Literature DB >> 16429961

Data interchange standards in healthcare IT--computable semantic interoperability: now possible but still difficult, do we really need a better mousetrap?

Charles N Mead1.   

Abstract

The following article on HL7 Version 3 will give readers a glimpse into the significant differences between "what came before"--that is, HL7 Version 2.x--and "what today and the future will bring," which is the HL7 Version 3 family of data interchange specifications. The difference between V2.x and V3 is significant, and it exists because the various stakeholders in the HL7 development process believe that the increased depth, breadth, and, to some degree, complexity that characterize V3 are necessary to solve many of today's and tomorrow's increasingly wide, deep and complex healthcare information data interchange requirements. Like many healthcare or technology discussions, this discussion has its own vocabulary of somewhat obscure, but not difficult, terms. This article will define the minimum set that is necessary for readers to appreciate the relevance and capabilities of HL7 Version 3, including how it is different than HL7 Version 2. After that, there will be a brief overview of the primary motivations for HL7 Version 3 in the presence of the unequivocal success of Version 2. In this context, the article will give readers an overview of one of the prime constructs of Version 3, the Reference Information Model (RIM). There are 'four pillars that are necessary but not sufficient to obtain computable semantic interoperability." These four pillars--a cross-domain information model; a robust data type specification; a methodology for separating domain-specific terms from, as well as binding them to, the common model; and a top-down interchange specification methodology and tools for using 1, 2, 3 and defining Version 3 specification--collectively comprise the "HL7 Version 3 Toolkit." Further, this article will present a list of questions and answers to help readers assess the scope and complexity of the problems facing healthcare IT today, and which will further enlighten readers on the "reality" of HL7 Version 3. The article will conclude with a "pseudo-code" argument in favor of the adoption of HL7 Version 3, framed by citing the recommendation of the Interoperability Consortium for the use of HL7 Version 3 as a critical component in the National Health Information Infrastructure.

Mesh:

Year:  2006        PMID: 16429961

Source DB:  PubMed          Journal:  J Healthc Inf Manag        ISSN: 1099-811X


  24 in total

1.  Enabling Better Interoperability for HealthCare: Lessons in Developing a Standards Based Application Programing Interface for Electronic Medical Record Systems.

Authors:  Suranga N Kasthurirathne; Burke Mamlin; Harsha Kumara; Grahame Grieve; Paul Biondich
Journal:  J Med Syst       Date:  2015-10-07       Impact factor: 4.460

2.  The BRIDG project: a technical report.

Authors:  Douglas B Fridsma; Julie Evans; Smita Hastak; Charles N Mead
Journal:  J Am Med Inform Assoc       Date:  2007-12-20       Impact factor: 4.497

3.  The SAGE Guideline Model: achievements and overview.

Authors:  Samson W Tu; James R Campbell; Julie Glasgow; Mark A Nyman; Robert McClure; James McClay; Craig Parker; Karen M Hrabak; David Berg; Tony Weida; James G Mansfield; Mark A Musen; Robert M Abarbanel
Journal:  J Am Med Inform Assoc       Date:  2007-06-28       Impact factor: 4.497

4.  Vital signs in intensive care: automatic acquisition and consolidation into electronic patient records.

Authors:  Telmo Fonseca; Cristina Ribeiro; Cristina Granja
Journal:  J Med Syst       Date:  2009-02       Impact factor: 4.460

5.  Biomedical ontologies in action: role in knowledge management, data integration and decision support.

Authors:  O Bodenreider
Journal:  Yearb Med Inform       Date:  2008

6.  Predefined headings in a multiprofessional electronic health record system.

Authors:  Annika Terner; Helena Lindstedt; Karin Sonnander
Journal:  J Am Med Inform Assoc       Date:  2012-06-28       Impact factor: 4.497

7.  Data standards for clinical research data collection forms: current status and challenges.

Authors:  Rachel L Richesson; Prakash Nadkarni
Journal:  J Am Med Inform Assoc       Date:  2011-05-01       Impact factor: 4.497

8.  2014 ACC/AHA Key Data Elements and Definitions for Cardiovascular Endpoint Events in Clinical Trials: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Data Standards (Writing Committee to Develop Cardiovascular Endpoints Data Standards).

Authors:  Karen A Hicks; James E Tcheng; Biykem Bozkurt; Bernard R Chaitman; Donald E Cutlip; Andrew Farb; Gregg C Fonarow; Jeffrey P Jacobs; Michael R Jaff; Judith H Lichtman; Marian C Limacher; Kenneth W Mahaffey; Roxana Mehran; Steven E Nissen; Eric E Smith; Shari L Targum
Journal:  J Nucl Cardiol       Date:  2015-10       Impact factor: 5.952

9.  Structurally Mapping Healthcare Data to HL7 FHIR through Ontology Alignment.

Authors:  Athanasios Kiourtis; Argyro Mavrogiorgou; Andreas Menychtas; Ilias Maglogiannis; Dimosthenis Kyriazis
Journal:  J Med Syst       Date:  2019-02-05       Impact factor: 4.460

10.  A Framework for Data Quality Assessment in Clinical Research Datasets.

Authors:  Kathleen Lee; Nicole Weiskopf; Jyotishman Pathak
Journal:  AMIA Annu Symp Proc       Date:  2018-04-16
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