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Navigating artificial intelligence in home healthcare: challenges and opportunities in nursing wound care
Halmstad University, School of Health and Welfare.ORCID iD: 0000-0002-5257-2524
Halmstad University, School of Health and Welfare.ORCID iD: 0000-0002-4341-660X
Halmstad University, School of Health and Welfare.ORCID iD: 0000-0001-7874-7970
Halmstad University, School of Health and Welfare.ORCID iD: 0000-0001-5865-2632
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2025 (English)In: BMC Nursing, E-ISSN 1472-6955, Vol. 24, no 1, article id 660Article in journal (Refereed) Published
Abstract [en]

Background: Artificial intelligence (AI) is increasingly introduced into healthcare, promising improved efficiency and clinical decision-making. While research has mainly focused on AI in hospital settings and physician perspectives, less is known about how AI may challenge the values that guide nursing practices. This study explores nurses’ perceptions of wound care in municipal home healthcare and the opportunities and challenges with the integration of AI technologies into their practices.

Methods: An exploratory qualitative study using semi-structured interviews was conducted with 14 registered nurses from two municipalities in Sweden. Participants were recruited through purposive sampling, and data were collected through individual interviews, either in person or via video call. Interviews were transcribed verbatim and analyzed inductively, inspired by the Gioia methodology. This approach allowed themes to emerge from the data while maintaining close alignment with participants’ perspectives. In a subsequent phase, the data were interpreted through the lens of Mol’s Logic of Care to deepen understanding of the relational, embodied, and adaptive nature of wound care. Ethical approval was obtained, and the study adhered to the Consolidated Criteria for Reporting Qualitative Research (COREQ).

Results: Three interconnected dimensions emerged from the data: relational, embodied, and adaptive practices. Nurses emphasized the importance of relational work in wound care, highlighting the trust and continuity necessary for effective wound care, which AI-driven automation might overlook. Embodied practices, such as sensory engagement through touch, sight, and smell, were central to wound care, raising nurses’ concerns about AI’s ability to replicate these nuanced judgments. Adaptive practices, including improvisation and situational awareness in non-standardized home environments, were presented as challenges for AI integration, as existing digital systems were perceived as rigid and often increased administrative burdens rather than streamlining care.

Conclusions: Home healthcare nurses’ perspectives highlight the complex interplay between technology and caregiving. While AI could support documentation and diagnostic processes, its current limitations in relational, sensory, and adaptive aspects raised the nurses’ concerns about its suitability for wound care in home settings. Successful AI integration should account for the realities of nursing practice, ensuring that technological tools enhance the embodied, relational, and adaptive dimensions of wound care. Applying Mol’s Logic of Care helps illuminate how good care emerges through ongoing, situated practices that resist full automation. Future research could further explore how AI aligns with professional nursing values and decision-making in real-world care settings.

 © The Author(s) 2025.

Place, publisher, year, edition, pages
London: BioMed Central (BMC), 2025. Vol. 24, no 1, article id 660
Keywords [en]
Artificial intelligence, Machine learning, Digitalization, Home healthcare, Municipal care, Wound care, Nursing, Nursing practice
National Category
Nursing
Research subject
Health Innovation, IDC; Health Innovation
Identifiers
URN: urn:nbn:se:hh:diva-56835DOI: 10.1186/s12912-025-03348-7ISI: 001511869800001PubMedID: 40537760Scopus ID: 2-s2.0-105008686607OAI: oai:DiVA.org:hh-56835DiVA, id: diva2:1981447
Funder
Halmstad UniversityKnowledge Foundation, 20200208 01 HKnowledge Foundation, 20210047 H 02Knowledge Foundation, 20170309
Note

This research is included in the CAISR Health research profile.

Available from: 2025-07-04 Created: 2025-07-04 Last updated: 2025-10-01Bibliographically approved

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Karnehed, SaraLarsson, IngridPetersson, LenaErlandsson, Lena-KarinTyskbo, Daniel

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