hindering data exchange, queryability, and integration into health data infrastructures.

OBJECTIVES: This study aimed to automate the extraction and mapping of internal lab codes to LOINC to improve structured data integration by utilizing web scraping and terminology mapping, we sought to create a FHIR-compliant ConceptMap.

METHODS: Guided by key requirements for structured data integration, we developed a Python-based workflow to extract and process laboratory data from an internal lab index. Using Selenium, BeautifulSoup, and Pandas, the extracted data was mapped to LOINC codes and transformed into a FHIR-compliant ConceptMap.

RESULTS: The workflow extracted 2,870 analytes, mapping 768 (27%) to LOINC. The automated process demonstrated feasibility and scalability.

CONCLUSION: The approach enables structured laboratory data integration but highlights the need for direct LIS integration and expanded LOINC coverage for legacy data.

PMID:40270418 | DOI:10.3233/SHTI250194

Stroke. 2025 Apr 24. doi: 10.1161/STROKEAHA.124.050207. Online ahead of print.

ABSTRACT

BACKGROUND: Women less frequently receive secondary prevention medications at discharge poststroke than men. It is unclear whether similar sex differences exist in the long term poststroke, after accounting for age and clinical characteristics. We aimed to evaluate sex differences in medication prescription, initiation, and discontinuation poststroke or transient ischemic attack.

METHODS: A retrospective cohort study using person-level linked data from the Australian Stroke Clinical Registry (42 hospitals; Victoria and Queensland; 2012-2016). We included all adults with first-ever ischemic stroke, intracerebral hemorrhage, or transient ischemic attack who survived >60 days post-discharge. For each major class of secondary prevention medication (antihypertensive, antithrombotic, or lipid lowering), we evaluated sex differences in prescription at hospital discharge, initiation within 60 days, and discontinuation within 2 years post-discharge. Sex differences were assessed using multivariable models, adjusted for sociodemographics and comorbidities. Where effect modification by age was found (Pinteraction≤0.05), age-specific odds ratios were reported.

RESULTS: Among 8108 women (median age, 74.3 years) and 10 344 men (median age, 70.5 years) with first-ever stroke (≈8% intracerebral hemorrhage) or transient ischemic attack, women were less likely to be prescribed antihypertensive medications on discharge (odds ratio, 0.82 [95% CI, 0.74-0.91]). Women were less likely to initiate antihypertensive (odds ratio, 0.76 [95% CI, 0.69-0.84]) and antithrombotic (odds ratio, 0.89 [95% CI, 0.82-0.96]) medications within 60 days than men. While there was no overall difference in discontinuation between men and women, interactions were observed with age (Pinteraction, all <0.002). Younger women (aged <45 years) and older women (aged >90 years) were more likely to discontinue secondary prevention medications than men of equivalent age.

CONCLUSIONS: Sex differences exist for prescription, initiation, and discontinuation of secondary prevention medications poststroke. With many sex differences being age specific, there is a critical need for targeted interventions to improve prevention medication use in these patient subgroups.

PMID:40270283 | DOI:10.1161/STROKEAHA.124.050207

J Alzheimers Dis. 2025 Apr 21:13872877251331222. doi: 10.1177/13872877251331222. Online ahead of print.

ABSTRACT

BackgroundConvolutional neural network (CNN) based volumetry of MRI data can help differentiate Alzheimer's disease (AD) and the behavioral variant of frontotemporal dementia (bvFTD) as causes of cognitive decline and dementia. However, existing CNN-based MRI volumetry tools lack a structured hierarchical representation of brain anatomy, which would allow for aggregating regional pathological information and automated computational inference.ObjectiveDevelop a computational ontology pipeline for quantifying hierarchical pathological abnormalities and visualize summary charts for brain atrophy findings, aiding differential diagnosis.MethodsUsing FastSurfer, we segmented brain regions and measured volume and cortical thickness from MRI scans pooled across multiple cohorts (N = 3433; ADNI, AIBL, DELCODE, DESCRIBE, EDSD, and NIFD), including healthy controls, prodromal and clinical AD cases, and bvFTD cases. Employing the Web Ontology Language (OWL), we built a semantic model encoding hierarchical anatomical information. Additionally, we created summary visualizations based on sunburst plots for visual inspection of the information stored in the ontology.ResultsOur computational framework dynamically estimated and aggregated regional pathological deviations across different levels of neuroanatomy abstraction. The disease similarity index derived from the volumetric and cortical thickness deviations achieved an AUC of 0.88 for separating AD and bvFTD, which was also reflected by distinct atrophy profile visualizations.ConclusionsThe proposed automated pipeline facilitates visual comparison of atrophy profiles across various disease types and stages. It provides a generalizable computational framework for summarizing pathologic findings, potentially enhancing the physicians' ability to evaluate brain pathologies robustly and interpretably.

