Case-based visualization of a patient cohort using SEER epidemiologic data - Maier, Christian; Bürkle, Thomas; Prokosch, Hans-Ulrich; Ganslandt, Thomas (Studies in health technology and informatics, 2014)
Data from cancer registries can be used to track the epidemiology of cancer and can potentially serve to guide individual diagnostic and treatment decisions. Even though some cancer registry datasets have been made publicly available for scientific and clinical use, few applications have so far provided direct access to these data from within the patient context of an electronic patient record. The goal of this project was to implement a proof-of-concept integration of the public SEER (Surveillance, Epidemiology and End Results) cancer registry dataset with a digital breast cancer tumor board at a German university hospital and to determine its utility in the clinical settings. The integration was successfully established, using data from routine documentation to provide dynamic visualizations of cohort composition and Kaplan-Meier survival plots. Evaluation feedback was favorable regarding the concept and implementation, but highlighted that important data elements, e.g. receptor status data, were missing in the SEER dataset, limiting clinical value of the system.
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Designing a Framework of Components to Support Patient Engagement in Research - Öfelein, Matthias; Reichold, Michael; Maier, Christian; Prokosch, Hans-Ulrich (German Medical Data Sciences: Shaping Change – Creative Solutions for Innovative Medicine, 2019)
In the context of the German Medical Informatics Initiative (MII), where data reuse and data sharing are major goals, cross-site, long-term research on patient care data can only be conducted lawfully with informed patient consent. Thus, the MII consent working group developed a template form for patient information and broad consent based on work that has been done for a former biobank project. The broad consent enables the patient to consent to the use of a wide range of the documented data including research purposes. Therefore, a user-friendly tool is needed which not only supports the storage and maintenance of the patient's consents but also allows him to easily review or withdraw his consents. Furthermore, the tool should allow the patient to review the use of his data in research projects and possible publications. This is why we developed a concept of how such a tool could be integrated into the clinical and research system landscape and implemented a prototype as a proof of concept.
CRIS-Team Scopus Importer:2019-09-24
Keywords: Consent Management; Data Sharing; Informed Consent; Patient Participation

Digital patient communication: Improving the hospital-patient relationship - Prokosch, Hans-Ulrich; Schüttler, Christina; Schraudt, Michaela; Öfelein, Matthias; Maier, Christian (Healthcare of the Future - Bridging the Information Gap, 2019)
Digitally engaging patients in their care processes was for many years limited to sharing care related documents (e.g. laboratory or radiology findings, discharge letters) with them through personal electronic health records. Newer concepts have led to the establishment of patient portals as patient frontends to a hospital´s electronic health record. Rarely however have complete patient pathways with pre-hospitalization, inpatient stay and post-hospitalization been evaluated to identify chains of communication processes involving clinical care scenarios, as well as subsequent home monitoring scenarios. Neither have such approaches been integrated with digital communication processes related to a patient´s engagement in medical research projects. In order to enhance hospital-patient relationships in a holistic manner, we hypothesize that an integrated environment (e.g. patient portal) supporting shared decision making and communication in a patient´s care situation and in the same time providing communication processes for patient research engagement will optimize the patient-hospital relationship and be supportive in binding a patient to this care providing institution.
CRIS-Team Scopus Importer:2019-05-02
Keywords: Digital patient communication; Electronic patient portal

Digital Patient Communication: Improving the Hospital-Patient Relationship. - Prokosch, Hans-Ulrich; Schüttler, Christina; Schraudt, Michaela; Öfelein, Matthias; Maier, Christian (Studies in health technology and informatics, 2019)
Digitally engaging patients in their care processes was for many years limited to sharing care related documents (e.g. laboratory or radiology findings, discharge letters) with them through personal electronic health records. Newer concepts have led to the establishment of patient portals as patient frontends to a hospitalś electronic health record. Rarely however have complete patient pathways with pre-hospitalization, inpatient stay and post-hospitalization been evaluated to identify chains of communication processes involving clinical care scenarios, as well as subsequent home monitoring scenarios. Neither have such approaches been integrated with digital communication processes related to a patientś engagement in medical research projects. In order to enhance hospital-patient relationships in a holistic manner, we hypothesize that an integrated environment (e.g. patient portal) supporting shared decision making and communication in a patient´s care situation and in the same time providing communication processes for patient research engagement will optimize the patient-hospital relationship and be supportive in binding a patient to this care providing institution.
