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Copyright of Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Digitalization in cardiovascular emergencies is rapidly evolving, analogous to the development in medicine, driven by the increasingly broader availability of digital structures and improved networks, electronic health records and the interconnectivity of systems. The potential use of digital health in patients with acute chest pain starts even in the prehospital phase with the transmission of a digital electrocardiogram (ECG) as well as telemedical support and digital emergency management, which facilitate optimization of the rescue pathways and reduce critical time intervals. The increasing dissemination and acceptance of guideline apps and clinical decision support tools as well as integrated calculators and electronic scores are anticipated to improve guideline adherence, translating into a better quality of treatment and improved outcomes. Implementation of artificial intelligence to support image analysis and also the prediction of coronary artery stenosis requiring interventional treatment or impending cardiovascular events, such as heart attacks or death, have an enormous potential especially as conventional instruments frequently yield suboptimal results; however, there are barriers to the rapid dissemination of corresponding decision aids, such as the regulatory rules related to approval as a medical product, data protection issues and other legal liability aspects, which must be considered., (© 2024. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Health data are extremely important in today's data-driven world. Through automation, healthcare processes can be optimized, and clinical decisions can be supported. For any reuse of data, the quality, validity, and trustworthiness of data are essential, and it is the only way to guarantee that data can be reused sensibly. Specific requirements for the description and coding of reusable data are defined in the FAIR guiding principles for data stewardship. Various national research associations and infrastructure projects in the German healthcare sector have already clearly positioned themselves on the FAIR principles: both the infrastructures of the Medical Informatics Initiative and the University Medicine Network operate explicitly on the basis of the FAIR principles, as do the National Research Data Infrastructure for Personal Health Data and the German Center for Diabetes Research.To ensure that a resource complies with the FAIR principles, the degree of FAIRness should first be determined (so-called FAIR assessment), followed by the prioritization for improvement steps (so-called FAIRification). Since 2016, a set of tools and guidelines have been developed for both steps, based on the different, domain-specific interpretations of the FAIR principles.Neighboring European countries have also invested in the development of a national framework for semantic interoperability in the context of the FAIR (Findable, Accessible, Interoperable, Reusable) principles. Concepts for comprehensive data enrichment were developed to simplify data analysis, for example, in the European Health Data Space or via the Observational Health Data Sciences and Informatics network. With the support of the European Open Science Cloud, among others, structured FAIRification measures have already been taken for German health datasets., (© 2024. The Author(s).)

Background and Objective: Psychometric tests can provide important information for diagnostics and progression in chronic pain patients. Between 2008 and 2018, the electronic system painDETECT® was used in the outpatient pain clinic of the Hannover Medical School (MHH). The aim of this retrospective study was to evaluate the pain symptomatology data recorded using painDETECT® and the treatment procedures used in the patient cohort examined over a period of 15 months., Material and Methods: A statistical analysis of baseline and follow-up data was performed. The analysis comprised pain-related parameters recorded by use of the painDETECT® system as well as outpatient records., Results: Baseline data of 459 patients (66% women) could be evaluated. The most common clinical pictures were spinal pain, headache, facial pain, and somatoform disorders, mostly with many years of previous treatment. Approximately 40% showed evidence of neuropathic pain components or central sensitization. With a mean pain intensity of VAS 6 (0-10), a predominantly high degree of chronicity was present. Approximately one third showed a high degree of pain-related functional impairment. Slightly more than half showed evidence of clinically relevant depression. Approximately 80% showed clinically relevant sleep disturbances. Follow-up data were available for 145 patients (31.6%). The proportion of patients receiving a nonpharmacological form of treatment increased by 44.1% (physical therapy) and by 24.1% (psychotherapeutic procedures) during the observation period. The use of co-analgesics increased by approximately 30% over the course., Conclusion: In the outpatient setting, an extension of treatment can be successful for high-grade chronic pain patients. Close structural networking with the clinics for rehabilitation medicine and for psychosomatics and psychotherapy at the MHH can be a favorable prerequisite for this., (© 2022. The Author(s).)

