Background:The standard Fast Healthcare Interoperability Resources (FHIR) is widely used in health information technology. However, its use as a standard for health research is still less prevalent. To use existing data sources more efficiently for health research, data interoperability becomes increasingly important. FHIR provides solutions by offering resource domains such as "Public Health & Research" and "Evidence-Based Medicine" while using already established web technologies. Therefore, FHIR could help standardize data across different data sources and improve interoperability in health research. Objective:The aim of our study was to provide a systematic review of existing literature and determine the current state of FHIR implementations in health research and possible future directions. Methods:We searched the PubMed/MEDLINE, Embase, Web of Science, IEEE Xplore, and Cochrane Library databases for studies published from 2011 to 2022. Studies investigating the use of FHIR in health research were included. Articles published before 2011, abstracts, reviews, editorials, and expert opinions were excluded. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and registered this study with PROSPERO (CRD42021235393). Data synthesis was done in tables and figures. Results:We identified a total of 998 studies, of which 49 studies were eligible for inclusion. Of the 49 studies, most (73%, n=36) covered the domain of clinical research, whereas the remaining studies focused on public health or epidemiology (6%, n=3) or did not specify their research domain (20%, n=10). Studies used FHIR for data capture (29%, n=14), standardization of data (41%, n=20), analysis (12%, n=6), recruitment (14%, n=7), and consent management (4%, n=2). Most (55%, 27/49) of the studies had a generic approach, and 55% (12/22) of the studies focusing on specific medical specialties (infectious disease, genomics, oncology, environmental health, imaging, and pulmonary hypertension) reported their solutions to be conferrable to other use cases. Most (63%, 31/49) of the studies reported using additional data models or terminologies: Systematized Nomenclature of Medicine Clinical Terms (29%, n=14), Logical Observation Identifiers Names and Codes (37%, n=18), International Classification of Diseases 10th Revision (18%, n=9), Observational Medical Outcomes Partnership common data model (12%, n=6), and others (43%, n=21). Only 4 (8%) studies used a FHIR resource from the domain "Public Health & Research." Limitations using FHIR included the possible change in the content of FHIR resources, safety, legal matters, and the need for a FHIR server. Conclusions:Our review found that FHIR can be implemented in health research, and the areas of application are broad and generalizable in most use cases. The implementation of international terminologies was common, and other standards such as the Observational Medical Outcomes Partnership common data model could be used as a complement to FHIR. Limitations such...

BACKGROUND The standard Fast Healthcare Interoperability Resources (FHIR) is widely used in health information technology. However, its use as a standard for health research is still less prevalent. To use existing data sources more efficiently for health research, data interoperability becomes increasingly important. FHIR provides solutions by offering resource domains such as “Public Health & Research” and “Evidence-Based Medicine” while using already established web technologies. Therefore, FHIR could help to standardize data across different data sources and improve interoperability in health research. OBJECTIVE The aim of our study was to provide a systematic review of existing literature and determine the current state of FHIR implementations in health research and possible future directions. METHODS We searched PubMed/Medline, EMBASE, Web of Science, IEEE Xplore and the Cochrane Library databases for studies published from 2010 to 2021. Studies investigating the use of FHIR in health research were included. Articles published before 2010, abstracts, reviews, editorials and expert opinions were excluded. We followed the PRISMA guidelines and registered this study with PROSPERO, CRD42021235393. Data synthesis was done in tables and figures. RESULTS We identified a total of 674 studies, of which 28 studies were eligible for inclusion. Most studies covered the domain of clinical research (22/28) while the remaining studies focused on public health/ epidemiology (3/28) or did not specify their research domain (3/28). Studies used FHIR for data capture (11/28), standardization of data (7/28), analysis (4/28), recruitment (4/28) and consent management (2/28). Most studies had a generic approach (15/28) and nine of 13 studies focusing on specific medical specialties (infectious disease, genomics, oncology, environmental health, imaging, pulmonary hypertension) reported their solutions to be conferrable to other use cases. Half of the studies reported using additional data models or terminologies: SNOMED CT (8/14), LOINC (8/14), ICD-10 (6/14), OMOP CDM (3/14) and others (9/14). Only one study used a FHIR resource from the domain “Public Health & Research”. Limitations using FHIR included the possible change in the content of FHIR resources, safety and legal matters and the need for a FHIR server. CONCLUSIONS Our review found that FHIR can be implemented in health research and that the areas of application are broad and generalizable in most use cases. Implementation of international terminologies was common and other standards such as OMOP CDM could be used complementary with FHIR. Limitations such as change of FHIR content, lack of FHIR implementation, safety and legal matters need to be addressed in future releases to expand the use of FHIR and therefore interoperability in health research.

Background: Tricuspid regurgitation (TR) after left ventricular assist device (LVAD) implantation is associated with a poor prognosis. This study evaluates the development of TR and right ventricular (RV) performance after LVAD implantation. Methods: Retrospective analysis of patients who underwent LVAD implantation between March 2018 and June 2019. Patients who underwent concomitant tricuspid valve surgery and patients with congenital heart disease were excluded.Results: A total of 155 patients underwent LVAD implantation. Fourteen patients were excluded. Of the remaining patients, thirty-one died during the first six months, six were lost to follow-up and two underwent transplantation. 102 patients presented at 6.3 months (5.8 to 7.0). Patients were supported with HeartWare HVAD (74%) or HeartMate 3 (26%). 50.4% were rated as INTERMACS profile 1 or 2. At six months, systolic pulmonary artery pressure dropped from 36 to 21 mmHg (P<0.001). Tricuspid annular plane systolic excursion decreased from 17.3 to 14.3 mm (P<0.001), RV fractional area change did not change (P=0.839).

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BackgroundThe COVID-19 pandemic has spurred large-scale, inter-institutional research efforts. To enable these efforts, the German Corona Consensus (GECCO) dataset has been developed previously as a harmonized, interoperable collection of the most relevant data elements for COVID-19-related patient research. As GECCO has been developed as a compact core dataset across all medical fields, the focused research within particular medical domains demanded the definition of extension modules that include those data elements that are most relevant to the research performed in these individual medical specialties.Main bodyWe created GECCO extension modules for the immunization, pediatrics, and cardiology domains with respect to the pandemic requests. The data elements included in each of these modules were selected in a consensus-based process by working groups of medical experts from the respective specialty to ensure that the contents are aligned with the research needs of the specialty. The selected data elements were mapped to international standardized vocabularies and data exchange specifications were created using HL7 FHIR profiles on the appropriate resources. All steps were performed in close interdisciplinary collaboration between medical domain experts, medical information scientists and FHIR developers. The profiles and vocabulary mappings were syntactically and semantically validated in a two-stage process. In that way, we defined dataset specifications for a total number of 23 (immunization), 59 (pediatrics), and 50 (cardiology) data elements that augment the GECCO core dataset. We created and published implementation guides and example implementations as well as dataset annotations for each extension module.ConclusionsWe here present extension modules for the GECCO core dataset that contain data elements most relevant to COVID-19-related patient research in immunization, pediatrics and cardiology. These extension modules were defined in an interdisciplinary, iterative, consensus-based approach that may serve as a blueprint for the development of further dataset definitions and GECCO extension modules. The here developed GECCO extension modules provide a standardized and harmonized definition of specialty-related datasets that can help to enable inter-institutional and cross-country COVID-19 research in these specialties.