■对电子健康记录(EHR)和数据类型(即,诊断,药物,和实验室数据)要求评估其数据质量作为一种基本方法,特别是由于需要确定患有慢性病的适当分母人群,例如2型糖尿病(T2D),使用通常可用的可计算表型定义(即,表型)。
■为了弥合这一差距,我们的研究旨在评估表型中的EHR数据质量和变异以及稳健性(或缺乏)问题如何对分母群体的识别产生潜在影响.
■大约208,000名T2D患者被纳入我们的研究,该研究使用了约翰·霍普金斯大学医疗机构(JHMI)2017-2019年的回顾性EHR数据。我们的评估包括4个已发表的表型和1个来自Hopkins专家小组的定义。我们对人口统计进行了描述性分析(即,年龄,性别,种族,和种族),使用医疗保健(住院和急诊室就诊),和每个表型的平均Charlson合并症指数得分。然后,我们使用不同的方法来诱导或模拟完整性的数据质量问题,准确度,和时效性分别跨每个表型。对于诱发的数据不完整,我们的模型随机放弃诊断,药物,和实验室代码以10%的增量独立;对于诱导的数据不准确,我们的模型用相同数据类型的另一个代码随机替换诊断或药物代码,并在实验室结果值中从-100%到+10%引起2%的增量变化;最后,为了及时性,数据被建模为诱导的日期记录增量转移30天到365天.
■在使用EHR的所有表型中,不到四分之一(n=47,326,23%)的人口重叠。通过每种表型识别的群体在数据类型的所有组合中变化。诱发的不完整性识别出每次增加的患者较少;例如,在100%诊断不完整的情况下,慢性病数据仓库表型确定为零患者,因为其表型特征仅包括诊断代码。诱导的不准确性和及时性类似地证明了每个表型的性能变化,因此,每次增加的变化导致更少的患者被识别。
■我们使用EHR数据进行诊断,药物,和来自大型三级医院系统的实验室数据类型,以了解T2D表型差异和性能。我们使用诱导数据质量方法来了解数据质量问题如何影响临床分母群体的识别(例如,临床研究和试验,人口健康评估)和财务或运营决策。我们研究的新结果可能为未来塑造可应用于临床信息学的常见T2D可计算表型定义的方法提供信息。管理慢性病,以及整个行业在医疗保健方面的额外努力。
UNASSIGNED: Increasing and substantial reliance on electronic health records (EHRs) and data types (ie, diagnosis, medication, and laboratory data) demands assessment of their data quality as a fundamental approach, especially since there is a need to identify appropriate denominator populations with chronic conditions, such as type 2 diabetes (T2D), using commonly available computable phenotype definitions (ie, phenotypes).
UNASSIGNED: To bridge this gap, our
study aims to assess how issues of EHR data quality and variations and robustness (or lack thereof) in phenotypes may have potential impacts in identifying denominator populations.
UNASSIGNED: Approximately 208,000 patients with T2D were included in our
study, which used retrospective EHR data from the Johns Hopkins Medical Institution (JHMI) during 2017-2019. Our assessment included 4 published phenotypes and 1 definition from a panel of experts at Hopkins. We conducted descriptive analyses of demographics (ie, age, sex, race, and ethnicity), use of health care (inpatient and emergency room visits), and the average Charlson Comorbidity Index score of each phenotype. We then used different methods to induce or simulate data quality issues of completeness, accuracy, and timeliness separately across each phenotype. For induced data incompleteness, our model randomly dropped diagnosis, medication, and laboratory codes independently at increments of 10%; for induced data inaccuracy, our model randomly replaced a diagnosis or medication code with another code of the same data type and induced 2% incremental change from -100% to +10% in laboratory result values; and lastly, for timeliness, data were modeled for induced incremental shift of date records by 30 days to 365 days.
UNASSIGNED: Less than a quarter (n=47,326, 23%) of the population overlapped across all phenotypes using EHRs. The population identified by each phenotype varied across all combinations of data types. Induced incompleteness identified fewer patients with each increment; for example, at 100% diagnostic incompleteness, the Chronic Conditions Data Warehouse phenotype identified zero patients, as its phenotypic characteristics included only diagnosis codes. Induced inaccuracy and timeliness similarly demonstrated variations in performance of each phenotype, therefore resulting in fewer patients being identified with each incremental change.
UNASSIGNED: We used EHR data with diagnosis, medication, and laboratory data types from a large tertiary hospital system to understand T2D phenotypic differences and performance. We used induced data quality methods to learn how data quality issues may impact identification of the denominator populations upon which clinical (eg, clinical research and trials, population health evaluations) and financial or operational decisions are made. The novel results from our
study may inform future approaches to shaping a common T2D computable phenotype definition that can be applied to clinical informatics, managing chronic conditions, and additional industry-wide efforts in health care.