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Introduction <p>The use of integrated artificial intelligence (AI) in prehospital emergency services can not only increase the quality of services, but also help save patients’ lives and improve treatment outcomes. Given the challenges in emergency medicine, this technology is recognized as an effective tool for increasing the efficiency and accuracy of medical services. The present study aims to determine the barriers to the use of this technology in the provision of Emergency Medical Services (EMS).</p> Methods <p>This qualitative conventional content analysis was conducted in Iran using purposive sampling. Data were collected through in-depth interviews with 38 participants, including prehospital managers, Emergency Operations Center (EOC) officers, faculty members, and Information Technology (IT) specialists, conducted between November 2024 and May 2025. Data analysis followed Graneheim and Lundman’s approach, and Lincoln and Guba’s criteria were applied to ensure trustworthiness.</p> Results <p>The qualitative analysis of interviews identified seven main categories and nineteen sub-categories related to the barriers to implementing AI in Iran’s prehospital emergency services. These barriers fell into technical, human, legal, operational, data-related, managerial, and algorithmic domains. Key challenges included inadequate technological infrastructure, poor data quality and completeness, organizational resistance, lack of standardized protocols, legal ambiguities, and technical limitations of algorithms.</p> Conclusion <p>The implementation of AI in Iran’s prehospital emergency systems is a complex and multifaceted process that requires structural reforms, targeted policymaking, and cross-sectoral collaboration at the national level. Success in this endeavor depends on strengthening technological capacity, enhancing professional digital literacy, establishing data-driven ethical regulations, and adopting an integrated approach to digital transformation in health system governance.</p> Clinical trial number <p>Not applicable.</p>

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Struggling to integrate artificial intelligence in prehospital emergency care in a developing country: exploration of the Iranian experts’ views based on qualitative content analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月8日

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  • Marziye Hadian,
  • Nader Tavakoli,
  • Mohammadreza Jabbari Khanbebin,
  • Mohsen Nouri,
  • Aziz Rezapour,
  • Tahereh Shafaght,
  • Hojjat Farahmandnia

摘要

Introduction

The use of integrated artificial intelligence (AI) in prehospital emergency services can not only increase the quality of services, but also help save patients’ lives and improve treatment outcomes. Given the challenges in emergency medicine, this technology is recognized as an effective tool for increasing the efficiency and accuracy of medical services. The present study aims to determine the barriers to the use of this technology in the provision of Emergency Medical Services (EMS).

Methods

This qualitative conventional content analysis was conducted in Iran using purposive sampling. Data were collected through in-depth interviews with 38 participants, including prehospital managers, Emergency Operations Center (EOC) officers, faculty members, and Information Technology (IT) specialists, conducted between November 2024 and May 2025. Data analysis followed Graneheim and Lundman’s approach, and Lincoln and Guba’s criteria were applied to ensure trustworthiness.

Results

The qualitative analysis of interviews identified seven main categories and nineteen sub-categories related to the barriers to implementing AI in Iran’s prehospital emergency services. These barriers fell into technical, human, legal, operational, data-related, managerial, and algorithmic domains. Key challenges included inadequate technological infrastructure, poor data quality and completeness, organizational resistance, lack of standardized protocols, legal ambiguities, and technical limitations of algorithms.

Conclusion

The implementation of AI in Iran’s prehospital emergency systems is a complex and multifaceted process that requires structural reforms, targeted policymaking, and cross-sectoral collaboration at the national level. Success in this endeavor depends on strengthening technological capacity, enhancing professional digital literacy, establishing data-driven ethical regulations, and adopting an integrated approach to digital transformation in health system governance.

Clinical trial number

Not applicable.