RESUMEN
A patient's electronic health record (EHR) contains extensive documentation of the patient's medical history but is difficult for clinicians to review and find what they are looking for under the time constraints of the clinical setting. Although recent advances in artificial intelligence (AI) in healthcare have shown promise in enhancing clinical diagnosis and decision-making in clinicians' day-to-day tasks, the problem of how to implement and scale such computationally expensive analytics remains an open issue. In this work, we present a system architecture that generates AI-based insights from analysis of the entire patient medical record for a multispecialty outpatient facility of over 700,000 patients. Our resulting system is able to generate insights efficiently while handling complexities of scheduling to deliver the results in a timely manner, and handle more than 30,000 updates per day while achieving desirable operating cost-performance goals.