A scoping review of cloud computing in healthcare - Griebel, Lena; Prokosch, Hans-Ulrich; Köpcke, Felix; Toddenroth, Dennis; Christoph, Jan; Leb, Ines; Engel, Igor; Sedlmayr, Martin (BMC Medical Informatics and Decision Making, 2015)
Cloud computing is a recent and fast growing area of development in healthcare. Ubiquitous, on-demand access to virtually endless resources in combination with a pay-per-use model allow for new ways of developing, delivering and using services. Cloud computing is often used in an "OMICS-context", e.g. for computing in genomics, proteomics and molecular medicine, while other field of application still seem to be underrepresented. Thus, the objective of this scoping review was to identify the current state and hot topics in research on cloud computing in healthcare beyond this traditional domain.MEDLINE was searched in July 2013 and in December 2014 for publications containing the terms "cloud computing" and "cloud-based". Each journal and conference article was categorized and summarized independently by two researchers who consolidated their findings.102 publications have been analyzed and 6 main topics have been found: telemedicine/teleconsultation, medical imaging, public health and patient self-management, hospital management and information systems, therapy, and secondary use of data. Commonly used features are broad network access for sharing and accessing data and rapid elasticity to dynamically adapt to computing demands. Eight articles favor the pay-for-use characteristics of cloud-based services avoiding upfront investments. Nevertheless, while 22 articles present very general potentials of cloud computing in the medical domain and 66 articles describe conceptual or prototypic projects, only 14 articles report from successful implementations. Further, in many articles cloud computing is seen as an analogy to internet-/web-based data sharing and the characteristics of the particular cloud computing approach are unfortunately not really illustrated.Even though cloud computing in healthcare is of growing interest only few successful implementations yet exist and many papers just use the term "cloud" synonymously for "using virtual machines" or "web-based" with no described benefit of the cloud paradigm. The biggest threat to the adoption in the healthcare domain is caused by involving external cloud partners: many issues of data safety and security are still to be solved. Until then, cloud computing is favored more for singular, individual features such as elasticity, pay-per-use and broad network access, rather than as cloud paradigm on its own.
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Comparison of electronic health record system functionalities to support the patient recruitment process in clinical trials - Schreiweis, Bjoern; Trinczek, Benjamin; Köpcke, Felix; Leusch, Thomas; Majeed, Raphael W.; Wenk, Joachim; Bergh, Bjoern; Ohmann, Christian; Roehrig, Rainer; Dugas, Martin; Prokosch, Hans-Ulrich (International Journal of Medical Informatics, 2014)
Reusing data from electronic health records for clinical and translational research and especially for patient recruitment has been tackled in a broader manner since about a decade. Most projects found in the literature however focus on standalone systems and proprietary implementations at one particular institution often for only one singular trial and no generic evaluation of EHR systems for their applicability to support the patient recruitment process does yet exist. Thus we sought to assess whether the current generation of EHR systems in Germany provides modules/tools, which can readily be applied for IT-supported patient recruitment scenarios.We first analysed the EHR portfolio implemented at German University Hospitals and then selected 5 sites with five different EHR implementations covering all major commercial systems applied in German University Hospitals. Further, major functionalities required for patient recruitment support have been defined and the five sample EHRs and their standard tools have been compared to the major functionalities.In our analysis of the site's hospital information system environments (with four commercial EHR systems and one self-developed system) we found that - even though no dedicated module for patient recruitment has been provided - most EHR products comprise generic tools such as workflow engines, querying capabilities, report generators and direct SQL-based database access which can be applied as query modules, screening lists and notification components for patient recruitment support. A major limitation of all current EHR products however is that they provide no dedicated data structures and functionalities for implementing and maintaining a local trial registry.At the five sites with standard EHR tools the typical functionalities of the patient recruitment process could be mostly implemented. However, no EHR component is yet directly dedicated to support research requirements such as patient recruitment. We recommend for future developments that EHR customers and vendors focus much more on the provision of dedicated patient recruitment modules.
