Martin Sedlmayr

A scoping review of cloud computing in healthcare

BackgroundCloud 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.MethodsMEDLINE 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.Results102 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.ConclusionsEven 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.

Secure Secondary Use of Clinical Data with Cloud-based NLP Services. Towards a Highly Scalable Research Infrastructure.

OBJECTIVES 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. METHODS 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. RESULTS 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. CONCLUSIONS 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.

eHealth Literacy Research – Quo vadis?

ABSTRACT The concept of electronic health (eHealth) literacy evolved from the social and information sciences and describes competencies necessary to use electronic health services. As it is a rather new topic, and as there is no current overview of the state of the art in research, it is not possible to identify research gaps. Therefore, the objective of this viewpoint article is to increase knowledge on the current state of the art of research in eHealth literacy and to identify gaps in scientific research which should be focused on by the research community in the future. The article provides a current viewpoint of the concept of eHealth literacy and related research. Gaps can be found in terms of a missing “gold standard” regarding both the definition and the measurement of eHealth literacy. Furthermore, there is a need for identifying the implications on eHealth developers, which evolve from the measurement of eHealth literacy in eHealth users. Finally, a stronger inclusion of health professionals, both in the evolving concept and in the measurement of eHealth literacy, is needed in the future.

Health Economic Impact of a Pulmonary Artery Pressure Sensor for Heart Failure Telemonitoring: A Dynamic Simulation.

AIMS Recently, a permanently implantable wireless system, designed to monitor and manage pulmonary artery (PA) pressures remotely, demonstrated significant reductions in heart failure (HF) hospitalizations in high-risk symptomatic patients, regardless of ejection fraction. The objectives of this study were to simulate the estimated clinical and economic impact in Germany of generalized use of this PA pressure monitoring system considering reductions of HF hospitalizations and the improvement in Quality of Life. MATERIALS AND METHODS Based on the Prospective Health Technology Assessment approach, we simulated the potential of the widespread application of PA pressure monitoring on the German healthcare system for the period 2009-2021. RESULTS This healthcare economic simulation formulated input assumptions based on results from the CHAMPION Trial, a multicenter, prospective, randomized controlled U.S. trial that demonstrated a 37% reduction of hospitalizations in persistently symptomatic previous HF patients. Based on these results, an estimated 114,800 hospitalizations would expected to be avoided. This effect would potentially save an estimated €522 million, an equivalent of

Integrating clinical decision support systems for pharmacogenomic testing into clinical routine - a scoping review of designs of user-system interactions in recent system development

575 million, during the entire simulation period. CONCLUSION This healthcare economic modeling of the PA pressure monitoring systems impact demonstrates substantial clinical and economic benefits in the German healthcare system.

Acceptance by laypersons and medical professionals of the personalized eHealth platform, eHealthMonitor

BackgroundPharmacogenomic clinical decision support systems (CDSS) have the potential to help overcome some of the barriers for translating pharmacogenomic knowledge into clinical routine. Before developing a prototype it is crucial for developers to know which pharmacogenomic CDSS features and user-system interactions have yet been developed, implemented and tested in previous pharmacogenomic CDSS efforts and if they have been successfully applied. We address this issue by providing an overview of the designs of user-system interactions of recently developed pharmacogenomic CDSS.MethodsWe searched PubMed for pharmacogenomic CDSS published between January 1, 2012 and November 15, 2016. Thirty-two out of 118 identified articles were summarized and included in the final analysis. We then compared the designs of user-system interactions of the 20 pharmacogenomic CDSS we had identified.ResultsAlerts are the most widespread tools for physician-system interactions, but need to be implemented carefully to prevent alert fatigue and avoid liabilities. Pharmacogenomic test results and override reasons stored in the local EHR might help communicate pharmacogenomic information to other internal care providers. Integrating patients into user-system interactions through patient letters and online portals might be crucial for transferring pharmacogenomic data to external health care providers. Inbox messages inform physicians about new pharmacogenomic test results and enable them to request pharmacogenomic consultations. Search engines enable physicians to compare medical treatment options based on a patient’s genotype.ConclusionsWithin the last 5 years, several pharmacogenomic CDSS have been developed. However, most of the included articles are solely describing prototypes of pharmacogenomic CDSS rather than evaluating them. To support the development of prototypes further evaluation efforts will be necessary. In the future, pharmacogenomic CDSS will likely include prediction models to identify patients who are suitable for preemptive genotyping.

