{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T19:50:30Z","timestamp":1791921030394,"version":"4.3.4"},"reference-count":35,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Hum Factors"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n 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\u2019 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\u2019s effectiveness (reflexive monitoring).<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n 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\u2019s implementation. Further, the impact of the AI tool and suggested mitigation strategies in workflows, costs, and staff and patient outcomes may be decisive.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/93420","type":"journal-article","created":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T20:50:06Z","timestamp":1789073406000},"page":"e93420-e93420","source":"Crossref","is-referenced-by-count":0,"title":["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"],"prefix":"10.2196","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9912-6345","authenticated-orcid":false,"given":"Yolanda","family":"Barrado-Mart\u00edn","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0259-8184","authenticated-orcid":false,"given":"Sanjoli","family":"Mathur","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2958-9555","authenticated-orcid":false,"given":"Zarnie","family":"Khadjesari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5679-2535","authenticated-orcid":false,"given":"Sigr\u00fan Eyr\u00fanard\u00f3ttir","family":"Clark","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7859-1646","authenticated-orcid":false,"given":"Cecilia","family":"Vindrola-Padros","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4542-3915","authenticated-orcid":false,"given":"Laurence B","family":"Lovat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2139-2309","authenticated-orcid":false,"given":"Fiona","family":"Stevenson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,10,13]]},"reference":[{"key":"R1","unstructured":"New commission to help accelerate NHS use of AI. 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