PMID:40255031 | DOI:10.1177/13872877251331222

Sci Data. 2025 Apr 15;12(1):628. doi: 10.1038/s41597-025-04938-5.

ABSTRACT

Ontologies have gained popularity in the scientific community as a way to standardize terminologies in organizations' data. Although certain cohorts have created frameworks with rules and guidelines on creating ontologies, there exist significant variations in how Materials Science ontologies are currently developed. We seek to provide guidance in the form of a unified automated framework for developing interoperable and modular ontologies for Materials Data Science that simplifies the ontology terms matching by establishing a semantic bridge up to the Basic Formal Ontology(BFO). This framework provides key recommendations on how ontologies should be positioned within the semantic web, what knowledge representation language is recommended, and where ontologies should be published online to boost their findability and interoperability. Two fundamental components of the MDS-Onto framework are the bilingual package called FAIRmaterials for ontology creation and FAIRLinked, for FAIR data creation. To showcase the practical capabilities of FAIRmaterials, we present two exemplar domain ontologies of MDS-Onto: Synchrotron X-Ray Diffraction and Photovoltaics.

PMID:40234492 | DOI:10.1038/s41597-025-04938-5

Front Artif Intell. 2025 Mar 26;8:1451865. doi: 10.3389/frai.2025.1451865. eCollection 2025.

ABSTRACT

As we become increasingly dependent on technology in our daily lives, the usability of HCIs is a key driver of individual empowerment for us all. A primary focus of AI systems has been to make HCIs easier to use by identifying what users need and agentively taking over some of the cognitive work users would have otherwise performed, as such, they are becoming our delegates. To become effective and reliable delegates, AI agents need to understand all relevant situational semantic context surrounding a user's need and how the tools of the HCI can be leveraged. Current ML systems have fundamental semantic gaps in bespoke human context, real-time world knowledge, and how those relate to HCI tooling. These challenges are difficult to close due factors such as privacy, continual learning, access to real-time context, and how deeply integrated the semantics are with in-context learning. As such, we need to research and explore new ways to safely capture, compactly model, and incrementally evolve semantics in ways that can efficiently integrate into how AI systems act on our behalf. This article presents a thought experiment called the Game of Delegation as a lens to view the effectiveness of delegation and the semantic efficiency with which the delegation was achieved.

PMID:40206708 | PMC:PMC11979188 | DOI:10.3389/frai.2025.1451865

J Insect Sci. 2025 Mar 14;25(2):8. doi: 10.1093/jisesa/ieaf019.

ABSTRACT

Taxonomy provides a general foundation for research on insects. Using stored product pest (SPP) arthropods as a model group, this article overviews the historical impacts of taxonomy on applied entomology. The article surveys the dynamics of historical descriptions of new species in various SPP taxa; the majority of all species (90%) were described prior to 1925, while the key pests were described prior to 1866. The review shows that process of describing new SPP species is not random but is influenced by following factors: (i) larger species tend to be described earlier than smaller and SPP moths and beetles are described earlier than psocids and mites; (ii) key economic pests are on average described earlier than less significant ones. Considering a species name as a "password" to unique information resources, this review also assesses the historical number of synonymous or duplicate names of SPP species. Pests belonging to some higher taxa Lepidoptera and Coleoptera has accumulated more scientific synonyms than those others belonging to Psocoptera and Acari. Number of synonyms positively correlated with the economic importance of SPP species. The review summarized semantic origin of SPP names showing minor proportion of names (17.6%) are toponyms (geography) or eponyms (people), while the majority (82.4%) fall into other categories (descriptive, etc.). It is concluded that awareness of taxonomic advances, including changes to species and higher taxa names, should be effectively communicated to pest control practitioners and applied entomology students, and specifically addressed in relevant textbooks, web media, and databases.

