Literature DB >> 15691548

Representation of ophthalmology concepts by electronic systems: adequacy of controlled medical terminologies.

Michael F Chiang1, Daniel S Casper, James J Cimino, Justin Starren.   

Abstract

OBJECTIVE: To assess the adequacy of 5 controlled medical terminologies (International Classification of Diseases 9, Clinical Modification [ICD9-CM]; Current Procedural Terminology 4 [CPT-4]; Systematized Nomenclature of Medicine, Clinical Terms [SNOMED-CT]; Logical Identifiers, Names, and Codes [LOINC]; Medical Entities Dictionary [MED]) for representing concepts in ophthalmology.
DESIGN: Noncomparative case series. PARTICIPANTS: Twenty complete ophthalmology case presentations were sequentially selected from a publicly available ophthalmology journal.
METHODS: Each of the 20 cases was parsed into discrete concepts, and each concept was classified along 2 axes: (1) diagnosis, finding, or procedure and (2) ophthalmic or medical concept. Electronic or paper browsers were used to assign a code for every concept in each of the 5 terminologies. Adequacy of assignment for each concept was scored on a 3-point scale. Findings from all 20 case presentations were combined and compared based on a coverage score, which was the average score for all concepts in that terminology. MAIN OUTCOME MEASURES: Adequacy of assignment for concepts in each terminology, based on a 3-point Likert scale (0, no match; 1, partial match; 2, complete match).
RESULTS: Cases were parsed into 1603 concepts. SNOMED-CT had the highest mean overall coverage score (1.625+/-0.667), followed by MED (0.974+/-0.764), LOINC (0.781+/-0.929), ICD9-CM (0.280+/-0.619), and CPT-4 (0.082+/-0.337). SNOMED-CT also had higher coverage scores than any of the other terminologies for concepts in the diagnosis, finding, and procedure categories. Average coverage scores for ophthalmic concepts were lower than those for medical concepts.
CONCLUSIONS: Controlled terminologies are required for electronic representation of ophthalmology data. SNOMED-CT had significantly higher content coverage than any other terminology in this study.

Entities:  

Mesh:

Year:  2005        PMID: 15691548     DOI: 10.1016/j.ophtha.2004.09.032

Source DB:  PubMed          Journal:  Ophthalmology        ISSN: 0161-6420            Impact factor:   12.079


  9 in total

1.  Migrating existing clinical content from ICD-9 to SNOMED.

Authors:  Prakash M Nadkarni; Jonathan A Darer
Journal:  J Am Med Inform Assoc       Date:  2010 Sep-Oct       Impact factor: 4.497

2.  Use of SNOMED CT to represent clinical research data: a semantic characterization of data items on case report forms in vasculitis research.

Authors:  Rachel L Richesson; James E Andrews; Jeffrey P Krischer
Journal:  J Am Med Inform Assoc       Date:  2006-06-23       Impact factor: 4.497

3.  Evaluation of the Systematized Nomenclature of Dentistry using case reports: preliminary results.

Authors:  Miguel Humberto Torres-Urquidy; Titus Schleyer
Journal:  AMIA Annu Symp Proc       Date:  2006

4.  Reliability of SNOMED-CT coding by three physicians using two terminology browsers.

Authors:  Michael F Chiang; John C Hwang; Alexander C Yu; Daniel S Casper; James J Cimino; Justin B Starren
Journal:  AMIA Annu Symp Proc       Date:  2006

Review 5.  A review of auditing methods applied to the content of controlled biomedical terminologies.

Authors:  Xinxin Zhu; Jung-Wei Fan; David M Baorto; Chunhua Weng; James J Cimino
Journal:  J Biomed Inform       Date:  2009-03-12       Impact factor: 6.317

6.  Determining correspondences between high-frequency MedDRA concepts and SNOMED: a case study.

Authors:  Prakash M Nadkarni; Jonathan D Darer
Journal:  BMC Med Inform Decis Mak       Date:  2010-10-28       Impact factor: 2.796

7.  Detection and characterization of usability problems in structured data entry interfaces in dentistry.

Authors:  Muhammad F Walji; Elsbeth Kalenderian; Duong Tran; Krishna K Kookal; Vickie Nguyen; Oluwabunmi Tokede; Joel M White; Ram Vaderhobli; Rachel Ramoni; Paul C Stark; Nicole S Kimmes; Meta E Schoonheim-Klein; Vimla L Patel
Journal:  Int J Med Inform       Date:  2012-06-29       Impact factor: 4.046

8.  Evaluation of electronic health record implementation in ophthalmology at an academic medical center (an American Ophthalmological Society thesis).

Authors:  Michael F Chiang; Sarah Read-Brown; Daniel C Tu; Dongseok Choi; David S Sanders; Thomas S Hwang; Steven Bailey; Daniel J Karr; Elizabeth Cottle; John C Morrison; David J Wilson; Thomas R Yackel
Journal:  Trans Am Ophthalmol Soc       Date:  2013-09

9.  An exploration of the properties of the CORE problem list subset and how it facilitates the implementation of SNOMED CT.

Authors:  Kin Wah Fung; Julia Xu
Journal:  J Am Med Inform Assoc       Date:  2015-02-26       Impact factor: 4.497

  9 in total

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