Literature DB >> 11079953

Assessing the accuracy of an automated coding system in emergency medicine.

W C Morris, D T Heinze, H R Warner Jr, A Primack, A E Morsch, R E Sheffer, M A Jennings, M L Morsch, M A Jimmink.   

Abstract

Accuracy and speed are imperative when it comes to coding medical records. Completely automated approaches to coding are faster than human coders, but are they as accurate? To measure accuracy, a "gold standard" is required; however, establishing a standard for medical records coding is problematic given the inherent ambiguity in some of the coding rules and guidelines. This paper presents statistics regarding the variability amongst experienced coders and compares this variability with an automated system, LifeCode. The authors conclude that LifeCode is as accurate as the human coders used in this study and offers the potential for increased coding consistency and productivity.

Entities:  

Mesh:

Year:  2000        PMID: 11079953      PMCID: PMC2244078     

Source DB:  PubMed          Journal:  Proc AMIA Symp        ISSN: 1531-605X


  3 in total

1.  Coding errors encountered in DRG study.

Authors:  L A Schraffenberger
Journal:  J Am Med Rec Assoc       Date:  1986-07

2.  Medicare reimbursement accuracy under the prospective payment system, 1985 to 1988.

Authors:  D C Hsia; C A Ahern; B P Ritchie; L M Moscoe; W M Krushat
Journal:  JAMA       Date:  1992-08-19       Impact factor: 56.272

3.  Accuracy of diagnostic coding for Medicare patients under the prospective-payment system.

Authors:  D C Hsia; W M Krushat; A B Fagan; J A Tebbutt; R P Kusserow
Journal:  N Engl J Med       Date:  1988-02-11       Impact factor: 91.245

  3 in total
  8 in total

Review 1.  Detecting adverse events using information technology.

Authors:  David W Bates; R Scott Evans; Harvey Murff; Peter D Stetson; Lisa Pizziferri; George Hripcsak
Journal:  J Am Med Inform Assoc       Date:  2003 Mar-Apr       Impact factor: 4.497

Review 2.  A systematic literature review of automated clinical coding and classification systems.

Authors:  Mary H Stanfill; Margaret Williams; Susan H Fenton; Robert A Jenders; William R Hersh
Journal:  J Am Med Inform Assoc       Date:  2010 Nov-Dec       Impact factor: 4.497

3.  Medical i2b2 NLP smoking challenge: the A-Life system architecture and methodology.

Authors:  Daniel T Heinze; Mark L Morsch; Brian C Potter; Ronald E Sheffer
Journal:  J Am Med Inform Assoc       Date:  2007-10-18       Impact factor: 4.497

4.  Natural language processing framework to assess clinical conditions.

Authors:  Henry Ware; Charles J Mullett; V Jagannathan
Journal:  J Am Med Inform Assoc       Date:  2009-04-23       Impact factor: 4.497

5.  Can structured EHR data support clinical coding? A data mining approach.

Authors:  José Carlos Ferrão; Mónica Duarte Oliveira; Filipe Janela; Henrique M G Martins; Daniel Gartner
Journal:  Health Syst (Basingstoke)       Date:  2020-03-01

6.  Need of informatics in designing interoperable clinical registries.

Authors:  Majid Rastegar-Mojarad; Sunghwan Sohn; Liwei Wang; Feichen Shen; Troy C Bleeker; William A Cliby; Hongfang Liu
Journal:  Int J Med Inform       Date:  2017-10-10       Impact factor: 4.046

7.  Comparing paper-based with electronic patient records: lessons learned during a study on diagnosis and procedure codes.

Authors:  Jurgen Stausberg; Dietrich Koch; Josef Ingenerf; Michael Betzler
Journal:  J Am Med Inform Assoc       Date:  2003-06-04       Impact factor: 4.497

8.  Facilitating accurate health provider directories using natural language processing.

Authors:  Matthew J Cook; Lixia Yao; Xiaoyan Wang
Journal:  BMC Med Inform Decis Mak       Date:  2019-04-04       Impact factor: 2.796

  8 in total

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