Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis

VER the past 10 years, hospital adoption of electronic health record (EHR) systems has skyrocketed, in part due to the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009, which provided $30 billion in incentives for hospitals and physician practices to adopt EHR systems [1]. According to the latest report from the Office of the National Coordinator for Health Information Technology (ONC), nearly 84% of hospitals have adopted at least a basic EHR system, a 9-fold increase since 2008 [2]. Additionally, office-based physician adoption of basic and certified EHRs has more than doubled from 42% to 87% [3]. EHR systems store data associated with each patient encounter, including demographic information, diagnoses, laboratory tests and results, prescriptions, radiological images, clinical notes, and more [1]. While primarily designed for improving healthcare efficiency from an operational standpoint, many studies have found secondary use for clinical informatics applications [4], [5].