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作 者:刘媛[1] 汪刚 Liu Yuan;Wang Gang(Statistics Center,Shanghai Chest Hospital,Shanghai Jiao Tong University School of Medicine,Shanghai 200030,China)
机构地区:[1]上海市胸科医院,上海交通大学医学院附属胸科医院,统计中心,上海市200030
出 处:《中国病案》2024年第4期23-25,共3页Chinese Medical Record
摘 要:目的 核查某三甲专科医院肺神经内分泌肿瘤形态学编码,分析错误原因,并提出相应的质控改进措施,以提高形态学编码准确性。方法 从某医院病案信息系统调取2022年1月1日-2022年12月31日出院的主要诊断ICD-10编码为C34、主要手术ICD-9-CM-3编码为32.2-32.5,且病理诊断符合神经内分泌肿瘤的病案共121例,分析形态学编码情况。结果 121例肺神经内分泌肿瘤中小细胞癌占比最高,占48.8%。形态学编码错误26例,错误占比较高的病理类型为大细胞神经内分泌癌、小细胞癌、小细胞-大细胞癌、不典型类癌,分别为26.9%、19.2%、15.4%、15.4%。错误原因主要为按冰冻病理结果编码、病理知识欠缺和编码知识更新滞后,分别导致的错误占比为34.6%、34.6%、30.8%。结论 肺神经内分泌肿瘤形态学编码错误占比较高,编码员需加强对病理学分类知识的学习,综合利用不同形态学编码版本,并利用信息化技术,以提高肺神经内分泌肿瘤形态学编码质量。Objectives To verify the morphological coding of lung neuroendocrine tumors in a Three A and Tertiary Hospital,analyze the causes of errors,and propose corresponding quality control improvement measures to improve the accuracy of morphological coding.Methods A total of 121 medical records which had the main discharge diagnostic ICD-10 codes of C34,the main surgical ICD-9-CM-3 codes of 32.2-32.5 and pathological diagnosis of neuroendocrine neoplasms,were retrieved from the hospital medical record information system from January 1st,2022 to December 31st,2022.The morphological coding was checked.Results The proportion of small cell carcinoma in 121 cases of lung neuroendocrine tumors was the highest,accounting for 48.8%.There were 26 cases of morphological coding errors,and the pathological types with a high proportion of coding errors were large cell neuroendocrine carcinoma,small cell carcinoma,combined small cell-large cell carcinoma,and atypical carcinoid,with 26.9%,19.2%,15.4%and 15.4%,respectively.The main reasons for morphological coding errors were coding based on frozen pathological results,lack of pathological knowledge,and delayed updating of coding knowledge,with errors accounting for 34.6%,34.6%and 30.8%,respectively.Conclusions The error proportion of morphological coding in lung neuroendocrine neoplasms was relatively high.Coders needed to improve the quality of morphological coding in lung neuroendocrine neoplasms by enhancing their learning of pathological classification knowledge,comprehensively utilizing different morphological coding versions,and utilizing information technology.
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