PMID:40178352 | DOI:10.1093/jisesa/ieaf019

JMIR Serious Games. 2025 Mar 29. doi: 10.2196/66781. Online ahead of print.

ABSTRACT

BACKGROUND: The waste problem is a global issue all developed and developing countries face. Like many developing countries, Indonesia has inadequate infrastructure to process an extremely high volume of waste produced throughout the country and minimal public participation in proper waste management. Although the Indonesian government regulates Waste Bank as a community-based waste management solution, there is lack of integrated technological innovation to support Waste Bank. This study fills the gap by developing Circonomy, a model combining IoT, gamification, and semantic web technologies to advance community-based circular waste management.

OBJECTIVE: The proposed model Circonomy is inspired by the Waste Bank, the Indonesian Government's community-based waste management initiative. This research has objective to develop Circonomy as a circular waste model that integrate IoT-based smart-bin, semantic web, and gamification as an innovative technological solution.

METHODS: We identify the problem faced by the Indonesian Waste Bank from three locations in Jakarta and Yogyakarta as a basis for the Circonomy model and prototype development. The evaluation of the model focuses on Technical Performance and User Experience. The Technical Performance has three indicators, i.e., Bin Capacity Accuracy with a minimum of 80% precision, Bin Lid Response Time should be less than 5 seconds at a minimum of 80% of trials, and Data Transmission Success Rate at a minimum of 80%. While User Experience Metrics has two indicators, i.e., a minimum of 80% reported high usability and ease of use, and at least 80% of users feel more motivated using the prototype than the traditional Waste Bank. We select 10 random participants from ages 18 to 60 to perform User Experience evaluation on our prototype.

RESULTS: The Circonomy prototype demonstrates sound and stable performances related to Technical Performance and User Experience. Circonomy performs with at least 80% technical performance accuracy, comparable to industry standards. The accuracy problem lies in the placement of the ultrasonic sensor. The waste should be placed directly under the ultrasonic sensor to ensure the bin's capacity measurement accuracy. The User Experience testing results from 10 participants indicate that Circonomy has excellent user engagement, whereas 100% felt motivated by gamification, and 80% found the mobile application easy to use.

CONCLUSIONS: The testing result shows that Circonomy has acceptable performances for early-stage prototyping with at least 80% accuracy rate in technical performance and user experience. This ensures that Circonomy operates effectively in real-world conditions while remaining cost-efficient and scalable. For future development, Circonomy will prioritize enhancing the accuracy and reliability of sensor-based occupancy detection through improved sensor placement, multiple sensor integration, and exploring alternative technologies for regions with limited IT resources. In addition, more gamification features such as challenges and quiz should be added to improve the user experience and motivation.

PMID:40157387 | DOI:10.2196/66781

PeerJ Comput Sci. 2025 Mar 5;11:e2664. doi: 10.7717/peerj-cs.2664. eCollection 2025.

ABSTRACT

The internet has been inundated with an ocean of information, and hence, information retrieval systems are failing to provide optimal results to the user. In order to meet the challenge, query expansion techniques have emerged as a game-changer and are improving the results of information retrieval significantly. Of late, semantic query expansion techniques have attracted increased interest among researchers since these techniques offer more pertinent and practical results to the users. These allow the user to retrieve more meaningful and useful information from the web. Currently, few research works provide a comprehensive review on semantic query expansion; usually, they cannot provide a full view on recent advances, diversified data application, and practical challenges. Therefore, it is imperative to go deep in review in order to explain these advances and assist researchers with concrete insights for future development. This article represents the comprehensive review of the query expansion methods, with a particular emphasis on semantic approaches. It overviews the recent frameworks that have been developed within a period of 2015-2024 and reviews the limitations of each approach. Further, it discusses challenges that are inherent in the semantic query expansion field and identifies some future research directions. This article emphasizes that the linguistic approach is the most effective and flexible direction for researchers to follow, while the ontology approach better suits domain-specific search applications. This, in turn, means that development of the ontology field may further open new perspectives for semantic query expansion. Moreover, by employing artificial intelligence (AI) and making most of the query context without relying on user intervention, improvements toward the optimal expanded query can be achieved.