The interoperability Working Group of the Medical Informatics Initiative (MII) is the platform for the coordination of overarching procedures, data structures, and interfaces between the data integration centers (DIC) of the university hospitals and national and international interoperability committees. The goal is the joint content-related and technical design of a distributed infrastructure for the secondary use of healthcare data that can be used via the Research Data Portal for Health. Important general conditions are data privacy and IT security for the use of health data in biomedical research. To this end, suitable methods are used in dedicated task forces to enable procedural, syntactic, and semantic interoperability for data use projects. The MII core dataset was developed as several modules with corresponding information models and implemented using the HL7® FHIR® standard to enable content-related and technical specifications for the interoperable provision of healthcare data through the DIC. International terminologies and consented metadata are used to describe these data in more detail. The overall architecture, including overarching interfaces, implements the methodological and legal requirements for a distributed data use infrastructure, for example, by providing pseudonymized data or by federated analyses. With these results of the Interoperability Working Group, the MII is presenting a future-oriented solution for the exchange and use of healthcare data, the applicability of which goes beyond the purpose of research and can play an essential role in the digital transformation of the healthcare system., (© 2024. The Author(s).)

Medication analyses by ward pharmacists are an important measure of drug therapy safety (DTS). Medication-related problems (MRPs) are identified and resolved with the attending clinicians. However, staff resources for extended medication analyses and complete documentation are often limited. Until now, data required for the identification of risk patients and for an extended medication analysis often had to be collected from various parts of the institution's internal electronic medical record (EMR). This error-prone and time-consuming process is to be improved in the INTERPOLAR (INTERventional POLypharmacy-Drug interActions-Risks) project using an IT tool provided by the data integration centers (DIC).INTERPOLAR is a use case of the Medical Informatics Initiative (MII) that focuses on the topic of DTS. The planning phase took place in 2023, with routine implementation planned from 2024. DTS-relevant data from the EMR is to be presented and the documentation of MRPs in routine care is to be facilitated. The prospective multicenter, cluster-randomized INTERPOLAR‑1 study serves to evaluate the benefits of IT support in routine care. The aim is to show that more MRPs can be detected and resolved with the help of IT support. For this purpose, six normal wards will be selected at each of eight university hospitals, so that 48 clusters (with a total of at least 70,000 cases) are available for randomization., (© 2024. The Author(s).)

Background: With targeted inhibition of type 2 inflammation, biologics represent the standard add-on therapy for inadequately controlled severe forms of chronic rhinosinusitis with nasal polyps (CRSwNP). Despite standardization with paper-based checklists, the documentation of medical history and current findings pertinent to indication criteria are a significant challenge for physicians. Through development of an application based on structured reporting, the current study aimed to improve documentation quality and simplify the decision-making process. Previously available paper checklists served as a comparison., Methods: For this study, a digital incremental tool was programmed to record current findings and check for fulfilment of indication criteria. The tool was compared with other checklists in terms of completeness, time required, and readability., Results: A total of 20 findings were collected for each of the three documentation options and included in the analysis. Documentation with the two paper-based checklists had comparable information content: 17.5 ± 5.1/21.7 ± 7.6 points out of a maximum of 43 points; p > 0.05. Documentation using the digital application led to a significant increase in information content compared to all paper-based documentation. The average score was 38.25 ± 3.7 (88.9% of maximum; p < 0.001). On average, user satisfaction was high (9.6/10). Use of the digital application was initially more time consuming, but as more cases were documented, the time taken improved significantly., Conclusion: In the future, structured reporting using apps could replace paper-based reporting for the indication of biologic therapy in CRSwNP patients and offer additional benefits in terms of data quality and traceability of results. The increasing volume of documentation in the future, the progress of digitalization, and the possibility of networking between individual centers make introduction of the app in the near future both likely and economical., (© 2024. The Author(s).)

