Menoci: lightweight extensible web portal enhancing data management for biomedical research projects.

BACKGROUND: Biomedical research projects deal with data management requirements from multiple sources like funding agencies' guidelines, publisher policies, discipline best practices, and their own users' needs. We describe functional and quality requirements based on many years of experience implementing data management for the CRC 1002 and CRC 1190. A fully equipped data management software should improve documentation of experiments and materials, enable data storage and sharing according to the FAIR Guiding Principles while maximizing usability, information security, as well as software sustainability and reusability. RESULTS: We introduce the modular web portal software menoci for data collection, experiment documentation, data publication, sharing, and preservation in biomedical research projects. Menoci modules are based on the Drupal content management system which enables lightweight deployment and setup, and creates the possibility to combine research data management with a customisable project home page or collaboration platform. CONCLUSIONS: Management of research data and digital research artefacts is transforming from individual researcher or groups best practices towards project- or organisation-wide service infrastructures. To enable and support this structural transformation process, a vital ecosystem of open source software tools is needed. Menoci is a contribution to this ecosystem of research data management tools that is specifically designed to support biomedical research projects.

Integrated Data Repository Toolkit (IDRT). A Suite of Programs to Facilitate Health Analytics on Heterogeneous Medical Data.

BACKGROUND: In recent years, research data warehouses moved increasingly into the focus of interest of medical research. Nevertheless, there are only a few center-independent infrastructure solutions available. They aim to provide a consolidated view on medical data from various sources such as clinical trials, electronic health records, epidemiological registries or longitudinal cohorts. The i2b2 framework is a well-established solution for such repositories, but it lacks support for importing and integrating clinical data and metadata. OBJECTIVES: The goal of this project was to develop a platform for easy integration and administration of data from heterogeneous sources, to provide capabilities for linking them to medical terminologies and to allow for transforming and mapping of data streams for user-specific views. METHODS: A suite of three tools has been developed: the i2b2 Wizard for simplifying administration of i2b2, the IDRT Import and Mapping Tool for loading clinical data from various formats like CSV, SQL, CDISC ODM or biobanks and the IDRT i2b2 Web Client Plugin for advanced export options. The Import and Mapping Tool also includes an ontology editor for rearranging and mapping patient data and structures as well as annotating clinical data with medical terminologies, primarily those used in Germany (ICD-10-GM, OPS, ICD-O, etc.). RESULTS: With the three tools functional, new i2b2-based research projects can be created, populated and customized to researcher's needs in a few hours. Amalgamating data and metadata from different databases can be managed easily. With regards to data privacy a pseudonymization service can be plugged in. Using common ontologies and reference terminologies rather than project-specific ones leads to a consistent understanding of the data semantics. CONCLUSIONS: i2b2's promise is to enable clinical researchers to devise and test new hypothesis even without a deep knowledge in statistical programing. The approach presented here has been tested in a number of scenarios with millions of observations and tens of thousands of patients. Initially mostly observant, trained researchers were able to construct new analyses on their own. Early feedback indicates that timely and extensive access to their "own" data is appreciated most, but it is also lowering the barrier for other tasks, for instance checking data quality and completeness (missing data, wrong coding).

Integration of genomic data in Electronic Health Records--opportunities and dilemmas.

OBJECTIVES: In this paper we give an overview about the challenge the postgenomic era poses on biomedical informaticists. The occurrence of new (genomic) data types necessitates new data models, new viewing metaphors and methods to deal with the disclosure of genomic data. We discuss integration issues when inferring phenotype and genotype data. Another challenge is to find the right phenotype to genotype data in order to get appropriate case numbers for sound clinical genotype-phenotype inference studies. METHODS: Genomic data could be integrated in an Electronic Health Record (EHR) in several ways. We describe patient-centered and pointer-based integration strategies and the corresponding data types and data models. The inference mechanisms for the interpretation of row data contain different agents. We describe vertical, horizontal and temporal agents. RESULTS: We have to deal with several new data types, not being standardized for EHR integration. Genomic data tends to be more structured than phenotype data. Beyond the development of new data models, vertical, horizontal and temporal agents have to be developed in order to link genotype and phenotype. As the genomic EHR will contain very sensitive data, confidentiality and privacy concerns have to be addressed. CONCLUSIONS: Given the necessity to capture both environment and genomic state of a patient and their interaction, clinical information systems have to be redesigned. While genotyping seems to be automatable easily, this is not the case for clinical information. More integration work on terminologies and ontologies has to be done.

Unlocking Data for Clinical Research - The German i2b2 Experience.