PMID:40134880 | PMC:PMC11935759 | DOI:10.7717/peerj-cs.2664

Clin Pract. 2025 Mar 7;15(3):54. doi: 10.3390/clinpract15030054.

ABSTRACT

Background/Objectives: During the last decade, there has been an increased interest in phenolic compound-rich natural products as natural therapies for regulating the molecular pathways behind central obesity and associated metabolic disorders. The present scoping review presents the outcomes of clinical and preclinical studies examining the anti-obesity effects of high phenolic extra virgin olive oil (HP-EVOO) and its possible underlying molecular mechanisms. Methods: Studies published between 2014 and 2024 were searched via MEDLINE, Scopus, Cochrane, the Web of Science, Semantic Scholar, Google Scholar, Science.gov, and Clinicaltrials.gov databases. A combination of keywords and Boolean logic was used to search throughout the last decade in all databases, including "hyperglycemia" or "hypertension" or "metabolic syndrome" or "dyslipidemia" or "hyperlipidemia" or "hypoglycemia" or "obesity" or "macrovascular diabetic complications" or "microvascular diabetic complications" or "cardiovascular disease" or "overweight" or "insulin sensitivity" or "insulin resistance" and "extra virgin olive oil" or "high phenolic olive oil" and "human" or "animal model". Results: The 10-year literature survey identified 21 studies in both animal models and humans, indicating that HP-EVOO improves inflammation, glycemic control, oxidative stress and endothelial function, potentially protecting against metabolic syndrome, hypertension and type 2 diabetes, even compared to EVOO. Moreover, HP-EVOO's antiplatelet effect and improvement in HDL functionality reduce cardiovascular risk. Conclusions: The evidence presented in this study demonstrates that HP-EVOO represents an effective preventive and therapeutic dietary approach to cardiometabolic diseases.

PMID:40136590 | DOI:10.3390/clinpract15030054

Sci Rep. 2025 Mar 19;15(1):9516. doi: 10.1038/s41598-025-92192-5.

ABSTRACT

In recent years, there has been a growing field of research focused on comprehending complexity in relation to web platform accessibility. It has shown that it is quite difficult to accurately assess and identify web accessibility concerns while taking multifaceted factors into account. It is imperative to prioritize multi-dimensional characteristics as they facilitate the integration of many aspects into the assessment process, which is a critical component in enhancing the accessibility evaluation process. Although many existing solutions with varying degrees of computational success have been proposed by scholars, they are confined to (1) following a certain set of rules of a specific guideline; (2) limited evaluation properties; (3) disregard for user criteria; and (4) complex functional properties or architectural design. To address these problems, we present in this work a straightforward yet precise model that assesses webpage accessibility by taking into account common features of the structural and visual elements of webpages that are part of the HTML Document Object Model (DOM) structure. In order to predict a webpage's accessibility status, we implemented three distinct algorithms to analyze web features/objects considering both semantic and non-semantic aspects. We performed experimental work to validate 20 university webpages in Hungary through our developed tool. The computed result of the developed tool was assessed by comparing the result with a user study where we performed user testing that included 40 users' 80 reviews on the same 20 university webpages in Hungary. Additionally, we compared our developed tool with other scientific models (that already exist) and existing ten open-source commercial automated testing tools considering several functional characteristics or properties. This two-phase assessment result shows that the developed tool has several advanced properties and the potential to predict the accessibility issues of the tested webpages.

PMID:40108199 | DOI:10.1038/s41598-025-92192-5

Mol Divers. 2025 Mar 14. doi: 10.1007/s11030-025-11164-z. Online ahead of print.

ABSTRACT

Biomedical knowledge graphs have emerged as powerful tools for drug discovery, but existing platforms often suffer from outdated information, limited accessibility, and insufficient integration of complex data. This study presents MedKG, a comprehensive and continuously updated knowledge graph designed to address these challenges in precision medicine and drug discovery. MedKG integrates data from 35 authoritative sources, encompassing 34 node types and 79 relationships. A Continuous Integration/Continuous Update pipeline ensures MedKG remains current, addressing a critical limitation of static knowledge bases. The integration of molecular embeddings enhances semantic analysis capabilities, bridging the gap between chemical structures and biological entities. To demonstrate MedKG's utility, a novel hybrid Relational Graph Convolutional Network for disease-drug link prediction, MedLINK was developed and used in case studies on clinical trial data for disease drug link prediction. Furthermore, a web-based application with user-friendly APIs and visualization tools was built, making MedKG accessible to both technical and non-technical users, which is freely available at http://pitools.niper.ac.in/medkg/.

