Journal of Yeungnam Medical Science

Yeungnam University College of Medicine, Yeungnam University Institute Medical Science (영남대 의대 학술지 편집위원회)
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Computer-based clinical coding activity analysis for neurosurgical terms

  • Lee, Jong Hyuk (Convergence Medical Institute of Technology, Pusan National University Hospital) ;
  • Lee, Jung Hwan (Department of Neurosurgery, Pusan National University Hospital) ;
  • Ryu, Wooseok (Department of Healthcare Information Management, Catholic University of Pusan) ;
  • Choi, Byung Kwan (Department of Neurosurgery, Pusan National University Hospital) ;
  • Han, In Ho (Department of Neurosurgery, Pusan National University Hospital) ;
  • Lee, Chang Min (Convergence Medical Institute of Technology, Pusan National University Hospital)
  • Received : 2019.04.01
  • Accepted : 2019.05.24
  • Published : 2019.09.30
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Abstract

Background: It is not possible to measure how much activity is required to understand and code a medical data. We introduce an assessment method in clinical coding, and applied this method to neurosurgical terms. Methods: Coding activity consists of two stages. At first, the coders need to understand a presented medical term (informational activity). The second coding stage is about a navigating terminology browser to find a code that matches the concept (code-matching activity). Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) was used for the coding system. A new computer application to record the trajectory of the computer mouse and record the usage time was programmed. Using this application, we measured the time that was spent. A senior neurosurgeon who has studied SNOMED CT has analyzed the accuracy of the input coding. This method was tested by five neurosurgical residents (NSRs) and five medical record administrators (MRAs), and 20 neurosurgical terms were used. Results: The mean accuracy of the NSR group was 89.33%, and the mean accuracy of the MRA group was 80% (p=0.024). The mean duration for total coding of the NSR group was 158.47 seconds, and the mean duration for total coding of the MRA group was 271.75 seconds (p=0.003). Conclusion: We proposed a method to analyze the clinical coding process. Through this method, it was possible to accurately calculate the time required for the coding. In neurosurgical terms, NSRs had shorter time to complete the coding and higher accuracy than MRAs.

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References

  1. Nishimura A, Nishimura K, Kada A, Iihara K; J-ASPECT Study GROUP. Status and future perspectives of utilizing big data in neurosurgical and stroke research. Neurol Med Chir (Tokyo) 2016;56:655-63. https://doi.org/10.2176/nmc.ra.2016-0174
  2. Yoon D, Ahn EK, Park MY, Cho SY, Ryan P, Schuemie MJ, et al. Conversion and data quality assessment of electronic health record data at a Korean tertiary teaching hospital to a common data model for distributed network research. Healthc Inform Res 2016;22:54-8. https://doi.org/10.4258/hir.2016.22.1.54
  3. Popovic JR. Distributed data networks: a blueprint for Big Data sharing and healthcare analytics. Ann N Y Acad Sci 2017;1387:105-11. https://doi.org/10.1111/nyas.13287
  4. Carter KJ, Rinehart S, Kessler E, Caccamo LP, Ritchey NP, Erickson BA, et al. Quality assurance in anatomic pathology: automated SNOMED coding. J Am Med Inform Assoc 1996;3:270-2. https://doi.org/10.1136/jamia.1996.96413134
  5. Wasserman H, Wang J. An applied evaluation of SNOMED CT as a clinical vocabulary for the computerized diagnosis and problem list. AMIA Annu Symp Proc 2003:699-703.
  6. Haliasos N, Rezajooi K, O'neill KS, Van Dellen J, Hudovsky A, Nouraei S. Financial and clinical governance implications of clinical coding accuracy in neurosurgery: a multidisciplinary audit. Br J Neurosurg 2010;24:191-5. https://doi.org/10.3109/02688690903536595
  7. Yoon D, Chang BC, Kang SW, Bae H, Park RW. Adoption of electronic health records in Korean tertiary teaching and general hospitals. Int J Med Inform 2012;81:196-203. https://doi.org/10.1016/j.ijmedinf.2011.12.002
  8. Lee D, de Keizer N, Lau F, Cornet R. Literature review of SNOMED CT use. J Am Med Inform Assoc 2014;21:e11-9. https://doi.org/10.1136/amiajnl-2013-001636
  9. Sohn S, Kim J, Chung CK, Lee NR, Sohn MJ, Kim SH. A nation-wide epidemiological study of newly diagnosed primary spine tumor in the adult Korean population, 2009-2011. J Korean Neurosurg Soc 2017;60:195-204. https://doi.org/10.3340/jkns.2016.0505.011
  10. Choi BK, Han IH, Cho WH, Cha SH. Inferiorly migrated disc fragment at t1 body treated by t1 transcorporeal approach. J Korean Neurosurg Soc 2011;49:61-4. https://doi.org/10.3340/jkns.2011.49.1.61
  11. de Lusignan S. The barriers to clinical coding in general practice: a literature review. Med Inform Internet Med 2005;30:89-97. https://doi.org/10.1080/14639230500298651

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