Appl Clin Inform 2012; 03(03): 349-355
DOI: 10.4338/ACI-2012-05-CR-0019
Case Report
Schattauer GmbH

Case Report Medical Eponyms

An Applied Clinical Informatics Opportunity
L.N. Guptha Munugoor Baskaran
1   Departments of Information Systems
2   Internal Medicine
5   Center for Clinical Informatics Research and Education; The MetroHealth System and School of Medicine, Case Western Reserve University, Cleveland OH
,
P.J. Greco
1   Departments of Information Systems
2   Internal Medicine
5   Center for Clinical Informatics Research and Education; The MetroHealth System and School of Medicine, Case Western Reserve University, Cleveland OH
,
D.C. Kaelber
2   Internal Medicine
4   Epidemiology, and Biostatistics
5   Center for Clinical Informatics Research and Education; The MetroHealth System and School of Medicine, Case Western Reserve University, Cleveland OH
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Correspondence to:

David C Kaelber, MD, PhD, MPH
3158 Kingsley Road
Shaker Heights OH 44122
216–409–0538

Publikationsverlauf

received: 19. Juni 2012

accepted: 29. August 2012

Publikationsdatum:
16. Dezember 2017 (online)

 

Summary

Medical eponyms are medical words derived from people’s names. Eponyms, especially similar sounding eponyms, may be confusing to people trying to use them because the terms themselves do not contain physiologically descriptive words about the condition they refer to. Through the use of electronic health records (EHRs), embedded applied clinical informatics tools including synonyms and pick lists that include physiologically descriptive terms associated with any eponym appearing in the EHR can significantly enhance the correct use of medical eponyms. Here we describe a case example of two similar sounding medical eponyms – Wegener’s disease and Wegner’s disease – which were confused in our EHR. We describe our solution to address this specific example and our suggestions and accomplishments developing more generalized approaches to dealing with medical eponyms in EHRs. Integrating brief physiologically descriptive terms with medical eponyms provides an applied clinical informatics opportunity to improve patient care.


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Conflict of Interest

No authors had any conflict of interest related to this work. No external funding was involved in the work.

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  • 14 The World Health Organization.. International Statistical Classification of Diseases and Related Health Problems, 10th Revision: 2010. c2012 [cited 07/07/2012]. Available from: http://apps.who.int/classifi/cations/icd10/browse/2010/en .
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Correspondence to:

David C Kaelber, MD, PhD, MPH
3158 Kingsley Road
Shaker Heights OH 44122
216–409–0538

  • References

  • 1 Enersen O D.. Whonamedit –A dictionary of medical eponyms [Internet]. c1994–2012 [cited 27 April 2012]. Available from http://www.whonamedit.com.
  • 2 Yee A. Andrew Yee’s medical eponym page [Internet]. c2000–2012 [updated 10 August 2008; cited 27 April 2012]. Available from http://eponyms.net..
  • 3 Woywodt A, Matteson E. Should eponyms be abandoned? Yes. BMJ 2007; 335: 424.
  • 4 Whitworth JA. Should eponyms be abandoned?. No. BMJ 2007; 335:: 425.
  • 5 Matteson EL, Woywodt A. Eponymophilia in rheumatology. Rheumatology 2006; 45: 1328-1330.
  • 6 Waseem M, Khan M, Hussain N, Giannoudis PV, Fischer J, Smith RM. Eponyms: errors in clinical practice and scientific writing. Acta Orthop Belg 2005; 71: 1-8.
  • 7 Lazarus R, Klompas M, Campion FX, McNabb SJN, Hou X, Daniel J. et al. The practice of informatics: viewpoint paper: electronic support for public health: validated case finding and reporting for notifiable diseases using electronic medical data. JAMIA 2009; 16: 18-24. doi:10.1197/jamia. M2848.
  • 8 Klompas M, Lazarus R, Daniel J, Haney GA, Campion FX, Kruska BA. et al. Automated detection and reporting of notifiable diseases using electronic medical records versus passive surveillance. MMWR Weekly 2008; 57 (14) 373-376.
  • 9 Klompas M, Lazarus R, Daniel J, Haney GA, Campion FX, Kruska BA. et al. Electronic medical record support for public health (ESP): automated detection and reporting of statutory notifiable diseases to public health authorities. Advances in Disease Surveillance 2007; 3: 3.
  • 10 Centers for Disease Control and Prevention.. International Classification of Disease, Ninth Revision, Clinical Modification (ICD-9-CM). CDC/National Center for Health Statistics Office of Information Services. c2012 [updated 23 September 2011; cited 04 April 2012]. Available from: http://www.cdc.gov/nchs/icd icd9cm.htm.
  • 11 Unified Medical Language System (UMLS) Medical Subject Headings (MeSH).. US National Library of Medicine, National Institutes of Health, Department of Health and Human Services. c1993–2012 [updated 28 August 2011; cited 05/13/2012]. Available from: http://www.nlm.nih.gov/mesh/MBrowser.html.
  • 12 Osheroff JA, Teich JM, Levick D, Saldana L, Velasco FT, Sittig DF. et al. Improving outcomes with clinical decision support: An implementer’s guide. Second edition. HIMSS. 2012
  • 13 Collins SA, Currie LM, Bakken S, and Cimino JJ. Case report: information needs, infobutton manager use, and satisfaction by clinician type: a case study. JAMIA 2009; 16: 140-142.
  • 14 The World Health Organization.. International Statistical Classification of Diseases and Related Health Problems, 10th Revision: 2010. c2012 [cited 07/07/2012]. Available from: http://apps.who.int/classifi/cations/icd10/browse/2010/en .
  • 15 International Health Terminology Standards Development Organisation.. Integrated Terminology Server –Systematized Nomenclature of Medicine (SNOMED) Clinical Terms Version: 20120131. c2002–2012 [updated 1/2012; cited 07/07/2012]. Available from: http://www.itserver.es/ITServer/Browser/snomedct/browser.faces.