scholarly journals The German Corona Consensus Dataset (GECCO): A standardized dataset for COVID-19 research

2020 ◽  
Author(s):  
Julian Sass ◽  
Alexander Bartschke ◽  
Moritz Lehne ◽  
Andrea Essenwanger ◽  
Eugenia Rinaldi ◽  
...  
Keyword(s):  
Data Exchange ◽  
Professional Associations ◽  
Case Report Form ◽  
Snomed Ct ◽  
University Hospitals ◽  
Research Activity ◽  
Response Options ◽  
Core Dataset ◽  
Icd 10 ◽  
Data Elements

Background: The current COVID-19 pandemic has led to a surge of research activity. While this research provides important insights, the multitude of studies results in an increasing segmentation of information. To ensure comparability across projects and institutions, standard datasets are needed. Here, we introduce the "German Corona Consensus Dataset" (GECCO), a uniform dataset that uses international terminologies and health IT standards to improve interoperability of COVID-19 data. Methods: Based on previous work (e.g., the ISARIC-WHO COVID-19 case report form) and in coordination with experts from university hospitals, professional associations and research initiatives, data elements relevant for COVID-19 research were collected, prioritized and consolidated into a compact core dataset. The dataset was mapped to international terminologies, and the Fast Healthcare Interoperability Resources (FHIR) standard was used to define interoperable, machine-readable data formats. Results: A core dataset consisting of 81 data elements with 281 response options was defined, including information about, for example, demography, anamnesis, symptoms, therapy, medications or laboratory values of COVID-19 patients. Data elements and response options were mapped to SNOMED CT, LOINC, UCUM, ICD-10-GM and ATC, and FHIR profiles for interoperable data exchange were defined. Conclusion: GECCO provides a compact, interoperable dataset that can help to make COVID-19 research data more comparable across studies and institutions. The dataset will be further refined in the future by adding domain-specific extension modules for more specialized use cases.

scholarly journals The German Corona Consensus Dataset (GECCO): A standardized dataset for COVID-19 research

2020 ◽  
Author(s):  
Sylvia Thun ◽  
Julian Sass ◽  
Alexander Bartschke ◽  
Moritz Lehne ◽  
Andrea Essenwanger ◽  
...  
Keyword(s):  
Data Exchange ◽  
Professional Associations ◽  
Case Report Form ◽  
Snomed Ct ◽  
University Hospitals ◽  
Research Activity ◽  
Response Options ◽  
Core Dataset ◽  
Icd 10 ◽  
Data Elements

Abstract Background: The current COVID-19 pandemic has led to a surge of research activity. While this research provides important insights, the multitude of studies results in an increasing segmentation of information. To ensure comparability across projects and institutions, standard datasets are needed. Here, we introduce the “German Corona Consensus Dataset” (GECCO), a uniform dataset that uses international terminologies and health IT standards to improve interoperability of COVID-19 data.Methods: Based on previous work (e.g., the ISARIC-WHO COVID-19 case report form) and in coordination with experts from university hospitals, professional associations and research initiatives, data elements relevant for COVID-19 research were collected, prioritized and consolidated into a compact core dataset. The dataset was mapped to international terminologies, and the Fast Healthcare Interoperability Resources (FHIR) standard was used to define interoperable, machine-readable data formats.Results: A core dataset consisting of 81 data elements with 281 response options was defined, including information about, for example, demography, anamnesis, symptoms, therapy, medications or laboratory values of COVID-19 patients. Data elements and response options were mapped to SNOMED CT, LOINC, UCUM, ICD-10-GM and ATC, and FHIR profiles for interoperable data exchange were defined.Conclusion: GECCO provides a compact, interoperable dataset that can help to make COVID-19 research data more comparable across studies and institutions. The dataset will be further refined in the future by adding domain-specific extension modules for more specialized use cases.

scholarly journals The German Corona Consensus Dataset (GECCO): A standardized dataset for COVID-19 research in university medicine and beyond

2020 ◽  
Author(s):  
Julian Sass ◽  
Alexander Bartschke ◽  
Moritz Lehne ◽  
Andrea Essenwanger ◽  
Eugenia Rinaldi ◽  
...  
Keyword(s):  
Data Exchange ◽  
Professional Associations ◽  
Case Report Form ◽  
Snomed Ct ◽  
University Hospitals ◽  
Research Activity ◽  
Response Options ◽  
University Medicine ◽  
Core Dataset ◽  
Data Elements

