JMIR Publications

ABSTRACT

Background:

Artificial intelligence (AI) is increasingly prevalent within healthcare, however, its implementation within the hospital setting may come with its own limitations given the high pressured and sensitive environment. Only a few studies have qualitatively explored barriers and facilitators to the implementation of AI within hospitals and there is a scarcity of qualitative research on the implementation of AI guided tools to improve air quality.

Objective:

This study aims to qualitatively explore, through a Normalisation Process Theory (NPT) lens, the anticipated barriers and facilitators to the successful implementation of an AI tool to guide the placement of mitigation strategies to reduce airborne transmission of infections within the hospital setting. Our objectives were to: a) Understand the day-to-day work of key hospital staff involved in reducing airborne transmission of infections; b) Identify potential barriers to the implementation of an AI tool to guide the placement of mitigation strategies in the hospital setting; and c) recognise necessary conditions for the successful implementation of the AI tool within the hospital setting.

Methods:

We conducted ethnographic observations (n=4) and semi-structured interviews (n=9) with hospital staff following preliminary and wider stakeholder engagement work involving patients, members of the public, national leaders in ventilation working within academic and industry environments, and hospital staff (n=70). Interview transcripts, fieldnotes and summaries from preliminary wider engagement work activities were analysed in NVivo 12, to facilitate triangulation through comparisons between data. Findings were organised according to the core constructs of NPT.

Results:

Hospital staff understanding of the AI guided tool and their preparedness to invest time in its implementation was variable across hospital staff at this pre-implementation stage (coherence and cognitive participation). The work hospital staff will need to do to successfully implement the AI guided tool and its recommended mitigation strategies, is expected to be a significant challenge for the implementation of the AI guided tool at scale given the differences in the ways of working among Trusts and the competition for limited resources (collective action). The effectiveness of the AI guided tool will need to be proved and anticipated concerns around its future refinement addressed (reflexive monitoring).

Conclusions:

This pre-implementation study suggests that the successful implementation of an AI guided tool advising on the placement of mitigation strategies to improve air quality in the hospital setting depends not only on the effectiveness demonstrated by the AI tool at trial stage but the way outcomes are shared with relevant stakeholders within the hospital setting. While initial resistance by hospital staff is expected, the impact of the AI tool and suggested mitigation strategies in workflows, costs and staff and patient outcomes, together with the way this information is conveyed to hospital staff may be decisive.