The German Medical Informatics Initiative (MII) strives to enhance the interoperability and reusability of clinical routine data for research applications. Among the substantial achievements of the MII effort stands a uniform German core data set (CDS), to be generated by over 31 data integration centers (DIZ) operating under a rigorous protocol. The HL7/FHIR standard facilitates the distribution of data. Data storage and retrieval frequently utilize locally situated classical data warehouses. We are motivated to probe the benefits of a graph database in this specific application. The MII CDS, after being transitioned into a graph format and housed within a graph database, and further enhanced with supporting metadata, offers significant prospects for more complex data exploration and analysis. As a proof of concept, we describe the extract-transform-load procedure that was established to enable data transformation and provide access to a graph-based common core dataset.

HealthECCO powers the COVID-19 knowledge graph, which incorporates data from multiple biomedical domains. SemSpect, an interface designed for graph-based data exploration, constitutes one method for accessing CovidGraph. We highlight three distinct use cases, stemming from the integration of a wide array of COVID-19 related data sources over the last three years, within the (bio-)medical realm. Access to the open-source COVID-19 graph is straightforward, facilitated by the downloadable resource at https//healthecco.org/covidgraph/. The covidgraph project's source code and documentation can be accessed at the GitHub link https//github.com/covidgraph.

The contemporary clinical research study landscape is marked by the prevalent application of eCRFs. An ontological model is presented here for these forms, permitting detailed description, expression of their granularity, and connections to relevant entities within the context of the relevant study. Stemming from a psychiatry project, this development's versatility could lead to a wider range of applications.

Within the context of the Covid-19 pandemic outbreak, the need for swiftly gathering and utilising large volumes of data became clear. The Corona Data Exchange Platform (CODEX), originally developed within the German Network University Medicine (NUM), underwent an expansion in 2022. This expansion included a new segment devoted to the implementation of FAIR science principles. Research networks utilize the FAIR principles to determine their adherence to current standards in open and reproducible science. To ensure transparency and to provide guidance on how NUM scientists can boost the reusability of data and software, an online survey was disseminated within the NUM. We're presenting the findings and the crucial insights gained.

Digital health projects, unfortunately, often encounter obstacles during their pilot or test periods. Transiliac bone biopsy The introduction of new digital health services is often hampered by the absence of clear, step-by-step implementation plans, creating the need for significant changes to existing work processes and procedures. This investigation delves into the development of the Verified Innovation Process for Healthcare Solutions (VIPHS), a methodical approach for digital health innovation and deployment, using service design principles. To develop a prehospital model, a multiple case study was conducted, involving two cases, participant observation, role-playing exercises, and semi-structured interviews. Innovative digital health projects could benefit from the model's support, enabling a holistic, disciplined, and strategic approach to their realization.

In the 11th revision of the International Classification of Diseases (ICD-11), Chapter 26 now incorporates Traditional Medicine into Western Medicine practices. Traditional Medicine's approach to healing and care stems from the integration of deeply held beliefs, carefully considered theories, and collective experiential knowledge. It is not readily apparent how much Traditional Medicine data is encompassed within the Systematized Nomenclature of Medicine – Clinical Terms (SCT), the global healthcare lexicon. GSK2256098 cell line This research endeavors to resolve this uncertainty and investigate the proportion of ICD-11-CH26's conceptual framework that aligns with the SCT's parameters. A comparison of hierarchical structures is conducted for concepts found in ICD-11-CH26, when identical or similar concepts are present within the SCT taxonomy. Pending the preceding steps, an ontology concerning Traditional Chinese Medicine, utilizing concepts from the Systematized Nomenclature of Medicine, will be created.

Our society is witnessing a rising trend of individuals taking various medications concurrently. The use of these medications together presents a risk, potentially leading to dangerous interactions. The multifaceted task of predicting all potential drug-type interactions is exceedingly complicated, as a complete list of such interactions is unavailable. Machine learning algorithms have been incorporated into models to help accomplish this assignment. The output of these models, unfortunately, lacks the necessary structure for its application in clinical reasoning processes related to interactions. We formulate, in this research, a clinically relevant and technically feasible drug interaction model and strategy.

