Assessing Revisit Risk in Emergency Department Patients: Machine Learning Approach.

Journal: JMIR AI
Published Date:

Abstract

BACKGROUND: Overcrowded emergency rooms might degrade the quality of care and overload the clinic staff. Assessing unscheduled return visits (URVs) to the emergency department (ED) is a quality assurance procedure to identify ED-discharged patients with a high likelihood of bounce-back, to ensure patient safety, and ultimately to reduce medical costs by decreasing the frequency of URVs. The field of machine learning (ML) has evolved considerably in the past decades, and many ML applications have been deployed in various contexts.

Authors

  • Wang-Chuan Juang
    Quality Management Center, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.
  • Zheng-Xun Cai
    Department of Information Management, National Sun Yat-sen University, Kaohsiung Taiwan.
  • Chia-Mei Chen
    Department of Information Management, National Sun Yat-sen University, Kaohsiung Taiwan.
  • Zhi-Hong You
    Department of Information Management, College of Management, National Sun Yat-sen University, Kaohsiung, Taiwan.

Keywords

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