[Development of competencies in the Medical Informatics Initiative (MII)-educational offers for a competent and secure handling of medical data]. / Kompetenzentwicklung in der Medizininformatik-Initiative (MII) ­ Lehrangebote für einen souveränen und sicheren Umgang mit medizinischen Daten.

In order to achieve the goals of the Medical Informatics Initiative (MII), staff with skills in the field of medical informatics and data science are required. Each consortium has established training activities. Further, cross-consortium activities have emerged. This article describes the concepts, implemented programs, and experiences in the consortia. Fifty-one new professorships have been established and 10 new study programs have been created: 1 bachelor's degree and 6 consecutive and 3 part-time master's degree programs. Further, learning and training opportunities can be used by all MII partners. Certification and recognition opportunities have been created.The educational offers are aimed at target groups with a background in computer science, medicine, nursing, bioinformatics, biology, natural science, and data science. Additional qualifications for physicians in computer science and computer scientists in medicine seem to be particularly important. They can lead to higher quality in software development and better support for treatment processes by application systems.Digital learning methods were important in all consortia. They offer flexibility for cross-location and interprofessional training. This enables learning at an individual pace and an exchange between professional groups.The success of the MII depends largely on society's acceptance of the multiple use of medical data in both healthcare and research. The information required for this is provided by the MII's public relations work. There is also an enormous need in society for medical and digital literacy.

[Germany-wide, Web-based ParaReg Registry for Lifelong Monitoring of People with Spinal Cord Injury: Data Model, Ethico-legal Prerequisites and Technical Implementation]. / Das deutschlandweite, webbasierte ParaReg-Register zur lebenslangen Dokumentation von Querschnittgelähmten ­ Datenmodell, rechtlich-ethische Voraussetzungen und technische Implementierung.

OBJECTIVE: In Germany, treatment paths for patients with acute spinal cord injury (SCI) differ considerably depending on intrinsic, disease-specific and extrinsic factors. Which of these factors are associated with improved outcome with fewer subsequent complications and inpatient re-admissions is not clear. The German-wide, patient-centered, web-based ParaReg registry will be implemented to improve the long-term quality of patient care and the planning of treatment paths with increased cost-effectiveness. METHODS: In the 2017-18 conceptualization phase, the data model of the registry was developed in an iterative process of the ParaReg steering committee together with the extended DMGP board and patient representatives. In ParaReg, routine social and medical data as well as internationally established neurological, functional and participation scores will be documented. The assignment of a unique patient ID allows a lifelong, cross-center documentation of inpatient stays in one of the 27 SCI centers organized in the German-speaking Medical Society for SCI (DMGP). The ParaReg data protection concept and patient information/consent are based on the Open Source Registry for Rare Diseases (OSSE) which were extended by GDPR-relevant aspects. RESULTS: In the realization phase, which started in 2019, the information technology infrastructure was implemented according to the clinical ID management module of the Technology and Methods Platform for Networked Medical Research (TMF). In parallel, the legal and ethical prerequisites for registry operation under the patronage of the DMGP were created. Recommendations of the working group data protection of the TMF were integrated into ParaReg's data protection concept. Based on the feedback from the alpha test phase with documentation of the hospitalization data of 40 patients, the ergonomics of the electronic case report forms were improved in particular for data entry on mobile devices. CONCLUSION: After completion of the monocentric alpha test phase, the multicenter data acquisition was started in 5 DMGP-SCI centers. The sustainability of ParaReg is ensured by the structural and financial support of the DMGP after expiry of the funding by the German Federal Ministry of Education and Research (BMBF).

vivaGen - a survival data set generator for software testing.

An amendment to this paper has been published and can be accessed via the original article.

Graph-Representation of Patient Data: a Systematic Literature Review.

Graph theory is a well-established theory with many methods used in mathematics to study graph structures. In the field of medicine, electronic health records (EHR) are commonly used to store and analyze patient data. Consequently, it seems straight-forward to perform research on modeling EHR data as graphs. This systematic literature review aims to investigate the frontiers of the current research in the field of graphs representing and processing patient data. We want to show, which areas of research in this context need further investigation. The databases MEDLINE, Web of Science, IEEE Xplore and ACM digital library were queried by using the search terms health record, graph and related terms. Based on the "Preferred Reporting Items for Systematic Reviews and Meta-Analysis" (PRISMA) statement guidelines the articles were screened and evaluated using full-text analysis. Eleven out of 383 articles found in systematic literature review were finally included for analysis in this literature review. Most of them use graphs to represent temporal relations, often representing the connection among laboratory data points. Only two papers report that the graph data were further processed by comparing the patient graphs using similarity measurements. Graphs representing individual patients are hardly used in research context, only eleven papers considered such kind of graphs in their investigations. The potential of graph theoretical algorithms, which are already well established, could help increasing this research field, but currently there are too few papers to estimate how this area of research will develop. Altogether, the use of such patient graphs could be a promising technique to develop decision support systems for diagnosis, medication or therapy of patients using similarity measurements or different kinds of analysis.

