Improving the usability of large emergency 911 data reporting systems: A machine learning case study using emergency incident descriptions.

Journal: Journal of safety research
Published Date: Jul 1, 2025

Abstract

INTRODUCTION: Emergency 9-1-1 incident data are recorded voluntarily within fire-department-specific computer-aided dispatch systems. The National Fire Incident Reporting System serves as a repository for these data, but inconsistency and variability in reporting practices across departments often lead to challenges in data quality and utility. This study aims to enhance emergency incident categorization and explore the feasibility of an automated system using free-text incident data from the National Fire Operations Reporting System (NFORS).

Authors

  • N Katherine Yoon
    Centers for Disease Control and Prevention (CDC)/National Institute for Occupational Safety and Health (NIOSH). Pittsburgh, PA, United States. Electronic address: NYoon@cdc.gov.
  • Tyler D Quinn
    West Virginia University. Morgantown, WV, United States. Electronic address: tyler.quinn1@hsc.wvu.edu.
  • Alexa Furek
    FORMER CDC/NIOSH. Austin, TX, United States. Electronic address: Amf180@pitt.edu.
  • Nora Y Payne
    CDC/NIOSH, Pittsburgh, PA, United States. Electronic address: ttt2@cdc.gov.
  • Emily J Haas
    CDC/NIOSH, Pittsburgh, PA, United States. Electronic address: wcq3@cdc.gov.

Keywords

External Resources

Popular Topics
Recent Journals