Literature DB >> 16779205

Comparison of accuracy captured by different controlled languages in oral pathology diagnoses.

Jung-Wei Chen1, Catherine Flaitz, Todd Johnson.   

Abstract

This project was comparing the accuracy of capturing the oral pathology diagnoses among different coding systems. 55 diagnoses were selected for comparison among 5 coding systems. The results of accuracy in capturing oral diagnoses are: AFIP (96.4%), followed by Read 99 (85.5%), SNOMED 98 (74.5%), ICD-9 (43.6%), and CDT-3 (14.5%). It shows that the currently used coding systems, ICD-9 and CDT-3, were inadequate, whereas the AFIP coding system captured the majority of oral diagnoses. In conclusion, the most commonly used medical and dental coding systems lack terms for the diagnosis of oral and dental conditions.

Mesh:

Year:  2005        PMID: 16779205      PMCID: PMC1560686     

Source DB:  PubMed          Journal:  AMIA Annu Symp Proc        ISSN: 1559-4076


  1 in total

Review 1.  Review paper: coding systems in health care.

Authors:  J J Cimino
Journal:  Methods Inf Med       Date:  1996-12       Impact factor: 2.176

  1 in total
  5 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.  Measuring the Information Gain of Diagnosis vs. Diagnosis Category Coding.

Authors:  William R Hogan; Vergil N Slee
Journal:  AMIA Annu Symp Proc       Date:  2010-11-13

3.  Using SNOMED CT-encoded problems to improve ICD-10-CM coding-A randomized controlled experiment.

Authors:  Kin Wah Fung; Julia Xu; S Trent Rosenbloom; James R Campbell
Journal:  Int J Med Inform       Date:  2019-03-05       Impact factor: 4.046

4.  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

5.  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

  5 in total

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