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Background: The COVID-19 pandemic revealed a need for better collaboration among research, care, and management in Germany as well as globally. Initially, there was a high demand for broad data collection across Germany, but as the pandemic evolved, localized data became increasingly necessary. Customized dashboards and tools were rapidly developed to provide timely and accurate information. In Saxony, the DISPENSE project was created to predict short-term hospital bed capacity demands, and while it was successful, continuous adjustments and the initial monolithic system architecture of the application made it difficult to customize and scale. Methods: To analyze the current state of the DISPENSE tool, we conducted an in-depth analysis of the data processing steps and identified data flows underlying users' metrics and dashboards. We also conducted a workshop to understand the different views and constraints of specific user groups, and brought together and clustered the information according to content-related service areas to determine functionality-related service groups. Based on this analysis, we developed a concept for the system architecture, modularized the main services by assigning specialized applications and integrated them into the existing system, allowing for self-service reporting and evaluation of the expert groups' needs. Results: We analyzed the applications' dataflow and identified specific user groups. The functionalities of the monolithic application were divided into specific service groups for data processing, data storage, predictions, content visualization, and user management. After composition and implementation, we evaluated the new system architecture against the initial requirements by enabling self-service reporting to the users. Discussion: By modularizing the monolithic application and creating a more flexible system, the challenges of rapidly changing requirements, growing need for information, and high administrative efforts were addressed. Conclusion: We demonstrated an improved adaptation towards the needs of various user groups, increased efficiency, and reduced burden on administrators, while also enabling self-service functionalities and specialization of single applications on individual service groups. [ABSTRACT FROM AUTHOR]

The applicant Syngenta Crop Protection AG submitted a request to the competent national authority in Germany to evaluate the confirmatory data that were identified for S‐metolachlor in the framework of the maximum residue level (MRL) review under Article 12 of Regulation (EC) No 396/2005 as not available. To address the data gaps, a new freezer storage stability study for S‐metolachlor in commodities with high acid content was submitted. However, the data gap on storage stability was considered only partially addressed for strawberries because, in the absence of information on the storage time interval of the samples of the residue trials performed on strawberries, no conclusion on the validity of these trials could be drawn. The data gap on the lack of trials to support the good agricultural practice (GAP) on pineapples was not addressed. Consequently, the existing tentative MRLs for strawberries and pineapple cannot be confirmed and EFSA recommends lowering these MRLs to the enforcement limit of quantification (LOQ). Since a new lower LOQ for enforcement in high acid content commodities of 0.01 mg/kg has been validated, it is proposed to change the existing MRL value from 0.05* to 0.01* mg/kg for these two commodities. [ABSTRACT FROM AUTHOR]

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Surveillance studies have long argued that electronic databases are designed to maximize state surveillance as a "superpanopticon" or "surveillant assemblage." But how are databases being implemented in practice, and do they actually enhance control? This article addresses these questions by examining the case of the German Central Foreigners Register (Ausländerzentralregister [AZR]). Established in 1953, the AZR was one of the first databases on migrants in the western liberal world, and remains a pillar of Germany's migration control system today. By analyzing internal ministerial records from the 1950s to the 1970s – the time when this database was introduced, expanded, and automatized while still relatively free from legal or public constraints – this article examines whether, or how, databases enhance state control. I argue that the AZR did not provide the "perfect surveillance" it was intended to deliver; rather, it produced major bureaucratic and political challenges and a series of malfunctions. This case study confirms that database surveillance, such as the German AZR in the 1970s and European databases today, depends on three basic conditions: shared expectations regarding data usages, cooperation in data supply, and capacities of data storage and maintenance. Moreover, databases serve the additional symbolic function of reassuring the self‐imagination of sovereign, modern state power. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases are directly or indirectly responsible for up to 38.5% of all deaths in Germany and thus represent the most frequent cause of death. At present, heart diseases are mainly discovered by chance during routine visits to the doctor or when acute symptoms occur. However, there is no practical method to proactively detect diseases or abnormalities of the heart in the daily environment and to take preventive measures for the person concerned. Long-term ECG devices, as currently used by physicians, are simply too expensive, impractical, and not widely available for everyday use. This work aims to develop an ECG device suitable for everyday use that can be worn directly on the body. For this purpose, an already existing hardware platform will be analyzed, and the corresponding potential for improvement will be identified. A precise picture of the existing data quality is obtained by metrological examination, and corresponding requirements are defined. Based on these identified optimization potentials, a new ECG device is developed. The revised ECG device is characterized by a high integration density and combines all components directly on one board except the battery and the ECG electrodes. The compact design allows the device to be attached directly to the chest. An integrated microcontroller allows digital signal processing without the need for an additional computer. Central features of the evaluation are a peak detection for detecting R-peaks and a calculation of the current heart rate based on the RR interval. To ensure the validity of the detected R-peaks, a model of the anatomical conditions is used. Thus, unrealistic RR-intervals can be excluded. The wireless interface allows continuous transmission of the calculated heart rate. Following the development of hardware and software, the results are verified, and appropriate conclusions about the data quality are drawn. As a result, a very compact and wearable ECG device with different wireless technologies, data storage, and evaluation of RR intervals was developed. Some tests yelled runtimes up to 24 hours with wireless Lan activated and streaming. [ABSTRACT FROM AUTHOR]

