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Zusammenfassung Hintergrund In Deutschland gibt es bisher keine Gesundheitsberichterstattung zu sektorenübergreifenden Versorgungsverläufen im Kontext einer Notaufnahmeversorgung. Das Projekt INDEED (Inanspruchnahme und sektorenübergreifende Versorgungsmuster von Patienten in Notfallversorgungsstrukturen in Deutschland) erhebt Routinedaten aus 16 Notaufnahmen, die mit ambulanten Abrechnungsdaten der Jahre 2014 bis 2017 personenbezogen zusammengeführt werden. Ziel der Arbeit Die methodischen Herausforderungen der Planung der internen Zusammenführung von klinischen und administrativen Routinedaten aus Notaufnahmen in Deutschland bis zur finalen Datenextraktion werden hier gemeinsam mit Lösungsansätzen dargestellt. Methodik Die Auswahl der Notaufnahmedaten erfolgte in einem iterativen Prozess unter Berücksichtigung der Forschungsfragen, medizinischen Relevanz und angenommenen Datenverfügbarkeit. Nach einer Vorbereitungsphase zur Klärung der Rahmenbedingungen (u. a. Datenschutz, Ethik), zur Prüfung von Testdaten und ggf. Korrekturen, erfolgte die verschlüsselte und pseudonyme Datenausleitung. Ergebnisse Die Daten der 16 kooperierenden Notaufnahmen stammten in der Regel aus dem Notaufnahme- und dem Krankenhausinformationssystem. Die Datenlage war sehr heterogen. Nicht alle Variablen waren in jeder Notaufnahme verfügbar, da sie beispielsweise nicht standardisiert und digital vorlagen oder der Extraktionsaufwand als zu hoch bewertet wurde. Schlussfolgerung Relevante Daten aus Notaufnahmen liegen unterschiedlich strukturiert und in mehreren IT-Systemen vor. Die notwendige Bildung eines klinikübergreifenden vergleichbaren Datensatzes erfordert erhebliche Ressourcen auf Seiten der Kliniken sowie der datenaufbereitenden Stelle. Dies muss für zukünftige Projekte großzügig kalkuliert werden.

In Germany there is currently no health reporting on cross-sectoral care patterns in the context of an emergency department care treatment. The INDEED project (Utilization and trans-sectoral patterns of care for patients admitted to emergency departments in Germany) collects routine data from 16 emergency departments, which are later merged with outpatient billing data from 2014 to 2017 on an individual level.The methodological challenges in planning of the internal merging of routine clinical and administrative data from emergency departments in Germany up to the final data extraction are presented together with possible solution approaches.Data were selected in an iterative process according to the research questions, medical relevance, and assumed data availability. After a preparatory phase to clarify formalities (including data protection, ethics), review test data and correct if necessary, the encrypted and pseudonymous data extraction was performed.Data from the 16 cooperating emergency departments came mostly from the emergency department and hospital information systems. There was considerable heterogeneity in the data. Not all variables were available in every emergency department because, for example, they were not standardized and digitally available or the extraction effort was judged to be too high.Relevant data from emergency departments are stored in different structures and in several IT systems. Thus, the creation of a harmonized data set requires considerable resources on the part of the hospital as well as the data processing unit. This needs to be generously calculated for future projects.HINTERGRUND: In Deutschland gibt es bisher keine Gesundheitsberichterstattung zu sektorenübergreifenden Versorgungsverläufen im Kontext einer Notaufnahmeversorgung. Das Projekt INDEED (Inanspruchnahme und sektorenübergreifende Versorgungsmuster von Patienten in Notfallversorgungsstrukturen in Deutschland) erhebt Routinedaten aus 16 Notaufnahmen, die mit ambulanten Abrechnungsdaten der Jahre 2014 bis 2017 personenbezogen zusammengeführt werden.Die methodischen Herausforderungen der Planung der internen Zusammenführung von klinischen und administrativen Routinedaten aus Notaufnahmen in Deutschland bis zur finalen Datenextraktion werden hier gemeinsam mit Lösungsansätzen dargestellt.Die Auswahl der Notaufnahmedaten erfolgte in einem iterativen Prozess unter Berücksichtigung der Forschungsfragen, medizinischen Relevanz und angenommenen Datenverfügbarkeit. Nach einer Vorbereitungsphase zur Klärung der Rahmenbedingungen (u. a. Datenschutz, Ethik), zur Prüfung von Testdaten und ggf. Korrekturen, erfolgte die verschlüsselte und pseudonyme Datenausleitung.Die Daten der 16 kooperierenden Notaufnahmen stammten in der Regel aus dem Notaufnahme- und dem Krankenhausinformationssystem. Die Datenlage war sehr heterogen. Nicht alle Variablen waren in jeder Notaufnahme verfügbar, da sie beispielsweise nicht standardisiert und digital vorlagen oder der Extraktionsaufwand als zu hoch bewertet wurde.Relevante Daten aus Notaufnahmen liegen unterschiedlich strukturiert und in mehreren IT-Systemen vor. Die notwendige Bildung eines klinikübergreifenden vergleichbaren Datensatzes erfordert erhebliche Ressourcen auf Seiten der Kliniken sowie der datenaufbereitenden Stelle. Dies muss für zukünftige Projekte großzügig kalkuliert werden.

