gms | German Medical Science

62. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

17.09. - 21.09.2017, Oldenburg

Building an IT research platform in a hospital setting

Meeting Abstract

  • Petar Horki - Institute for Medical Biometry and Statistics, Faculty of Medicine, University of Freiburg, Freiburg, Deutschland; Comprehensive Cancer Center Freiburg, Medical Center - University of Freiburg, Freiburg, Deutschland
  • Christian Haverkamp - Medical Center - University of Freiburg, Freiburg, Deutschland
  • Adrian Tassoni - Clinical Trials Unit of the Medical Center - University of Freiburg, Freiburg, Deutschland
  • Martin Boeker - Institute for Medical Biometry and Statistics, Faculty of Medicine, University of Freiburg, Freiburg, Deutschland; Comprehensive Cancer Center Freiburg, Medical Center - University of Freiburg, Freiburg, Deutschland

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 62. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS). Oldenburg, 17.-21.09.2017. Düsseldorf: German Medical Science GMS Publishing House; 2017. DocAbstr. 221

doi: 10.3205/17gmds165, urn:nbn:de:0183-17gmds1654

Published: August 29, 2017

© 2017 Horki et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. See license information at http://creativecommons.org/licenses/by/4.0/.


Outline

Text

Introduction (incl. Objective / Requirements): Patient recruitment for studies and hypotheses exploration in research projects are two areas that can greatly benefit from IT research platforms derived from the primary clinical and departmental systems. Building such a platform in UK Freiburg, one main requirement was to maintain the stability of the primary systems at all time. The objective of our work was to ensure this stability by decoupling the primary systems and the IT research platform through a clinical data warehouse (CDWH). Such a CDWH is also considered a core element for further improvements in research and patient care [1].

State of the art (related Work & short commings): Notable examples of software platforms and data models for CDWH include i2b2 [2], TranSMART [3], and OMOP/OHDS [4]. We chose the i2b2 platform for the following reasons:

  • there are numerous demonstration of its successful deployment in the United States [5]
  • it has been successfully evaluated in Germany [6]
  • there are open source tools supporting its deployment [7]

Furthermore, the i2b2 is compatible with OMOP [8], and full i2b2 integration is scheduled for an upcoming TranSMART version.

Concept: Building a CDWH, the following requirements had to be met:

  • Data can only be imported and exported via defined processes
  • Heterogeneous data structures of the primary systems need to be harmonised
  • Data protection must be ensured

To fulfil the first requirement, we developed a range of extract, transform, and load (ETL) processes. These ETL processes also allowed us to harmonise heterogeneous data structures by annotating them with classification systems. To ensure data protection, we cooperated with the Clinical Trials Unit in Freiburg to implement several security measures based on ECRIN (http://www.ecrin.org).

Implementation: The core IT infrastructure was provided by the Medical Computer Department. We developed the first version of the CDWH using the "i2b2 wizard" component of the IDRT software [7]. Next, we installed i2b2 1.7.08b manually on SUSE Linux Enterprise Server 12 running a PostgreSQL 9.4 database.

We implemented several security measures before loading the actual patient data, including setting up a firewall within a secure network, implementing a reverse proxy with password authentication for the web client, and restricting the password-protected database access to specific clients only.

We developed the ETL processes in Talend Open Studio, and linked the data to the ICD-10-GM and OPS classification systems.

Lessons Learned: Even though our CDWH is still work in progress, it is already being applied for patient recruitment in an ongoing study, and is scheduled for application in a medication research project. The main lesson learned so far is that the CDWH should start small, and grow into the requirements it is meant to fulfil. For example, the patient recruitment is an iterative process, where each query yields requirements for the CDWH, and each query result yields insights in how the query can be further refined. Another lesson learned is that the data use and access policies are as important as the technical aspects of the CDWH.



Die Autoren geben an, dass kein Interessenkonflikt besteht.

Die Autoren geben an, dass kein Ethikvotum erforderlich ist.


References

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