Abstract Background: The current COVID-19 pandemic has led to a surge of research activity. While this research provides important insights, the multitude of studies results in an increasing fragmentation of information. To ensure comparability across projects and institutions, standard datasets are needed. Here, we introduce the “German Corona Consensus Dataset” (GECCO), a uniform dataset that uses international terminologies and health IT standards to improve interoperability of COVID-19 data, in particular for university medicine. Methods: Based on previous work (e.g., the ISARIC-WHO COVID-19 case report form) and in coordination with experts from university hospitals, professional associations and research initiatives, data elements relevant for COVID-19 research were collected, prioritized and consolidated into a compact core dataset. The dataset was mapped to international terminologies, and the Fast Healthcare Interoperability Resources (FHIR) standard was used to define interoperable, machine-readable data formats. Results: A core dataset consisting of 81 data elements with 281 response options was defined, including information about, for example, demography, medical history, symptoms, therapy, medications or laboratory values of COVID-19 patients. Data elements and response options were mapped to SNOMED CT, LOINC, UCUM, ICD-10-GM and ATC, and FHIR profiles for interoperable data exchange were defined. Conclusion: GECCO provides a compact, interoperable dataset that can help to make COVID-19 research data more comparable across studies and institutions. The dataset will be further refined in the future by adding domain-specific extension modules for more specialized use cases.

scholarly journals The German Corona Consensus Dataset (GECCO): a standardized dataset for COVID-19 research in university medicine and beyond

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Julian Sass ◽  
Alexander Bartschke ◽  
Moritz Lehne ◽  
Andrea Essenwanger ◽  
Eugenia Rinaldi ◽  
...  
Keyword(s):  
Data Exchange ◽  
Professional Associations ◽  
Case Report Form ◽  
Snomed Ct ◽  
University Hospitals ◽  
Research Activity ◽  
Response Options ◽  
University Medicine ◽  
Core Dataset ◽  
Data Elements

Abstract Background The current COVID-19 pandemic has led to a surge of research activity. While this research provides important insights, the multitude of studies results in an increasing fragmentation of information. To ensure comparability across projects and institutions, standard datasets are needed. Here, we introduce the “German Corona Consensus Dataset” (GECCO), a uniform dataset that uses international terminologies and health IT standards to improve interoperability of COVID-19 data, in particular for university medicine. Methods Based on previous work (e.g., the ISARIC-WHO COVID-19 case report form) and in coordination with experts from university hospitals, professional associations and research initiatives, data elements relevant for COVID-19 research were collected, prioritized and consolidated into a compact core dataset. The dataset was mapped to international terminologies, and the Fast Healthcare Interoperability Resources (FHIR) standard was used to define interoperable, machine-readable data formats. Results A core dataset consisting of 81 data elements with 281 response options was defined, including information about, for example, demography, medical history, symptoms, therapy, medications or laboratory values of COVID-19 patients. Data elements and response options were mapped to SNOMED CT, LOINC, UCUM, ICD-10-GM and ATC, and FHIR profiles for interoperable data exchange were defined. Conclusion GECCO provides a compact, interoperable dataset that can help to make COVID-19 research data more comparable across studies and institutions. The dataset will be further refined in the future by adding domain-specific extension modules for more specialized use cases.

scholarly journals The German Corona Consensus Dataset (GECCO): A standardized dataset for COVID-19 research in university medicine and beyond

2020 ◽  
Author(s):  
Julian Sass ◽  
Alexander Bartschke ◽  
Moritz Lehne ◽  
Andrea Essenwanger ◽  
Eugenia Rinaldi ◽  
...  
Keyword(s):  
Data Exchange ◽  
Professional Associations ◽  
Case Report Form ◽  
Snomed Ct ◽  
University Hospitals ◽  
Research Activity ◽  
Response Options ◽  
University Medicine ◽  
Core Dataset ◽  
Data Elements

Abstract Background: The current COVID-19 pandemic has led to a surge of research activity. While this research provides important insights, the multitude of studies results in an increasing fragmentation of information. To ensure comparability across projects and institutions, standard datasets are needed. Here, we introduce the “German Corona Consensus Dataset” (GECCO), a uniform dataset that uses international terminologies and health IT standards to improve interoperability of COVID-19 data, in particular for university medicine.Methods: Based on previous work (e.g., the ISARIC-WHO COVID-19 case report form) and in coordination with experts from university hospitals, professional associations and research initiatives, data elements relevant for COVID-19 research were collected, prioritized and consolidated into a compact core dataset. The dataset was mapped to international terminologies, and the Fast Healthcare Interoperability Resources (FHIR) standard was used to define interoperable, machine-readable data formats.Results: A core dataset consisting of 81 data elements with 281 response options was defined, including information about, for example, demography, medical history, symptoms, therapy, medications or laboratory values of COVID-19 patients. Data elements and response options were mapped to SNOMED CT, LOINC, UCUM, ICD-10-GM and ATC, and FHIR profiles for interoperable data exchange were defined.Conclusion: GECCO provides a compact, interoperable dataset that can help to make COVID-19 research data more comparable across studies and institutions. The dataset will be further refined in the future by adding domain-specific extension modules for more specialized use cases.