• Data-driven train delay forecasting using machine learning at scale. • Using operational data from Deutsche Bahn for 25,000 trains daily. • Online method combines machine learning and simulation. • Key pre-processing steps handled automatically. A large-scale ensemble prediction model to predict train delays is presented. The ensemble model uses a disparate set of models, two statistical and one simulation-based to generate forecasts of train delays. The first statistical model is a context-aware random forest that accounts for network traffic states, such as likely stretch conflicts and current headway's, exogenous weather, event, and work zone information. The second model is a kernel regression that captures train-specific dynamics. A mesoscopic simulation model that accounts for travel and dwell time variations as well as inferred track occupation conflicts, train connections and rolling stock rotations, is additionally considered. The models have been used in a proof of concept to forecast delays for nationwide passenger services network of Deutsche Bahn, which operates roughly 25,000 trains daily in Germany. Results demonstrate a 25% improvement potential in forecast correctness (fraction of predictions within one minute) and 50% reduction in root mean squared errors compared to the published schedule. The paper describes the models along with the big data challenges that were addressed in data storage, feature and model building, and computation. [ABSTRACT FROM AUTHOR]

Conventional ultrasound (US) is the first-line imaging method for abdominal pathologies, but its diagnostic accuracy is operator-dependent, and data storage is usually limited to two-dimensional images. A novel tomographic US system (Curefab CS, Munich, Germany) processes imaging data combined with three-dimensional spatial information using a magnetic field tracking. This enables standardized image presentation in axial planes and a review of the entire examination. The applicability and diagnostic performance of this tomographic US approach was analyzed in an abdominal setting using conventional US as reference. Tomographic US data were successfully compiled in all subjects of a training cohort (20 healthy volunteers) and in 50 patients with abdominal lesions. Image quality (35% and 79% for training and patient cohort respectively) and completeness of organ visualization (45% and 44%) were frequently impaired in tomographic US compared to conventional US. Conventional and tomographic US showed good agreement for measurement of organ sizes in the training cohort (right liver lobe and both kidneys with a median deviation of 5%). In the patient cohort, tomographic US identified 57 of 74 hepatic or renal lesions detected by conventional ultrasound (sensitivity 77%). In conclusion, this study illustrates the diagnostic potential of abdominal tomographic US, but current significant limitations of the tomographic ultrasound device demand further technical improvements before this and comparable approaches can be implemented in clinical practice. [ABSTRACT FROM AUTHOR]

Data harmonization is an important step in large-scale data analysis and for generating evidence on real world data in healthcare. With the OMOP common data model, a relevant instrument for data harmonization is available that is being promoted by different networks and communities. At the Hannover Medical School (MHH) in Germany, an Enterprise Clinical Research Data Warehouse (ECRDW) is established and harmonization of that data source is the focus of this work. We present MHH's first implementation of the OMOP common data model on top of the ECRDW data source and demonstrate the challenges concerning the mapping of German healthcare terminologies to a standardized format.

In order to provide the scientists with a stable, future-oriented service for their research results, since September 2012 TU Berlin has developed an infrastructure for research data. It shall guarantee the long-term storage and availability of research data and research publications that are generated in TU Berlin. The organizational and technical components of this infrastructure will be realized in a two year long, TU-wide project. The university library, the IT service center and the research department of TU Berlin have cooperated in this project. The following article presents the concept and informs about the status of the project. [ABSTRACT FROM AUTHOR]

The article discusses the study on the applicability of Kurt Lewin's technique in change management to the health informatics project at Erlangen University Hospital in Germany. The project has implemented changes in the documentation of costly materials in the surgical department based on the field theory and group dynamics of Lewin in a data warehouse. It notes that Lewin's cost effective approach to change management is applicable to the hospital setting with a data warehouse.

