Tools
Tools
Barrado-Martín, Yolanda, Mathur, Sanjoli, Khadjesari, Zarnie 
ORCID: https://orcid.org/0000-0002-2958-9555, Clark, Sigrún Eyrúnardóttir, Vindrola-Padros, Cecilia, Lovat, Laurence B. and Stevenson, Fiona
(2026)
Anticipated Barriers and Facilitators to the Implementation of an AI Tool Advising on the Placement of Mitigation Strategies to Improve Air Quality in the Hospital Setting: Qualitative Study.
JMIR Human Factors, 13.
ISSN 2292-9495
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Abstract
Background: AI is increasingly prevalent within health care; however, its implementation within the hospital setting may come with its own limitations, given the high-pressure and sensitive environment. Barriers and facilitators to the implementation of AI within hospitals have been largely explored quantitatively, and there is a scarcity of qualitative research on the implementation of AI tools to improve air quality. Objective: This study aims to qualitatively explore, through a normalization 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. Specifically, we sought to (1) understand the day-to-day work of key hospital staff involved in reducing airborne transmission of infections, (2) identify potential barriers to the implementation of an AI tool to guide the placement of mitigation strategies in the hospital setting, and (3) recognize necessary conditions for the successful implementation of the AI tool within the hospital setting. Methods: We conducted ethnographic observations (n=4) and semistructured interviews (n=18) with hospital staff following preliminary one-on-one meetings and group discussions involving patients, members of the public, national leaders in ventilation working within academic and industry environments, and hospital staff (n=70). Data were collected between October 2025 and May 2026 in the United Kingdom. Interview transcripts, field notes, and summaries from preliminary wider engagement work activities were analyzed in NVivo (Lumivero; version 12) following codebook thematic analysis to facilitate confirmatory triangulation. Findings were organized according to the core constructs of NPT. Results: Hospital staff understanding of the AI tool and their preparedness to invest time in its implementation varied across those consulted. The anticipated work required by hospital staff to successfully implement the AI tool and its recommended mitigation strategies was identified as a significant challenge for its scalability. Differences in the ways of working among trusts and the competition for limited resources (collective action), and issues associated with hospitals’ physical estate (coherence) were seen as particularly problematic. The need for local champions (cognitive participation) was raised as a facilitator, as well as expert validation and the assessment of the AI tool’s effectiveness (reflexive monitoring). Conclusions: This preimplementation study suggests that the successful implementation of an AI tool to improve air quality in the hospital setting depends not only on the effectiveness demonstrated by the AI tool but also on the way trial outcomes are conveyed to hospital staff. Hospital staff anticipate an initial resistance to the AI tool’s implementation. Further, the impact of the AI tool and suggested mitigation strategies in workflows, costs, and staff and patient outcomes may be decisive.
| Item Type: | Article |
|---|---|
| Additional Information: | Data Availability: The datasets analyzed during this study are not publicly available due to their containing information that could identify participants and they did not provide consent to these data being shared outside the research team beyond anonymized quotes in publications. Data supporting the results have been included in this published article. If you would like more information about the nature of the data, please contact the corresponding author. |
| Uncontrolled Keywords: | barriers and facilitators,artificial intelligence,implementation science,hospital,cross-infections,air filters,infection control,qualitative research,normalization process theory,das in place,data availability the datasets analyzed during this study are not publicly available due to their containing information that could identify participants and they did not provide consent to these data being shared outside the research team beyond anonymized quotes in publications. data supporting the results have been included in this published article. if you would like more information about the nature of the data, please contact the corresponding author.,2026 ,/dt/atira/pure/keyword/DAStatement/das_in_place |
| Faculty \ School: | Faculty of Medicine and Health Sciences > School of Health Sciences |
| UEA Research Groups: | Faculty of Medicine and Health Sciences > Research Centres > Public Health Faculty of Medicine and Health Sciences > Research Groups > Implementation Science EAST Faculty of Medicine and Health Sciences > Research Centres > Norwich Institute for Healthy Aging Faculty of Medicine and Health Sciences > Research Groups > Behaviour Change EAST |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 15 Oct 2026 14:06 |
| Last Modified: | 18 Oct 2026 05:33 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/104798 |
| DOI: | 10.2196/93420 |
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