An electronic health record to support patients and institutions of the health care system

The department of Medical Informatics of the University Hospital Münster and the Gesakon GmbH (an university offspring) initiated the cooperative development of an electronic health record (EHR) called "akteonline.de" in 2000. From 2001 onwards several clinics of the university hospital have already offered this EHR (within pilot projects) as an additional service to selected subsets of their patients. Based on the experiences of those pilot projects the system architecture and the basic data model underwent several evolutionary enhancements, e.g. implementations of electronic interfaces to other clinical systems (considering for example data interchange methods like the Clinical Document Architecture - standardized within the HL7 group - and also interfacing architectures of German GP systems, such as VCS and D2D). "akteonline.de" in its current structure supports patients as well as health care professionals and aims at providing a collaborative health information system which perfectly supports the clinical workflow even across institutional boundaries and including the patient himself. Since such an EHR needs to strictly fulfill high data security and data protection requirements, a complex authorization and access control component has been included. Furthermore the EHR data are encrypted within the database itself and during their transfer across the internet

A RESTful interface to pseudonymization services in modern web applications

BACKGROUND: Medical research networks rely on record linkage and pseudonymization to determine which records from different sources relate to the same patient. To establish informational separation of powers, the required identifying data are redirected to a trusted third party that has, in turn, no access to medical data. This pseudonymization service receives identifying data, compares them with a list of already reported patient records and replies with a (new or existing) pseudonym. We found existing solutions to be technically outdated, complex to implement or not suitable for internet-based research infrastructures. In this article, we propose a new RESTful pseudonymization interface tailored for use in web applications accessed by modern web browsers. METHODS: The interface is modelled as a resource-oriented architecture, which is based on the representational state transfer (REST) architectural style. We translated typical use-cases into resources to be manipulated with well-known HTTP verbs. Patients can be re-identified in real-time by authorized users' web browsers using temporary identifiers. We encourage the use of PID strings for pseudonyms and the EpiLink algorithm for record linkage. As a proof of concept, we developed a Java Servlet as reference implementation. RESULTS: The following resources have been identified: Sessions allow data associated with a client to be stored beyond a single request while still maintaining statelessness. Tokens authorize for a specified action and thus allow the delegation of authentication. Patients are identified by one or more pseudonyms and carry identifying fields. Relying on HTTP calls alone, the interface is firewall-friendly. The reference implementation has proven to be production stable. CONCLUSION: The RESTful pseudonymization interface fits the requirements of web-based scenarios and allows building applications that make pseudonymization transparent to the user using ordinary web technology. The open-source reference implementation implements the web interface as well as a scientifically grounded algorithm to generate non-speaking pseudonyms

A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research

Recent advances in sequencing and biotechnological methodologies have led to the generation of large volumes of molecular data of different omics layers, such as genomics, transcriptomics, proteomics and metabolomics. Integration of these data with clinical information provides new opportunities to discover how perturbations in biological processes lead to disease. Using data-driven approaches for the integration and interpretation of multi-omics data could stably identify links between structural and functional information and propose causal molecular networks with potential impact on cancer pathophysiology. This knowledge can then be used to improve disease diagnosis, prognosis, prevention, and therapy. This review will summarize and categorize the most current computational methodologies and tools for integration of distinct molecular layers in the context of translational cancer research and personalized therapy. Additionally, the bioinformatics tools Multi-Omics Factor Analysis (MOFA) and netDX will be tested using omics data from public cancer resources, to assess their overall robustness, provide reproducible workflows for gaining biological knowledge from multi-omics data, and to comprehensively understand the significantly perturbed biological entities in distinct cancer types. We show that the performed supervised and unsupervised analyses result in meaningful and novel findings

Social Media im Gesundheitswesen - Chancen, Risiken, Trends

Hartz T, Fangerau H, Ückert F, Albrecht U-V. Social Media im Gesundheitswesen - Chancen, Risiken, Trends. In: GMDS 2014. 59. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. Düsseldorf: German Medical Science GMS Publishing House; 2014: DocAbstr. 252

Mapping Cancer Registry Data to the Episode Domain of the Observational Medical Outcomes Partnership Model (OMOP)

A great challenge in the use of standardized cancer registry data is deriving reliable, evidence-based results from large amounts of data. A solution could be its mapping to a common data model such as OMOP, which represents knowledge in a unified semantic base, enabling decentralized analysis. The recently released Episode Domain of the OMOP CDM allows episodic modelling of a patient’ disease and treatment phases. In this study, we mapped oncology registry data to the Episode Domain. A total of 184,718 Episodes could be implemented, with the Concept of Cancer Drug Treatment most frequently. Additionally, source data were mapped to new terminologies as part of the release. It was possible to map ≈ 73.8% of the source data to the respective OMOP standard. Best mapping was achieved in the Procedure Domain with 98.7%. To evaluate the implementation, the survival probabilities of the CDM and source system were calculated (n = 2756/2902, median OAS = 82.2/91.1 months, 95% Cl = 77.4–89.5/84.4–100.9). In conclusion, the new release of the CDM increased its applicability, especially in observational cancer research. Regarding the mapping, a higher score could be achieved if terminologies which are frequently used in Europe are included in the Standardized Vocabulary Metadata Repository

Mapping Cancer Registry Data to the Episode Domain of the Observational Medical Outcomes Partnership Model (OMOP)

A great challenge in the use of standardized cancer registry data is deriving reliable, evidence-based results from large amounts of data. A solution could be its mapping to a common data model such as OMOP, which represents knowledge in a unified semantic base, enabling decentralized analysis. The recently released Episode Domain of the OMOP CDM allows episodic modelling of a patient’ disease and treatment phases. In this study, we mapped oncology registry data to the Episode Domain. A total of 184,718 Episodes could be implemented, with the Concept of Cancer Drug Treatment most frequently. Additionally, source data were mapped to new terminologies as part of the release. It was possible to map ≈ 73.8% of the source data to the respective OMOP standard. Best mapping was achieved in the Procedure Domain with 98.7%. To evaluate the implementation, the survival probabilities of the CDM and source system were calculated (n = 2756/2902, median OAS = 82.2/91.1 months, 95% Cl = 77.4–89.5/84.4–100.9). In conclusion, the new release of the CDM increased its applicability, especially in observational cancer research. Regarding the mapping, a higher score could be achieved if terminologies which are frequently used in Europe are included in the Standardized Vocabulary Metadata Repository

HCHSGraphXplore: Visualizing Complex Medical Data with Knowledge Graphs

This dataset capures statistical analysis of the HCHS cohort study using a knowledge graph and dashboard. Properties of 10,000 participants were analyzed for their association with cardiovascular disease as well as for their relationships among each other. The data is presented in the form of Neo4J database dumps and can be explored following the given user guide