OBJECTIVE: Data from clinical care is increasingly being used for research purposes. The i2b2 platform has been introduced in some US research communities as a tool for data integration and querying by clinical users. The purpose of this project was to assess the applicability of i2b2 in Germany regarding use cases, functionality and integration with privacy enhancing tools. METHODS: A set of four research usage scenarios was chosen, including the transformation and import of ontology and fact data from existing clinical data collections into i2b2 v1.4 instances. Query performance was measured in comparison to native SQL queries. A setup and administration tool for i2b2 was developed. An extraction tool for CDISC ODM data was programmed. Interfaces for the TMF privacy enhancing tools (PID Generator, Pseudonymization Service) were implemented. RESULTS: Data could be imported in all tested scenarios from various source systems, including the generation of i2b2 ontology definitions. The integration of TMF privacy enhancing tools was possible without modification of the platform. Limitations were found regarding query performance in comparison to native SQL and certain temporal queries. CONCLUSIONS: i2b2 is a viable platform for data query tasks in use cases typical for networked medical research in Germany. The integration of privacy enhancing tools facilitates the use of i2b2 within established data protection concepts. Entry barriers should be lowered by providing tools for simplified setup and import of medical standard formats like CDISC ODM.

Registers for Networked Medical Research in Germany: Situation and prospects.

BACKGROUND: Several disease specific registers are operated by members of the 'TMF - Technology, Methods, and Infrastructure for Networked Medical Research', an umbrella organization of research networks in Germany. OBJECTIVE: To describe the coverage and the current state as well as financial and organizational issues of registers operated by member networks of the TMF, to identify their requirements and needs, and to recommend best practice models. METHODS: A survey with a self-completion questionnaire including all 55 TMF member networks was carried out in winter 2007/2008. Interviews focusing on technological issues were conducted and analyzed in summer 2009 with a convenience sample of 10 registers. RESULTS: From 55 TMF member networks, 11 provided information about 14 registers. Six registers address diseases of the circulatory system with more than 150,000 registered patients. The interviews revealed a typical setting of "research registers". Research registers are an important mean to generate hypotheses for clinical research, to identify eligible patients, and to share data with clinical trials. Concerning technical solutions, we found a remarkable heterogeneity. The analysis of the most efficient registers revealed a structure with five levels as best practice model of register management: executive, operations, IT-management, software, hardware. CONCLUSION: In the last ten years, the TMF member networks established disease specific registers in Germany mainly to support clinical research. The heterogeneity of organizational and technical solutions as well as deficits in register planning motivated the development of respective recommendations. The TMF will continue to assist the registers in quality improvement.

A data protection scheme for medical research networks. Review after five years of operation.

BACKGROUND: The data protection requirements matured in parallel to new clinical tests generating more personal data since the 1960s. About ten years ago it was recognized that a generic data protection scheme for medical research networks is required, which reinforces patient rights but also allows economically feasible medical research compared to "hand-carved" individual solutions. OBJECTIVES: To give recommendations for more efficient IT infrastructures for medical research networks in compliance with data protection requirements. METHODS: The IT infrastructures of three medical research networks were reviewed with respect to the relevant data management modules. Recommendations are derived to increase cost efficiency in research networks assessing the consequences of a service provider approach without lowering the data protection level. RESULTS: The existing data protection schemes are very complex. Smaller research networks cannot afford the implementation of such schemes. Larger networks struggle to keep them sustainable. Due to a modular redesign in the medical research network community, a new approach offers opportunities for an efficient sustainable IT infrastructure involving a service provider concept. For standard components 70-80% of the costs could be cut down, for open source components about 37% over a three-year period. CONCLUSIONS: Future research networks should switch to a service-oriented approach to achieve a sustainable, cost-efficient IT infrastructure.

IT Infrastructure Components for Biobanking.

OBJECTIVE: Within translational research projects in the recent years large biobanks have been established, mostly supported by homegrown, proprietary software solutions. No general requirements for biobanking IT infrastructures have been published yet. This paper presents an exemplary biobanking IT architecture, a requirements specification for a biorepository management tool and exemplary illustrations of three major types of requirements. METHODS: We have pursued a comprehensive literature review for biobanking IT solutions and established an interdisciplinary expert panel for creating the requirements specification. The exemplary illustrations were derived from a requirements analysis within two university hospitals. RESULTS: The requirements specification comprises a catalog with more than 130 detailed requirements grouped into 3 major categories and 20 subcategories. Special attention is given to multitenancy capabilities in order to support the project-specific definition of varying research and bio-banking contexts, the definition of workflows to track sample processing, sample transportation and sample storage and the automated integration of preanalytic handling and storage robots. CONCLUSION: IT support for biobanking projects can be based on a federated architectural framework comprising primary data sources for clinical annotations, a pseudonymization service, a clinical data warehouse with a flexible and user-friendly query interface and a biorepository management system. Flexibility and scalability of all such components are vital since large medical facilities such as university hospitals will have to support biobanking for varying monocentric and multicentric research scenarios and multiple medical clients.