Research

Interests

Methodology:
- data analytics, machine learning methods, game theory, econometric modeling, causal inference, decision analysis, simulation

Applications:
- health economics and outcomes, payment and reimbursement models, clinical decision-making, real-world evidence, pharmaceutical supply chain, telehealth

Publications

  1. A. ElHabr, S. Merdan, R. Duscak, M. Horný, T. Hanna, A. Prater, T. Ayer, D. Hughes (2022). Increasing Utilization of Emergency Department Neuroimaging From 2007 Through 2017. American Journal of Roentgenology, 218(1), 165-173. paper
  2. A. ElHabr, J. Katz, J. Wang, M. Bastani, G. Martinez, M. Gribko, D. Hughes, P. Sanelli (2021). Predicting 90-Day Modified Rankin Scale Score with Discharge Information in Acute Ischaemic Stroke Patients Following Treatment. BMJ Neurology Open, 3(1). paper

Working Papers

  1. A. ElHabr, T. Ayer, C. Zhang (2022). Outcome-based Pharmaceutical Contracting with Heterogenous Patient Groups.
  2. A. ElHabr, T. Ayer, J. Newsome, N. Kokabi, R. Smith, J. Gichoya (2022). Racial Disparities in Utilization of Interventional Radiology, Operative, and Non-operative Hemorrhage Management for Patients with Traumatic Injuries: A National Trauma Data Bank Study.
  3. A. ElHabr, T. Ayer, J. Katz, J. Wang, M. Bastani, M. Gribko, D. Hughes, P. Sanelli (2022). Development and Validation of an Administrative Claims Stroke Severity Score via Ensembling Across Multiple Databases.
  4. N. Shiban, J. Gaul, H. Zhang, A. ElHabr, N. Kokabi, J. Johnson, T. Hanna, J. Schrager, J. Gichoya, I. Banarjee, H. Trivedi (2021). Machine Learning Methods to Predict Survival in Patients Following Traumatic Aortic Injury. medRxiv. paper

Presentations