The secondary utilization of medical data for research is commendable due to inherent ethical, financial, and intrinsic merits. The question of making such datasets accessible to a larger target audience over the long term is critical within this context. Datasets are not usually extracted unexpectedly from the primary systems, because their processing is focused on quality and detail (following the principles of FAIR data). For this specific need, specialized data repositories are being constructed at present. A study of the conditions needed for reusing clinical trial data within a data repository, leveraging the Open Archiving Information System (OAIS) reference model, is presented in this paper. An Archive Information Package (AIP) design, in particular, emphasizes a cost-effective compromise between the data producer's creation expenditures and the data consumer's data understanding.

Autism Spectrum Disorder (ASD), a neurodevelopmental condition, is characterized by enduring difficulties in both social communication and interaction, and restricted, repetitive patterns of behavior. Children are impacted by this, and the effects continue into adolescence and adulthood. The root causes and the associated psychopathological pathways of this condition are unknown and need to be discovered. The TEDIS cohort study, spanning the period from 2010 to 2022, encompassed 1300 patient files within the Ile-de-France region, each containing current health information, notably data derived from ASD assessments. Researchers and decision-makers benefit from reliable data, leading to improved knowledge and practical application for autistic patients.

Real-world data (RWD) is steadily increasing its role within research initiatives. The European Medicines Agency (EMA) is presently engaged in building a multinational research network that leverages RWD for research endeavors. While this is true, achieving data consistency across nations requires a careful methodology to avoid misclassification and prejudice.
The research presented in this paper investigates the level of accuracy in assigning RxNorm ingredients to medication orders using only ATC codes.
The University Hospital Dresden (UKD) dataset of 1,506,059 medication orders underwent analysis, harmonized with the Observational Medical Outcomes Partnership's (OMOP) ATC vocabulary, incorporating relevant relationship linkages to RxNorm.
Following our analysis of all medication orders, we determined that 70.25% of the prescriptions consisted of a single drug ingredient with a direct mapping to the RxNorm classification. Yet, a substantial challenge existed in the mapping of other medication orders, which was displayed in an interactive scatterplot visualization.
Single-ingredient medication orders, accounting for 70.25% of those under observation, are readily standardized to RxNorm. However, combination drugs present a challenge due to the varied ingredient assignments seen in ATC compared to RxNorm. With the aid of the visualization, research teams can achieve a more in-depth understanding of concerning data points and subsequently pursue further investigation of any issues uncovered.
Within the observed medication orders, a substantial percentage (70.25%) comprises single-ingredient drugs easily cataloged using RxNorm's system. However, combination drugs pose a difficulty because their ingredient assignments vary significantly between the Anatomical Therapeutic Chemical Classification System (ATC) and RxNorm. To facilitate a better grasp of problematic data, the visualization helps research teams further investigate identified problems.

Mapping local healthcare data to standardized terminology is a prerequisite for achieving interoperability. This paper benchmarks various methods for implementing HL7 FHIR Terminology Module operations, assessing the resulting performance for a terminology client, to highlight the strengths and weaknesses of each approach. The methods demonstrate remarkably distinct performance, while maintaining a local client-side cache for all operations is exceptionally vital. Our investigation underscores the significance of careful consideration of the integration environment, potential bottlenecks, and implementation strategies.

Knowledge graphs have become a dependable instrument in clinical practices, improving patient care and assisting in the discovery of treatments for new diseases. mediator subunit A wide range of healthcare information retrieval systems have felt the consequences of their actions. A disease database is enhanced in this study with a knowledge graph constructed using Neo4j, a knowledge graph tool, enabling streamlined responses to complex queries that formerly required considerable time and effort. Existing semantic relations within a medical knowledge graph, combined with its reasoning capacity, enable the derivation of new information.