Background An essential step in any medical research project after identifying the research question is to determine if there are sufficient patients available for a study and where to find them. Pursuing digital feasibility queries on available patient data registries has proven to be an excellent way of reusing existing real-world data sources. To support multicentric research, these feasibility queries should be designed and implemented to run across multiple sites and securely access local data. Working across hospitals usually involves working with different data formats and vocabularies. Recently, the Fast Healthcare Interoperability Resources (FHIR) standard was developed by Health Level Seven to address this concern and describe patient data in a standardized format. The Medical Informatics Initiative in Germany has committed to this standard and created data integration centers, which convert existing data into the FHIR format at each hospital. This partially solves the interoperability problem; however, a distributed feasibility query platform for the FHIR standard is still missing. Objective This study described the design and implementation of the components involved in creating a cross-hospital feasibility query platform for researchers based on FHIR resources. This effort was part of a large COVID-19 data exchange platform and was designed to be scalable for a broad range of patient data. Methods We analyzed and designed the abstract components necessary for a distributed feasibility query. This included a user interface for creating the query, backend with an ontology and terminology service, middleware for query distribution, and FHIR feasibility query execution service. Results We implemented the components described in the Methods section. The resulting solution was distributed to 33 German university hospitals. The functionality of the comprehensive network infrastructure was demonstrated using a test data set based on the German Corona Consensus Data Set. A performance test using specifically created synthetic data revealed the applicability of our solution to data sets containing millions of FHIR resources. The solution can be easily deployed across hospitals and supports feasibility queries, combining multiple inclusion and exclusion criteria using standard Health Level Seven query languages such as Clinical Quality Language and FHIR Search. Developing a platform based on multiple microservices allowed us to create an extendable platform and support multiple Health Level Seven query languages and middleware components to allow integration with future directions of the Medical Informatics Initiative. Conclusions We designed and implemented a feasibility platform for distributed feasibility queries, which works directly on FHIR-formatted data and distributed it across 33 university hospitals in Germany. We showed that developing a feasibility platform directly on the FHIR standard is feasible.
Background The COVID-19 pandemic highlighted the importance of making research data from all German hospitals available to scientists to respond to current and future pandemics promptly. The heterogeneous data originating from proprietary systems at hospitals' sites must be harmonized and accessible. The German Corona Consensus Dataset (GECCO) specifies how data for COVID-19 patients will be standardized in Fast Healthcare Interoperability Resources (FHIR) profiles across German hospitals. However, given the complexity of the FHIR standard, the data harmonization is not sufficient to make the data accessible. A simplified visual representation is needed to reduce the technical burden, while allowing feasibility queries. Objective This study investigates how a search ontology can be automatically generated using FHIR profiles and a terminology server. Furthermore, it describes how this ontology can be used in a user interface (UI) and how a mapping and a terminology tree created together with the ontology can translate user input into FHIR queries. Methods We used the FHIR profiles from the GECCO data set combined with a terminology server to generate an ontology and the required mapping files for the translation. We analyzed the profiles and identified search criteria for the visual representation. In this process, we reduced the complex profiles to code value pairs for improved usability. We enriched our ontology with the necessary information to display it in a UI. We also developed an intermediate query language to transform the queries from the UI to federated FHIR requests. Separation of concerns resulted in discrepancies between the criteria used in the intermediate query format and the target query language. Therefore, a mapping was created to reintroduce all information relevant for creating the query in its target language. Further, we generated a tree representation of the ontology hierarchy, which allows resolving child concepts in the process. Results In the scope of this project, 82 (99%) of 83 elements defined in the GECCO profile were successfully implemented. We verified our solution based on an independently developed test patient. A discrepancy between the test data and the criteria was found in 6 cases due to different versions used to generate the test data and the UI profiles, the support for specific code systems, and the evaluation of postcoordinated Systematized Nomenclature of Medicine (SNOMED) codes. Our results highlight the need for governance mechanisms for version changes, concept mapping between values from different code systems encoding the same concept, and support for different unit dimensions. Conclusions We developed an automatic process to generate ontology and mapping files for FHIR-formatted data. Our tests found that this process works for most of our chosen FHIR profile criteria. The process established here works directly with FHIR profiles...
