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The rapid growth of clinical data, driven by digital technologies and high-resolution sensors, presents significant challenges for health care organizations aiming to support advanced artificial intelligence research and improve patient care. Traditional data management approaches may struggle to handle the large, diverse, and rapidly updating datasets prevalent in modern clinical environments. This study aimed to compare 3 clinical data management architectures-clinical data warehouses, clinical data lakes, and clinical data lakehouses-by analyzing their performance using the FAIR (findable, accessible, interoperable, and reusable) principles and the big data 5 V's (volume, variety, velocity, veracity, and value). The aim was to provide guidance on selecting an architecture that balances robust data governance with the flexibility required for advanced analytics. We developed a comprehensive analysis framework that integrates aspects of data governance with technical performance criteria. A rapid literature review was conducted to synthesize evidence from multiple studies, focusing on how each architecture manages large, heterogeneous, and dynamically updating clinical data. The review assessed key dimensions such as scalability, real-time processing capabilities, metadata consistency, and the technical expertise required for implementation and maintenance. The results show that clinical data warehouses offer strong data governance, stability, and structured reporting, making them well suited for environments that require strict compliance and reliable analysis. However, they are limited in terms of real-time processing and scalability. In contrast, clinical data lakes offer greater flexibility and cost-effective scalability for managing heterogeneous data types, although they may suffer from inconsistent metadata management and challenges in maintaining data quality. Clinical data lakehouses combine the strengths of both approaches by supporting real-time data ingestion and structured querying; however, their hybrid nature requires high technical expertise and involves complex integration efforts. The optimal data management architecture for clinical applications depends on an organization's specific needs, available resources, and strategic goals. Health care institutions need to weigh the trade-offs between robust data governance, operational flexibility, and scalability to build future-proof infrastructures that support both clinical operations and artificial intelligence research. Further research should focus on simplifying the complexity of hybrid models and improving the integration of clinical standards to improve overall system reliability and ease of implementation.

Background To gain insight into the real-life care of patients in the healthcare system, data from hospital information systems and insurance systems are required. Consequently, linking clinical data with claims data is necessary. To ensure their syntactic and semantic interoperability, the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) from the Observational Health Data Sciences and Informatics (OHDSI) community was chosen. However, there is no detailed guide that would allow researchers to follow a generic process for data harmonization, i.e. the transformation of local source data into the standardized OMOP CDM format. Thus, the aim of this paper is to conceptualize a generic data harmonization process for OMOP CDM. Methods For this purpose, we conducted a literature review focusing on publications that address the harmonization of clinical or claims data in OMOP CDM. Subsequently, the process steps used and their chronological order as well as applied OHDSI tools were extracted for each included publication. The results were then compared to derive a generic sequence of the process steps. Results From 23 publications included, a generic data harmonization process for OMOP CDM was conceptualized, consisting of nine process steps: dataset specification, data profiling, vocabulary identification, coverage analysis of vocabularies, semantic mapping, structural mapping, extract-transform-load-process, qualitative and quantitative data quality analysis. Furthermore, we identified seven OHDSI tools which supported five of the process steps. Conclusions The generic data harmonization process can be used as a step-by-step guide to assist other researchers in harmonizing source data in OMOP CDM.

The vast and heterogeneous data being constantly generated in clinics can provide great wealth for patients and research alike. The quickly evolving field of medical informatics research has contributed numerous concepts, algorithms, and standards to facilitate this development. However, these difficult relationships, complex terminologies, and multiple implementations can present obstacles for people who want to get active in the field. With a particular focus on medical informatics research conducted in Germany, we present in our Viewpoint a set of 10 important topics to improve the overall interdisciplinary communication between different stakeholders (eg, physicians, computational experts, experimentalists, students, patient representatives). This may lower the barriers to entry and offer a starting point for collaborations at different levels. The suggested topics are briefly introduced, then general best practice guidance is given, and further resources for in-depth reading or hands-on tutorials are recommended. In addition, the topics are set to cover current aspects and open research gaps of the medical informatics domain, including data regulations and concepts; data harmonization and processing; and data evaluation, visualization, and dissemination. In addition, we give an example on how these topics can be integrated in a medical informatics curriculum for higher education. By recognizing these topics, readers will be able to (1) set clinical and research data into the context of medical informatics, understanding what is possible to achieve with data or how data should be handled in terms of data privacy and storage; (2) distinguish current interoperability standards and obtain first insights into the processes leading to effective data transfer and analysis; and (3) value the use of newly developed technical approaches to utilize the full potential of clinical data.

