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Prediction of patient disposition: comparison of computer and human approaches and a proposed synthesis

This on-demand webinar does not offer CE credit.

Lead author Yuval Barak-Corren, MD, MS, discusses this month’s JAMIA Journal Club selection:

Barak-Corren Y, Agarwal I, Michelson KA, et al. Prediction of patient disposition: comparison of computer and human approaches and a proposed synthesis [published online ahead of print, 2021 May 9]. J Am Med Inform Assoc. 2021;ocab076. doi:10.1093/jamia/ocab076

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Yuval Barak-Corren, MD, MS
Predictive Medicine Group, Computational Health Informatics Program
Boston Children's Hospital

Yuval Barak-Corren, MD, MS, is a researcher at the Predictive Medicine Group at Boston Children’s Hospital. His work focuses on the intersection of computer science and healthcare and includes the development of advanced prediction models for both clinical and operational purposes. In addition to his research work, Yuval is a practicing pediatrician and is currently pursuing a fellowship in pediatric cardiology at the Schneider Children's Medical Center of Israel. Yuval also serves as a consultant for startups and for the Israeli ministry of health on topics of digital health.


Hannah Burkhardt
PhD candidate
University of Washington School of Medicine
Biomedical Informatics and Medical Education


Chao Yan, PhD Candidate
PhD Candidate
Department of Electrical Engineering and Computer Science
Vanderbilt University

Statement of Purpose

Computer models have been developed to predict patient disposition from the emergency department (ED), yet it is not clear if their ability outperforms that of experienced ED clinicians. We sought to compare the accuracy of computer vs. human physician predictions, to study the factors driving each prediction, and to explore the potential synergies of hybrid physician-computer models.

Target Audience

The target audience for this activity is professionals and students interested in health informatics.

Learning Objectives

The general learning objective for all of the JAMIA Journal Club webinars is that participants will

  • Use a critical appraisal process to assess article validity and to gauge article findings' relevance to practice

After participating in this webinar, the listener should be able to:

  • Understand the strengths and weaknesses of both machine-learning and clinician derived predictions, in the context of predicting hospitalizations from the ED.
  • Learn ways to integrate computer and clinician predictions, and to acquire better understanding of the added value of such hybrid models.
Dates and Times: -
Type: Webinar
Course Format(s): On Demand
Price: Free for everyone; AMIA members and non-members