Trade data interchange. Trade data elements directory

Semantic interoperability is the key to achieving global interoperability in healthcare information technology. The benefits are tremendous – the sharing of clinical data for multiple uses including patient care, research, reimbursement, audit and analyses, education, health surveillance, and many other uses. Patient safety, higher quality healthcare, more effective and efficient healthcare, increased outcomes, and potentially improved performance, higher quality of life and longer lifetimes are potential results. Decision support and the immediate linking of knowledge to the care process become easier. Semantic interoperability is a worthy goal. There are many barriers to achieving semantic interoperability. Key among these is the resolution of the many issues relating to the terminologies used in defining, describing and documenting health care. Each of these controlled terminologies has a reason for being and a following. The terminologies conflict and overlap; the granularity is not sufficiently rich for direct clinical use; there are gaps that prevent an exhaustive set; there are major variances in cost and accessibility; and no one appears eager or willing to make the ultimate decisions required to solve the problem. This chapter defines and describes the purpose and characteristics of the major terminologies in use in healthcare today. Terminology sets are compared in purpose, form and content. Finally, a proposed solution is presented based on a global master metadictionary of data elements with a rich set of attributes including names that may come from existing controlled terminologies, precise definitions to remove ambiguity in use, and complete value sets of possible values. The focus is on data elements because data elements are the basic unit of data interchange.

The Common Data Elements for Cancer Research: Remarks on Functions and Structure

Methods of Information in Medicine ◽

10.1055/s-0038-1634121 ◽

2006 ◽

Vol 45 (06) ◽

pp. 594-601 ◽

Cited By ~ 30

Author(s):

C. A. Brandt ◽

P. M. Nadkarni

Keyword(s):

Cancer Research ◽

Community Integration ◽

Controlled Vocabulary ◽

Common Data Elements ◽

Metadata Model ◽

Data Interchange ◽

The Common ◽

Set Up ◽

Definition Of ◽

Data Elements

Summary Objectives: The National Cancer Institute (NCI) has developed the Common Data Elements (CDE) to serve as a controlled vocabulary of data descriptors for cancer research, to facilitate data interchange and inter-oper-ability between cancer research centers. We evaluated CDE’s structure to see whether it could represent the elements necessary to support its intended purpose, and whether it could prevent errors and inconsistencies from being accidentally introduced. We also performed automated checks for certain types of content errors that provided a rough measure of curation quality. Methods: Evaluation was performed on CDE content downloaded via the NCI’s CDE Browser, and transformed into relational database form. Evaluation was performed under three categories: 1) compatibility with the ISO/IEC 11179 metadata model, on which CDE structure is based, 2) features necessary for controlled vocabulary support, and 3) support for a stated NCI goal, set up of data collection forms for cancer research. Results: Various limitations were identified both with respect to content (inconsistency, insufficient definition of elements, redundancy) as well as structure – particularly the need for term and relationship support, as well as the need for metadata supporting the explicit representation of electronic forms that utilize sets of common data elements. Conclusions: While there are numerous positive aspects to the CDE effort, there is considerable opportunity for improvement. Our recommendations include review of existing content by diverse experts in the cancer community; integration with the NCI thesaurus to take advantage of the latter’s links to nationally used controlled vocabularies, and various schema enhancements required for electronic form support.

Data elements in terminological entries

Terminology ◽

10.1075/term.1.1.05wri ◽

1994 ◽

Vol 1 (1) ◽

pp. 41-59 ◽

Cited By ~ 2

Author(s):

Sue Ellen Wright ◽

Gerhard Budin

Keyword(s):

Internal Structure ◽

Working Group ◽

Practical Approach ◽

Data Element ◽

Data Preparation ◽

Common Data Element ◽

Empirical Examination ◽

Data Interchange ◽

Preparation Stage ◽

Data Elements

Differing theoretical and methodological views and working-group needs have spawned a wide diversity in the content, layout and internal structure of terminological entries in database environments, which in turn complicates standardization and data interchange. Major criticisms lodged against the data element list provided in ISO 6156 (MATER) prompted the authors to conduct an empirical examination of over thirty existing databases to ascertain which data elements are truly used in practice (as opposed to those which are espoused or rejected in theory). Their results reveal that designation of data elements, like other terminological products, are subject to the vagaries of polysemy and synonymy. They conclude that, given the widespread differences in approach evidenced in existing databases, the most practical approach to data element concerns during interchange is to compile an open-ended dictionary of common data element types for use as a mapping device during the data preparation stage.

