https://www.infirmiere-canadienne.com/blogs/ic-contenu/2024/10/21/au-dela-du-battage-lintelligence-artificielle
8 recommendations for how nurses can maximize their use of AI
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It is important for nurses to take the lead in thinking through how AI and an increasing number of generative AI applications are implemented in various contexts.
There is a lot of excitement about the use of artificial intelligence (AI), including generative AI, in health care. Applications such as natural language processing, medical image generation, drug discovery, and behavioural modelling are advancing the use of this technology at the point of care.
However, practical applications of AI, and generative AI within nursing specifically, are not well known among the profession, nor are the implications well understood. So, while there is some buy-in around the potential of AI technologies in nursing, there is also a feeling of caution, driven by such concerns as data bias and representation, ethics, reliability and interpretability, regulatory issues, and integration into nursing workflows.
This article highlights the potential use of AI and/or generative AI in nursing, and provides recommendations for how nursing can assume more prominent leadership to ensure that AI is implemented in meaningful and appropriate ways.
Use case 1: AI scribes and speech recognition technology
Documentation burden continues to be a major challenge for nursing. An AI scribe is an artificial intelligence-powered tool designed to assist nurses and other health professionals in documenting patient care in the electronic health record by listening to the clinical interaction and generating the preliminary content of documentation.
Speech recognition technology is similar. With this technology, nurses’ verbal dictation of the clinical interaction is transcribed into text in real time. The reasons behind documentation burden are complex, and so are the solutions. AI scribes and speech recognition technology may play an important role in addressing this challenge.
Use case 2: Clinical decision support system (CDSS)
Nurses are constantly making decisions to support care. Some of these decisions can be meaningfully supported by analyzing vast amounts of data that cannot realistically be done during in-the-moment case processes.
AI-powered CDSSs assist nurses in making clinical decisions by analyzing patient data, medical literature, and best practices to provide evidence-based recommendations. CDSSs can help nurses with such tasks as medication management, dosage calculation, and care planning. For example, these systems can alert nurses to potential drug interactions, suggest appropriate interventions based on patient history and current condition, and provide decision support for complex clinical scenarios.
Recommendations for nursing
It is important for nurses to take the lead in thinking through how AI and an increasing number of generative AI applications are implemented in various contexts. This scrutiny requires proactive and immediate efforts from the nursing profession to ensure that AI technologies are deployed ethically, responsibly and equitably.
Below are eight recommendations for how the nursing profession can and should act now to maximize our influence on this significant technological shift:
- Invest in education and training programs to familiarize nurses with AI technologies, including AI’s capabilities, limitations, and ethical considerations. It is paramount to provide opportunities for ongoing learning and skill development to empower nurses in practice and training to leverage AI tools in their future work where appropriate.
- Develop and adhere to ethical guidelines and standards for the responsible use of AI in nursing practice. This includes collaborating with professional organizations, regulatory bodies, and interdisciplinary stakeholders (including other health disciplines) to establish ethical frameworks that prioritize patient safety, privacy, autonomy, and equity in the face of this advancing technology. Advocacy for AI to be highlighted as practice standards and codes of ethics are revised and updated is a priority even as stand-alone frameworks are under development.
- Encourage nurses to evaluate AI solutions before implementation, considering such factors as data bias, transparency, accountability, and the potential impact on patient care and outcomes. It is important to engage in interdisciplinary discussions and to bring in the evidence base regarding the efficacy of AI technologies in nursing practice. This technology, generative AI in particular, has been shown in recent studies to be an effective generator of health misinformation. It is therefore critical that nurses monitor this risk in outputs for both their own use and, perhaps even more importantly, for the use of patients.
- Maintain a patient-centred approach to care delivery when integrating AI technologies into nursing practice. This includes prioritizing patient preferences and values in decision-making, and ensuring that AI tools enhance, rather than replace, the human aspects of nursing care. The concept of intelligence amplification (IA) encourages nurses to remember that these tools are means to expand ways of knowing in evidence-informed practice. Compassionate, humanistic care is, after all, a cornerstone of nursing.
- Foster collaboration and interdisciplinary communication among nurses, health-care providers, technologists, policy-makers, and patients to co-design/develop and implement AI solutions that meet the needs of diverse patient populations and health-care settings. To do so, nurses must engage in open dialogue to address concerns, share best practices, and promote transparency and accountability in AI implementation.
- Advocate for health equity, social justice, and the environment in the development, deployment, and evaluation of AI technologies. This must be done to ensure that AI solutions are accessible, inclusive, culturally appropriate, and advanced in consideration of environmental impact. The energy demands of AI’s computational needs are substantial, necessitating significant power consumption due to the operation and cooling of servers within data centres, among other functions. Nurses can be key players in advocating for policies and approaches that address this environmental concern, along with disparities in health-care access, outcomes, and representation in the ongoing development and integration of AI technologies. Health equity and social justice are also essential aspects of nursing practice in Canada that must be ensured with the deployment of AI tools.
- Establish mechanisms for continuous monitoring and evaluation of AI systems in nursing practice, including monitoring for biases, errors, and unintended consequences. Nursing can advocate to implement processes for feedback, quality improvement, and iterative refinement of AI to enhance its safety, effectiveness, and usability over time.
- Take an active role in professional leadership and governance structures to influence policy decisions, standards of practice, and regulatory frameworks related to AI in nursing. The profession must advocate for nurses’ voices to be represented in discussions about the ethical, legal, and societal implications of AI technologies in health care.
The nursing profession may stand to benefit significantly if AI, including generative AI, is meaningfully implemented to support nursing care and advance the profession. However, this implementation must be done with nursing leading the way. This article suggests recommendations for how the profession can do just that.
Gillian Strudwick, RN, PhD, FAMIA, FCAN, is a senior scientist and the chief clinical informatics officer at the Centre for Addiction and Mental Health.
Tracie Risling, RN, PhD, is an associate professor and the associate dean of innovation in the Faculty of Nursing at the University of Calgary.
The authors are members of the Canadian Nurse editorial advisory board.
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