Development and evaluation of MADDIE: Method to Acquire Delivery Date Information from Electronic health records

•We designed an algorithm - MADDIE to extract patient delivery date information from Electronic Health Records (EHR) data.•MADDIE was 98.5 % accurate with an F1 score of 92.1 %.•We validated the algorithm against an independent gold-standard birth log.•MADDIE enables EHR billing data to be used to identify patient delivery episodes and delivery dates. 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 (F1-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.