Methods for a similarity measure for clinical attributes based on survival data analysis.

BACKGROUND: Case-based reasoning is a proven method that relies on learned cases from the past for decision support of a new case. The accuracy of such a system depends on the applied similarity measure, which quantifies the similarity between two cases. This work proposes a collection of methods for similarity measures especially for comparison of clinical cases based on survival data, as they are available for example from clinical trials. METHODS: Our approach is intended to be used in scenarios, where it is of interest to use longitudinal data, such as survival data, for a case-based reasoning approach. This might be especially important, where uncertainty about the ideal therapy decision exists. The collection of methods consists of definitions of the local similarity of nominal as well as numeric attributes, a calculation of attribute weights, a feature selection method and finally a global similarity measure. All of them use survival time (consisting of survival status and overall survival) as a reference of similarity. As a baseline, we calculate a survival function for each value of any given clinical attribute. RESULTS: We define the similarity between values of the same attribute by putting the estimated survival functions in relation to each other. Finally, we quantify the similarity by determining the area between corresponding curves of survival functions. The proposed global similarity measure is designed especially for cases from randomized clinical trials or other collections of clinical data with survival information. Overall survival can be considered as an eligible and alternative solution for similarity calculations. It is especially useful, when similarity measures that depend on the classic solution-describing attribute "applied therapy" are not applicable. This is often the case for data from clinical trials containing randomized arms. CONCLUSIONS: In silico evaluation scenarios showed that the mean accuracy of biomarker detection in k = 10 most similar cases is higher (0.909-0.998) than for competing similarity measures, such as Heterogeneous Euclidian-Overlap Metric (0.657-0.831) and Discretized Value Difference Metric (0.535-0.671). The weight calculation method showed a more than six times (6.59-6.95) higher weight for biomarker attributes over non-biomarker attributes. These results suggest that the similarity measure described here is suitable for applications based on survival data.

Past and next 10 years of medical informatics.

More than 10 years ago Haux et al. tried to answer the question how health care provision will look like in the year 2013. A follow-up workshop was held in Braunschweig, Germany, for 2 days in May, 2013, with 20 invited international experts in biomedical and health informatics. Among other things it had the objectives to discuss the suggested goals and measures of 2002 and how priorities on MI research in this context should be set from the viewpoint of today. The goals from 2002 are now as up-to-date as they were then. The experts stated that the three goals: "patient-centred recording and use of medical data for cooperative care"; "process-integrated decision support through current medical knowledge" and "comprehensive use of patient data for research and health care reporting" have not been reached yet and are still relevant. A new goal for ICT in health care should be the support of patient centred personalized (individual) medicine. MI as an academic discipline carries out research concerning tools that support health care professionals in their work. This research should be carried out without the pressure that it should lead to systems that are immediately and directly accepted in practice.

Pseudonymization of patient identifiers for translational research.

BACKGROUND: The usage of patient data for research poses risks concerning the patients' privacy and informational self-determination. Next-generation-sequencing technologies and various other methods gain data from biospecimen, both for translational research and personalized medicine. If these biospecimen are anonymized, individual research results from genomic research, which should be offered to patients in a clinically relevant timeframe, cannot be associated back to the individual. This raises an ethical concern and challenges the legitimacy of anonymized patient samples. In this paper we present a new approach which supports both data privacy and the possibility to give feedback to patients about their individual research results. METHODS: We examined previously published privacy concepts regarding a streamlined de-pseudonymization process and a patient-based pseudonym as applicable to research with genomic data and warehousing approaches. All concepts identified in the literature review were compared to each other and analyzed for their applicability to translational research projects. We evaluated how these concepts cope with challenges implicated by personalized medicine. Therefore, both person-centricity issues and a separation of pseudonymization and de-pseudonymization stood out as a central theme in our examination. This motivated us to enhance an existing pseudonymization method regarding a separation of duties. RESULTS: The existing concepts rely on external trusted third parties, making de-pseudonymization a multistage process involving additional interpersonal communication, which might cause critical delays in patient care. Therefore we propose an enhanced method with an asymmetric encryption scheme separating the duties of pseudonymization and de-pseudonymization. The pseudonymization service provider is unable to conclude the patient identifier from the pseudonym, but assigns this ability to an authorized third party (ombudsman) instead. To solve person-centricity issues, a collision-resistant function is incorporated into the method. These two facts combined enable us to address essential challenges in translational research. A productive software prototype was implemented to prove the functionality of the suggested translational, data privacy-preserving method. Eventually, we performed a threat analysis to evaluate potential hazards connected with this pseudonymization method. CONCLUSIONS: The proposed method offers sustainable organizational simplification regarding an ethically indicated, but secure and controlled process of de-pseudonymizing patients. A pseudonym is patient-centered to allow correlating separate datasets from one patient. Therefore, this method bridges the gap between bench and bedside in translational research while preserving patient privacy. Assigned ombudsmen are able to de-pseudonymize a patient, if an individual research result is clinically relevant.

