@article{sleimann_implementation_2023,
	title = {Implementation of a clinical long-term follow-up database for adult childhood cancer survivors in {Germany}: a feasibility study at two specialised late effects clinics},
	issn = {1432-1335},
	shorttitle = {Implementation of a clinical long-term follow-up database for adult childhood cancer survivors in {Germany}},
	url = {https://doi.org/10.1007/s00432-023-05145-8},
	doi = {10.1007/s00432-023-05145-8},
	abstract = {Childhood cancer survivors (CCS) are at risk for increased morbidity and reduced quality of life associated with treatment-related late effects. In Germany, however, only a few of the more than 40,000 CCS registered in the German Childhood Cancer Registry (GCCR) currently benefit from adequate clinical long-term follow-up (LTFU) structures. To establish a comprehensive knowledge base on CCS’ long-term health in Germany, a database was developed in cooperation with the GCCR. Following a first evaluation phase at two German university centres, this database will be implemented more widely within Germany allowing longitudinal documentation of clinical LTFU data.},
	language = {en},
	urldate = {2023-07-18},
	journal = {Journal of Cancer Research and Clinical Oncology},
	author = {Sleimann, Madelaine and Balcerek, Magdalena and Cytera, Chirine and Richter, Franziska and Borgmann-Staudt, Anja and Wörmann, Bernhard and Kronziel, Lea Louisa and Calaminus, Gabriele and Kock-Schoppenhauer, Ann-Kristin and Grabow, Desiree and Baust, Katja and Neumann, Anke and Langer, Thorsten and Gebauer, Judith},
	month = jul,
	year = {2023},
	keywords = {Childhood cancer survivors, Epidemiology, Health, Late effects, Long-term follow-up, Quality of life},
}

@article{drenkhahn_waspweb_2023,
	title = {{WASP}—{A} {Web} {Application} to {Support} {Syntactically} and {Semantically} {Correct} {SNOMED} {CT} {Postcoordination}},
	volume = {13},
	issn = {2076-3417},
	url = {https://www.mdpi.com/2076-3417/13/10/6114},
	doi = {10.3390/app13106114},
	abstract = {Expressive clinical terminologies are of utmost importance for achieving a semantically interoperable data exchange and reuse in healthcare. SNOMED CT, widely respected as the most comprehensive terminology in medicine, provides formal concept definitions based on description logic which not only allows for advanced querying of SNOMED-CT-coded data but also for flexibly augmenting its 350,000 concepts by allowing a controlled combination of these. This ability for postcoordination largely increases the expressivity of the terminology but correlates with an intrinsic complexity. Complicated by the current lack of tooling support, postcoordination is widely either ignored or applied in an error-prone way. To help facilitate the adoption of postcoordination, we implemented a web application that guides users through the creation of postcoordinated expressions (PCEs) while ensuring adherence to syntactic and semantic constraints. Our approach was largely facilitated by making use of the extensive SNOMED CT specifications as well as advanced HL7 FHIR Terminology Services. Qualitative evaluations confirmed the usability of the developed application and the correctness of the PCEs created with it.},
	language = {en},
	number = {10},
	urldate = {2023-05-22},
	journal = {Applied Sciences},
	author = {Drenkhahn, Cora and Ohlsen, Tessa and Wiedekopf, Joshua and Ingenerf, Josef},
	month = may,
	year = {2023},
	pages = {6114},
}

@article{wiedekopf_termicron_2022,
	title = {{TermiCron} - {Bridging} the {Gap} {Between} {FHIR} {Terminology} {Servers} and {Metadata} {Repositories}},
	volume = {290},
	issn = {1879-8365},
	url = {https://doi.org/10.3233/SHTI220034},
	doi = {10.3233/SHTI220034},
	abstract = {The large variability of data models, specifications, and interpretations of data elements is particular to the healthcare domain. Achieving semantic interoperability is the first step to enable reuse of healthcare data. To ensure interoperability, metadata repositories (MDR) are increasingly used to manage data elements on a structural level, while terminology servers (TS) manage the ontologies, terminologies, coding systems and value sets on a semantic level. In practice, however, this strict separation is not always followed; instead, semantical information is stored and maintained directly in the MDR, as a link between both systems is missing. This may be reasonable up to a certain level of complexity, but it quickly reaches its limitations with increasing complexity. The goal of this approach is to combine both components in a compatible manner. We present TermiCron, a synchronization engine that provides synchronized value sets from TS in MDRs, including versioning and annotations. Prototypical results were shown for the terminology server Ontoserver and two established MDR systems. Bridging the semantic and structural gap between the two infrastructure components, this approach enables shared use of metadata and reuse of corresponding health information by establishing a clear separation of the two systems and thus serves to strengthen reuse as well as to increase quality.},
	language = {eng},
	journal = {Studies in Health Technology and Informatics},
	author = {Wiedekopf, Joshua and Ulrich, Hannes and Drenkhahn, Cora and Kock-Schoppenhauer, Ann-Kristin and Ingenerf, Josef},
	month = jun,
	year = {2022},
	pmid = {35672973},
	keywords = {Delivery of Health Care, HL7 FHIR, Health Facilities, Metadata, Metadata Repository, Secondary Use, Semantics},
	pages = {71--75},
}

