Hernandez-Boussard Tina, Blayney Douglas W, Brooks James D
Department of Medicine, Stanford University, Stanford, California.
Department of Biomedical Data Science, Stanford University, Stanford, California.
Cancer Epidemiol Biomarkers Prev. 2020 Apr;29(4):816-822. doi: 10.1158/1055-9965.EPI-19-0873. Epub 2020 Feb 17.
Efficient capture of routine clinical care and patient outcomes is needed at a population-level, as is evidence on important treatment-related side effects and their effect on well-being and clinical outcomes. The increasing availability of electronic health records (EHR) offers new opportunities to generate population-level patient-centered evidence on oncologic care that can better guide treatment decisions and patient-valued care.
This study includes patients seeking care at an academic medical center, 2008 to 2018. Digital data sources are combined to address missingness, inaccuracy, and noise common to EHR data. Clinical concepts were identified and extracted from EHR unstructured data using natural language processing (NLP) and machine/deep learning techniques. All models are trained, tested, and validated on independent data samples using standard metrics.
We provide use cases for using EHR data to assess guideline adherence and quality measurements among patients with cancer. Pretreatment assessment was evaluated by guideline adherence and quality metrics for cancer staging metrics. Our studies in perioperative quality focused on medications administered and guideline adherence. Patient outcomes included treatment-related side effects and patient-reported outcomes.
Advanced technologies applied to EHRs present opportunities to advance population-level quality assessment, to learn from routinely collected clinical data for personalized treatment guidelines, and to augment epidemiologic and population health studies. The effective use of digital data can inform patient-valued care, quality initiatives, and policy guidelines.
A comprehensive set of health data analyzed with advanced technologies results in a unique resource that facilitates wide-ranging, innovative, and impactful research on prostate cancer. This work demonstrates new ways to use the EHRs and technology to advance epidemiologic studies and benefit oncologic care.
在人群层面需要有效获取常规临床护理和患者结局，以及关于重要治疗相关副作用及其对幸福感和临床结局影响的证据。电子健康记录（EHR）可用性的不断提高为生成以患者为中心的人群层面肿瘤护理证据提供了新机会，这些证据可以更好地指导治疗决策和以患者价值为导向的护理。
本研究纳入了2008年至2018年在一家学术医疗中心寻求治疗的患者。整合数字数据源以解决EHR数据常见的缺失、不准确和噪声问题。使用自然语言处理（NLP）和机器学习/深度学习技术从EHR非结构化数据中识别和提取临床概念。所有模型均使用标准指标在独立数据样本上进行训练、测试和验证。
我们提供了使用EHR数据评估癌症患者指南依从性和质量测量的用例。通过癌症分期指标的指南依从性和质量指标评估预处理评估。我们在围手术期质量方面的研究重点是用药情况和指南依从性。患者结局包括治疗相关副作用和患者报告的结局。
应用于EHR的先进技术为推进人群层面的质量评估、从常规收集的临床数据中学习以制定个性化治疗指南以及加强流行病学和人群健康研究提供了机会。有效利用数字数据可为以患者价值为导向的护理、质量改进措施和政策指南提供信息。
用先进技术分析的一套全面的健康数据产生了一种独特的资源，有助于对前列腺癌进行广泛、创新且有影响力的研究。这项工作展示了利用EHR和技术推进流行病学研究并使肿瘤护理受益的新方法。
