Literature DB >> 9357676

Standardized problem list generation, utilizing the Mayo canonical vocabulary embedded within the Unified Medical Language System.

P L Elkin1, D N Mohr, M S Tuttle, W G Cole, G E Atkin, K Keck, T B Fisk, B H Kaihoi, K E Lee, M C Higgins, H J Suermondt, N Olson, P L Claus, P C Carpenter, C G Chute.   

Abstract

UNLABELLED: VOCABULARY: The Mayo problem list vocabulary is a clinically derived lexicon created from the entries made to the Mayo Clinic's Master Sheet Index and the problem list entries made to the Impression/ Report/Plan section of the Clinical Notes System over the last three years. The vocabulary was reduced by eliminating repetition including lexical variants, spelling errors, and qualifiers (Administrative or Operational terms). Qualifiers are re-coordinated with other terms, at run-time, which greatly increased the number of input strings which our system is capable of recognizing. IMPLEMENTATION: The Problem Manager is implemented using standard windows tools in a Windows NT environment. The interface is designed using Object Pascal. HTTP calls are passed over the World Wide Web to a UNIX based vocabulary server. The server returns a document, which is read into Object Pascal structures, parsed, filtered and displayed. STUDY: This paper reports the results of a recent Usability Trial focused on assessing the viability of this mechanism for standardized problem entry. Eight clinicians engaged in eleven scenarios and responded as to their satisfaction with the systems performance. These responses were observed, videotaped and tabulated. Clinicians in this study were able to find acceptable diagnoses in 91.1% of the scenarios. The response time was acceptable in 92.5% of the scenarios. The presentation of related terms was stated to be useful in at least one scenario by seven of the eight participants. All clinicians wanted to make use of shortcuts which would minimize the amount of typing necessary to encode the concept they were searching for (e.g. Abbreviations, Word Completion).
CONCLUSIONS: Clinicians are willing to choose a canonical term from a suggested list (as opposed to their own wording). Clinicians want an "intelligent" system, which would suggest terms within a category (e.g. Types of "Migraine"). They are able to make functional use of our system, in its current state of development. Finally, all clinicians appreciate the value of encoding their problems in a standardized vocabulary, toward improved research, education and practice.

Entities:  

Mesh:

Year:  1997        PMID: 9357676      PMCID: PMC2233586     

Source DB:  PubMed          Journal:  Proc AMIA Annu Fall Symp        ISSN: 1091-8280


  2 in total

1.  A schematic analysis of the Unified Medical Language System.

Authors:  Y Yang; C G Chute
Journal:  Proc Annu Symp Comput Appl Med Care       Date:  1991

2.  Adding your terms and relationships to the UMLS Metathesaurus.

Authors:  M S Tuttle; D D Sherertz; M S Erlbaum; W D Sperzel; L F Fuller; N E Olson; S J Nelson; J J Cimino; C G Chute
Journal:  Proc Annu Symp Comput Appl Med Care       Date:  1991
  2 in total
  24 in total

1.  Desiderata for a clinical terminology server.

Authors:  C G Chute; P L Elkin; D D Sherertz; M S Tuttle
Journal:  Proc AMIA Symp       Date:  1999

2.  A large-scale evaluation of terminology integration characteristics.

Authors:  F S McDonald; C G Chute; P V Ogren; D Wahner-Roedler; P L Elkin
Journal:  Proc AMIA Symp       Date:  1999

3.  A randomized controlled trial of concept based indexing of Web page content.

Authors:  P L Elkin; A Ruggieri; L Bergstrom; B A Bauer; M Lee; P V Ogren; C G Chute
Journal:  Proc AMIA Symp       Date:  2000

4.  UMLS concept indexing for production databases: a feasibility study.

Authors:  F S McDonald; P L Elkin
Journal:  J Am Med Inform Assoc       Date:  2001 Sep-Oct       Impact factor: 4.497

5.  Usability evaluation of the progress note construction set.

Authors:  S H Brown; S Hardenbrook; L Herrick; J St Onge; K Bailey; P L Elkin
Journal:  Proc AMIA Symp       Date:  2001

6.  A randomized controlled trial of the accuracy of clinical record retrieval using SNOMED-RT as compared with ICD9-CM.

Authors:  P L Elkin; A P Ruggieri; S H Brown; J Buntrock; B A Bauer; D Wahner-Roedler; S C Litin; J Beinborn; K R Bailey; L Bergstrom
Journal:  Proc AMIA Symp       Date:  2001

7.  The UMLS-CORE project: a study of the problem list terminologies used in large healthcare institutions.

Authors:  Kin Wah Fung; Clement McDonald; Suresh Srinivasan
Journal:  J Am Med Inform Assoc       Date:  2010 Nov-Dec       Impact factor: 4.497

8.  Information retrieval performance of probabilistically generated, problem-specific computerized provider order entry pick-lists: a pilot study.

Authors:  Adam S Rothschild; Harold P Lehmann
Journal:  J Am Med Inform Assoc       Date:  2005-01-31       Impact factor: 4.497

9.  Automation of a problem list using natural language processing.

Authors:  Stephane Meystre; Peter J Haug
Journal:  BMC Med Inform Decis Mak       Date:  2005-08-31       Impact factor: 2.796

10.  Using SNOMED CT to represent two interface terminologies.

Authors:  S Trent Rosenbloom; Steven H Brown; David Froehling; Brent A Bauer; Dietlind L Wahner-Roedler; William M Gregg; Peter L Elkin
Journal:  J Am Med Inform Assoc       Date:  2008-10-24       Impact factor: 4.497

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