@article{ohlsen_mapping_2022,
	title = {Mapping of {ICD}-{O} {Tuples} to {OncoTree} {Codes} {Using} {SNOMED} {CT} {Post}-{Coordination}},
	volume = {294},
	issn = {1879-8365},
	url = {https://doi.org/10.3233/SHTI220464},
	doi = {10.3233/SHTI220464},
	abstract = {Around 500,000 oncological diseases are diagnosed in Germany every year which are documented using the International Classification of Diseases for Oncology (ICD-O). Apart from this, another classification for oncology, OncoTree, is often used for the integration of new research findings in oncology. For this purpose, a semi-automatic mapping of ICD-O tuples to OncoTree codes was developed. The implementation uses a FHIR terminology server, pre-coordinated or post-coordinated SNOMED CT expressions, and subsumption testing. Various validations have been applied. The results were compared with reference data of scientific papers and manually evaluated by a senior pathologist, confirming the applicability of SNOMED CT in general and its post-coordinated expressions in particular as a viable intermediate mapping step. Resulting in an agreement of 84,00 \% between the newly developed approach and the manual mapping, it becomes obvious that the present approach has the potential to be used in everyday medical practice.},
	language = {eng},
	journal = {Studies in Health Technology and Informatics},
	author = {Ohlsen, Tessa and Kruse, Valerie and Krupar, Rosemarie and Banach, Alexandra and Ingenerf, Josef and Drenkhahn, Cora},
	month = may,
	year = {2022},
	pmid = {35612082},
	keywords = {Germany, HL7 FHIR, ICD-O, International Classification of Diseases, Medical Oncology, OncoTree, Ontoserver, SNOMED CT, Systematized Nomenclature of Medicine, post-coordination, terminology server},
	pages = {307--311},
}

@article{wiedekopf_terminodiff_2022,
	title = {{TerminoDiff} - {Detecting} {Semantic} {Differences} in {HL7} {FHIR} {CodeSystems}},
	volume = {294},
	issn = {1879-8365},
	url = {https://doi.org/10.3233/SHTI220475},
	doi = {10.3233/SHTI220475},
	abstract = {While HL7 FHIR and its terminology package have seen a rapid uptake by the research community, in no small part due to the wide availability of tooling and resources, there are some areas where tool availability is still lacking. In particular, the comparison of terminological resources, which supports the work of terminologists and implementers alike, has not yet been sufficiently addressed. Hence, we present TerminoDiff, an application to semantically compare FHIR R4 CodeSystem resources. Our tool considers differences across all levels required, i.e. metadata and concept differences, as well as differences in the edge graph, and surfaces them in a visually digestible fashion.},
	language = {eng},
	journal = {Studies in Health Technology and Informatics},
	author = {Wiedekopf, Joshua and Drenkhahn, Cora and Rosenau, Lorenz and Ulrich, Hannes and Kock-Schoppenhauer, Ann-Kristin and Ingenerf, Josef},
	month = may,
	year = {2022},
	pmid = {35612097},
	keywords = {Delivery of Health Care, Drug Packaging, Electronic Health Records, HL7 FHIR, Health Information Exchange, Health Level Seven, Metadata, Semantics, Terminology as Topic, Vocabulary},
	pages = {362--366},
}

