Research Interest

My research interests focus on data-driven healthcare analytics, emphasizing technology adoption, operational improvement, and workforce productivity.

Specifically, my research theme revolves around 1) workforce management, including problems such as workforce attrition and workload management, in healthcare organizations; 2) technology adoption, which incorporates technology in healthcare practitioners’ care delivery processes and delivers operational improvement.

Operational Overload: The Impact of Workload on High-Skilled Workforce Attrition

Authors: Blair Liu, Diwas KC, Bradley Staats, Michael Fundora*

Workforce attrition is critical and costly, disrupting operations across industries and leading to significant productivity losses. In healthcare, nurse attrition poses even greater challenges, exacerbated by the persistent shortage and increasing burnout. In 2025 National Council of State report, about 130,000 registered nurses left the workforce since 2022, and nearly 40% of total registered nurses intend to leave by 2029.

Despite its importance, nurse attrition remains underexplored in operations management (OM) literature, particularly concerning how different workload dimensions influence voluntary attrition and what solutions exist to retain nurses. This study addresses this gap by investigating how workload dimensions, including nurse responsibility, overtime shift, emotional toll, and cumulative workload, affect voluntary attrition among nurses.

In addition, we demonstrate how supportive coworkers act as a buffer against burnout-induced attrition. Finally, we offer actionable strategies for managers to enhance workforce retention: monitoring workloads to prevent fatigue-driven attrition, implementing flexible scheduling to allow recovery, and fostering peer support systems.

Bridging the Nursing Gap: How Virtual Nurse Adoption Impacts Patient Outcomes

Authors: Blair Liu, Yuqian Xu, Bradley Staats

The growing shortage of registered nurses (RNs) has placed unprecedented strain on U.S. hospitals, prompting healthcare systems to seek scalable and innovative staffing solutions. One such intervention is the Virtual Nurse (VN) model, which allows RNs to deliver non-clinical inpatient services, such as admissions and discharges, through synchronous video technology. Yet despite its increasing adoption, the model’s impact on patient outcomes remains largely unexplored.

To address this gap, we leverage encounter-level data from a large East Coast healthcare system. Using a difference-in-differences (DiD) design, we find that VN adoption significantly improves patient care efficiency and quality. VN adoption significantly improved patient outcomes, reducing length of stay (LOS) by 7.18% and decreasing 30- and 60-day readmission rates by 2.00% and 1.95%.

Our mechanism analyses reveal when, where, and who drives these operational improvements. In short, VN engagement during patient admission is pivotal in driving improvements in both LOS and readmission outcomes; VN effectiveness is significantly enhanced when used under moderate department congestion ,and delivered by relatively more experienced VN nurses.