PMID:40085402 | DOI:10.1007/s11030-025-11164-z

Syst Rev. 2025 Mar 1;14(1):52. doi: 10.1186/s13643-025-02793-4.

ABSTRACT

INTRODUCTION: Empathy is a crucial skill that enhances the quality of patient care, reduces burnout among healthcare professionals, and fosters professionalism in medical students. Clinical practice and standardized patient-based education provide opportunities to enhance empathy, but a lack of consistency and reproducibility as well as significant dependency on resources are impediments. The COVID-19 pandemic has further restricted these opportunities, highlighting the need for alternative approaches. Virtual patients through standardized scenarios ensure consistency and reproducibility while offering safe, flexible, and repetitive learning opportunities unconstrained by time or location. Empathy education using virtual patients could serve as a temporary alternative during the COVID-19 pandemic and address the limitations of traditional face-to-face learning methods. This review aims to comprehensively map existing literature on the use of virtual patients in empathy education and identify research gaps.

METHODS: This scoping review will follow the Joanna Briggs Institute's guidelines and be reported according to PRISMA-P. The search strategy includes a comprehensive search across databases such as PubMed (MEDLINE), CINAHL, Web of Science, Scopus, ERIC, Google, Google Scholar, and Semantic Scholar, covering both published and gray literature without language restrictions. Both quantitative and qualitative studies will be included. Two independent researchers will screen all titles/abstracts and full texts for eligibility. Data will be extracted to summarize definitions of empathy, characteristics of virtual patient scenarios, and methods for measuring their impact on empathy development. Results will be presented in narrative and tabular formats to highlight key findings and research gaps.

DISCUSSION: As this review analyzes existing literature, ethical approval is not required. Findings will be actively disseminated through academic conferences and peer-reviewed publications, providing educators and researchers with valuable insights into the potential of virtual patients to enhance empathy in medical education. This study goes beyond the mere synthesis of academic knowledge by contributing to the advancement of medical education and clinical practice by clarifying virtual patient scenario design and evaluation methods in empathy education. The findings provide a critical foundation for our ongoing development of a medical education platform aimed at enhancing empathy through the use of virtual patients.

PMID:40025554 | DOI:10.1186/s13643-025-02793-4

JMIR Med Inform. 2025 Feb 28;13:e67984. doi: 10.2196/67984.

ABSTRACT

BACKGROUND: The digitalization of health care has increased the demand for efficient data exchange, emphasizing semantic interoperability. SNOMED Clinical Terms (SNOMED CT), a comprehensive terminology with over 360,000 medical concepts, supports this need. However, it cannot cover all medical scenarios, particularly in complex cases. To address this, SNOMED CT allows postcoordination, where users combine precoordinated concepts with new expressions. Despite SNOMED CT's potential, the creation and validation of postcoordinated expressions (PCEs) remain challenging due to complex syntactic and semantic rules.

OBJECTIVE: This work aims to develop a tool that validates postcoordinated SNOMED CT expressions, focusing on providing users with detailed, automated correction instructions for syntactic and semantic errors. The goal is not just validation, but also offering user-friendly, actionable suggestions for improving PCEs.

METHODS: A tool was created using the Fast Healthcare Interoperability Resource (FHIR) service $validate-code and the terminology server Ontoserver to check the correctness of PCEs. When errors are detected, the tool processes the SNOMED CT Concept Model in JSON format and applies predefined error categories. For each error type, specific correction suggestions are generated and displayed to users. The key added value of the tool is in generating specific correction suggestions for each identified error, which are displayed to the users. The tool was integrated into a web application, where users can validate individual PCEs or bulk-upload files. The tool was tested with real existing PCEs, which were used as input and validated. In the event of errors, appropriate error messages were generated as output.

