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 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.

Establishing the common oncology data model for the national biobank consortium of Taiwan.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e19283-e19283
Author(s):  
Yang Yi-Hsin ◽  
Li-Tzong Chen ◽  
Shiu-Feng Huang
Keyword(s):  
National Health ◽  
Real World ◽  
Data Model ◽  
Data Science ◽  
Common Data Model ◽  
Data Interoperability ◽  
Cancer Data ◽  
Common Definition ◽  
The Common ◽  
Data Elements

e19283 Background: Taiwan has 32 biobanks under Government’ governance. The Ministry of Health and Welfare have established a National Biobank Consortium of Taiwan to unify the specimen quality and the medical record database. The total recruited participants exceed 350,000. The National Health Research Institutes in Taiwan hold the responsibility of establish a common data model for aggregating data elements from electronic health records (EHRs) of institutes through direct feeds. The goals are to assemble a set of common oncology data elements and to facilitate cancer data interoperability for patient care and research across institutes of Biobank Consortium. Methods: We first conduct a thorough review of available EHR data elements for patient characteristics, diagnosis/staging, treatments, laboratory results, vital signs and outcomes. The data dictionary was organized based on HL7 FHIR and also included data elements from Taiwan Cancer Registry (TCR) and National Health Insurance (NHI) Program, which the common definition has already been established and implemented for years. Data elements suggested by ASCO CancerLinQ and minimal Common Oncology Data Elements (mCODE) are also referenced during planning. The final common model was then reviewed by a panel of experts consisting oncologists as well as data science specialists. Results: There are finally 9 data tables with 281 data elements, in which 248 of them are from the routinely uploaded data elements to government agencies (TCR & NHI) and 33 elements are collected with partial common definition among institutes. There are 164 data elements which are to be collected one observation per case, while 117 elements will be accumulated periodically. Conclusions: A comprehensive understanding of genetics, phenotypes, disease variation as well as treatment responses is crucial to fulfill the needs of real-world studies, which potentially would lead to personalized treatment and drug development. At the first stage of this project, we aim to accumulate available EHR structured data elements and to maintain sufficient cancer data quality. Consequently, the database can provide real-world evidence to promote evidence-based & data-driven cancer care.

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.

OBDMR: An Ontology-Based Data Model for Railways

2020 ◽  
Vol 17 (1) ◽  
pp. 273-283
Author(s):  
Kumar Abhishek ◽  
M. P. Singh ◽  
Deepika Shukla ◽  
Sachin Gupta
Keyword(s):  
Data Model ◽  
Data Exchange ◽  
Current Data ◽  
Generic Model ◽  
Data Interoperability ◽  
Data Resource ◽  
Railway Systems ◽  
New Information ◽  
Railway System ◽  
The Given

The domain of the railway system is vast and complex since it includes several sub-domains hierarchy in it. These sub-domains include different branches of technology and operational hierarchy. Many types of research are running on and have happened in this vast domain along with different technologies. Among all available technologies ontology is the single one which talks about semantics and thus supports the decision support system. This paper proposes an OBDMR model for railway systems to integrate the information at the knowledge level. The paper has used railML (version 2.2) as a data resource as railML covers all the aspects of the railway system. railML (Railway Mark-up Language) is an open, XML-based data exchange format for data interoperability of railway application. The proposed ontology adds the semantics to the given data and even allows to infer new information from current data which XML cannot do. OBDMR is capable of taking decisions by automated reasoning using software agents. A generic model proposed in this paper satiates the standards and specifications of most countries’ railway systems. A use-case for Indian Railways is discussed with some examples.

scholarly journals The IeDEA Data Exchange Standard: a common data model for global HIV cohort collaboration

2020 ◽  
Author(s):  
Stephany N Duda ◽  
Beverly S Musick ◽  
Mary-Ann Davies ◽  
Annette H Sohn ◽  
Bruno Ledergerber ◽  
...  
Keyword(s):  
Data Model ◽  
Data Exchange ◽  
Governance Structure ◽  
Data Models ◽  
Hiv Care ◽  
Common Data Model ◽  
Resource Limited Settings ◽  
Resource Limited ◽  
Hiv Research ◽  
Data Exchange Standard

