Literature DB >> 7949924

Automatic SNOMED coding.

G W Moore1, J J Berman.   

Abstract

Medical coding has become an important new industry that has originated from the field of medical informatics. Automatic coding of specimens has emerged as a way of relieving hospitals from the cost of paying professional coders and for achieving uniform coding for all specimens. Unfortunately, automatic coding, like manual coding, has numerous pitfalls. Further, the coding algorithms employed by manufacturers of automatic coders are typically proprietary. We have developed a method for automatic coding of pathology reports. Using this public domain autocoder, we have previously demonstrated that automatic SNOMED coding was superior to manual coding in several measurable categories, including the overall number of codes generated and the number of distinct code entities provided. In this report, we describe an algorithm that executes this strategy in the M-Technology environment.

Mesh:

Year:  1994        PMID: 7949924      PMCID: PMC2247836     

Source DB:  PubMed          Journal:  Proc Annu Symp Comput Appl Med Care        ISSN: 0195-4210


  3 in total

1.  Comparison of manual data coding errors in two hospitals.

Authors:  P A Hall; N R Lemoine
Journal:  J Clin Pathol       Date:  1986-06       Impact factor: 3.411

2.  Performance analysis of manual and automated systemized nomenclature of medicine (SNOMED) coding.

Authors:  G W Moore; J J Berman
Journal:  Am J Clin Pathol       Date:  1994-03       Impact factor: 2.493

3.  Progress in medical information management. Systematized nomenclature of medicine (SNOMED).

Authors:  R A Côté; S Robboy
Journal:  JAMA       Date:  1980 Feb 22-29       Impact factor: 56.272

  3 in total
  3 in total

Review 1.  The role of the pathologist as tissue refiner and data miner: the impact of functional genomics on the modern pathology laboratory and the critical roles of pathology informatics and bioinformatics.

Authors:  M J Becich
Journal:  Mol Diagn       Date:  2000-12

2.  Identifying primary and recurrent cancers using a SAS-based natural language processing algorithm.

Authors:  Justin A Strauss; Chun R Chao; Marilyn L Kwan; Syed A Ahmed; Joanne E Schottinger; Virginia P Quinn
Journal:  J Am Med Inform Assoc       Date:  2012-07-21       Impact factor: 4.497

3.  Resources for comparing the speed and performance of medical autocoders.

Authors:  Jules J Berman
Journal:  BMC Med Inform Decis Mak       Date:  2004-06-15       Impact factor: 2.796

  3 in total

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