@article{gruendner_architecture_2022,
	title = {Architecture for a feasibility query portal for distributed {COVID}-19 {Fast} {Healthcare} {Interoperability} {Resources} ({FHIR}) patient data repositories: {Design} and {Implementation} {Study}},
	issn = {2291-9694},
	shorttitle = {Architecture for a feasibility query portal for distributed {COVID}-19 {Fast} {Healthcare} {Interoperability} {Resources} ({FHIR}) patient data repositories},
	url = {https://doi.org/10.2196/36709},
	doi = {10.2196/36709},
	abstract = {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 securely access local data. Working across hospitals usually also means working with different data formats and vocabularies. Recently, the Fast Healthcare Interoperability Resources (FHIR) standard has been developed by HL7 to address this concern and describe patient data in a standardized format. The Medical Informatics Initiative (MII) 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 describes the design and implementation of the components involved in creating a cross-hospital feasibility query platform for researchers 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 analyzed 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 FHIR feasibility query 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). A performance test using specifically created synthetic data revealed the applicability of our solution to datasets containing millions of FHIR resources. The solution can be easily deployed across the hospitals and supports feasibility queries, combining multiple inclusion 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 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.
CLINICALTRIAL:},
	language = {eng},
	journal = {JMIR medical informatics},
	author = {Gruendner, Julian and Deppenwiese, Noemi and Folz, Michael and Köhler, Thomas and Kroll, Björn and Prokosch, Hans-Ulrich and Rosenau, Lorenz and Rühle, Mathias and Scheidl, Marc-Anton and Schüttler, Christina and Sedlmayr, Brita and Twrdik, Alexander and Kiel, Alexander and Majeed, Raphael W.},
	month = apr,
	year = {2022},
	pmid = {35486893},
}

@article{rosenau_generation_2022,
	title = {Generation of a {Fast} {Healthcare} {Interoperability} {Resources} ({FHIR})-based {Ontology} for {Federated} {Feasibility} {Queries} in the {Context} of {COVID}-19: {Feasibility} {Study}},
	volume = {10},
	issn = {2291-9694},
	shorttitle = {Generation of a {Fast} {Healthcare} {Interoperability} {Resources} ({FHIR})-based {Ontology} for {Federated} {Feasibility} {Queries} in the {Context} of {COVID}-19},
	url = {https://doi.org/10.2196/35789},
	doi = {10.2196/35789},
	abstract = {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 and a terminology server, making it extendable to other FHIR profiles and demonstrating that automatic ontology generation on FHIR profiles is feasible.},
	language = {eng},
	number = {4},
	journal = {JMIR medical informatics},
	author = {Rosenau, Lorenz and Majeed, Raphael W. and Ingenerf, Josef and Kiel, Alexander and Kroll, Björn and Köhler, Thomas and Prokosch, Hans-Ulrich and Gruendner, Julian},
	month = apr,
	year = {2022},
        pmid = {35380548},
        pages = {e35789},
}

@article{bastian_ubtfatxn7l3_2022,
	title = {{UBTF}::{ATXN7L3} gene fusion defines novel {B} cell precursor {ALL} subtype with {CDX2} expression and need for intensified treatment},
	issn = {0887-6924, 1476-5551},
	shorttitle = {{UBTF}},
	url = {https://www.nature.com/articles/s41375-022-01557-6},
	doi = {10.1038/s41375-022-01557-6},
	language = {en},
	urldate = {2022-04-12},
	journal = {Leukemia},
	author = {Bastian, Lorenz and Hartmann, Alina M. and Beder, Thomas and Hänzelmann, Sonja and Kässens, Jan and Bultmann, Miriam and Hoeppner, Marc P. and Franzenburg, Sören and Wittig, Michael and Franke, Andre and Nagel, Inga and Spielmann, Malte and Reimer, Niklas and Busch, Hauke and Schwartz, Stefan and Steffen, Björn and Viardot, Andreas and Döhner, Konstanze and Kondakci, Mustafa and Wulf, Gerald and Wendelin, Knut and Renzelmann, Andrea and Kiani, Alexander and Trautmann, Heiko and Neumann, Martin and Gökbuget, Nicola and Brüggemann, Monika and Baldus, Claudia D.},
	month = apr,
	year = {2022},
}

