Lead by Silvia Canelon, PhD, along Senior Author and PISC Senior Scholar Mary Regina Boland, and colleagues designed an algorithm to extract patient delivery date (PDD) information from Electronic Health Records (EHR) data.
The algorithm is called MADDIE, Method to Acquire Delivery Date Information from Electronic Health Records. MADDIE 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. MADDIE was 98.6 % accurate 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.