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EHR-Independent Predictive Decision Support Architecture Based on OMOP - Unberath, Philipp; Prokosch, Hans-Ulrich; Gründner, Julian; Erpenbeck, Marcel; Maier, Christian; Christoph, Jan (Applied Clinical Informatics, 2020)
BACKGROUND: The increasing availability of molecular and clinical data of cancer patients combined with novel machine learning techniques has the potential to enhance clinical decision support, example, for assessing a patient's relapse risk. While these prediction models often produce promising results, a deployment in clinical settings is rarely pursued. OBJECTIVES: In this study, we demonstrate how prediction tools can be integrated generically into a clinical setting and provide an exemplary use case for predicting relapse risk in melanoma patients. METHODS: To make the decision support architecture independent of the electronic health record (EHR) and transferable to different hospital environments, it was based on the widely used Observational Medical Outcomes Partnership (OMOP) common data model (CDM) rather than on a proprietary EHR data structure. The usability of our exemplary implementation was evaluated by means of conducting user interviews including the thinking-aloud protocol and the system usability scale (SUS) questionnaire. RESULTS: An extract-transform-load process was developed to extract relevant clinical and molecular data from their original sources and map them to OMOP. Further, the OMOP WebAPI was adapted to retrieve all data for a single patient and transfer them into the decision support Web application for enabling physicians to easily consult the prediction service including monitoring of transferred data. The evaluation of the application resulted in a SUS score of 86.7. CONCLUSION: This work proposes an EHR-independent means of integrating prediction models for deployment in clinical settings, utilizing the OMOP CDM. The usability evaluation revealed that the application is generally suitable for routine use while also illustrating small aspects for improvement.
CRIS-Team Scopus Importer:2020-06-16

Experiences of Transforming a Complex Nephrologic Care and Research Database into i2b2 Using the IDRT Tools - Maier, Christian; Christoph, Jan; Schmidt, Danilo; Ganslandt, Thomas; Prokosch, Hans-Ulrich; Kraus, Stefan; Sedlmayr, Martin (Journal of Healthcare Engineering, 2019)
The secondary use of data from electronic medical records has become an important factor to determine and to identify various causes of disease. For this reason, applications like informatics for integrating biology and the bedside (i2b2) offer a GUI-based front end to select patient cohorts. To make use of those tools, however, clinical data need to be extracted from the Electronic Health Record (EHR) system and integrated into the data schema of i2b2. We used TBase, a documentation system for nephrologic transplantations, as a source system and applied the Integrated Data Repository Toolkit (IDRT) for the Extract, Transform, and Load (ETL) process to load the data into i2b2. Since i2b2 uses an entity-attribute-value (EAV) schema, which is a fundamentally different way of modeling data in comparison to a standard relational schema in TBase, we evaluated if (a) the data relationship of the source system entities can still be represented in the i2b2 schema and if (b) the IDRT is a suitable solution for loading the data of a comprehensive data schema like TBase into i2b2. For that reason, we identified entities in the TBase data schema which were relevant for answering questions on cohort identification. By doing so, we found out that the entities had different structures that needed to be handled differently for the ETL process. Furthermore, the use of IDRT revealed shortcomings with regard to large input data and specific data structures that are part of most modern EHR systems. However, this project also showed that our way of modeling the TBase data in i2b2 has been proven to be successful in terms of answering the most common questions of clinicians on cohort identification.
CRIS-Team WoS Importer:2019-03-06

Optimizing R with SparkR on a commodity cluster for biomedical research - Sedlmayr, Martin; Würfl, Tobias; Maier, Christian; Haeberle, Lothar; Fasching, Peter; Prokosch, Hans-Ulrich; Christoph, Jan (Computer Methods and Programs in Biomedicine, 2016)
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.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.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.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.
