Literature DB >> 23616894

Implementing SNOMED CT for Quality Reporting: Avoiding Pitfalls.

G Wade1.   

Abstract

OBJECTIVE: To implement the SNOMED CT electronic specifications for reporting quality measures and to identify critical issues that affect implementation.
BACKGROUND: The Centers for Medicare and Medicaid (CMS) have issued the electronic specifications for reporting quality measures requiring vendors and hospital systems to use standardized data elements to provide financial incentives for eligible providers.
METHODS: The electronic specifications from CMS were downloaded and extracted. All SNOMED CT codes were examined individually as part of the creation of a mapping table for distribution by a vendor for incorporation into electronic health record systems. A qualitative and quantitative evaluation of the SNOMED CT codes was done as a follow up to the mapping project.
RESULTS: A total of 10643 SNOMED codes were examined for the 44 measures. The approved SNOMED CT code sets contain aberrancies in content such as incomplete IDs, the use of description IDs instead of concept IDs, inactive codes, morphology and observable codes for clinical findings and the inclusion of non-human content.
CONCLUSION: Implementers of these approved specifications must do additional rigorous review and make edits in order to avoid incorporating errors into their EHR products and systems.

Entities:  

Keywords:  SNOMED CT; electronic health records; national health policy

Year:  2011        PMID: 23616894      PMCID: PMC3613001          DOI: 10.4338/ACI-2011-10-RA-0056

Source DB:  PubMed          Journal:  Appl Clin Inform        ISSN: 1869-0327            Impact factor:   2.342


  14 in total

1.  Addressing SNOMED CT implementation challenges through multi-disciplinary collaboration.

Authors:  Justin Liu; Kelly Lane; Elisa Lo; Mary Lam; Tran Truong; Christian Veillette
Journal:  Stud Health Technol Inform       Date:  2010

2.  Investigating subsumption in SNOMED CT: an exploration into large description logic-based biomedical terminologies.

Authors:  Olivier Bodenreider; Barry Smith; Anand Kumar; Anita Burgun
Journal:  Artif Intell Med       Date:  2007-01-22       Impact factor: 5.326

3.  Auditing description-logic-based medical terminological systems by detecting equivalent concept definitions.

Authors:  Ronald Cornet; Ameen Abu-Hanna
Journal:  Int J Med Inform       Date:  2007-08-10       Impact factor: 4.046

4.  Definitions and qualifiers in SNOMED CT.

Authors:  Ronald Cornet
Journal:  Methods Inf Med       Date:  2009-02-18       Impact factor: 2.176

5.  SNOMED reaching its adolescence: ontologists' and logicians' health check.

Authors:  Stefan Schulz; Boontawee Suntisrivaraporn; Franz Baader; Martin Boeker
Journal:  Int J Med Inform       Date:  2008-09-12       Impact factor: 4.046

6.  A version management system for SNOMED CT.

Authors:  Josef Ingenerf; Thomas Beisiegel
Journal:  Stud Health Technol Inform       Date:  2008

7.  Getting the foot out of the pelvis: modeling problems affecting use of SNOMED CT hierarchies in practical applications.

Authors:  Alan L Rector; Sam Brandt; Thomas Schneider
Journal:  J Am Med Inform Assoc       Date:  2011-04-21       Impact factor: 4.497

8.  Adopting Graph Traversal Techniques for Context-Driven Value Sets Extraction from Biomedical Knowledge Sources.

Authors:  Jyotishman Pathak; Guoqian Jiang; Sridhar O Dwarkanath; James D Buntrock; Christopher G Chute
Journal:  Proc IEEE Int Conf Semant Comput       Date:  2008-08-12

9.  The impact of SNOMED CT revisions on a mapped interface terminology: terminology development and implementation issues.

Authors:  Geraldine Wade; S Trent Rosenbloom
Journal:  J Biomed Inform       Date:  2009-03-12       Impact factor: 6.317

10.  Experiences mapping a legacy interface terminology to SNOMED CT.

Authors:  Geraldine Wade; S Trent Rosenbloom
Journal:  BMC Med Inform Decis Mak       Date:  2008-10-27       Impact factor: 2.796

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  2 in total

1.  Methods and applications for visualization of SNOMED CT concept sets.

Authors:  A R Højen; E Sundvall; K R Gøeg
Journal:  Appl Clin Inform       Date:  2014-02-19       Impact factor: 2.342

2.  Using Artificial Intelligence With Natural Language Processing to Combine Electronic Health Record's Structured and Free Text Data to Identify Nonvalvular Atrial Fibrillation to Decrease Strokes and Death: Evaluation and Case-Control Study.

Authors:  Peter L Elkin; Sarah Mullin; Jack Mardekian; Christopher Crowner; Sylvester Sakilay; Shyamashree Sinha; Gary Brady; Marcia Wright; Kimberly Nolen; JoAnn Trainer; Ross Koppel; Daniel Schlegel; Sashank Kaushik; Jane Zhao; Buer Song; Edwin Anand
Journal:  J Med Internet Res       Date:  2021-11-09       Impact factor: 5.428

  2 in total

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