Systems thinking and predictive analytics to improve veteran healthcare scheduling

Abstract

As a culture, the United States acknowledges that the delivery of veteran services to those who risked their lives and suffered to protect our nation is a top national priority. Over the past several years, however, the media have reported stark examples of how these services are lacking, particularly in the case of medical appointment scheduling. At the same time, the Veterans Health Administration is plagued by strikingly high no-show rates at its medical outpatient clinics and a resulting handicap in resource allocation. We bring to bear systems thinking to address these issues. As a result, we developed a model for a dynamic overbooking system that receives the probability of a patient arriving on-time for their appointment from the patient's phone and couples this real-time probability with prior probability derived from existing VA data. Note that the system protects patient privacy by never transmitting nor sharing location data. When the arrival probability of a patient falls below a given threshold, an algorithm can automatically cancel a patient's appointment and re-assign it to another patient drawn from a pool of wait-list and other patients with high arrival probabilities given their current location. In this presentation, we share the progress to date on our approach, and our proposals for future work and implementation.