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
Watch the Recording
Author
You Chen, PhD, FAMIA
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.
Manager
Hannah Burkhardt
Moderator
Ziyou Ren, PhD
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
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Reflect on the challenges and opportunities of using AI/ML to improve hospital discharge prediction
Format
- 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