@article{unberath_searching_2022,
	title = {Searching of {Clinical} {Trials} {Made} {Easier} in {cBioPortal} {Using} {Patients}' {Genetic} and {Clinical} {Profiles}},
	volume = {13},
	issn = {1869-0327},
	url = {http://www.thieme-connect.de/DOI/DOI?10.1055/s-0042-1743560},
	doi = {10.1055/s-0042-1743560},
	abstract = {Abstract
            Background Molecular tumor boards (MTBs) cope with the complexity of an increased usage of genome sequencing data in cancer treatment. As for most of these patients, guideline-based therapy options are exhausted, finding matching clinical trials is crucial. This search process is often performed manually and therefore time consuming and complex due to the heterogeneous and challenging dataset.
            Objectives In this study, a prototype for a search tool was developed to demonstrate how cBioPortal as a clinical and genomic patient data source can be integrated with ClinicalTrials.gov, a database of clinical studies to simplify the search for trials based on genetic and clinical data of a patient. The design of this tool should rest on the specific needs of MTB participants and the architecture of the integration should be as lightweight as possible and should not require manual curation of trial data in advance with the goal of quickly and easily finding a matching study.
            Methods Based on a requirements analysis, interviewing MTB experts, a prototype was developed. It was further refined using a user-centered development process with multiple feedback loops. Finally, the usability of the application was evaluated with user interviews including the thinking-aloud protocol and the system usability scale (SUS) questionnaire.
            Results The integration of ClinicalTrials.gov in cBioPortal is achieved by a new tab in the patient view where the genomic profile for the search is prefilled and additional parameters can be adjusted. These parameters are then used to query the application programming interface (API) of ClinicalTrials.gov. The returned search results subsequently are ranked and presented to the user. The evaluation of the application resulted in an SUS score of 83.5.
            Conclusion This work demonstrates the integration of cBioPortal with ClinicalTrials.gov to use clinical and genomic patient data to search for appropriate trials within an MTB.},
	language = {en},
	number = {02},
	urldate = {2022-03-31},
	journal = {Applied Clinical Informatics},
	author = {Unberath, Philipp and Mahlmeister, Lukas and Reimer, Niklas and Busch, Hauke and Boerries, Melanie and Christoph, Jan},
	month = mar,
	year = {2022},
	pages = {363--369},
}

@article{vogl_luma_2022,
	title = {{LUMA}: {A} {Mapping} {Assistant} for {Standardizing} the {Units} of {LOINC}-{Coded} {Laboratory} {Tests}},
	volume = {12},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {2076-3417},
	shorttitle = {{LUMA}},
	url = {https://www.mdpi.com/2076-3417/12/12/5848},
	doi = {10.3390/app12125848},
	abstract = {The coding system Unified Code for Units of Measure (UCUM) serves the unambiguous electronic communication of physical quantities and their measurements and has faced a slow uptake. Despite being closely related to popular healthcare standards such as LOINC, laboratories still majorly report results using proprietary unit terms. Currently available methods helping users create mappings between their units and UCUM are not flexible and automated enough to be of great use in trying to remedy this. We propose the “LOINC to UCUM Mapping Assistant” (LUMA) as a tool able to overcome the drawbacks of existing approaches while being more accessible even to inexperienced users. By mapping LOINC’s Property axis to representations within UCUM reflecting its semantics, we were able to formalize the association between the two. An HL7 FHIR back-end provides LUMA with UCUM unit recommendations sourced from existing lookup tables simply by providing it with a LOINC code. Additionally, the mappings users created may be used to perform unit conversions from proprietary units to UCUM. The tool was evaluated with five participants from the LADR laboratory network in Germany, who valued the streamlined approach to creating the mappings and particularly emphasized the utility of being able to perform unit conversions within the tool.},
	language = {en},
	number = {12},
	urldate = {2022-06-08},
	journal = {Applied Sciences},
	author = {Vogl, Kai and Ingenerf, Josef and Kramer, Jan and Chantraine, Christine and Drenkhahn, Cora},
	month = jan,
	year = {2022},
	note = {Number: 12
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {HL7 FHIR, LOINC, RELMA, REST, UCUM, mapping, standardization},
	pages = {5848},
}

@article{ulrich_proposal_2022,
	title = {Proposal of {Semantic} {Annotation} for {German} {Metadata} {Using} {Bidirectional} {Recurrent} {Neural} {Networks}},
	url = {https://ebooks.iospress.nl/doi/10.3233/SHTI220474},
	doi = {10.3233/SHTI220474},
	urldate = {2022-08-16},
	journal = {Challenges of Trustable AI and Added-Value on Health},
	author = {Ulrich, Hannes and Uzunova, Hristina and Handels, Heinz and Ingenerf, Josef},
	year = {2022},
	note = {Publisher: IOS Press},
	pages = {357--361},
}