Objective To describe content domains and applications of the IeDEA Data Exchange Standard, its development history, governance structure, and relationships to other established data models, as well as to share open source, reusable, scalable, and adaptable implementation tools with the informatics community. Methods In 2012, the International Epidemiology Databases to Evaluate AIDS (IeDEA) collaboration began development of a data exchange standard, the IeDEA DES, to support collaborative global HIV epidemiology research. With the HIV Cohorts Data Exchange Protocol as a template, a global group of data managers, statisticians, clinicians, informaticians, and epidemiologists reviewed existing data schemas and clinic data procedures to develop the HIV data exchange model. The model received a substantial update in 2017, with annual updates thereafter. Findings The resulting IeDEA DES is a patient-centric common data model designed for HIV research that has been informed by established data models from US-based electronic health records, broad experience in data collection in resource-limited settings, and informatics best practices. The IeDEA DES is inherently flexible and continues to grow based on the ongoing stewardship of the IeDEA Data Harmonization Working Group with input from external collaborators. Use of the IeDEA DES has improved multiregional collaboration within and beyond IeDEA, expediting over 95 multiregional research projects using data from more than 400 HIV care and treatment sites across seven global regions. A detailed data model specification and REDCap data entry templates that implement the IeDEA DES are publicly available on GitHub. Conclusions The IeDEA common data model and related resources are powerful tools to foster collaboration and accelerate science across research networks. While currently directed towards observational HIV research and data from resource-limited settings, this model is flexible and extendable to other areas of health research.

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.

The Tissue Microarray Object Model: A Data Model for Storage, Analysis, and Exchange of Tissue Microarray Experimental Data

2006 ◽  
Vol 130 (7) ◽  
pp. 1004-1013
Author(s):  
Hye Won Lee ◽  
Yu Rang Park ◽  
Jaehyun Sim ◽  
Rae Woong Park ◽  
Woo Ho Kim ◽  
...  
Keyword(s):  
Gene Expression ◽  
Experimental Data ◽  
Tissue Microarray ◽  
Data Model ◽  
Data Exchange ◽  
Object Model ◽  
Microarray Gene Expression ◽  
Common Data Elements ◽  
Microarray Gene ◽  
Data Elements

Abstract Context.—Tissue microarray (TMA) is an array-based technology allowing the examination of hundreds of tissue samples on a single slide. To handle, exchange, and disseminate TMA data, we need standard representations of the methods used, of the data generated, and of the clinical and histopathologic information related to TMA data analysis. Objective.—To create a comprehensive data model with flexibility that supports diverse experimental designs and with expressivity and extensibility that enables an adequate and comprehensive description of new clinical and histopathologic data elements. Design.—We designed a tissue microarray object model (TMA-OM). Both the array information and the experimental procedure models are created by referring to the microarray gene expression object model, minimum information specification for in situ hybridization and immunohistochemistry experiments, and the TMA data exchange specifications. The clinical and histopathologic information model is created by using College of American Pathologists cancer protocols and National Cancer Institute common data elements. Microarray Gene Expression Data Ontology, the Unified Medical Language System, and the terms extracted from College of American Pathologists cancer protocols and NCI common data elements are used to create a controlled vocabulary for unambiguous annotation. Result.—The TMA-OM consists of 111 classes in 17 packages to represent clinical and histopathologic information as well as experimental data for any type of cancer. We implemented a Web-based application for TMA-OM, supporting data export in XML format conforming to the TMA data exchange specifications or the document type definition derived from TMA-OM. Conclusions.—The TMA-OM provides a comprehensive data model for storage, analysis, and exchange of TMA data and facilitates model-level integration of other biological models.

scholarly journals Patient Cohort Identification on Time Series Data Using the OMOP Common Data Model

2021 ◽  
Vol 12 (01) ◽  
pp. 057-064
Author(s):  
Christian Maier ◽  
Lorenz A. Kapsner ◽  
Sebastian Mate ◽  
Hans-Ulrich Prokosch ◽  
Stefan Kraus
Keyword(s):  
Data Model ◽  
Time Series Data ◽  
Series Data ◽  
Common Data Model ◽  
Eligibility Criteria ◽  
Data Set ◽  
Cohort Identification ◽  
Data Elements ◽  
Clinical Source ◽  
Arden Syntax

Abstract Background 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 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 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 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 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.

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