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JYMS : Journal of Yeungnam Medical Science

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1 "Jong Hyuk Lee"
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Original article
Computer-based clinical coding activity analysis for neurosurgical terms
Jong Hyuk Lee, Jung Hwan Lee, Wooseok Ryu, Byung Kwan Choi, In Ho Han, Chang Min Lee
Yeungnam Univ J Med. 2019;36(3):225-230.   Published online June 4, 2019
DOI: https://doi.org/10.12701/yujm.2019.00220
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AbstractAbstract PDF
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.

Citations

Citations to this article as recorded by  
  • Are ICD codes reliable for observational studies? Assessing coding consistency for data quality
    Stuart J. Nelson, Ying Yin, Eduardo A. Trujillo Rivera, Yijun Shao, Phillip Ma, Mark S. Tuttle, Jennifer Garvin, Qing Zeng-Treitler
    DIGITAL HEALTH.2024;[Epub]     CrossRef

JYMS : Journal of Yeungnam Medical Science
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