RESULTS: In the validation of 136 PCEs from 304 FHIR Questionnaires, 18 (13.2%) PCEs were invalid, with the most common errors being invalid attribute values. Additionally, 868 OncoTree codes were evaluated, resulting in 161 (20.9%) PCEs containing inactive concepts, which were successfully replaced with valid alternatives. A user survey reflects a favorable evaluation of the tool's functionality. Participants found the error categorization and correction suggestions to be precise, offering clear guidance for addressing issues. However, there is potential for enhancement, particularly regarding the level of detail in the error messages.

CONCLUSIONS: The validation tool significantly improves the accuracy of postcoordinated SNOMED CT expressions by not only identifying errors but also offering detailed correction instructions. This approach supports health care professionals in ensuring that their PCEs are syntactically and semantically valid, enhancing data quality and interoperability across systems.

PMID:40019788 | DOI:10.2196/67984

BMC Nurs. 2025 Feb 25;24(1):210. doi: 10.1186/s12912-025-02847-x.

ABSTRACT

INTRODUCTION: Enhancing clinical outcomes and patient satisfaction can be achieved through interprofessional collaboration between physicians and nurses. Conversely, a lack of nurse-physician interprofessional collaboration compromises patient safety, care, and improvement, and creates moral discomfort for healthcare professionals. Studies indicate that failures in interprofessional collaboration between nurses and physicians lead to adverse medical events, including hospital-acquired infections, medication administration errors, and unnecessary health-related costs.

OBJECTIVE: This systematic review and meta-analysis aimed to investigate the pooled proportions of the interprofessional collaborations towards patient care and associated factors among nurses and physicians in Ethiopia, 2024.

METHODS: A comprehensive search was conducted to find articles on interprofessional collaboration towards patient care and associated factors among nurses and physicians in Ethiopia. The study included cross-sectional studies conducted in Ethiopia and published in English from inception up to August 20, 2024. Excluded were conference proceedings, qualitative research, commentaries, editorial letters, case reports, case series, and monthly and annual police reports. The search encompassed full-text publications written in English and databases such as PubMed/MEDLINE, African Journals Online (AJOL), Semantic Scholar, Google Scholar, and Google. A checklist from the Joanna Briggs Institute (JBI) was used to evaluate the quality of the studies. Two independent reviewers performed data extraction, critical appraisal, and article screening. Statistical analysis was performed using STATA-17 software. A random-effects model was employed to estimate pooled proportions, and effect sizes with 95% confidence intervals were used to analyze determinants of interprofessional collaboration in patient care among nurses and physicians. Funnel plots and Egger's test were used to examine the possibility of publication bias (p-value < 0.10), and the trim-and-fill method by Duval and Tweedie was applied to adjust for publication bias.

RESULTS: Five studies with a total of 1686 study participants that are conducted in three Ethiopian regions and meet the inclusion criteria were reviewed and pooled for this evaluation. The pooled proportions of the interprofessional collaboration towards patient care in Ethiopia is 52.73% (95% CI = 44.66, 60.79%, I2 = 91.5%). Factors such as attitude (favorable attitude towards collaboration) (OR = 1.13, 95% CI: 0.13, 9.89, I2 = 97.7%) and organizational support (satisfaction towards organizational support) (OR = 0.38, 95% CI: 0.07, 2.10, I2 = 97.5%) were not significantly associated with interprofessional collaboration towards patient care.

CONCLUSION: In summary, this systematic review and meta-analysis reveal that interprofessional collaboration between nurses and physicians in Ethiopia is moderately common, with a pooled proportion of 52.73%. This finding underscores the need for ongoing efforts to enhance collaborative practices to further improve patient care outcomes. Additionally, the review identified two potential contributors to interprofessional collaboration: satisfaction with organizational support and favorable attitudes towards collaboration. However, the pooled effects of these factors did not show a significant association with interprofessional collaboration. This highlights the necessity for further primary research to identify additional factors that may influence interprofessional collaboration and enhance patient care outcomes. Notable limitations of this study include significant variation among studies, a small number of studies, a focus solely on public hospitals, restriction to English-language publications, only observational studies, and limited access to databases such as EMBASE, CINAHL, and Web of Science.

