The use of Artificial Intelligence in the out of hospital care settings: A Scoping Review
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Background
Out of hospital services face significant challenges, including growing patient demand, workforce limitations, and evolving care pathways. Artificial Intelligence (AI) technologies offer potential solutions, but their application in out-of-hospital settings remains inconsistently implemented and poorly understood.
Objectives
To identify the types of AI technologies being applied in out-of-hospital settings, explore their purposes and implementation contexts, and examine associated outcomes.
Methods
Six electronic databases were searched for English-language studies published between 2013-2024. Eligible studies involved AI technologies in the out-of-hospital emergency services setting. Data were synthesised according to five implementation domains: system level, dispatch zone, response zone, on-scene zone, and onward prognosis.
Results
From 236 publications, we identified diverse AI applications across the care pathway. System-level implementations (46 studies) featured AI for demand forecasting, optimal resource allocation, and strategic facility location, with demonstrated improvements in coverage efficiency of 10-20%. In the dispatch zone (32 studies), AI-enhanced emergency call triage and ambulance allocation reduced response times by up to 10-20%. Response-level applications (43 studies) included intelligent traffic management and real-time route optimisation, reducing travel times by 15-30%. On-scene zone implementations (75 studies) supported clinical decision-making with cardiac arrest rhythm detection, achieving an area under the curve (AUC) values exceeding 0.90 and acute coronary syndrome prediction sensitivities of 85-90%. Onward prognosis models (19 studies) predicted patient outcomes with AUC values of 0.80-0.90 for survival forecasting, enabling better resource allocation and early intervention. Further inferential analysis applications (21 studies) were also identified that provide higher-level insights through secondary analyses of out-of-hospital data.
Conclusions
AI demonstrates significant potential across the care pathway, from operational optimisation to clinical decision support. Future development should focus on real-time adaptive systems, ethical implementation, improved data integration across the care continuum, and rigorous evaluation of real-time patient outcomes. Cross-disciplinary collaboration and standardised reporting of AI implementations will be essential to realise the full potential of these technologies in improving out-of-hospital care delivery.
Key Messages
What is already known on this topic
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AI technologies show theoretical promise for enhancing out-of-hospital care services, but limited information exists about their implementation and real-world impact.
What this study adds
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This first comprehensive scoping review identifies a critical implementation gap: of 236 publications describing AI applications in out-of-hospital care, fewer than 15% report functional clinical deployments, and fewer than 5% document sustained implementation with evaluation.
How this study might affect research, practice or policy
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Our findings suggest an urgent need to shift focus from developing novel AI applications to implementing and evaluating existing ones, addressing key barriers, including technical integration challenges, regulatory hurdles, evidence requirements, and organisational change management.