scholarly journals From OpenEHR to FHIR and OMOP Data Model for Microbiology Findings

2021 ◽  
Author(s):  
Eugenia Rinaldi ◽  
Sylvia Thun
Keyword(s):  
Infection Control ◽  
Data Model ◽  
Data Exchange ◽  
Control Group ◽  
Common Data Model ◽  
University Hospitals ◽  
Data Set ◽  
Related Data ◽  
National Initiative ◽  
Data Elements

HiGHmed is a German Consortium where eight University Hospitals have agreed to the cross-institutional data exchange through novel medical informatics solutions. The HiGHmed Use Case Infection Control group has modelled a set of infection-related data in the openEHR format. In order to establish interoperability with the other German Consortia belonging to the same national initiative, we mapped the openEHR information to the Fast Healthcare Interoperability Resources (FHIR) format recommended within the initiative. FHIR enables fast exchange of data thanks to the discrete and independent data elements into which information is organized. Furthermore, to explore the possibility of maximizing analysis capabilities for our data set, we subsequently mapped the FHIR elements to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM). The OMOP data model is designed to support the conduct of research to identify and evaluate associations between interventions and outcomes caused by these interventions. Mapping across standard allows to exploit their peculiarities while establishing and/or maintaining interoperability. This article provides an overview of our experience in mapping infection control related data across three different standards openEHR, FHIR and OMOP CDM.

Semantic Interoperability in Czech Healthcare Environment Supported by HL7 Version 3

2010 ◽  
Vol 49 (02) ◽  
pp. 186-195 ◽  
Author(s):  
P. Hanzlícek ◽  
P. Precková ◽  
A. Ríha ◽  
M. Dioszegi ◽  
L. Seidl ◽  
...  
Keyword(s):  
Data Exchange ◽  
Semantic Interoperability ◽  
International Standards ◽  
National Standards ◽  
Snomed Ct ◽  
Healthcare Environment ◽  
Coding Systems ◽  
Information Models ◽  
Icd 10 ◽  
Implementation Guidelines

Summary Objectives: The data interchange in the Czech healthcare environment is mostly based on national standards. This paper describes a utilization of international standards and nomenclatures for building a pilot semantic interoperability platform (SIP) that would serve to exchange information among electronic health record systems (EHR-Ss) in Czech healthcare. The work was performed by the national research project of the “Information Society” program. Methods: At the beginning of the project a set of requirements the SIP should meet was formulated. Several communication standards (open EHR, HL7 v3, DICOM) were analyzed and HL7 v3 was selected to exchange health records in our solution. Two systems were included in our pilot environment: WinMedicalc 2000 and ADAMEKj EHR. Results: HL7-based local information models were created to describe the information content of both systems. The concepts from our original information models were mapped to coding systems supported by HL7 (LOINC, SNOMED CT and ICD-10) and the data exchange via HL7 v3 messages was implemented and tested by querying patient administration data. As a gateway between local EHR systems and the HL7 message-based infrastructure, a configurable HL7 Broker was developed. Conclusions: A nationwide implementation of a full-scale SIP based on HL7 v3 would include adopting and translating appropriate international coding systems and nomenclatures, and developing implementation guidelines facilitating the migration from national standards to international ones. Our pilot study showed that our approach is feasible but it would demand a huge effort to fully integrate the Czech healthcare system into the European e-health context.

scholarly journals Towards a Newborn Screening Common Data Model: The Utah Newborn Screening Data Model

2021 ◽  
Vol 7 (4) ◽  
pp. 70
Author(s):  
David Jones ◽  
Jianyin Shao ◽  
Heidi Wallis ◽  
Cody Johansen ◽  
Kim Hart ◽  
...  
Keyword(s):  
Newborn Screening ◽  
Data Model ◽  
Data Exchange ◽  
Healthcare Providers ◽  
Common Data Model ◽  
Snomed Ct ◽  
Electronic Data ◽  
Data Interoperability ◽  
Screening Programs ◽  
Data Elements

