Clinical intelligence: The rise of AI-enhanced decision-making in ANP/AMP practice

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Reference: September-October 2025 | Issue 5 | Vol 18 | Page 20

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Artificial intelligence (AI) is rapidly transforming healthcare delivery and clinical workflows across the globe. Healthcare systems are becoming increasingly complex due to a combination of demographic, epidemiological, and systemic pressures. An ageing population is contributing to higher rates of multimorbidity and greater healthcare utilisation, placing added strain on services and resources.

At the same time, the prevalence of chronic diseases such as diabetes, cardiovascular conditions, and respiratory illnesses continues to rise, requiring long-term management, multidisciplinary care, and personalised treatment approaches. Advanced nurse practitioners (ANPs) and advanced midwife practitioners (AMPs) are expected to respond to these pressures with expert clinical judgement and timely decision-making. These challenges are further compounded by growing patient expectations, workforce shortages, and the need for more efficient, data-driven care delivery.

As AI continues to rapidly evolve and reshape healthcare delivery, ANPs and AMPs will increasingly be required to integrate AI-supported decision-making into their everyday clinical practice.^1,2,3

As healthcare becomes more data-driven, adopting AI tools will be essential for enhancing diagnostic accuracy, streamlining workflows, and improving patient outcomes. AI offers new possibilities for improving clinical reasoning and improving outcomes.

Staying at the forefront of modern healthcare will demand not only an understanding of AI technologies, but also the ability to critically evaluate and apply AI-generated insights to enhance clinical reasoning, diagnosis, and patient management. Incorporating AI tools effectively will enable ANPs and AMPs to improve workflow efficiency, personalise patient care, and support evidence-based interventions.

To fully harness these benefits, practitioners must also engage in ongoing education, ethical deliberation, and interdisciplinary collaboration to ensure that AI is used safely, responsibly, and in alignment with professional standards of care.^1,2,3

Defined as the ability of machines to perform tasks that typically require human intelligence, AI now contributes to multiple aspects of healthcare delivery. Within advanced nursing and midwifery practice, AI tools can provide support in diagnosis, risk stratification, documentation, and personalised care planning.

As practice boundaries expand, ANPs and AMPs must be equipped to critically appraise and ethically integrate these technologies into their clinical environments.^1,2,3

AI in clinical decision support

AI applications in healthcare encompass a range of capabilities. Predictive analytics uses statistical and machine learning models to forecast patient outcomes such as risk of readmission, deterioration, or disease progression.

Natural language processing (NLP) allows AI systems to analyse and interpret unstructured clinical notes and patient narratives. In diagnostics, computer vision enables pattern recognition in imaging studies such as radiology or dermatology.

Symptom checkers and triage algorithms assist in early assessment by generating differential diagnoses based on patient-reported symptoms. These AI functions are commonly embedded in clinical decision support systems (CDSS), which are designed to provide real-time, evidence-based recommendations to clinicians at the point of care. CDSS tools are not intended to replace the clinical judgement of ANPs, AMPs, or other healthcare professionals, but rather to augment it by providing timely insights drawn from large datasets.^1,2,3

The integration of AI into clinical decision-making holds the potential for improving healthcare outcomes. AI systems can analyse various forms of patient data, including medical records, lab results, and imaging tests, providing healthcare professionals with evidence-based insights and recommendations.³

This can lead to more accurate diagnoses, personalised treatment plans, and better patient outcomes. AI algorithms contribute to clinical planning by identifying patterns, detecting anomalies, and predicting potential risks.⁴ AI’s capability to quickly and efficiently process large volumes of data can enhance decision-making accuracy, performance, and responsiveness, ultimately addressing the individual needs of each patient in clinical settings.⁵

Healthcare professionals have voiced concern that AI could potentially replace human judgement in clinical decision-making, raising important questions about the future role of clinicians and the preservation of patient-centred care.^1,2 Clinicians often worry that relying too heavily on AI could undermine the essential human elements of care, such as empathy, communication, and critical thinking.

The reluctance of healthcare professionals to embrace AI can be attributed to several factors, including apprehensions about potential job displacement, limited familiarity with AI technologies and concerns regarding their reliability and accuracy.³

While AI can assist with data analysis and offer decision support, it cannot replicate the nuanced judgement, ethical considerations, or understanding of patient context that experienced practitioners bring to clinical practice.

