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Predicting next-day discharge via electronic health record access logs

Author You Chen, PhD, FAMIA discusses this month's JAMIA Journal Club selection: 

Zhang X, Yan C, Malin BA, Patel MB, Chen Y. Predicting next-day discharge via electronic health record access logs [published online ahead of print, 2021 Sep 30]. J Am Med Inform Assoc. 2021; ocab211. doi:10.1093/jamia/ocab211 

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You Chen, PhD, FAMIA
Assistant Professor
Vanderbilt University Medical Center
Nashville, TN

You Chen, PhD, FAMIA, is an Assistant Professor of Biomedical Informatics at Vanderbilt University Medical Center. He is the director of the Optimization of Health ProcEsses and Networks Laboratory (OHPENLab). He uses sophisticated data mining, machine learning, and network analysis to mine the vast stores of data held in electronic health records, identifying patterns representing good practice in the implementation of collaborative patient-centered care. 

Dr. Chen’s research is funded through various grants from the National Institutes of Health (NIH) and National Science Foundation (NSF) to construct methodologies and technologies that optimize the healthcare process via the learning healthcare systems. 

Dr. Chen’s research foci include artificial intelligence in healthcare, network analysis in healthcare, care coordination, team science, telehealth, patient safety, predictive analytics, drug-drug interactions, and clinician burnout. Dr. Chen’s research findings were published in high-impact clinical (e.g., AJRCCM) and medical informatics journals (e.g., JAMIA, JBI, JMIR, and IJMI). Dr. Chen is an Associate Editor in the Journal Informatics and Smart Health. He holds a doctoral degree in computer science from the Chinese Academy of Sciences. He has been working in biomedical informatics since his graduation in 2010.


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


Ziyou Ren, PhD
University of Chicago, Center for Research Informatics
Chicago, IL

Statement of Purpose

Hospital capacity management depends on accurate real-time estimates of hospital-wide discharges. Estimation of patient discharge by a clinician requires a large amount of effort and, even when attempted, accuracy in forecasting next-day patient-level discharge is poor. A machine learning approach may support next-day discharge predictions by incorporating electronic health record (EHR) audit log data for discharge prediction. Such an approach can assist hospital administrators in more accurately predicting time of discharge, with the potential of aligning timely care services with a patient’s needs and streamlining inpatient flow of hospitals. 

Target Audience

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

Learning Objectives

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

  • Consider the use of EHR audit log data in an AI/ML approach to improving hospital discharge prediction
  • Reflect on the challenges and opportunities of using AI/ML to improve hospital discharge prediction


  • 35-minute presentation by article author(s) considering salient features of the published study and its potential impact on practice
  • 25-minute discussion of questions submitted by listeners via the webinar tools and moderated by JAMIA Student Editorial Board members 

Accreditation Statement

The American Medical Informatics Association is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.

Commercial Support

No commercial support was received for this activity.

Disclosures for this Activity

The following planners and staff who are in a position to control the content of this activity disclose that they have no relevant financial relationships with commercial interests/ineligible entities:

Presenter: You Chen

JAMIA Journal Club planners: Hannah Burkhardt,  Ziyou Ren, Kirk E. Roberts

AMIA staff: Susanne Arnold, Pesha Rubinstein


Dates and Times: -
Type: Webinar
Course Format(s): On Demand
Price: Free