University of North Carolina at Charlotte, Charlotte, NC.
Wake Forest School of Medicine, Winston Salem, NC.
JCO Clin Cancer Inform. 2020 Jun;4:529-538. doi: 10.1200/CCI.19.00114.
Diagnosis (DX) information is key to clinical data reuse, yet accessible structured DX data often lack accuracy. Previous research hints at workflow differences in cancer DX entry, but their link to clinical data quality is unclear. We hypothesized that there is a statistically significant relationship between workflow-describing variables and DX data quality.
We extracted DX data from encounter and order tables within our electronic health records (EHRs) for a cohort of patients with confirmed brain neoplasms. We built and optimized logistic regressions to predict the odds of fully accurate (ie, correct neoplasm type and anatomic site), inaccurate, and suboptimal (ie, vague) DX entry across clinical workflows. We selected our variables based on correlation strength of each outcome variable.
Both workflow and personnel variables were predictive of DX data quality. For example, a DX entered in departments other than oncology had up to 2.89 times higher odds of being accurate ( < .0001) compared with an oncology department; an outpatient care location had up to 98% fewer odds of being inaccurate ( < .0001), but had 458 times higher odds of being suboptimal ( < .0001) compared with main campus, including the cancer center; and a DX recoded by a physician assistant had 85% fewer odds of being suboptimal ( = .005) compared with those entered by physicians.
These results suggest that differences across clinical workflows and the clinical personnel producing EHR data affect clinical data quality. They also suggest that the need for specific structured DX data recording varies across clinical workflows and may be dependent on clinical information needs. Clinicians and researchers reusing oncologic data should consider such heterogeneity when conducting secondary analyses of EHR data.
诊断（DX）信息是临床数据再利用的关键，但可访问的结构化 DX 数据通常准确性不足。先前的研究表明癌症 DX 录入的工作流程存在差异，但它们与临床数据质量的关系尚不清楚。我们假设工作流程描述变量与 DX 数据质量之间存在统计学上显著的关系。
我们从电子病历（EHR）中的就诊和医嘱表中提取了一组确诊脑肿瘤患者的 DX 数据。我们构建并优化了逻辑回归模型，以预测在不同临床工作流程下 DX 录入完全准确（即正确的肿瘤类型和解剖部位）、不准确和次优（即模糊）的概率。我们根据每个结果变量的相关性强度选择变量。
工作流程和人员变量都可预测 DX 数据质量。例如，与肿瘤科相比，在肿瘤科以外的科室录入的 DX 准确性更高，其准确性的几率高达 2.89 倍（<0.0001）；门诊护理地点不准确的几率降低了 98%（<0.0001），但次优的几率增加了 458 倍（<0.0001），高于主校区，包括癌症中心；而由医师助理重新编码的 DX 次优的几率降低了 85%（=0.005），低于医师录入的 DX。
这些结果表明，临床工作流程和产生 EHR 数据的临床人员之间的差异会影响临床数据质量。它们还表明，特定结构化 DX 数据记录的需求因临床工作流程而异，可能取决于临床信息需求。在对 EHR 数据进行二次分析时，重新使用肿瘤学数据的临床医生和研究人员应考虑这种异质性。