Background: Digital transformation is shaping the future of orthopedics and trauma surgery. Telemedicine, digital health applications, electronic patient records and artificial intelligence play a central role in this. These technologies have the potential to improve medical care, enable individualized patient treatment plans and reduce the burden on the treatment process. However, there are currently challenges in the areas of infrastructure, regulation, reimbursement and data protection., Realising the Transformation: Effective transformation requires a deep understanding of both technology and clinical practice. Orthopedic and trauma surgeons need to take a leadership role by actively engaging with new technologies, designing new treatment processes and enhancing their medical skills with digital and AI competencies. The integration of digital skills into medical education and specialist training will be crucial for actively shaping the digital transformation and exploiting its full potential., (© 2024. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Background: Data collected by general practitioners (GPs) may provide potential for health services research. In this study, we investigated if clinical questions can be answered with GPs' electronic medical records (EMRs) by means of diagnosing community-acquired pneumonia (CAP)., Method: Patients diagnosed with CAP, defined as ICD code J18.9, were identified in the fourth quarter of 2021. The data were derived from the EMR system (Medical Office®) of a central German association of 30 general practices, using three different approaches: 1. The integrated statistic tool was used to record whether patients were referred for radiological diagnostic confirmation. 2. Retrospectively, EMRs were evaluated manually by a doctor familiar with the EMR. 3. The raw data of the EMR system were extracted by automated export. The information obtained through the three types of access was compared. For each patient case, detailed comments on problems and specifics were documented and evaluated by qualitative content analysis (QCA) according to Mayring., Results: In total, 164 patients diagnosed with CAP were identified. The numbers of documented radiological diagnostic confirmations varied between data approaches: While the manual evaluation of the EMRs revealed 60 referred patients, the statistics tool identified 38 of these cases. The export of the raw data identified 58 referrals to radiography after adjustment. According to QCA, there was a high variation in applied diagnostics and time of diagnosis. Referrals for radiography were made both before and after coding of the diagnosis. In case of hospitalization, X-rays were usually performed during the inpatient stay. Laboratory tests were performed as an alternative to radiography. There was also a high variation in the documentation of risk factors and diagnostic certainty., Discussion: The statistics tool integrated into the EMR system is a quick way to perform simple queries but proved to be impracticable for complex questions. The EMRs provide detailed information but need to be evaluated manually. An automated data export from the raw data offers both detailed information and access to large volumes of data but requires complex preparation and appropriate IT expertise., Conclusion: Based on the example of diagnosed CAP in a GP setting, the use of data extracted from an EMR system seems to be feasible to answer simple clinical questions. However, it is necessary to adapt the data export, and a comparison with a small number of manually evaluated cases is useful to achieve valid results., (Copyright © 2024. Published by Elsevier GmbH.)

Objective: The article tackles various issues arising in the context of the process of digitalization in the health sector. The communication and availability of health data, health registers, the electronic health record, consent procedures for the transfer of data and access to health data for research are considered., Methods: The study is based on a computer-assisted telephone survey (dual-frame) of a random sample of adult people living in Germany. Data was collected in the period between June 01 and June 27, 2022 (n = 1,308)., Results: The level of knowledge concerning the transmission of health data to health insurers is good, whereas the existence of central death-, vaccination- and health registers as well as the access to health data by treating physicians is overestimated. The general acceptance of medical registers is very high. Half the population is unfamiliar with the electronic health record, and the willingness to use it is rather low. An opt-in procedure is preferred when transferring data, and more than eighty percent would release data in their electronic health file for research purposes. Three quarters would consent that their health data be handed over to general research, especially if reserach facilities were situated at German universities, under the condition that their data be treated confidentiallly. The willingness to release data correlates with the level of trust in the press as well as in universities and colleges and decreases when a data leak is considered to be serious., Discussion and Conclusion: In Germany, as in other European countries, we observe a great willingness of people to release health data for research purposes. However, the propensity to use the electronic health file is comparatively low, as is the acceptance of an opt-out procedure, which in the literature is considered a prerequisite for the successful implementation of electronic health records in other countries. Unsurprisingly, a general trust in research and government agencies that process health data is a key factor., (Copyright © 2023. Published by Elsevier GmbH.)

The analysis of real-world data (RWD) has become increasingly important in health research in recent years. With the BfArM Health Data Lab (HDL), which is currently being set up, researchers will in future be able to gain access to routine data from the statutory health insurance of around 74 million people in Germany. Data from electronic patient records can also be made available for research prospectively. In doing so, the Health Data Lab guarantees the highest data protection and IT security standards. The digital application process, the provision of data in secure processing environments as well as the features supporting the analyses such as catalogues of coding systems, a point-and-click analysis tool and predefined standard analyses increase user-friendliness for researchers. The use of the extensive health data accessible at HDL will open a wide range of future possibilities for improving the health system and the quality of care. This article begins by highlighting the advantages of the HDL and outlining the opportunities that the RWD offers for research in healthcare and for the population. The structure and central aspects of the HDL are explained afterwards. An outlook on the opportunities of linking different data is given. What the application and data usage processes at the HDL will look like is illustrated using the example of fictitious possibilities for analysing long COVID based on the routine data available at the HDL in the future., (© 2024. The Author(s).)

Broad access to health data offers great potential for science and research. However, health data often contains sensitive information that must be protected in a special way. In this context, the article deals with the re-identification potential of health data. After defining the relevant terms, we discuss factors that influence the re-identification potential. We summarize international privacy standards for health data and highlight the importance of background knowledge. Given that the reidentification potential is often underestimated in practice, we present strategies for mitigation based on the Five Safes concept. We also discuss classical data protection strategies as well as methods for generating synthetic health data. The article concludes with a brief discussion and outlook on the planned Health Data Lab at the Federal Institute for Drugs and Medical Devices., (© 2024. The Author(s).)