The article focuses on the German business services statistics panel. The German business services statistics panel contains a dataset that gives an overview about enterprise and professional persons in the business-related services sector. Variables included in the panel dataset are described. The article looks at the upcoming changes to the services statistics in the reporting year 2008. An overview on data access is presented.

The article focuses on the research data centre (RDC) of the statistical offices of the Länder in Germany. Safe scientific workstations and comprehensive use of microdata in all statistical offices are some of the advantages of RDC. The RDC is funded by the Federal Ministry for Education and Research. It relates the origins and content of the sub-project called Official Firm Data for Germany. It also covers the data sets that will be created during the course of the project.

Objective: To analyze the most common ophthalmologic disorders in pregnant women seen in a hospital in Munich in Germany using a big data analysis system, as well as to compare the results obtained with those from other epidemiological studies that used different data acquisition methods., Methods: We retrospectively analyzed electronic health records of pregnant women who were seen at the ophthalmology department from 2003 to 2019 at the Ludwig-Maximilians-Universität München hospital. The main complaints that led to ophthalmic consultations during this period were evaluated, and also the variation in intraocular pressure of patients throughout gestational trimesters by analyzing data from the data warehouse system., Results: A total of 27,326 electronic health records were analyzed. Of participants, 149 (0.54%) required eye care during pregnancy. Their mean intraocular pressure was 17mmHg in the first trimester, 12mmHg in the second trimester, and 14mmHg in the third trimester. The most prevalent findings were dry eye (29.3%) and conjunctivitis (16%), and ametropia (16%). The most common posterior segment problem was diabetic retinopathy (4.6%). The lower mean intraocular pressure in the second and third trimester found in our study is in accordance with other studies that used other method for data acquisition., Conclusion: The most common ophthalmic conditions found in this study population were dry eye, conjunctivitis, and ametropia. The use of data warehouse proved to be useful for acquiring and analyzing data from many patients. This study results are comparable with other studies in published literature that adopted different methodology.

The increasing digitalization of social life opens up new possibilities for modern health care. This article describes innovative application possibilities that could help to sustainably improve the treatment of severe injuries in the future with the help of methods such as big data, artificial intelligence, intelligence augmentation, and machine learning. For the successful application of these methods, suitable data sources must be available. The TraumaRegister DGU® (TR-DGU) currently represents the largest database in Germany in the field of care for severely injured patients that could potentially be used for digital innovations. In this context, it is a good example of the problem areas such as data transfer, interoperability, standardization of data sets, parameter definitions, and ensuring data protection, which still represent major challenges for the digitization of trauma care. In addition to the further development of new analysis methods, solutions must also continue to be sought to the question of how best to intelligently link the relevant data from the various data sources.

Background and Purpose: Mechanical thrombectomy, in addition to intravenous (i.v.) thrombolysis is recommended for treatment of acute stroke in patients with large vessel occlusions (LVO) in the anterior circulation up to 6 h after symptom onset. We compared thrombectomy rates of eight university hospitals of the MIRACUM consortium to analyze the implementation of this guideline in clinical routine., Methods: Anonymized billing data in a standardized format were loaded into a local i2b2 data warehouse by applying already existing extract, transform and load (ETL) routines. A locally executed uniform SQL (structured query language) query delivered aggregated site data for all inpatients with a discharge diagnosis of ischemic stroke (ICD-10 I63) containing counts for type of acute treatment, type of admission and age groups, which were centrally analyzed with R., Results: From 2014 to 2016, the thrombectomy rate almost doubled from a mean of 4.7% to 9.6%, although significant differences between centers exist (range in 2016: 5.8-17%). The number of drip-and-ship procedures increased in 3 out of 8 centers. There was no evidence for a decrease in thrombectomy rates during weekends/holiday or among patients older than 80 years, but this age group is more likely to receive i.v. recombinant tissue plasminogen activator (rtPA)., Conclusion: The observed increase of thrombectomy rates and drip-and-ship procedures without a significant difference between weekdays and weekends or patients of different ages is substantiating a rapid implementation of stroke guidelines within the analyzed neurovascular centers. The prototype of the MIRACUM Data Integration Center already contributes to health services research in Germany.