@article{ulrich_understanding_2022,
	title = {Understanding the {Nature} of {Metadata}: {Systematic} {Review}},
	volume = {24},
	shorttitle = {Understanding the {Nature} of {Metadata}},
	url = {https://www.jmir.org/2022/1/e25440},
	doi = {10.2196/25440},
	abstract = {Background: Metadata are created to describe the corresponding data in a detailed and unambiguous way and is used for various applications in different research areas, for example, data identification and classification. However, a clear definition of metadata is crucial for further use. Unfortunately, extensive experience with the processing and management of metadata has shown that the term “metadata” and its use is not always unambiguous.
Objective: This study aimed to understand the definition of metadata and the challenges resulting from metadata reuse.
Methods: A systematic literature search was performed in this study following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for reporting on systematic reviews. Five research questions were identified to streamline the review process, addressing metadata characteristics, metadata standards, use cases, and problems encountered. This review was preceded by a harmonization process to achieve a general understanding of the terms used.
Results: The harmonization process resulted in a clear set of definitions for metadata processing focusing on data integration. The following literature review was conducted by 10 reviewers with different backgrounds and using the harmonized definitions. This study included 81 peer-reviewed papers from the last decade after applying various filtering steps to identify the most relevant papers. The 5 research questions could be answered, resulting in a broad overview of the standards, use cases, problems, and corresponding solutions for the application of metadata in different research areas.
Conclusions: Metadata can be a powerful tool for identifying, describing, and processing information, but its meaningful creation is costly and challenging. This review process uncovered many standards, use cases, problems, and solutions for dealing with metadata. The presented harmonized definitions and the new schema have the potential to improve the classification and generation of metadata by creating a shared understanding of metadata and its context.},
	language = {EN},
	number = {1},
	urldate = {2022-01-12},
	journal = {Journal of Medical Internet Research},
	author = {Ulrich, Hannes and Kock-Schoppenhauer, Ann-Kristin and Deppenwiese, Noemi and Gött, Robert and Kern, Jori and Lablans, Martin and Majeed, Raphael W. and Stöhr, Mark R. and Stausberg, Jürgen and Varghese, Julian and Dugas, Martin and Ingenerf, Josef},
	month = jan,
	year = {2022},
	note = {Company: Journal of Medical Internet Research
Distributor: Journal of Medical Internet Research
Institution: Journal of Medical Internet Research
Label: Journal of Medical Internet Research
Publisher: JMIR Publications Inc., Toronto, Canada},
	keywords = {Humans, Metadata, Publications, Reference Standards, data classification, data identification, data integration, metadata, metadata definition, systematic review},
	pages = {e25440},
}

@inproceedings{banach_benefits_2021,
	address = {Kiel},
	title = {Benefits of {MII} {Core} {Dataset} and {HL7} {FHIR}-{Based} {Tooling} for {Automated} {Recruiting} {Purposes}},
	copyright = {This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License.},
	doi = {10.3205/21gmds027},
	language = {engl},
	publisher = {German Medical Science GMS Publishing House},
	author = {Banach, Alexandra and Ulrich, Hannes and Kroll, Björn and Kiel, Alexander and Ingenerf, Josef and Kock-Schoppenhauer, Ann-Kristin},
	month = sep,
	year = {2021},
	keywords = {CQL, FHIR, NLP, clinical trials, patient recruitment},
	pages = {DocAbstr. 125},
}

@article{ulrich_hands_2021,
	title = {Hands on the {Medical} {Informatics} {Initiative} {Core} {Data} {Set} - {Lessons} {Learned} from {Converting} the {MIMIC}-{IV}},
	volume = {283},
	issn = {1879-8365},
	doi = {10.3233/SHTI210549},
	abstract = {With the steady increase in the connectivity of the healthcare system, new requirements and challenges are emerging. In addition to the seamless exchange of data between service providers on a national level, the local legacy data must also meet the new requirements. For this purpose, the applications used must be tested securely and sufficiently. However, the availability of suitable and realistic test data is not always given. Therefore, this study deals with the creation of test data based on real electronic health record data provided by the Medical Information Mart for Intensive Care (MIMIC-IV) database. In addition to converting the data to the current FHIR R4, conversion to the core data sets of the German Medical Informatics Initiative was also presented and made available. The test data was generated to simulate a legacy data transfer. Moreover, four different FHIR servers were tested for performance. This study is the first step toward comparable test scenarios around shared datasets and promotes comparability among providers on a national level.},
	language = {eng},
	journal = {Studies in Health Technology and Informatics},
	author = {Ulrich, Hannes and Behrend, Paul and Wiedekopf, Joshua and Drenkhahn, Cora and Kock-Schoppenhauer, Ann-Kristin and Ingenerf, Josef},
	month = sep,
	year = {2021},
	pmid = {34545827},
	keywords = {Data Integration, Delivery of Health Care, Electronic Health Records, HL7 FHIR, Information Dissemination, MIMIC-IV, Medical Informatics},
	pages = {119--126},
}