Digitization offers a multitude of opportunities to gain insights into current diagnostics and therapies from retrospective data. In this context, real-world data and their accessibility are of increasing importance to support unbiased and reliable research on big data. However, routinely collected data are not readily usable for research owing to the unstructured nature of health care systems and a lack of interoperability between these systems. This challenge is evident in drug data. This study aimed to present an approach that identifies and increases the structuredness of drug data while ensuring standardization according to Anatomical Therapeutic Chemical (ATC) classification. Our approach was based on available drug prescriptions and a drug catalog and consisted of 4 steps. First, we performed an initial analysis of the structuredness of local drug data to define a point of comparison for the effectiveness of the overall approach. Second, we applied 3 algorithms to unstructured data that translated text into ATC codes based on string comparisons in terms of ingredients and product names and performed similarity comparisons based on Levenshtein distance. Third, we validated the results of the 3 algorithms with expert knowledge based on the 1000 most frequently used prescription texts. Fourth, we performed a final validation to determine the increased degree of structuredness. Initially, 47.73% (n=843,980) of 1,768,153 drug prescriptions were classified as structured. With the application of the 3 algorithms, we were able to increase the degree of structuredness to 85.18% (n=1,506,059) based on the 1000 most frequent medication prescriptions. In this regard, the combination of algorithms 1, 2, and 3 resulted in a correctness level of 100% (with 57,264 ATC codes identified), algorithms 1 and 3 resulted in 99.6% (with 152,404 codes identified), and algorithms 1 and 2 resulted in 95.9% (with 39,472 codes identified). As shown in the first analysis steps of our approach, the availability of a product catalog to select during the documentation process is not sufficient to generate structured data. Our 4-step approach reduces the problems and reliably increases the structuredness automatically. Similarity matching shows promising results, particularly for entries with no connection to a product catalog. However, further enhancement of the correctness of such a similarity matching algorithm needs to be investigated in future work.

In the Medical Informatics in Research and Care in University Medicine (MIRACUM) consortium, an IT-based clinical trial recruitment support system was developed based on the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Currently, OMOP CDM is populated with German Fast Healthcare Interoperability Resources (FHIR) using an Extract-Transform-Load (ETL) process, which was designed as a bulk load. However, the computational effort that comes with an everyday full load is not efficient for daily recruitment. The aim of this study is to extend our existing ETL process with the option of incremental loading to efficiently support daily updated data. Based on our existing bulk ETL process, we performed an analysis to determine the requirements of incremental loading. Furthermore, a literature review was conducted to identify adaptable approaches. Based on this, we implemented three methods to integrate incremental loading into our ETL process. Lastly, a test suite was defined to evaluate the incremental loading for data correctness and performance compared to bulk loading. The resulting ETL process supports bulk and incremental loading. Performance tests show that the incremental load took 87.5% less execution time than the bulk load (2.12 min compared to 17.07 min) related to changes of 1 day, while no data differences occurred in OMOP CDM. Since incremental loading is more efficient than a daily bulk load and both loading options result in the same amount of data, we recommend using bulk load for an initial load and switching to incremental load for daily updates. The resulting incremental ETL logic can be applied internationally since it is not restricted to German FHIR profiles.

