Facilitating the Implementation of Artificial Intelligence as Complex Health Interventions in Intensive Care Nursing

Abstract

Artificial intelligence (AI) has the potential to integrate and digest vast amounts of information to aid clinical decision-making and organise the logistics, processes and delivery of healthcare services, especially in the areas of patient data analytics, real-time and predictive diagnostics and syndromic surveillance and modelling. While research on AI-enabled technologies and applications in intensive care nursing are on the rise, much of this work remains exploratory and limited to retrospective model development, model testing and algorithmic prototyping. Studies that focussed on the clinical integration of AI in critical care nursing practice are scarce, or if present, have restricted generalisability, adaptability and usability. In this paper, we propose that instead of viewing AI-enabled technologies as simply computational technologies and predictive tools that need to be tested, such should be viewed as complex health interventions. Using a case, we mapped the implementation of AI-enabled technologies in intensive care nursing to the core elements of the UK Medical Research Council Framework for developing and evaluating complex interventions, and we presented a matrix of practical considerations that can be used when planning to implement a complex health intervention in the intensive care setting. By engaging with a complexity framework, nurses will be able to plan for and confront the components and interactions that make the integration of AI-enabled technologies into practice as complex, and aim for the implementation, integration and sustainability of AI-enabled technologies in intensive care nursing.

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