Background The Aligning Biobanking and Data Integration Centers Efficiently project aims to harmonize technologies and governance structures of German university hospitals and their biobanks to facilitate searching for patient data and biospecimens. The central element will be a feasibility tool for researchers to query the availability of samples and data to determine the feasibility of their study project. Objective The objectives of the study were as follows: an evaluation of the overall user interface usability of the feasibility tool, the identification of critical usability issues, comprehensibility of the underlying ontology operability, and analysis of user feedback on additional functionalities. From these, recommendations for quality-of-use optimization, focusing on more intuitive usability, were derived. Methods To achieve the study goal, an exploratory usability test consisting of 2 main parts was conducted. In the first part, the thinking aloud method (test participants express their thoughts aloud throughout their use of the tool) was complemented by a quantitative questionnaire. In the second part, the interview method was combined with supplementary mock-ups to collect users’ opinions on possible additional features. Results The study cohort rated global usability of the feasibility tool based on the System Usability Scale with a good score of 81.25. The tasks assigned posed certain challenges. No participant was able to solve all tasks correctly. A detailed analysis showed that this was mostly because of minor issues. This impression was confirmed by the recorded statements, which described the tool as intuitive and user friendly. The feedback also provided useful insights regarding which critical usability problems occur and need to be addressed promptly. Conclusions The findings indicate that the prototype of the Aligning Biobanking and Data Integration Centers Efficiently feasibility tool is headed in the right direction. Nevertheless, we see potential for optimization primarily in the display of the search functions, the unambiguous distinguishability of criteria, and the visibility of their associated classification system. Overall, it can be stated that the combination of different tools used to evaluate the feasibility tool provided a comprehensive picture of its usability.
BACKGROUND An essential step in any medical research project after having identified the research question is to find out if there are enough patients available for a study and where to find them. Pursuing digital feasibility queries on available patient data registries has proven to be an excellent way of reusing existing real world data sources. To support multicentric research, these feasibility queries should be designed and implemented to run across multiple sites and access local data in a secure manner. Working across hospitals usually also means working with different data formats and vocabularies. Recently the Fast Healhcare Interoperability Resources (FHIR) standard has been developed by HL7 to address this concern and describe patient data in an standardized format. The Medical Informatics Initiative (MII) in Germany has commited to this standard and created Data Integration Centers, which convert existing data into the FHIR format at each hospital. This partially solves the interoperability problem, however a distributed feasibility query platform for the FHIR standard is still missing. OBJECTIVE In this study we describe the design and implementation of the components involved in creating a privacy preserving cross-hospital feasibility query platform for researhchers based on FHIR resources. This effort is part of a larger COVID-19 data exchange platform (CODEX) and is designed to be scalable for a broad range of patient data. METHODS We analysed and designed the abstract components necessary for a distributed feasibility query. This included a user interface for creating the query, a backend with an ontology and terminology service, a middleware for query distribution and a feasibility FHIR execution service. RESULTS We implemented the components identified in the methods. The resulting solution was distributed to 33 German university hospitals. The functionality of the comprehensive network infrastructure was demonstrated using a test dataset based on the German Corona Consensus Dataset (GECCO). We found that our solution could be easily deployed across the hospitals and that it supported complex feasibility queries, combining multiple inclusion criteria and exclusion criteria using standard Health Level Seven (HL7) query languages such as the Clinical Quality Language (CQL) and FHIR Search. Developing a platform based on multiple microservices allowed us to create an extendable platform and support multiple HL7 query languages as well as middleware components necessary to allow integration with future directions of the Medical Informatics Initiative. CONCLUSIONS We designed and implemented a feasibility platform for privacy preserving distributed feasibility queries, which works directly on FHIR formatted data and distributed it across 33 university hospitals in Germany. We showed that developing a feasibility platform directly on the FHIR standard is feasible.
Testing for genetic alterations in tumor tissue allows clinicians to identify patients who most likely will benefit from molecular targeted treatment. EXLIQUID – exploiting liquid biopsies to advance cancer precision medicine – investigates the potential of additional non-invasive tools for guiding therapy decisions and monitoring of advanced cancer patients. The term “liquid biopsy” (LB) refers to non-invasive analysis of tumor-derived circulating material such as cell-free DNA in blood samples from cancer patients. Although recent technological advances allow sensitive and specific detection of LB biomarkers, only few LB assays have entered clinical routine to date. EXLIQUID is a German Cancer Consortium (DKTK)-wide joint funding project that aims at establishing LBs as a minimally-invasive tool to analyze molecular changes in circulating tumor DNA (ctDNA). Here, we present the structure, clinical aim, and methodical approach of the new DKTK EXLIQUID consortium. Within EXLIQUID, we will set up a multicenter repository of high-quality LB samples from patients participating in DKTK MASTER and local molecular tumor boards, which use molecular profiles of tumor tissues to guide targeted therapies. We will develop LB assays for monitoring of therapy efficacy by the analysis of tumor mutant variants and tumor-specific DNA methylation patterns in ctDNA from these patients. By bringing together LB experts from all DKTK partner sites and exploiting the diversity of their particular expertise, complementary skills and technologies, the EXLIQUID consortium addresses the challenges of translating LBs into the clinic. The DKTK structure provides EXLIQUID a unique position for the identification of liquid biomarkers even in less common tumor types, thereby extending the group of patients benefitting from non-invasive LB testing. Besides its scientific aims, EXLIQUID is building a valuable precision oncology cohort and LB platform which will be available for future collaborative research studies within the DKTK and beyond.
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