Real-World Evidence
Causal Inference
Health Economics & Outcomes Research
Biopharma
Digital Health
Joseph L. Smith, PhD, MPH, MBA
He / Him / His / Himself
Adjunct Instructor
He / Him / His / Himself
Adjunct Instructor
Research & Practice Interests
Education
PhD, Public Health, Health Services Research, University of South Florida
MBA, Healthcare Managment, Entrepreneurship & Leadership, Johns Hopkins University
MPH, Health Policy & Management, Kent State University
BA, Psychology & Pre-Medicine, Kent State University
Publications
- Validation of the Klinrisk Machine Learning Model for CKD Progression in a Large Representative US Population
- Correction to: A step-by-step guide to causal study design using real-world data (Health Services and Outcomes Research Methodology, (2025), 25, 2, (182-196), 10.1007/s10742-024-00333-6)
- A step-by-step guide to causal study design using real-world data
- Real-world healthcare resource utilization, costs, and predictors of relapse among US patients with incident schizophrenia or schizoaffective disorder
- Exacerbations, treatment patterns, utilization, and costs before and after initiating of benralizumab for the treatment of severe eosinophilic asthma
TEACHING
Fundamentals of Applied Statistics
Biography
Joseph L. Smith, PhD, MPH, MBA, is a life sciences strategist and health services researcher specializing in real-world evidence (RWE), health economics and outcomes research (HEOR), and patient-centered research. He serves as Senior Director of Scientific Solutions & Strategy at Inspire, where he leads the group responsible design and execution of evidence-generation strategies that connect data to decision-making across medical affairs, market access, and clinical development. With over 15 years of experience spanning academia, payer organizations, and industry, his work focuses on translating complex data into actionable insights that improve patient outcomes and support value demonstration.
Dr. Smith also works at the intersection of AI and healthcare, applying machine learning and generative AI to enhance evidence generation, automate research workflows, and improve data integration and analysis. His recent work includes developing AI-enabled approaches for protocol design, analytic validation, and insight generation, as well as exploring how large language models can be applied to real-world data and scientific communication. He has authored multiple peer-reviewed publications and previously taught graduate-level biostatistics and U.S. health policy at Thomas Jefferson University, where he continues to contribute to advancing data-driven, technology-enabled healthcare and mentoring the next generation of researchers and leaders