Michael Y. Wang

Michael Y. Wang serves as Professor in the Department of Neurological Surgery at the University of Miami Miller School of Medicine. His research focuses on evidence-based neurosurgical outcomes with particular emphasis on cervical spondylotic myelopathy (CSM) and lumbar spine disorders. Dr. Wang actively contributes to the Quality Outcomes Database (QOD) Spine Collaborative Research Study Group, leading multiple longitudinal studies examining surgical efficacy across diverse patient populations.

His research interests span surgical outcomes measurement, comorbidity impact analysis, geriatric neurosurgery, and predictive modeling. Dr. Wang investigates how factors including age, ASA physical status classification, pain catastrophizing, and care dependency influence surgical results. Recent work integrates machine learning algorithms to predict long-term outcomes, demonstrating innovation in neurosurgical research methodology.

Analysis of Dr. Wang's recent publications reveals consistent focus on patient-centered outcomes using large-scale prospective databases. His work establishes that surgical intervention provides significant benefit across diverse populations including elderly patients, those with multimorbidity, and individuals with high pain catastrophizing scores. The research demonstrates similar minimal clinically important difference achievement rates across demographic and clinical subgroups.

Dr. Wang's scholarly contributions include numerous publications in Journal of Neurosurgery Spine, Neurosurgical Focus, and Clinical Spine Surgery. His work has established important findings regarding surgical indications and outcome expectations for complex spinal conditions.

As an active investigator in the Quality Outcomes Database consortium, Dr. Wang contributes to large-scale multicenter research that shapes contemporary neurosurgical practice. His work provides evidence supporting surgical intervention across diverse patient populations, challenging previous assumptions about contraindications based on age or comorbidities. The research demonstrates that careful patient selection and expectation management can optimize outcomes even in traditionally challenging cases.