The worldwide incidence of Crohn disease (CD) in childhood and adolescence has an increasing trend, with significant differences between different geographic regions and individual countries. This includes an increase in the incidence of CD in countries and geographic regions where CD was not previously prevalent. In response to the increasing incidence, the pediatric care landscape is facing growing challenges. This systematic review and meta-analysis were undertaken to comprehensively delineate the incidence rates of CD in pediatric populations across different countries and to explore potential influencing factors. We performed a systematic review of PubMed and Embase (via Ovid) for studies from January 1, 1970, to December 31, 2019. In addition, a manual search was performed in relevant and previously published reviews. The results were evaluated quantitatively. For this purpose, random effects meta-analyses and meta-regressions were performed to investigate the overall incidence rate and possible factors influencing the incidence. A qualitative synthesis of 74 studies was performed, with 72 studies included in the meta-analyses and 52 in the meta-regressions. The results of our meta-analysis showed significant heterogeneity between the individual studies, which cannot be explained by a sample effect alone. Our findings showed geographical differences in incidence rates, which increased with increasing distance from the equator, although no global temporal trend was apparent. The meta-regression analysis also identified geographic location, UV index, and Human Development Index as significant moderators associated with CD incidence. Our results suggest that pediatric CD incidence has increased in many countries since 1970 but varies widely with geographic location, which may pose challenges to the respective health care systems. We identified geographic, environmental, and socioeconomic factors that contribute to the observed heterogeneity in incidence rates. These results can serve as a basis for future research. To this end, implementations of internationally standardized and interoperable registries combined with the dissemination of health data through federated networks based on a common data model, such as the Observational Medical Outcomes Partnership, would be beneficial. This would deepen the understanding of CD and promote evidence-based approaches to preventive and interventional strategies as well as inform public health policies aimed at addressing the increasing burden of CD in children and adolescents. PROSPERO International prospective register of systematic reviews CRD42020168644; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=168644. RR2-10.1136/bmjopen-2020-037669.

Obtaining real-world data from routine clinical care is of growing interest for scientific research and personalized medicine. Despite the abundance of medical data across various facilities - including hospitals, outpatient clinics, and physician practices - the intersectoral exchange of information remains largely hindered due to differences in data structure, content, and adherence to data protection regulations. In response to this challenge, the Medical Informatics Initiative (MII) was launched in Germany, focusing initially on university hospitals to foster the exchange and utilization of real-world data through the development of standardized methods and tools, including the creation of a common core dataset. Our aim, as part of the Medical Informatics Research Hub in Saxony (MiHUBx), is to extend the MII concepts to non-university healthcare providers in a more seamless manner to enable the exchange of real-world data among intersectoral medical sites. We investigated what services are needed to facilitate the provision of harmonized real-world data for cross-site research. On this basis, we designed a Service Platform Prototype that hosts services for data harmonization, adhering to the globally recognized Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) international standard communication format and the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). Leveraging these standards, we implemented additional services facilitating data utilization, exchange and analysis. Throughout the development phase, we collaborated with an interdisciplinary team of experts from the fields of system administration, software engineering and technology acceptance to ensure that the solution is sustainable and reusable in the long term. We have developed the pre-built packages \"ResearchData-to-FHIR,\" \"FHIR-to-OMOP,\" and \"Addons,\" which provide the services for data harmonization and provision of project-related real-world data in both the FHIR MII Core dataset format (CDS) and the OMOP CDM format as well as utilization and a Service Platform Prototype to streamline data management and use. Our development shows a possible approach to extend the MII concepts to non-university healthcare providers to enable cross-site research on real-world data. Our Service Platform Prototype can thus pave the way for intersectoral data sharing, federated analysis, and provision of SMART-on-FHIR applications to support clinical decision making.