The SNOMED DICOM Microglossary: Controlled Terminology Resource for Data Interchange in Biomedical Imaging

Methods of Information in Medicine ◽

10.1055/s-0038-1634557 ◽

1998 ◽

Vol 37 (04/05) ◽

pp. 404-414 ◽

Cited By ~ 9

Author(s):

W. Dean Bidgood

Keyword(s):

Technical Assistance ◽

Diagnostic Image ◽

Domain Experts ◽

Proprietary Information ◽

Data Interchange ◽

Computer Based ◽

Mapping Resource ◽

Professional Specialty ◽

Data Elements ◽

Light Color

AbstractThis paper describes an authoritative, non-proprietary information resource that provides an efficient mechanism for embedding specialized clinical knowledge into the design of healthcare telecommunications systems. The resource marries two types of data interchange standards, a message/electronic-document standard and a terminology standard. In technical terms, it is part protocol and part database. Industry, academia, professional specialty societies, and the federal government participated in its development. The development of mUlti-specialty content has broadly engaged biomedical domain experts to an unprecedented degree in voluntary, non-proprietary message/document-standards development. The resource is the SNOMED DICOM Microglossary (SDM) [1], a message-terminology (or document-content) mapping resource. The message/electronicdocument standard is DICOM (Digital Imaging and Communications in Medicine) [2]. The terminology standard is SNOMED, (Systematized Nomenclature of Human and Veterinary Medicine) [31. The SDM specifies the mapping of multi-specialty imaging terminology from SNOMED to DICOM data elements. DICOM provides semantic constraints and a framework for discou rse that are lacking in SNOMED. Thus the message standard and the computerbased terminology both depend upon and complete each other. The combination is synergistic. By substitution of different templates of specialty terminology from the SDM, a generic message template, such as the DICOM Visible Light (Color Diagnostic) Image or the DICOM Structured Reporting specification can be reconfigured for diverse applications. Professional societies, with technical assistance from the College of American Pathologists, contribute and maintain their portions of the terminology, and can use SDM templates and term lists in clinical practice guidelines for the structure and content of computer-based patient records.

Electronic trade data interchange projects

Data Processing ◽

10.1016/0011-684x(85)90090-5 ◽

1985 ◽

Vol 27 (7) ◽

pp. 13-16

Author(s):

Alfredo Sarich

Keyword(s):

Electronic Trade ◽

Trade Data ◽

Data Interchange

Towards a European Food Composition Data Interchange Platform

International Journal for Vitamin and Nutrition Research ◽

10.1024/0300-9831/a000113 ◽

2012 ◽

Vol 82 (3) ◽

pp. 209-215 ◽

Cited By ~ 9

Author(s):

Simone Bell ◽

Heikki Pakkala ◽

Michael P. Finglas

Keyword(s):

Nutrient Content ◽

Food Composition ◽

Consumer Information ◽

Health Claims ◽

Composition Data ◽

European Committee ◽

Food Properties ◽

Data Interchange ◽

Food Composition Data ◽

Definition Of

Food composition data (FCD) comprises the description and identification of foods, as well as their nutrient content, other constituents, and food properties. FCD are required for a range of purposes including food labeling, supporting health claims, nutritional and clinical management, consumer information, and research. There have been differences within and beyond Europe in the way FCD are expressed with respect to food description, definition of nutrients and other food properties, and the methods used to generate data. One of the major goals of the EuroFIR NoE project (2005 - 10) was to provide tools to overcome existing differences among member states and parties with respect to documentation and interchange of FCD. The establishment of the CENs (European Committee for Standardisation) TC 387 project committee on Food Composition Data, led by the Swedish Standards Institute, and the preparation of the draft Food Data Standard, has addressed these deficiencies by enabling unambiguous identification and description of FCD and their quality, for dissemination and data interchange. Another major achievement of the EuroFIR NoE project was the development and dissemination of a single, authoritative source of FCD in Europe enabling the interchange and update of data between countries, and also giving access to users of FCD.

Intelligent Processing of Loosely Structured Documents as a Strategy for Organizing Electronic Health Care Records

Methods of Information in Medicine ◽

10.1055/s-0038-1634938 ◽

1993 ◽

Vol 32 (04) ◽

pp. 265-268 ◽

Cited By ~ 11

Author(s):

D. J. Essin

Keyword(s):

Health Care ◽

Health Care Organization ◽

Database Systems ◽

Relevant Information ◽

Full Potential ◽

Care Organization ◽

Intelligent Processing ◽

Data Interchange ◽

Structured Documents ◽

Health Care Records

AbstractLoosely structured documents can capture more relevant information about medical events than is possible using today’s popular databases. In order to realize the full potential of this increased information content, techniques will be required that go beyond the static mapping of stored data into a single, rigid data model. Through intelligent processing, loosely structured documents can become a rich source of detailed data about actual events that can support the wide variety of applications needed to run a health-care organization, document medical care or conduct research. Abstraction and indirection are the means by which dynamic data models and intelligent processing are introduced into database systems. A system designed around loosely structured documents can evolve gracefully while preserving the integrity of the stored data. The ability to identify and locate the information contained within documents offers new opportunities to exchange data that can replace more rigid standards of data interchange.