As newborn screening programs transition from paper-based data exchange toward automated, electronic methods, significant data exchange challenges must be overcome. This article outlines a data model that maps newborn screening data elements associated with patient demographic information, birthing facilities, laboratories, result reporting, and follow-up care to the LOINC, SNOMED CT, ICD-10-CM, and HL7 healthcare standards. The described framework lays the foundation for the implementation of standardized electronic data exchange across newborn screening programs, leading to greater data interoperability. The use of this model can accelerate the implementation of electronic data exchange between healthcare providers and newborn screening programs, which would ultimately improve health outcomes for all newborns and standardize data exchange across programs.

scholarly journals Towards Implementation of OMOP in a German University Hospital Consortium

2018 ◽  
Vol 09 (01) ◽  
pp. 054-061 ◽  
Author(s):  
C. Maier ◽  
L. Lang ◽  
H. Storf ◽  
P. Vormstein ◽  
R. Bieber ◽  
...  
Keyword(s):  
Data Integration ◽  
Large Data ◽  
University Hospital ◽  
Common Data Model ◽  
Ministry Of Education ◽  
University Hospitals ◽  
Core Element ◽  
Icd 10 ◽  
Pilot Implementation ◽  
Colorectal Cancer Patients

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.

Research ethics

2021 ◽  
pp. 57-82
Author(s):  
Aaron Williamon ◽  
Jane Ginsborg ◽  
Rosie Perkins ◽  
George Waddell
Keyword(s):  
Research Ethics ◽  
Professional Associations ◽  
Research Activity ◽  
Ethical Research ◽  
Ethical Approval ◽  
Music Research ◽  
Potential Benefits ◽  
Regulatory Bodies ◽  
Scientific Value ◽  
Selection Of

Chapter 3 of Performing Music Research explores the guiding principles on which ethical codes are based. These can be summarized as follows: people should not be harmed, nor their rights and dignity compromised, and research must be of scientific value and carried out with integrity. These issues must be considered and addressed in the earliest stages of research and in light of the potential benefits of the findings of the research to society. The chapter reflects on the philosophical underpinnings of ethical research and outlines the process whereby ethical approval is typically sought and obtained, with reference to a selection of codes of research ethics published by professional associations and regulatory bodies that guide and inform research activity.

scholarly journals 1123. Specificity of Diagnosis Codes and Adequacy of Supportive Documentation for Common Acute Pediatric Infections: Implications for Ambulatory Stewardship

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S399-S399
Author(s):  
Zachary Willis ◽  
Elizabeth Walters
Keyword(s):  
Acute Otitis Media ◽  
Antibiotic Prescribing ◽  
National Guidelines ◽  
Laboratory Findings ◽  
Healthcare Settings ◽  
Diagnosis Codes ◽  
Icd 10 ◽  
Pediatric Infections ◽  
Data Elements ◽  
Physical Findings

Abstract Background Assessing the appropriateness of antibiotic prescribing in ambulatory care generally relies on the accuracy of diagnosis codes, which is uncertain. It is also uncertain if documented history and physical findings support antibiotic indications (AI). We completed a retrospective study of pediatric primary care (PPC) encounters to determine: A) if documented findings supported documented AI; and B) whether diagnosis codes captured documented AI (figure). Methods We conducted point-prevalence audits of the 9 PPC clinics in our healthcare system, randomly selecting one weekday per month to review all visits between 9/2017 and 4/2018. We included only encounters with antibiotic prescribing. We reviewed clinician notes, orders, laboratory results, and ICD-10 diagnosis codes. We recorded demographics; visit date/location; AI as documented in notes; history, examination, and laboratory findings; and diagnosis codes. We used national guidelines to determine whether documentation supported AI. We calculated the sensitivity of diagnosis codes using documented AI as the gold standard. Results The sample included 452 encounters. The most common AI were acute otitis media (AOM), pharyngitis, and sinusitis. For AOM, 163 of 168 encounters (97.0%) had an appropriate diagnosis code; for pharyngitis, 127 of 138 (92.0%); and for sinusitis, 68 of 75 (90.7%). For AOM, 160 of 168 encounters (95.2%) had adequate documentation of supportive findings. For sinusitis, 44 of 75 encounters had adequate supporting history and/or examination findings (58.7%). For pharyngitis, while 135 of 139 (97.1%) had a positive streptococcal test, 104 of 139 (74.8%) had history and examination findings to support testing. Conclusion By chart review, we identified each AI and evaluated whether findings supported those AI. The sensitivity of diagnosis codes for AI ranged from 90.7–97.0% for common conditions; this result can inform the design of ambulatory stewardship programs. Only 74.8% of children treated for pharyngitis and 58.7% of children treated for sinusitis had sufficient supporting documentation. Use of discrete data elements alone (Figure 1) may result in overestimates of the proportion of children for whom antibiotics are appropriate. Further research is needed across healthcare settings. Disclosures All authors: No reported disclosures

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