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Consistency and accuracy of diagnostic cancer codes generated by automated registration: comparison with manual registration

机译:自动注册生成的诊断性癌症代码的一致性和准确性:与手动注册的比较

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Background Automated procedures are increasingly used in cancer registration, and it is important that the data produced are systematically checked for consistency and accuracy. We evaluated an automated procedure for cancer registration adopted by the Lombardy Cancer Registry in 1997, comparing automatically-generated diagnostic codes with those produced manually over one year (1997). Methods The automatically generated cancer cases were produced by Open Registry algorithms. For manual registration, trained staff consulted clinical records, pathology reports and death certificates. The social security code, present and checked in both databases in all cases, was used to match the files in the automatic and manual databases. The cancer cases generated by the two methods were compared by manual revision. Results The automated procedure generated 5027 cases: 2959 (59%) were accepted automatically and 2068 (41%) were flagged for manual checking. Among the cases accepted automatically, discrepancies in data items (surname, first name, sex and date of birth) constituted 8.5% of cases, and discrepancies in the first three digits of the ICD-9 code constituted 1.6%. Among flagged cases, cancers of female genital tract, hematopoietic system, metastatic and ill-defined sites, and oropharynx predominated. The usual reasons were use of specific vs. generic codes, presence of multiple primaries, and use of extranodal vs. nodal codes for lymphomas. The percentage of automatically accepted cases ranged from 83% for breast and thyroid cancers to 13% for metastatic and ill-defined cancer sites. Conclusion Since 59% of cases were accepted automatically and contained relatively few, mostly trivial discrepancies, the automatic procedure is efficient for routine case generation effectively cutting the workload required for routine case checking by this amount. Among cases not accepted automatically, discrepancies were mainly due to variations in coding practice.
机译:背景技术自动化程序越来越多地用于癌症登记中,重要的是系统地检查产生的数据的一致性和准确性。我们评估了1997年伦巴第癌症注册中心采用的自动癌症注册程序,将自动生成的诊断代码与一年以上手动生成的诊断代码进行了比较。方法自动产生的癌症病例通过Open Registry算法产生。对于手动注册,训练有素的工作人员请查阅临床记录,病理报告和死亡证明。在所有情况下,在两个数据库中都存在并检查过的社会保险代码用于匹配自动数据库和手动数据库中的文件。通过手动修订比较了两种方法产生的癌症病例。结果自动化程序产生了5027例:2959(59%)被自动接受,并且2068(41%)被标记为手动检查。在自动接受的案件中,数据项(姓,名,性别和出生日期)的差异占案件的8.5%,ICD-9代码前三位数字的差异占1.6%。在标记病例中,女性生殖道癌,造血系统癌,转移性病灶和不明确的部位以及口咽癌占主导地位。通常的原因是使用特定的或通用的编码,存在多个原发性编码以及淋巴瘤的结外编码与结点编码。自动接受病例的百分比范围从乳腺癌和甲状腺癌的83%到转移性和未明确定义的癌症部位的13%不等。结论由于59%的案件是自动接受的,并且包含的​​差异相对较小(大部分都是微不足道的),因此自动程序对于常规案件的生成非常有效,从而有效地减少了常规案件检查所需的工作量。在未自动接受的案例中,差异主要是由于编码实践的差异。

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