Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Jun 19;24(1):660.
doi: 10.1186/s12912-025-03348-7.

Navigating artificial intelligence in home healthcare: challenges and opportunities in nursing wound care

Sara Karnehed  1 , Ingrid Larsson  1 , Lena Petersson  1 , Lena-Karin Erlandsson  1 , Daniel Tyskbo  2
Affiliations

Navigating artificial intelligence in home healthcare: challenges and opportunities in nursing wound care

Sara Karnehed et al. BMC Nurs. .

Abstract

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.

Clinical trial number: Not applicable.

Keywords: Artificial intelligence; Digitalization; Home healthcare; Machine learning; Municipal care; Nursing; Nursing practice; Wound care.

PubMed Disclaimer

Conflict of interest statement

Declarations. Ethics approval and consent to participate: This study was approved by the Swedish ethical review authority (Approval No. 2022-05837-01). The study followed the Declaration of Helsinki [51]. Informed consent was obtained from all participants involved in the study. Data were stored according to data protection regulations. Consent for publication: All authors have approved the submitted version of the article and have agreed to be personally accountable for their contributions and ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, have been appropriately investigated and resolved. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Data structure illustrating the progression from raw data to aggregate dimensions, showing the relationship between first-order concepts, second-order themes, and aggregate dimensions
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Ali O, Abdelbaki W, Shrestha A, Elbasi E, Alryalat MAA, Dwivedi YK. A systematic literature review of artificial intelligence in the healthcare sector: benefits, challenges, methodologies, and functionalities. J Innov Knowl. 2023;8(1):100333.
    1. Ali S, Akhlaq F, Imran AS, Kastrati Z, Daudpota SM, Moosa M. The enlightening role of explainable artificial intelligence in medical & healthcare domains: A systematic literature review. Comput Biol Med. 2023;166:107555. - PubMed
    1. Ventura-Silva J, Martins MM, Trindade LL, Faria A, Pereira S, Zuge SS, Ribeiro O. Artificial intelligence in the organization of nursing care: A scoping review. Nurs Rep. 2024;14(4):2733–45. - PMC - PubMed
    1. Wangpitipanit S, Lininger J, Anderson N. Exploring the deep learning of artificial intelligence in nursing: a concept analysis with walker and avant’s approach. BMC Nurs. 2024;23(1):529. - PMC - PubMed
    1. Ye J. The role of health technology and informatics in a global public health emergency: practices and implications from the COVID-19 pandemic. JMIR Med Inf. 2020;8(7):e19866. - PMC - PubMed

LinkOut - more resources