Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, United States.
Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, United States.
Int J Med Inform. 2021 Jan;145:104339. doi: 10.1016/j.ijmedinf.2020.104339. Epub 2020 Nov 6.
To develop an algorithm that infers patient delivery dates (PDDs) and delivery-specific details from Electronic Health Records (EHRs) with high accuracy; enabling pregnancy-level outcome studies in women's health.
We obtained EHR data from 1,060,100 female patients treated at Penn Medicine hospitals or outpatient clinics between 2010-2017. We developed an algorithm called MADDIE: Method to Acquire Delivery Date Information from Electronic Health Records that infers a PDD for distinct deliveries based on EHR encounter dates assigned a delivery code, the frequency of code usage, and the time differential between code assignments. We validated MADDIE's PDDs against a birth log independently maintained by the Department of Obstetrics and Gynecology.
MADDIE identified 50,560 patients having 63,334 distinct deliveries. MADDIE was 98.6 % accurate (F-score 92.1 %) when compared to the birth log. The PDD was on average 0.68 days earlier than the true delivery date for patients with only one delivery (± 1.43 days) and 0.52 days earlier for patients with more than one delivery episode (± 1.11 days).
MADDIE is the first algorithm to successfully infer PDD information using only structured delivery codes and identify multiple deliveries per patient. MADDIE is also the first to validate the accuracy of the PDD using an external gold standard of known delivery dates as opposed to manual chart review of a sample.
MADDIE augments the EHR with delivery-specific details extracted with high accuracy and relies only on structured EHR elements while harnessing temporal information and the frequency of code usage to identify accurate PDDs.
开发一种算法，从电子健康记录（EHR）中高度准确地推断患者分娩日期（PDD）和分娩具体细节；使妇女健康的妊娠结局研究成为可能。
我们从 2010 年至 2017 年在宾夕法尼亚大学医学院医院或门诊接受治疗的 1,060,100 名女性患者中获得了 EHR 数据。我们开发了一种名为 MADDIE 的算法：从电子健康记录中获取分娩日期信息的方法，该算法根据分配了分娩代码的 EHR 就诊日期、代码使用频率以及代码分配之间的时间差，为每个分娩推断出一个 PDD。我们将 MADDIE 的 PDD 与妇产科独立维护的出生记录进行了验证。
MADDIE 确定了 50,560 名患者的 63,334 个不同分娩。与出生记录相比，MADDIE 的准确率为 98.6%（F 分数为 92.1%）。对于仅有一次分娩的患者，PDD 平均比实际分娩日期早 0.68 天（±1.43 天），对于有多次分娩的患者，PDD 平均早 0.52 天（±1.11 天）。
MADDIE 是第一个仅使用结构化分娩代码成功推断 PDD 信息并识别每位患者多次分娩的算法。MADDIE 也是第一个使用已知分娩日期的外部黄金标准而非手动审查样本来验证 PDD 准确性的算法。
MADDIE 使用高度准确的方法从 EHR 中提取分娩细节，并仅依赖结构化的 EHR 元素，同时利用时间信息和代码使用频率来识别准确的 PDD。