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Patient Cohort Identification on Time Series Data Using the OMOP Common Data Model - Maier, Christian; Kapsner, Lorenz; Mate, Sebastian; Prokosch, Hans-Ulrich; Kraus, Stefan (Applied Clinical Informatics, 2021)
Background â The identification of patient cohorts for recruiting patients into clinical trials requires an evaluation of study-specific inclusion and exclusion criteria. These criteria are specified depending on corresponding clinical facts. Some of these facts may not be present in the clinical source systems and need to be calculated either in advance or at cohort query runtime (so-called feasibility query). Objectives â We use the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) as the repository for our clinical data. However, Atlas, the graphical user interface of OMOP, does not offer the functionality to perform calculations on facts data. Therefore, we were in search for a different approach. The objective of this study is to investigate whether the Arden Syntax can be used for feasibility queries on the OMOP CDM to enable on-The-fly calculations at query runtime, to eliminate the need to precalculate data elements that are involved with researchers' criteria specification. Methods â We implemented a service that reads the facts from the OMOP repository and provides it in a form which an Arden Syntax Medical Logic Module (MLM) can process. Then, we implemented an MLM that applies the eligibility criteria to every patient data set and outputs the list of eligible cases (i.e., performs the feasibility query). Results â The study resulted in an MLM-based feasibility query that identifies cases of overventilation as an example of how an on-The-fly calculation can be realized. The algorithm is split into two MLMs to provide the reusability of the approach. Conclusion â We found that MLMs are a suitable technology for feasibility queries on the OMOP CDM. Our method of performing on-The-fly calculations can be employed with any OMOP instance and without touching existing infrastructure like the Extract, Transform and Load pipeline. Therefore, we think that it is a well-suited method to perform on-The-fly calculations on OMOP.
CRIS-Team Scopus Importer:2021-02-12
Keywords: Arden Syntax; clinical data; electronic health records; information storage and retrieval; Medical Logic Modules

Semi-automated De-identification of German Content Sensitive Reports for Big Data Analytics - Seuß, Hannes; Dankerl, Peter; Ihle, Matthias; Grandjean, Andrea; Hammon, Rebecca; Kaestle, Nicola; Fasching, Peter; Maier, Christian; Christoph, Jan; Sedlmayr, Martin; Uder, Michael; Hammon, Matthias; et al. (Röfo-Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren, 2017)
Purpose Projects involving collaborations between different institutions require data security via selective de-identification of words or phrases. A semi-automated de-identification tool was developed and evaluated on different types of medical reports natively and after adapting the algorithm to the text structure. Materials and Methods A semi-automated de-identification tool was developed and evaluated for its sensitivity and specificity in detecting sensitive content in written reports. Data from 4671 pathology reports (4105 + 566 in two different formats), 2804 medical reports, 1008 operation reports, and 6223 radiology reports of 1167 patients suffering from breast cancer were de-identified. The content was itemized into four categories: direct identifiers (name, address), indirect identifiers (date of birth/operation, medical ID, etc.), medical terms, and filler words. The software was tested natively (without training) in order to establish a baseline. The reports were manually edited and the model re-trained for the next test set. After manually editing 25, 50, 100, 250, 500 and if applicable 1000 reports of each type re-training was applied. Results In the native test, 61.3 % of direct and 80.8 % of the indirect identifiers were detected. The performance (P) increased to 91.4 % (P25), 96.7 % (P50), 99.5 % (P100), 99.6 % (P250), 99.7 % (P500) and 100 % (P1000) for direct identifiers and to 93.2 % (P25), 97.9 % (P50), 97.2 % (P100), 98.9 % (P250), 99.0 % (P500) and 99.3 % (P1000) for indirect identifiers. Without training, 5.3 % of medical terms were falsely flagged as critical data. The performance increased, after training, to 4.0 % (P25), 3.6 % (P50), 4.0 % (P100), 3.7 % (P250), 4.3 % (P500), and 3.1 % (P1000). Roughly 0.1 % of filler words were falsely flagged. Conclusion Training of the developed de-identification tool continuously improved its performance. Training with roughly 100 edited reports enables reliable detection and labeling of sensitive data in different types of medical reports. Key Points: · Collaborations between different institutions require de-identification of patients' data. · Software-based de-identification of content-sensitive reports grows in importance as a result of 'Big data'. · A de-identification software was developed and tested natively and after training. · The proposed de-identification software worked quite reliably, following training with roughly 100 edited reports. · A final check of the texts by an authorized person remains necessary. Citation Format · Seuss H, Dankerl P, Ihle M et al. Semi-automated De-identification of German Content Sensitive Reports for Big Data Analytics. Fortschr Röntgenstr 2017; 189: 661 - 671.
