Optimization of Electronic Medical Records for Data Mining Using a Common Data Model

Abstract

The increasing use of electronic health records (EHRs) in veterinary medicine creates an opportunity to utilize the high volume of electronic patient data for mining and data-driven analytics with the goal of improving patient care and outcomes. A central focus of the Clinical and Translational Science Award One Health Alliance (COHA) is to integrate efforts across multiple disciplines to better understand shared diseases in animals and people. The ability to combine veterinary and human medical data provides a unique resource to study the interactions and relationships between animals, humans, and the environment. However, to effectively answer these questions, veterinary EHR data must first be prepared in the same way it is now commonly being done in human medicine to enable data mining and development of analytics to facilitate knowledge formation and solutions that advance our understanding of disease processes, with the ultimate goal of improving outcomes for veterinary patients and their owners. As a first step, COHA member institutions implemented a Common Data Model to standardize EHR data. Herein we present the approach executed within the COHA framework to prepare and optimize veterinary EHRs for data mining and knowledge formation based on the adoption of the Observational Health Data Sciences and Informatics’ Observational Medical Outcomes Partnership Common Data Model.