Knowledge Acquisition and Construction of a RDF-Ontology for Computer-Assisted Surgery.

The integration of surgical knowledge into virtual planning systems plays a key role in computer-assisted surgery. The knowledge is often implicitly contained in the implemented algorithms. However, a strict separation would be desirable for reasons of maintainability, reusability and readability. Along with the Department of Oral and Maxillofacial Surgery at Heidelberg University Hospital, we are working on the development of a virtual planning system for mandibular reconstruction. In this work we describe a process for the structured acquisition and representation of surgical knowledge for mandibular reconstruction. Based on the acquired knowledge, an RDF(S) ontology was created. The ontology is connected to the virtual planning system via a SPARQL interface. The described process of knowledge acquisition can be transferred to other surgical use cases. Furthermore, the developed ontology is characterised by a reusable and easily expandable data model.

HiGHmed - An Open Platform Approach to Enhance Care and Research across Institutional Boundaries.

INTRODUCTION: This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. HiGHmed brings together 24 partners from academia and industry, aiming at improvements in care provision, biomedical research and epidemiology. By establishing a shared information governance framework, data integration centers and an open platform architecture in cooperation with independent healthcare providers, the meaningful reuse of data will be facilitated. Complementary, HiGHmed integrates a total of seven Medical Informatics curricula to develop collaborative structures and processes to train medical informatics professionals, physicians and researchers in new forms of data analytics. GOVERNANCE AND POLICIES: We describe governance structures and policies that have proven effective during the conceptual phase. These were further adapted to take into account the specific needs of the development and networking phase, such as roll-out, carerelated aspects and our focus on curricula development in Medical Inform atics. ARCHITECTURAL FRAMEWORK AND METHODOLOGY: To address the challenges of organizational, technical and semantic interoperability, a concept for a scalable platform architecture, the HiGHmed Platform, was developed. We outline the basic principles and design goals of the open platform approach as well as the roles of standards and specifications such as IHE XDS, openEHR, SNOMED CT and HL7 FHIR. A shared governance framework provides the semantic artifacts which are needed to establish semantic interoperability. USE CASES: Three use cases in the fields of oncology, cardiology and infection control will demonstrate the capabilities of the HiGHmed approach. Each of the use cases entails diverse challenges in terms of data protection, privacy and security, including clinical use of genome sequencing data (oncology), continuous longitudinal monitoring of physical activity (cardiology) and cross-site analysis of patient movement data (infection control). DISCUSSION: Besides the need for a shared governance framework and a technical infrastructure, backing from clinical leaders is a crucial factor. Moreover, firm and sustainable commitment by participating organizations to collaborate in further development of their information system architectures is needed. Other challenges including topics such as data quality, privacy regulations, and patient consent will be addressed throughout the project.

Can an EPR support the concept of family-centred, individualized developmental care of premature infants and newborns?

At the University Children's Hospital Heidelberg the concept of 'Developmental, Family-Centred, Individual Care of Premature Infants and Newborns' was introduced to support optimal growth of premature infants. This interdisciplinary concept requires cooperation of different specialists. A well operating communication is a precondition for such cooperation. As a patient's record is not only used for storing information but also for exchanging information, the question was if a complete electronic patient record (EPR), in contrast to the existing patient's record, could sensibly support this new concept of care. To answer this question the whole communication of the staff in the infants ward was analysed using different observation methods. These observations delivered several issues which showed that an EPR could improve communication and workflow. Therefore an EPR for the neonatology at the University Children's Hospital Heidelberg can now be designed on the basis of our communication concept.

Data Integration for Integrated Research and Care.