The healthcare sector is notable for its reliance on discrete, self-contained information systems, which are often characterised by the presence of disparate data silos. The growing demands for documentation, quality assurance, and secondary use of medical data for research purposes has underscored the necessity for solutions that are more flexible, straightforward to maintain and interoperable. In this context, modular systems have the potential to act as a catalyst for change, offering the capacity to encapsulate and combine functionalities in an adaptable manner. The objective of this scoping review is to determine the extent to which modular systems are employed in the medical field. The review will provide a detailed overview of the effectiveness of service-oriented or microservice architectures, the challenges that should be addressed during implementation, and the lessons that can be learned from countries with productive use of such modular architectures. The review shows a rise in the use of microservices, indicating a shift towards encapsulated autonomous functions. The implementation should use HL7 FHIR as communication standard, deploy RESTful interfaces and standard protocols for technical data exchange, and apply HIPAA security rule for security purposes. User involvement is essential, as is integrating services into existing workflows. Modular architectures can facilitate flexibility and scalability. However, there are well-documented performance issues associated with microservice architectures, namely a high communication demand. One potential solution to this problem may be to integrate modular architectures into a cloud computing environment, which would require further investigation.

The incidence of ulcerative colitis (UC) among children and adolescents is rising globally, albeit with notable discrepancies across countries. This systematic review and meta-analysis aims to provide a comprehensive overview of the incidence rates of pediatric UC in various countries and explore potential influencing factors. A systematic literature search was conducted in PubMed and EMBASE (via OVID) for studies published between January 1, 1970, and December 31, 2019. Additionally, a manual search was performed to identify relevant systematic reviews. Meta-analyses and meta-regressions were employed to determine the overall incidence rate and examine potential factors that may influence it. A total of 66 studies were included in the qualitative analysis, while 65 studies were included in the meta-analysis and 50 studies were meta-regression. The study reports a rising incidence of pediatric UC in several countries but significant differences across geographic regions, with no discernible global temporal trend. In addition, our meta-regression analysis showed that geographic location and socioeconomic factors significantly influenced the incidence of UC. Conclusion : Our findings indicate a rising incidence of pediatric UC in numerous countries since 1970, but with significant geographical variation, potentially presenting challenges for respective healthcare systems. We have identified geographic and socioeconomic factors that contribute to the observed heterogeneity in incidence rates. These findings provide a foundation for future research and health policies, aiming to tackle the growing burden of UC among children and adolescents. What is Known: • The incidence of ulcerative colitis in childhood and adolescence appears to be increasing worldwide and varies internationally. • Environmental and lifestyle factors are suspected as potential causes. What is New: • Our results highlight that the heterogeneity in incidence rates can be attributed to geographic and socio-economic factors.

The escalating worldwide prevalence of Crohn’s disease (CD) among children and adolescents, coupled with a trend toward earlier onset, presents significant challenges for healthcare systems. Moreover, the chronicity of this condition imposes substantial individual burdens. Consequently, the principal objective of CD treatment revolves around rapid inducing remission. This study scrutinizes the impact of age, gender, initial disease localization, and therapy on the duration to achieve disease activity amelioration. Data from the Saxon Pediatric IBD Registry in Germany were analyzed over a period of 15 years. In addition to descriptive methods, logistic and linear regression analyses were conducted to identify correlations. Furthermore, survival analyses and Cox regressions were utilized to identify factors influencing the time to improvement in disease activity. These effects were expressed as Hazard Ratios (HR) with 95% confidence intervals. Data on the clinical course of 338 children and adolescents with CD were available in the registry. The analyses showed a significant correlation between a young age of onset and the severity of disease activity. It was evident that treatment with anti-TNF (Infliximab) was associated with a more favorable prognosis in terms of the time required for improvement in disease activity. Similarly, favorable outcomes were observed with the combination therapies of infliximab with enteral nutrition therapy and Infliximab with immunosuppressants. Conclusion : Our analysis of data from the Saxon Pediatric IBD Registry revealed that the timeframe for improvement of disease activity in pediatric Crohn’s disease is influenced by several factors. Specifically, patient age, treatment modality, and initial site of inflammation were found to be significant factors. The study provides important findings that underline the need for individualized treatment.