Computer-based clinical coding activity analysis for neurosurgical terms
0
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
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.
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.
참고문헌 (11)
- Nishimura A, "Status and future perspectives of utilizing big data in neurosurgical and stroke research", Neurol Med Chir (Tokyo), 56, pp.655~663 (2016)
- 윤덕용, "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", Healthcare Informatics Research, 22(1), pp.54~58 (2016), 대한의료정보학회[DOI]
- Popovic JR, "Distributed data networks: a blueprint for Big Data sharing and healthcare analytics", Ann N Y Acad Sci, 1387, pp.105~111 (2017)
- Carter KJ, "Quality assurance in anatomic pathology: automated SNOMED coding", J Am Med Inform Assoc, 3, pp.270~272 (1996)
- Wasserman H, "An applied evaluation of SNOMED CT as a clinical vocabulary for the computerized diagnosis and problem list", AMIA Annu Symp Proc, pp.699~703 (2003)
- Haliasos N, "Financial and clinical governance implications of clinical coding accuracy in neurosurgery: a multidisciplinary audit", Br J Neurosurg, 24, pp.191~195 (2010)
- Yoon D, "Adoption of electronic health records in Korean tertiary teaching and general hospitals", Int J Med Inform, 81, pp.196~203 (2012)
- Lee D, "Literature review of SNOMED CT use", J Am Med Inform Assoc, 21, pp.e11~e19 (2014)
- 손세일, "A Nation-Wide Epidemiological Study of Newly Diagnosed Primary Spine Tumor in the Adult Korean Population, 2009-2011", 대한신경외과학회지, 60(2), pp.195~204 (2017), 대한신경외과학회
- 최병관, "Inferiorly Migrated Disc Fragment at T1 Body Treated by T1 Transcorporeal Approach", 대한신경외과학회지, 49(1), pp.61~64 (2011), 대한신경외과학회
- de Lusignan S, "The barriers to clinical coding in general practice: a literature review", Med Inform Internet Med, 30, pp.89~97 (2005)
