Lab News

Our new paper introduces a practical method for estimating how chronic kidney disease (CKD) progresses when patients’ health records are collected at irregular intervals.

The paper, “Estimating Chronic Kidney Disease Stage Transitions from Irregular Electronic Health Record Data Using an Expectation-Maximization Framework,” was published in MDM Policy & Practice. The study was led by Wendy Qi in collaboration with researchers from the University of Virginia’s Departments of Systems and Information Engineering, Public Health Sciences, and Urology.

During the summer, lab members presented their research at different conferences. 

Last week, I had the privilege of celebrating an important milestone as Wendy Qi successfully defended her Ph.D. in Systems and Information Engineering at the University of Virginia. As my first Ph.D. student, this moment was especially meaningful, marking the culmination of years of research.

Introduction

Every day, hospitals face a difficult balancing act inside the operating room (OR). Surgeries run longer than expected, procedures finish early, emergency cases interrupt schedules, and staff overtime accumulates quickly. While these disruptions may seem unavoidable, even small inaccuracies in surgical duration estimates can create major operational challenges for hospitals and stressful experiences for patients and providers alike.