Empirical Evaluation of the Emergency Care Ecosystem Using Artificial Intelligence for Decision-Makers' Support

Abstract

In a healthcare ecosystem, artificial intelligence (AI) enables more agile and precise interventions, improving the diagnostic process, particularly in medical emergencies, where time is a critical element in decision-making. In this context, AI mitigates human errors, improves decision-making, and results in clinical outcomes with fewer risks for patients. However, its implementation faces challenges, such as limited infrastructure, resistance to technological adoption, and ethical concerns related to data privacy. Based on the above, this research investigates the enablers and inhibitors of AI to speed up the flow of information and decisions in hospital emergencies. A multiple case study was conducted with actors linked to the emergency sector to achieve this objective. Based on the comparative analysis of the cases, we identified six inhibitors and eight facilitators for the adoption of AI in healthcare ecosystems. Strategies that overcome technical and cultural barriers are essential for effective implementation without compromising humanized care. The findings indicate that adopting AI can transform and improve the agility and accuracy of decision-making processes, especially in emergency services, such as triage and prioritization of care. The study reinforces the need for sustainable integration of AI as a transformative means to improve operational efficiency and the accuracy of care provided in AI-based emergency services.

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