Sollie Annet, Sijmons Rolf H, Helsper Charles, Numans Mattijs E
Department of General Practice & Elderly Care Medicine and the EMGO Institute for Health and Care Research, VU University Medical Centre, PO Box 7507, 1007 MB Amsterdam, The Netherlands; Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, PO Box 85500, 3508 GA Utrecht, The Netherlands.
Department of Genetics, University of Groningen, University Medical Centre Groningen, PO Box 30001, 9700 RB Groningen, The Netherlands.
Int J Med Inform. 2017 Mar;99:45-52. doi: 10.1016/j.ijmedinf.2016.08.004. Epub 2016 Aug 28.
To assess quality and reusability of coded cancer diagnoses in routine primary care data. To identify factors that influence data quality and areas for improvement.
A dynamic cohort study in a Dutch network database containing 250,000 anonymized electronic medical records (EMRs) from 52 general practices was performed. Coded data from 2000 to 2011 for the three most common cancer types (breast, colon and prostate cancer) was compared to the Netherlands Cancer Registry.
Data quality is expressed in Standard Incidence Ratios (SIRs): the ratio between the number of coded cases observed in the primary care network database and the expected number of cases based on the Netherlands Cancer Registry. Ratios were multiplied by 100% for readability.
The overall SIR was 91.5% (95%CI 88.5-94.5) and showed improvement over the years. SIRs differ between cancer types: from 71.5% for colon cancer in males to 103.9% for breast cancer. There are differences in data quality (SIRs 76.2% - 99.7%) depending on the EMR system used, with SIRs up to 232.9% for breast cancer. Frequently observed errors in routine healthcare data can be classified as: lack of integrity checks, inaccurate use and/or lack of codes, and lack of EMR system functionality.
Re-users of coded routine primary care Electronic Medical Record data should be aware that 30% of cancer cases can be missed. Up to 130% of cancer cases found in the EMR data can be false-positive. The type of EMR system and the type of cancer influence the quality of coded diagnosis registry. While data quality can be improved (e.g. through improving system design and by training EMR system users), re-use should only be taken care of by appropriately trained experts.
评估常规初级保健数据中编码癌症诊断的质量和可复用性。识别影响数据质量的因素及改进领域。
在一个荷兰网络数据库中进行了一项动态队列研究，该数据库包含来自52家全科诊所的250,000份匿名电子病历（EMR）。将2000年至2011年三种最常见癌症类型（乳腺癌、结肠癌和前列腺癌）的编码数据与荷兰癌症登记处的数据进行比较。
数据质量用标准发病率比（SIRs）表示：初级保健网络数据库中观察到的编码病例数与基于荷兰癌症登记处的预期病例数之比。为便于阅读，比率乘以100%。
总体SIR为91.5%（95%CI 88.5 - 94.5），且多年来有所改善。不同癌症类型的SIR不同：男性结肠癌为71.5%，乳腺癌为103.9%。根据所使用的EMR系统，数据质量存在差异（SIRs为76.2% - 99.7%），乳腺癌的SIR高达232.9%。常规医疗数据中经常出现的错误可分类为：缺乏完整性检查、编码使用不准确和/或缺乏编码以及EMR系统功能不足。
编码后的常规初级保健电子病历数据的再使用者应意识到可能会漏诊30%的癌症病例。在EMR数据中发现的癌症病例中，高达130%可能为假阳性。EMR系统类型和癌症类型会影响编码诊断登记的质量。虽然数据质量可以提高（例如通过改进系统设计和培训EMR系统用户），但再使用应由经过适当培训的专家进行。