A national German funding initiative for Medical Informatics focusing at data integration for medicine gives an opportunity to reopen a window to Germany. In the open window appears a best paper selection of the 2015 annual conference of the German Society of Medical Informatics, Biometry and Epidemiology and papers of the German journal GMS Medical Informatics, Biometry and Epidemiology (MIBE). The publications in focus deal with data integration by transferring clinical routine data into an electronic data capture (EDC) system, using natural language processing to make unstructured date processable, measuring quality of record linkage, and by using a unified metadata scheme for integrated documentation in laboratories. Two additional papers present methods for data analysis especially for change point detection in binary sequences and for analyzing categorial data.

The Use of Tools, Modelling Methods, Data Types, and Endpoints in Systems Medicine: A Survey on Projects of the German e:Med-Programme.

Systems medicine is the consequent continuation of research efforts on the road to an individualized medicine. Thereby, systems medicine tries to offer a holistic view on the patient by combining different data sources to highlight different perspectives on the patient's health. Our research question was to identify the main data types, modelling methods, analysis tools, and endpoints currently used and studied in systems medicine. Therefore, we conducted a survey on projects with a systems medicine background. Fifty participants completed this survey. The results of the survey were analyzed using histograms and cross tables, and finally compared to results of a former literature review with the same research focus. The data types reported in this survey were widely diversified. As expected, genomic and phenotype data were used most frequently. In contrast, environmental and behavioral data were rarely used in the projects. Overall, the cross tables of the data types in the survey and the literature review showed overlapping results.

Alignment of high-throughput sequencing data inside in-memory databases.

In times of high-throughput DNA sequencing techniques, performance-capable analysis of DNA sequences is of high importance. Computer supported DNA analysis is still an intensive time-consuming task. In this paper we explore the potential of a new In-Memory database technology by using SAP's High Performance Analytic Appliance (HANA). We focus on read alignment as one of the first steps in DNA sequence analysis. In particular, we examined the widely used Burrows-Wheeler Aligner (BWA) and implemented stored procedures in both, HANA and the free database system MySQL, to compare execution time and memory management. To ensure that the results are comparable, MySQL has been running in memory as well, utilizing its integrated memory engine for database table creation. We implemented stored procedures, containing exact and inexact searching of DNA reads within the reference genome GRCh37. Due to technical restrictions in SAP HANA concerning recursion, the inexact matching problem could not be implemented on this platform. Hence, performance analysis between HANA and MySQL was made by comparing the execution time of the exact search procedures. Here, HANA was approximately 27 times faster than MySQL which means, that there is a high potential within the new In-Memory concepts, leading to further developments of DNA analysis procedures in the future.

A framework for integrating heterogeneous clinical data for a disease area into a central data warehouse.

Structured collection of clinical facts is a common approach in clinical research. Especially in the analysis of rare diseases it is often necessary to aggregate study data from several sites in order to achieve a statistically significant cohort size. In this paper we describe a framework how to approach an integration of heterogeneous clinical data into a central register. This enables site-spanning queries for the occurrence of specific clinical facts and thus supports clinical research. The framework consists of three sequential steps, starting from a formal data harmonization process, to the data transformation methods and finally the integration into a proper data warehouse. We implemented reusable software templates that are based on our best practices in several projects in integrating heterogeneous clinical data. Our methods potentially increase the efficiency and quality for future data integration projects by reducing the implementation effort as well as the project management effort by usage of our approaches as a guideline.

Towards a comprehensive electronic patient record to support an innovative individual care concept for premature infants using the openEHR approach.

PURPOSE: This paper introduces the modelling of a prototype neonatology electronic patient record (EPR) using openEHR archetypes. The EPR is necessary to support the complex communication tasks of the innovative concept of 'Developmental, Family-Centred, Individual Care of Premature Infants and Newborns' established for the Department of Neonatology at Heidelberg University Hospital. METHODS: The data to be documented was analysed and modelled using the five step openEHR data modelling approach (odma). RESULTS: The analysis revealed a total of 1818 items, which could be arranged into 70 clinical concepts. The items and concepts were then mapped to 132 openEHR archetypes. Fifty-eight of these archetypes could be reused either directly or via specialisation from the existing openEHR archetypes. A further 67 archetypes were newly developed. To combine and constrain archetypes for local settings 16 templates were developed. CONCLUSION: By using the five step openEHR data modelling approach, semantic interoperability, and a reduced need for repeated documentation of the same data can be realised. This is of major importance within the hospital as well as for trans-institutional data exchange.