New forms of care for complex chronic diseases require substantial efforts in the collection, storage, and analysis of privacy sensible medical data. Additionally, providing practical support for those who coordinate the actual care management process within a diversified network of regional service providers is beneficial. In this paper, we present a follow-up with the current status, lessons learned, and preliminary results of the IT infrastructure in project STROKE OWL. The project aims at a comprehensive implementation of cross-sector care management for stroke patients. Patients are accompanied in their recovery by stroke pilots, who use a specially designed tablet app for the piloting. It is shown how the tablet app uses receiver-oriented encryption to support the piloting process while ensuring privacy.

Cancer risks may be influenced by local exposures such as working conditions or nuclear waste repositories. To find influences, local accumulations of cancer rates are used, for which finely granulated data should be utilized. In particular, high-resolution demographic data for a reference population are important, but often not available for data protection reasons. Therefore, estimation methods are necessary to approximate small-scale demographic data as accurately as possible. This paper presents an approach to project existing epidemiological and public data to a common granularity with respect to attribute characteristics such as place of residence, age or smoking status to allow for analyses such as local accumulations and consistently falls below an average relative error of 5%.

Research into new forms of care for complex chronic diseases requires substantial efforts in the collection, storage, and analysis of medical data. Additionally, providing practical support for those who coordinate the actual care management process within a diversified network of regional service providers is also necessary. For instance, for stroke units, rehabilitation partners, ambulatory actors, as well as health insurance funds. In this paper, we propose the concept of comprehensive and practical receiver-oriented encryption (ROE) as a guiding principle for such data-intensive, research-oriented case management systems, and illustrate our concept with the example of the IT infrastructure of the project STROKE OWL.

Caregivers and relatives of artificially ventilated persons are under high pressure as errors or miscues in care activities can lead to the death of the patients. Therefore, the joint research project "MeSiB" aims at implementing a safety and alerting system, which analyzes the sensor events, infers whether the situation is dangerous, and, if so, alarms the caregiver and/or a telemedicine station. Among other factors, the number of persons in the bed area of the patient is an essential information for this system. Therefore, a subsystem of this safety system detects the number of persons with an infrared array sensor (Panasonic Grid-EYE). This subsystem is presented in this work. The paper describes the general approach and system design, and how the subsystem integrates into the MeSiB architecture. The structure of the software is presented, and different machine learning approaches are tested. Finally, the results of an evaluation study in a realistic home care environment are presented and discussed. In this evaluation study, the Random Forest classifier was used and detection rates of 84% and 96% for two different scenarios were achieved.

In this paper, we present an approach for enhancing the safety of artificially ventilated patients using ambient process analysis. We propose to use an analysis system consisting of low-cost ambient sensors such as power sensor, RGB-D sensor, passage detector, and matrix infrared temperature sensor to reduce risks for artificially ventilated patients in both home and clinical environments. We describe the system concept and our implementation and show how the system can contribute to patient safety.

For patients with Cardiovascular Implantable Electronic Devices (CIEDs), telemonitoring promises improved quality of life and safety, since events recorded by the device or observed by the patient can alert a health professional. Taking into account the latest clinical guidelines when responding to such alerts, is a topic of active research addressed by the iCARDEA project. A key technical challenge is correlating telemonitoring CIED report data in a vendor-independent format with Electronic Health Record (EHR) data collected in the hospital and Personal Health Record (PHR) data entered by the patient, in guideline-driven care processes. The iCARDEA CIED exposure service component presented in this paper employs standards specifications from ISO/IEEE 11073 (Health Informatics, Point-of-care Medical Device Communication) and HL7v2.x in the context of Integrating the Healthcare Enterprise (IHE) profiles to deliver telemonitoring CIED report data from two different CIED vendors to the adaptive care planner that implements guideline-driven care plans. Experience gained with implementation and initial component testing is discussed, while challenges and expectations for future health information standards to effectively support EHR-integrated guide-line-driven telemonitoring services are highlighted.