REGISTRATION: This systematic review and meta-analysis was registered in Prospero with the registration ID and link as follows: CRD42024579370; https://www.crd.york.ac.uk/prospero/#recordDetails .

PMID:40001025 | DOI:10.1186/s12912-025-02847-x

JMIR Nurs. 2025 Feb 21;8:e60810. doi: 10.2196/60810.

ABSTRACT

BACKGROUND: The digitalization of health care in Germany holds great potential to improve patient care, resource management, and efficiency. However, strict data protection regulations, fragmented infrastructures, and resistance to change hinder progress. These challenges leave care institutions reliant on outdated paper-based workflows, particularly for patient data transmission, despite the pressing need for efficient tools to support health care professionals amid a nursing shortage and rising demand for care.

OBJECTIVE: This paper aims to analyze Germany's care transition record (CTR) and CTR transmission process as part of transition management and suggests improvements toward a seamless digital solution.

METHODS: To understand the current challenges of manual CTR transfers, we used a mixed methods approach, which included a web-based questionnaire with nursing professionals, field observations, business process model and notation modeling, semantic and frequency analysis of CTR entries, and user story mapping.

RESULTS: A web-based questionnaire involving German nursing professionals (N=59) revealed considerable delays in patient care due to manual, patient-transferred CTRs. Of the 33 usable responses (n=33), 70% (n=23) of the respondents advocating for digital transmission to improve efficiency. Observations (N=11) in care facilities (n=5, 45%) and a hospital (n=6, 55%) confirmed the high administrative burden, averaging 34.67 (SD 10.78) minutes per CTR within a hospital and 44.6 (SD 20.5) minutes in care facilities. A semantic analysis of various CTRs (N=4) highlighted their differences and complexity, stressing the need for standardization. Analyzing a new CTR standard (care information object CTR) and manually mapping an existing CTR to it showed that the procedure was ambiguous, and some associations remained unclear. A frequency analysis of CTR entities revealed which were most used. In addition, discussions with care staff pointed out candidates for the most relevant entities. On the basis of the key findings, a stepwise transition approach toward a road map proposal for a standardized, secure transfer of CTRs was conceptualized. This road map in the form of a user story map, encompassing a "CTR transformer" (mapping of traditional CTRs to a new standard) and "care information object CTR viewer/editor" (in short, CIO-CTR viewer and editor; a new standard for viewing, editing, and exporting), shows a possibility to bridge the transition time until all institutions fully support the new standard.

CONCLUSIONS: A future solution should simplify the overall CTR transmission process by minimizing manual transfers into in-house systems, standardizing the CTR, and providing a secure digital transfer. This could positively impact the overall care process and patient experience. With our solutions, we attempt to support care staff in their daily activities and processes until nationwide state regulations are implemented successfully, though the timeline for this remains uncertain.

PMID:39982779 | DOI:10.2196/60810

medRxiv [Preprint]. 2025 Feb 2:2025.01.31.25321503. doi: 10.1101/2025.01.31.25321503.

ABSTRACT

We developed a simulated process to show a software implementation to facilitate an approach to integrate the Informed Consent Ontology, a reference ontology of informed consent information, to express implicit description and implement conceptual permission from informed consent life cycle. An early study introduced an experimental method to use Semantic Web Rule Language (SWRL) to describe and represent permissions to computational deduce more information from the Informed Consent Ontology (ICO), demonstrated by the use of the All of Us informed consent documents. We show how incomplete information in informed consent documents can be elucidated using a computational model of permissions toward health information technology that integrates ontologies. Future goals entail applying our computational approach for specific sub-domains of the informed consent life cycle, specifically for vaccine informed consent.

PMID:39974098 | PMC:PMC11838618 | DOI:10.1101/2025.01.31.25321503

Sci Data. 2025 Feb 17;12(1):282. doi: 10.1038/s41597-025-04580-1.

ABSTRACT

The Provenance Ontology (PROV-O) is a World Wide Web Consortium (W3C) recommended ontology used to structure data about provenance across a wide variety of domains. Basic Formal Ontology (BFO) is a top-level ontology ISO/IEC standard used to structure a wide variety of ontologies, such as the OBO Foundry ontologies and the Common Core Ontologies (CCO). To enhance interoperability between these two ontologies, their extensions, and data organized by them, a mapping methodology and set of alignments are presented according to specific criteria which prioritize semantic and logical principles. The ontology alignments are evaluated by checking their logical consistency with canonical examples of PROV-O instances and querying terms that do not satisfy the alignment criteria as formalized in SPARQL. A variety of semantic web technologies are used in support of FAIR (Findable, Accessible, Interoperable, Reusable) principles.