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Evaluating predictive modeling algorithms to assess patient eligibility for clinical trials from routine data - Köpcke, Felix; Lubgan, Dorota; Fietkau, Rainer; Scholler, Axel; Nau, Carla; Stürzl, Michael; Croner, Roland S.; Prokosch, Hans-Ulrich; Toddenroth, Dennis (BMC Medical Informatics and Decision Making, 2013)
Background: The necessity to translate eligibility criteria from free text into decision rules that are compatible with data from the electronic health record (EHR) constitutes the main challenge when developing and deploying clinical trial recruitment support systems. Recruitment decisions based on case-based reasoning, i.e. using past cases rather than explicit rules, could dispense with the need for translating eligibility criteria and could also be implemented largely independently from the terminology of the EHR's database. We evaluated the feasibility of predictive modeling to assess the eligibility of patients for clinical trials and report on a prototype's performance for different system configurations. Methods. The prototype worked by using existing basic patient data of manually assessed eligible and ineligible patients to induce prediction models. Performance was measured retrospectively for three clinical trials by plotting receiver operating characteristic curves and comparing the area under the curve (ROC-AUC) for different prediction algorithms, different sizes of the learning set and different numbers and aggregation levels of the patient attributes. Results: Random forests were generally among the best performing models with a maximum ROC-AUC of 0.81 (CI: 0.72-0.88) for trial A, 0.96 (CI: 0.95-0.97) for trial B and 0.99 (CI: 0.98-0.99) for trial C. The full potential of this algorithm was reached after learning from approximately 200 manually screened patients (eligible and ineligible). Neither block- nor category-level aggregation of diagnosis and procedure codes influenced the algorithms' performance substantially. Conclusions: Our results indicate that predictive modeling is a feasible approach to support patient recruitment into clinical trials. Its major advantages over the commonly applied rule-based systems are its independency from the concrete representation of eligibility criteria and EHR data and its potential for automation. © 2013 Köpcke et al.; licensee BioMed Central Ltd.
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IT infrastructure components to support clinical care and translational research projects in a comprehensive cancer center - Prokosch, Hans-Ulrich; Ries, Markus; Beyer, Alexander; Schwenk, Martin; Seggewies, Christof; Köpcke, Felix; Mate, Sebastian; Martin, Marcus; Bärthlein, Barbara; Beckmann, Matthias; Stürzl, Michael; Croner, Roland; et al. (User Centred Networked Health Care, 2011)
This paper presents the concept of an integrated IT infrastructure framework established at the comprehensive cancer center at the University Hospital Erlangen. The framework is based on the single source concept where data from the electronic medical record are reused for clinical and translational research projects. The applicability of the approach is illustrated by two case studies from colon cancer and prostate cancer research projects. © 2011 European Federation for Medical Informatics. All rights reserved.
UnivIS-Import:2015-04-14:Pub.2011.tech.IMMD.infome.itinfr
Keywords: Cancer documentation; Center; Comprehensive cancer; IT infrastructure framework; Single source concept; Translational research

Secure Secondary Use of Clinical Data with Cloud-based NLP Services. Towards a Highly Scalable Research Infrastructure - Christoph, Jan; Griebel, Lena; Leb, Ines; Engel, Igor; Köpcke, Felix; Toddenroth, Dennis; Prokosch, Hans-Ulrich; Laufer, J.; Marquardt, K.; Sedlmayr, Martin (Methods of Information in Medicine, 2015)
The secondary use of clinical data provides large opportunities for clinical and translational research as well as quality assurance projects. For such purposes, it is necessary to provide a flexible and scalable infrastructure that is compliant with privacy requirements. The major goals of the cloud4health project are to define such an architecture, to implement a technical prototype that fulfills these requirements and to evaluate it with three use cases.The architecture provides components for multiple data provider sites such as hospitals to extract free text as well as structured data from local sources and de-identify such data for further anonymous or pseudonymous processing. Free text documentation is analyzed and transformed into structured information by text-mining services, which are provided within a cloud-computing environment. Thus, newly gained annotations can be integrated along with the already available structured data items and the resulting data sets can be uploaded to a central study portal for further analysis.Based on the architecture design, a prototype has been implemented and is under evaluation in three clinical use cases. Data from several hundred patients provided by a University Hospital and a private hospital chain have already been processed.Cloud4health has shown how existing components for secondary use of structured data can be complemented with text-mining in a privacy compliant manner. The cloud-computing paradigm allows a flexible and dynamically adaptable service provision that facilitates the adoption of services by data providers without own investments in respective hardware resources and software tools.
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