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Area of Science:

  • Health Informatics
  • Data Science
  • Emergency Medical Services Research

Background:

  • The 21st century witnessed significant expansion in emergency medical services (EMS) information technology, leading to vast data accumulation.
  • Integration of EMS systems with electronic health records (EHRs) and public databases is hindered by data structure inconsistencies, missing data, and regulatory barriers.

Purpose of the Study:

  • To present the history and fundamentals of record linkage techniques.
  • To provide an overview of historical applications of record linkage in EMS.
  • To explore future directions for integrating EMS data systems using advanced techniques, including machine learning.

Main Methods:

  • Review of historical and fundamental record linkage techniques.
  • Analysis of past applications of record linkage within EMS.
  • Exploration of emerging machine learning-based approaches for data integration.

Main Results:

  • Limited integration of EMS data with EHRs and public databases due to data quality and policy issues.
  • Record linkage science has evolved, offering potential solutions for EMS data integration.
  • Machine learning presents promising future avenues for overcoming integration challenges.

Conclusions:

  • Effective record linkage is crucial for overcoming barriers in EMS data integration.
  • Future integration efforts in EMS data systems will likely leverage advanced machine learning techniques.
  • Improved data integration can enhance the utility of EMS data within the broader healthcare ecosystem.