PMID:39962095 | DOI:10.1038/s41597-025-04580-1

J Biomed Inform. 2025 Feb 15:104804. doi: 10.1016/j.jbi.2025.104804. Online ahead of print.

ABSTRACT

OBJECTIVE: Visual representations generally serve as supplements to information, rather than as bearers of computable information themselves. Our objective is to develop a method for creating semantically-augmented graphic libraries that will serve as visual standards and can be implemented as visual assets in intelligent information systems.

METHODS: Graphics were developed using a composable approach and specified using SVG. OWL was used to represent the entities of our system, which include elements, units, graphics, graphic libraries, and library collections. A graph database serves as our data management system. Semantics are applied at multiple levels: (a) each element is associated with a semantic style class to link visual style to semantic meaning, (b) graphics are described using object properties and data properties, (c) relationships are specified between graphics, and (d) mappings are made between the graphics and outside resources.

RESULTS: The Graphic Library web application enables users to browse the libraries, view information pages for each graphic, and download individual graphics. We demonstrate how SPARQL can be employed to query the graphics database and the APIs can be used to retrieve the graphics and associated data for applications. In addition, this work shows that our method of designing composable graphics is well-suited to depicting variations in human anatomy.

CONCLUSION: This work provides a bridge between visual communication and the field of knowledge representation. We demonstrate a method for creating visual standards that are compatible with practices in biomedical ontology and implement a system for making them accessible to information systems.

PMID:39961540 | DOI:10.1016/j.jbi.2025.104804

J Biomed Inform. 2025 Feb 12:104788. doi: 10.1016/j.jbi.2025.104788. Online ahead of print.

ABSTRACT

BACKGROUND: If hospital Clinical Data Warehouses are to address today's focus in personalized medicine, they need to be able to track patients longitudinally and manage the large data sets generated by whole genome sequencing, RNA analyses, and complex imaging studies. Current Clinical Data Warehouses address neither issue. This paper reports on methods to enrich current systems by providing provenance data allowing patient histories to be followed longitudinally and managing the linking and versioning of large data sets from whatever source. The methods are open source and applicable to any clinical data warehouse system, whether data schema it uses.

METHOD: We introduce gitOmmix, an approach that overcomes these limitations, and illustrate its usefulness in the management of medical omics data. gitOmmix relies on (i) a file versioning system: git, (ii) an extension that handles large files: git-annex, (iii) a provenance knowledge graph: PROV-O, and (iv) an alignment between the git versioning information and the provenance knowledge graph.

RESULTS: Capabilities inherited from git and git-annex enable retracing the history of a clinical interpretation back to the patient sample, through supporting data and analyses. In addition, the provenance knowledge graph, aligned with the git versioning information, enables querying and browsing provenance relationships between these elements.

CONCLUSION: gitOmmix adds a provenance layer to CDWs, while scaling to large files and being agnostic of the CDW system. For these reasons, we think that it is a viable and generalizable solution for omics clinical studies.

PMID:39952627 | DOI:10.1016/j.jbi.2025.104788

Front Robot AI. 2025 Jan 29;11:1363150. doi: 10.3389/frobt.2024.1363150. eCollection 2024.

ABSTRACT

This article introduces a model-based design, implementation, deployment, and execution methodology, with tools supporting the systematic composition of algorithms from generic and domain-specific computational building blocks that prevent code duplication and enable robots to adapt their software themselves. The envisaged algorithms are numerical solvers based on graph structures. In this article, we focus on kinematics and dynamics algorithms, but examples such as message passing on probabilistic networks and factor graphs or cascade control diagrams fall under the same pattern. The tools rely on mature standards from the Semantic Web. They first synthesize algorithms symbolically, from which they then generate efficient code. The use case is an overactuated mobile robot with two redundant arms.

PMID:39944358 | PMC:PMC11813742 | DOI:10.3389/frobt.2024.1363150