Person-centred or nurse-centred: Nursing decision-making in the age of AI
Authors: Diana Santos1,2, Eduardo Santos1,3,4, Fernando Petronilho1,5, António Fernando Amaral1, José Miguel Seguro1,6, Mark Hayter7
1. Univ Coimbra, UICISA: E, ESEUC
2. Unidade Local de Saúde de Coimbra, Portugal
3. Polytechnic University of Viseu, School of Health, Portugal
4. JBI Portugal Centre for Evidence-Based Practice
5. University of Minho, School of Nursing, Portugal
6. Sanfil Medicina, Coimbra, Portugal
7. Manchester Metropolitan University, Manchester, UK
Involving people in nursing decision-making
Since the beginning of time, nursing has been seen as vital to patient-centred care. International organisations recognise the involvement of people in care as a strategy for improving health outcomes through a co-production approach to care. In nursing, specifically, this involvement can be integrated throughout the entire five-step nursing process, guiding decision-making and development, through the five stages of initial assessment, diagnostic activity, care planning, implementation and final evaluation.
Involving the patient throughout the entire nursing process enables them to become a partner in their care. This makes it possible to address their needs, considering their preferences, values and culture. This perspective is consistent with the promotion of both person-centred care and evidence-informed care, since evidence-based practice is described as clinical decision-making that takes into account the best available evidence, the context in which care is provided, the person’s preferences and the critical judgement of healthcare professionals.
However, achieving this level of involvement is not always easy in clinical practice. Time constraints, the demands of the care setting itself and the difficulty of implementing patient involvement can sometimes, albeit unintentionally, lead to a paternalistic (nurse-centred) approach. There is a gap between recognising the importance of patient involvement and successfully implementing it consistently in clinical practice.
Additionally, we also encounter patients who place the responsibility for decision-making on the nurse, often using phrases such as ‘you are the nurse, you know best’. This attitude may reflect various realities, such as a lack of knowledge about their rights, difficulty understanding the available information or even limited awareness of their own health status.
As a nurse in hospital practice (D.S.), promoting patients’ involvement in nursing decision-making is a constant concern. I, therefore, seek to create opportunities to listen to patients, understand what is important to them, provide the necessary information and involve them in making decisions whenever possible. This clinical experience has influenced my journey as a PhD student, in which I am developing a model designed to support patient involvement in nursing decision-making.
Nursing decision-making in the age of AI
In nursing, artificial intelligence (AI) can support data analysis, triage and decision-making, helping to increase efficiency, reduce the administrative burden and improve outcomes for people. However, its use requires continuous professional supervision and attention to issues such as bias, data privacy, legal liability and transparency. In this regard, a model is advocated in which AI supports, but does not replace, human clinical judgement. AI can support nursing care, but human connection, empathy and trust must remain at its core. In this context, explainable AI is particularly significant, as it makes AI predictions and recommendations more transparent and interpretable, helping healthcare professionals to understand how they are generated. This transparency can foster trust, accountability and ethical decision-making, while also facilitating the adoption of AI in clinical practice.
Perhaps one of the most tangible opportunities that AI brings to nursing is something much simpler: time. Nurses often cite a lack of time as an obstacle to meaningful contact with patients, while much of their working day is spent on administrative and coordination tasks, including communicating with support services, such as the pharmacy. If AI can significantly reduce this burden, its value could extend beyond mere efficiency. By freeing up nurses’ time, AI could enable them to spend more time on meaningful interactions with patients and on the relational aspect of care, which technology cannot replace.
From our experience, AI is beginning to influence nursing care and decision-making, although it is currently used informally. We are increasingly coming across AI through tools that nurses and patients use independently, rather than through AI systems that are formally integrated into clinical practice. This places us in a period of transition and presents a significant challenge: how can we incorporate these tools into nursing decision-making processes while maintaining a person-centred approach?
Lessons learned: Person-centred nursing decision-making and AI
There has been much discussion about people’s involvement in making decisions about their health, particularly through the co-creation of care and shared decision-making. At the same time, AI has been playing an increasingly significant role in healthcare. While some authors are already beginning to link these two themes in the new era of AI, this discussion is still in its infancy, and people’s preferences may not be given due consideration. As this conversation develops, there are a couple of important questions for us to ask: How is people’s involvement in decision-making regarding nursing care being promoted in the AI era? Is this involvement limited, or can it be enhanced? As these demonstrate, rather than asking whether AI should be part of decision-making in nursing, we should be asking how it can be integrated without silencing the person’s voice or undermining the nurse’s professional judgement.
There are also certain key considerations. On the one hand, when AI is used to support nurses’ decision-making, it is important that the decision-making process continues to be co-constructed with patients, with AI playing an advisory and/or supporting role to the nurse. In this regard, it is recommended that this AI-supported process should be validated by patients.
On the other hand, when patients themselves turn to AI for information to support their decisions, nurses can play a role in clarifying that information. If patients have the power to decide, it is important that the information underpinning their decisions is accurate. Nurses can facilitate this process, enabling patients and nurses to work together to make the best decisions.
Our clinical experience provides valuable perspective in this regard. We have observed an increasing use of AI tools by patients seeking health information. Some arrive in the post-operative period armed with information about possible complications, recovery times, diet, mobility and the care they may require. However, this information does not always reflect their specific situation or the best available evidence, making it difficult to assess critically. In the intermediate care unit, this issue is further complicated by patients not having access to their mobile phones, which makes it difficult to identify the sources they have consulted. In practice, we seek to understand the origin of the information, analyse it and discuss it with the patient to create opportunities for their informed involvement in decision-making.
In this way, our experience shows that nurses may play an important role in interpreting information introduced by patients through AI, considering the best available evidence and transforming information into knowledge, while promoting patient involvement and informed, person-centred decision-making.
Key messages/recommendations
- People should be partners in decision-making, not merely the recipients of AI-supported decisions: the patients’ voice must always be included
- AI can inform and support nursing decision-making, without replacing nurses’ professional judgement and patients’ involvement
- When people turn to AI for information, nurses may play an important role in clarifying the information, supporting decision-making and addressing misinformation
References
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Pearson, A., Wiechula, R., Court, A., & Lockwood, C. (2005). The JBI model of evidence-based healthcare. International Journal of Evidence-based Healthcare, 3(8), 207–215. https://doi.org/10.1111/j.1479-6988.2005.00026.x
Santos, D. G. S. M. D., Amaral, A. F. S., & Santos, E. J. F. D. (2024). Power to the people: Contributions to the involvement in decision-making about nursing care in hospital settings. Journal of Clinical Nursing, 33(12), 4525–4527. https://doi.org/10.1111/jocn.17492
Toney-Butler, T. J., & Thayer, J. M. (2023). Nursing process. https://www.ncbi.nlm.nih.gov/books/NBK499937/
Vitorino, L. M., Yoshinari Júnior, G. H., & Lopes-Júnior, L. C. (2025). Artificial intelligence in nursing: Advancing clinical judgment and decision-making. Revista Brasileira de Enfermagem, 78(4), e780401. https://doi.org/10.1590/0034-7167.2025780401
To link to this article - DOI: https://doi.org/10.70253/ZNKE8522
Links to additional resources
https://www.who.int/publications/m/item/artificial-intelligence-for-health
https://iris.who.int/items/9b35cc4a-8507-4b81-ae2b-fd4f0c13aa2c
Disclaimer
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