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The generic Informed Consent Service gICS®: implementation and benefits of a modular consent software tool to master the challenge of electronic consent management in research - Rau, Henriette; Geidel, Lars; Bialke, Martin; Blumentritt, Arne; Langanke, Martin; Liedtke, Wenke; Pasewald, Sandra; Stahl, Dana; Bahls, Thomas; Maier, Christian; Prokosch, Hans-Ulrich; Hoffmann, Wolfgang (Journal of Translational Medicine, 2020)
BACKGROUND: Defining and protecting participants' rights is the aim of several ethical codices and legal regulations. According to these regulations, the Informed Consent (IC) is an inevitable element of research with human subjects. In the era of "big data medicine", aspects of IC become even more relevant since research becomes more complex rendering compliance with legal and ethical regulations increasingly difficult. METHODS: Based on literature research and practical experiences gathered by the Institute for Community Medicine (ICM), University Medicine Greifswald, requirements for digital consent management systems were identified. RESULTS: To address the requirements, the free-of-charge, open-source software "generic Informed Consent Service" (gICS®) was developed by ICM to provide a tool to facilitate and enhance usage of digital ICs for the international research community covering various scenarios. gICS facilitates IC management based on IC modularisation and supports various workflows within research, including (1) electronic depiction of paper-based consents and (2) fully electronic consents. Numerous projects applied gICS and documented over 336,000 ICs and 2400 withdrawals since 2014. DISCUSSION: Since the consent's content is a prerequisite for securing participants' rights, application of gICS is no guarantee for legal compliance. However, gICS supports fine-granular consents and accommodation of differentiated consent states, which can be directly exchanged between systems, allowing automated data processing. CONCLUSION: gICS simplifies and supports sustained IC management as a major key to successfully conduct studies and build trust in research with human subjects. Therefore, interested researchers are invited to use gICS and provide feedback for further improvements.
CRIS-Team Scopus Importer:2020-08-07
Keywords: Consent management; GDPR; General data protection regulation; Informed consent

Two Years of tranSMART in a University Hospital for Translational Research and Education - Christoph, Jan; Knell, Christian; Naschberger, Elisabeth; Stürzl, Michael; Maier, Christian; Prokosch, Hans-Ulrich; Sedlmayr, Martin (Studies in health technology and informatics, 2017)
For translational research, software platforms such as tranSMART with an integrated view of both clinical and omics data have gained more and more attention in the last years.We wanted to examine the success and failures of tranSMART in the fields of translational research and education by looking at the examples of six use cases at our hospital. We wanted to point out suitable scenarios and user groups as well as still existing challenges and limitations.We sum up the experience we made with our use cases with a focus on lessons learned.tranSMART was successfully established by a bottom-up approach at our university hospital and has been running for more than two years now. It has been used in four translational research projects as well as in education in the context of lectures and bachelor/master theses.tranSMART can be a very useful tool for translational research and education. But it should be used with both care and statistical knowledge to avoid wrong conclusions. Some technical constraints, especially for data modeling, still limit many applications. Version control and data provenance are remaining challenges.
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Two years of tranSMART in a university hospital for translational research and education - Christoph, Jan; Knell, Christian; Naschberger, Elisabeth; Stürzl, Michael; Maier, Christian; Prokosch, Hans-Ulrich; Sedlmayr, Martin (Health informatics meets eHealth: Digital insight information-driven health & care; proceedings of the 11th eHealth2017 Conference, 2017)
UnivIS-Import:2018-09-11:Pub.2017.tech.IMMD.infome.twoyea
Keywords: Biomedical research; Medical education; Research personnel; Translational medical research