Identification, administrative, and common clinical data structure for intermittently connected devices used in healthcare (including machine readable cards)

The acquisition, preparation and distribution of machine-readable astrometric data by the Astronomical Data Center (ADC) are described. Examples of certain general changes in data structure and format to improve compatability with other computers and software processing systems and to increase storage efficiency are discussed, as are the present data archive and request history of the ADC. Current development work in the areas of astrometric and positional catalogs is described.

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Development of an Open Metadata Schema for Prospective Clinical Research (openPCR) in China

Methods of Information in Medicine ◽

10.3414/me13-01-0008 ◽

2014 ◽

Vol 53 (01) ◽

pp. 39-46 ◽

Cited By ~ 2

Author(s):

Z. Guan ◽

J. Sun ◽

Z. Wang ◽

Y. Geng ◽

W. Xu

Keyword(s):

Data Structure ◽

Clinical Research ◽

Clinical Data ◽

Early Stage ◽

Data Entry ◽

Data Management System ◽

Case Report Form ◽

Functional Requirements ◽

The Core ◽

Metadata Schema

SummaryObjectives: In China, deployment of electronic data capture (EDC) and clinical data management system (CDMS) for clinical research (CR) is in its very early stage, and about 90% of clinical studies collected and submitted clinical data manually. This work aims to build an open metadata schema for Prospective Clinical Research (openPCR) in China based on openEHR archetypes, in order to help Chinese researchers easily create specific data entry templates for registration, study design and clinical data collection.Methods: Singapore Framework for Dublin Core Application Profiles (DCAP) is used to develop openPCR and four steps such as defining the core functional requirements and deducing the core metadata items, developing archetype models, defining metadata terms and creating archetype records, and finally developing implementation syntax are followed.Results: The core functional requirements are divided into three categories: requirements for research registration, requirements for trial design, and requirements for case report form (CRF). 74 metadata items are identified and their Chinese authority names are created. The minimum metadata set of openPCR includes 3 documents, 6 sections, 26 top level data groups, 32 lower data groups and 74 data elements. The top level container in openPCR is composed of public document, internal document and clinical document archetypes. A hierarchical structure of openPCR is established according to Data Structure of Electronic Health Record Architecture and Data Stand -ard of China (Chinese EHR Standard). Meta-data attributes are grouped into six parts: identification, definition, representation, relation, usage guides, and administration.Discussions and Conclusion: OpenPCR is an open metadata schema based on research registration standards, standards of the Clinical Data Interchange Standards Consortium (CDISC) and Chinese healthcare related stand -ards, and is to be publicly available throughout China. It considers future integration of EHR and CR by adopting data structure and data terms in Chinese EHR Standard. Archetypes in openPCR are modularity models and can be separated, recombined, and reused. The authors recommend that the method to develop openPCR can be referenced by other countries when designing metadata schema of clinical research. In the next steps, openPCR should be used in a number of CR projects to test its applicability and to continuously improve its coverage. Besides, metadata schema for research protocol can be developed to structurize and standardize protocol, and syntactical interoperability of openPCR with other related standards can be considered.

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Improving Clinical Data Warehouse Performance via a Windowing Data Structure Architecture

2009 International Conference on Computational Science and Its Applications ◽

10.1109/iccsa.2009.23 ◽

2009 ◽

Cited By ~ 3

Author(s):

Joshua Nealon ◽

Wenny Rahayu ◽

Eric Pardede

Keyword(s):

Data Structure ◽

Data Warehouse ◽

Clinical Data ◽

Clinical Data Warehouse

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openEHR Is FAIR-Enabling by Design

Studies in Health Technology and Informatics - Public Health and Informatics ◽

10.3233/shti210131 ◽

2021 ◽

Author(s):

Francesca Frexia ◽

Cecilia Mascia ◽

Luca Lianas ◽

Giovanni Delussu ◽

Alessandro Sulis ◽

...

Keyword(s):

Knowledge Discovery ◽

Clinical Data ◽

Implementation Strategy ◽

Contextual Information ◽

Data Repositories ◽

Domain Specific ◽

Research Outcomes ◽

Fair Principles ◽

Reference Implementation ◽

Machine Readable

The FAIR Principles are a set of recommendations that aim to underpin knowledge discovery and integration by making the research outcomes Findable, Accessible, Interoperable and Reusable. These guidelines encourage the accurate recording and exchange of data, coupled with contextual information about their creation, expressed in domain-specific standards and machine-readable formats. This paper analyses the potential support to FAIRness of the openEHR specifications and reference implementation, by theoretically assessing their compliance with each of the 15 FAIR principles. Our study highlights how the openEHR approach, thanks to its computable semantics-oriented design, is inherently FAIR-enabling and is a promising implementation strategy for creating FAIR-compliant Clinical Data Repositories (CDRs).

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A Proposed Electronic Health Record Content Structure Based on Clinical Organizations Survey

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v15i13.5283 ◽

2014 ◽

Vol 15 (13) ◽

pp. 5233-5246 ◽

Cited By ~ 1

Author(s):

Dr. Ayman E. Khedr ◽

Fahad Kamal Alsheref

Keyword(s):

Data Structure ◽

Electronic Health Records ◽

Electronic Health Record ◽

Clinical Data ◽

Data Exchange ◽

Medical Information System ◽

Health Record ◽

Medical Organization ◽

Health Records ◽

Electronic Health

Computer systems and communication technologies made a strong and influential presence in the different fields of medicine. The cornerstone of a functional medical information system is the Electronic Health Records (EHR) management system. Several electronic health records systems were implemented in different states with different clinical data structures that prevent data exchange between systems even in the same state. This leads to the important barrier in implementing EHR system which is the lack of standards of EHR clinical data structure. In this paper we made a survey on several in international and Egyptian medical organization for implementing electronic health record systems for finding the best electronic health record clinical data structure that contains all patientâ€™s medical data. We proposed an electronic health record system with a standard clinical data structure based on the international and Egyptian medical organization survey and with avoiding the limitations in the other electronic health record that exists in the survey.

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