4. Facilitators and Barriers to the Adoption of Artificial Intelligence by Moroccan Nurses: an Exploratory Qualitative Study
Year: 2025
Type: article
Abstract: The adoption of artificial intelligence (AI) in nursing care in Morocco represents an indispensable opportunity to revolutionize professional practices, improve the quality of care and optimize clinical processes. However, this integration faces technological, organizational and ethical barriers, mainly the fear of a dehumanization of care. This study aims to investigate the factors driving AI adoption by Moroccan nurses and to propose strategies adapted to the national context in order to maximize the potential of AI. An exploratory qualitative study was conducted among 27 nurses working in public and private hospitals in Morocco, representing different clinical specialties. Data were collected through semi-structured interviews and then analyzed using a deductive thematic approach based on the framework developed by Braun and Clarke, which draws on the TAM-AIN theoretical model. Methodological rigor was ensured through independent double coding, triangulation by the researcher and validation of participants. Three main levers have emerged are: the development of skills through targeted continuing vocational training, the improvement of technological infrastructures and the interoperability of systems, the implementation of institutional measures to promote awareness, ethical support and interprofessional collaboration. The adaptation of AI tools to cultural and linguistic specificities was considered essential. Although artificial intelligence is seen as innovative and useful, it raises concerns about the loss of professional autonomy and the change in the relationship between caregiver and care receiver. An integrated strategy of training, technological investment and organizational support is essential for the gradual ethical introduction of AI in Moroccan healthcare.
Source: PRSM
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7101426508
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