Optimizing R with SparkR on a commodity cluster for biomedical research

ABSTRACT Introduction and background: Often, eHealth services are not accepted because of factors such as eHealth literacy or trust. Within this study, eHealthMonitor was evaluated in three European countries (Germany, Greece, and Poland) by medical professionals and laypersons with respect to numerous acceptance factors. Methods: Questionnaires were created on the basis of factors from literature and with the help of scales which have already been validated. A qualitative survey was conducted in Germany, Poland, and Greece. Results: The eHealth literacy of all participants was medium/high. Laypersons mostly agreed that they could easily become skillful with eHealthMonitor and that other people thought that they should use eHealthMonitor. Amongst medical professionals, a large number were afraid that eHealthMonitor could violate their privacy or the privacy of their patients. Overall, the participants thought that eHealthMonitor was a good concept and that they would use it. Discussion and conclusion: The main hindrances to the use of eHealthMonitor were found in trust issues including data privacy. In the future, more research on the linkage of all measured factors is needed, for example, to address the question of whether highly educated people tend to mistrust eHealth information more than people with lower levels of education.

User-centered design of a mobile medication management

BACKGROUND AND OBJECTIVES Medical researchers are challenged today by the enormous amount of data collected in healthcare. Analysis methods such as genome-wide association studies (GWAS) are often computationally intensive and thus require enormous resources to be performed in a reasonable amount of time. While dedicated clusters and public clouds may deliver the desired performance, their use requires upfront financial efforts or anonymous data, which is often not possible for preliminary or occasional tasks. We explored the possibilities to build a private, flexible cluster for processing scripts in R based on commodity, non-dedicated hardware of our department. METHODS For this, a GWAS-calculation in R on a single desktop computer, a Message Passing Interface (MPI)-cluster, and a SparkR-cluster were compared with regards to the performance, scalability, quality, and simplicity. RESULTS The original script had a projected runtime of three years on a single desktop computer. Optimizing the script in R already yielded a significant reduction in computing time (2 weeks). By using R-MPI and SparkR, we were able to parallelize the computation and reduce the time to less than three hours (2.6 h) on already available, standard office computers. While MPI is a proven approach in high-performance clusters, it requires rather static, dedicated nodes. SparkR and its Hadoop siblings allow for a dynamic, elastic environment with automated failure handling. SparkR also scales better with the number of nodes in the cluster than MPI due to optimized data communication. CONCLUSION R is a popular environment for clinical data analysis. The new SparkR solution offers elastic resources and allows supporting big data analysis using R even on non-dedicated resources with minimal change to the original code. To unleash the full potential, additional efforts should be invested to customize and improve the algorithms, especially with regards to data distribution.

Towards Implementation of OMOP in a German University Hospital Consortium

ABSTRACT Background: The use of a nationwide medication plan has been promoted as an effective strategy to improve patient safety in Germany. However, the medication plan only exists as a paper-based version, which is related to several problems, that could be circumvented by an electronic alternative. Objective: The main objective of this study was to report on the development of a mobile interface concept to support the management of medication information. Methods: The human-centered design (UCD) process was chosen. First the context of use was analyzed, and personas and an interaction concept were designed. Next, a paper prototype was developed and evaluated by experts. Based on those results, a medium-fidelity prototype was created and assessed by seven end-users who performed a thinking-aloud test in combination with a questionnaire based on the System Usability Scale (SUS). Results: Initially for one persona/user type, an interface design concept was developed, which received an average SUS-Score of 92.1 in the user test. Usability problems have been solved so that the design concept could be fixed for a future implementation. Contribution: The approach of the UCD process and the methods involved can be applied by other researchers as a framework for the development of similar applications.

Technology foresight for medical device development through hybrid simulation: The ProHTA Project

Background  In 2015, the German Federal Ministry of Education and Research initiated a large data integration and data sharing research initiative to improve the reuse of data from patient care and translational research. The Observational Medical Outcomes Partnership (OMOP) common data model and the Observational Health Data Sciences and Informatics (OHDSI) tools could be used as a core element in this initiative for harmonizing the terminologies used as well as facilitating the federation of research analyses across institutions. Objective  To realize an OMOP/OHDSI-based pilot implementation within a consortium of eight German university hospitals, evaluate the applicability to support data harmonization and sharing among them, and identify potential enhancement requirements. Methods  The vocabularies and terminological mapping required for importing the fact data were prepared, and the process for importing the data from the source files was designed. For eight German university hospitals, a virtual machine preconfigured with the OMOP database and the OHDSI tools as well as the jobs to import the data and conduct the analysis was provided. Last, a federated/distributed query to test the approach was executed. Results  While the mapping of ICD-10 German Modification succeeded with a rate of 98.8% of all terms for diagnoses, the procedures could not be mapped and hence an extension to the OMOP standard terminologies had to be made. Overall, the data of 3 million inpatients with approximately 26 million conditions, 21 million procedures, and 23 million observations have been imported. A federated query to identify a cohort of colorectal cancer patients was successfully executed and yielded 16,701 patient cases visualized in a Sunburst plot. Conclusion  OMOP/OHDSI is a viable open source solution for data integration in a German research consortium. Once the terminology problems can be solved, researchers can build on an active community for further development.