Department of Population Health, New York University Grossman School of Medicine, 180 Madison Ave, New York, NY, 10016, USA.
Division of Cancer Prevention and Control, Centers for Disease Control and Prevention, Atlanta, GA, 30333, USA.
BMC Med Inform Decis Mak. 2022 Apr 6;22(1):91. doi: 10.1186/s12911-022-01831-8.
State cancer prevention and control programs rely on public health surveillance data to set objectives to improve cancer prevention and control, plan interventions, and evaluate state-level progress towards achieving those objectives. The goal of this project was to evaluate the validity of using electronic health records (EHRs) based on common data model variables to generate indicators for surveillance of cancer prevention and control for these public health programs.
Following the methodological guidance from the PRISMA Extension for Scoping Reviews, we conducted a literature scoping review to assess how EHRs are used to inform cancer surveillance. We then developed 26 indicators along the continuum of the cascade of care, including cancer risk factors, immunizations to prevent cancer, cancer screenings, quality of initial care after abnormal screening results, and cancer burden. Indicators were calculated within a sample of patients from the New York City (NYC) INSIGHT Clinical Research Network using common data model EHR data and were weighted to the NYC population using post-stratification. We used prevalence ratios to compare these estimates to estimates from the raw EHR of NYU Langone Health to assess quality of information within INSIGHT, and we compared estimates to results from existing surveillance sources to assess validity.
Of the 401 identified articles, 15% had a study purpose related to surveillance. Our indicator comparisons found that INSIGHT EHR-based measures for risk factor indicators were similar to estimates from external sources. In contrast, cancer screening and vaccination indicators were substantially underestimated as compared to estimates from external sources. Cancer screenings and vaccinations were often recorded in sections of the EHR that were not captured by the common data model. INSIGHT estimates for many quality-of-care indicators were higher than those calculated using a raw EHR.
Common data model EHR data can provide rich information for certain indicators related to the cascade of care but may have substantial biases for others that limit their use in informing surveillance efforts for cancer prevention and control programs.
州癌症预防和控制计划依赖公共卫生监测数据来设定目标,以改善癌症预防和控制,规划干预措施,并评估州一级实现这些目标的进展情况。本项目的目标是评估使用基于通用数据模型变量的电子健康记录(EHR)生成这些公共卫生计划癌症预防和控制监测指标的有效性。
根据 PRISMA 扩展范围综述的方法学指导,我们进行了文献范围综述,以评估 EHR 如何用于癌症监测。然后,我们沿着护理级联的连续体开发了 26 个指标,包括癌症风险因素、预防癌症的免疫接种、癌症筛查、异常筛查结果后的初始护理质量以及癌症负担。使用纽约市(NYC)INSIGHT 临床研究网络中的患者样本计算了指标,使用后分层法将指标加权到 NYC 人口中。我们使用患病率比将这些估计值与 NYU Langone Health 的原始 EHR 中的估计值进行比较,以评估 INSIGHT 中的信息质量,并将估计值与现有监测来源的结果进行比较,以评估有效性。
在 401 篇已识别的文章中,有 15%的文章具有与监测相关的研究目的。我们的指标比较发现,INSIGHT EHR 基于风险因素指标的测量值与外部来源的估计值相似。相比之下,与外部来源的估计值相比,癌症筛查和疫苗接种指标被大大低估。癌症筛查和疫苗接种通常记录在 EHR 的未被通用数据模型捕获的部分中。许多质量护理指标的 INSIGHT 估计值高于使用原始 EHR 计算的值。
通用数据模型 EHR 数据可为与级联护理相关的某些指标提供丰富的信息,但对于其他指标可能存在重大偏差,限制了其在癌症预防和控制计划监测工作中的使用。