While AI holds great promise in enhancing clinical decision-making, it should be regarded as a complementary tool, supporting, but not replacing, the expertise and judgment of clinical practitioners. Maintaining the central role of professional judgment and preserving the human connection with patients is essential to delivering compassionate, high-quality care.^1,2,6

In Ireland, the Digital for Care: A Digital Health Framework for Ireland 2024-2030 outlines a national vision for harnessing digital technologies, including AI, to enhance care delivery while prioritising safety, equity, and patient empowerment.

The framework recognises the critical role of clinical leadership, particularly from advanced practitioners, in ensuring the ethical and effective integration of AI into healthcare systems. It also highlights the importance of building digital capacity across the health workforce through continuous professional development, digital literacy initiatives, and active engagement with emerging technologies to support high-quality, data-informed care.⁷

AI-powered clinical decision support systems have the potential to enhance healthcare and advanced nursing and midwifery practice. By analysing patient data like medical records, lab results, and imaging tests, AI can provide evidence-based insights that help make accurate diagnoses and create tailored treatment plans. AI algorithms can identify patterns, detect anomalies, and predict risks, improving clinical decision-making and patient outcomes. Its ability to process large volumes of data quickly allows for faster, more responsive care in clinical settings.³

AI can support nurses and midwives by offering real-time recommendations and identifying trends that may otherwise be overlooked. It can assist in predicting patient outcomes and personalising treatment strategies, ultimately enhancing patient monitoring by detecting early signs of deterioration.

While AI offers opportunities to advance healthcare delivery, its successful integration depends on overcoming important issues including data integrity, safeguarding patient privacy, and ensuring clear, transparent decision-making processes.³

AI algorithms have the capacity to improve care coordination within healthcare systems. Tasks such as appointment scheduling and resource management can be automated, increasing efficiency and reducing administrative burden. AI-driven platforms can enhance communication and collaboration among healthcare teams by enabling real-time information sharing, which helps reduce delays in care delivery. By analysing patient data, these systems can identify gaps in care and suggest timely, appropriate interventions.³

AI presents promising opportunities to support advanced practice, particularly in enhancing clinical decision-making. By processing large volumes of patient data, AI can assist in identifying patterns, recognising early signs of deterioration, and offering evidence-informed suggestions that may contribute to more accurate diagnoses and care planning. These tools have the potential to support risk assessment and help tailor treatment strategies to individual patient needs, contributing to efficient and responsive care.^3,8

AI-powered tools have the potential to significantly improve the distribution of nursing and midwifery workloads by promoting more efficient and equitable allocation of resources. These systems can analyse key variables such as patient acuity, staffing levels, and overall workload to support well-informed task assignments.

By factoring in elements like patient complexity, nurse/midwife experience, and time-sensitive care needs, AI could help ensure that work is distributed fairly across the team. This would not only reduce the risk of staff fatigue and clinical errors, but also contribute to higher job satisfaction among nurses and midwives.

Optimising workload distribution through AI would allow nurses and midwives to focus more on direct patient care, supporting both staff wellbeing, and improved patient outcomes.^3,8,9

The adoption of AI in nursing and midwifery should be approached with careful consideration, acknowledging its potential to improve practice while carefully considering the complexities and challenges it may bring. The accuracy and reliability of AI systems are highly dependent on the quality, diversity, and representativeness of the data on which they are trained. Limitations in data can lead to biased or incomplete recommendations.

While AI can aid clinical judgement, it cannot replace the critical thinking, ethical reasoning, and compassionate care that are core to nursing and midwifery practice. Concerns around data privacy, patient consent, transparency in algorithmic decisions, and equitable access must be carefully considered. Ensuring that AI remains a supportive tool rather than a substitute for clinicians’ judgement is important to preserving patient-centred, safe, and accountable care.³

Preparing nurses and midwives for the future: AI education and training

To enhance patient care, it is important for nursing and midwifery professionals to gain an understanding of AI concepts, including machine learning, data analysis, NLP, and AI algorithms.¹⁰ They must also be familiar with AI ethics, privacy concerns, and security measures.

Strong critical thinking skills are important for nurses and midwives to interpret AI insights and apply them effectively in practice.¹¹ They should also recognise AI’s limitations and potential biases, ensuring human oversight remains central in decision-making.¹²

Integrating AI education into nursing and midwifery curricula involves offering dedicated courses on core concepts, healthcare applications, and ethical considerations.¹³ Practical experiences such as hands-on AI tool usage and case studies can deepen understanding.

Collaborations with AI experts and healthcare organisations could provide valuable resources and insights.¹⁴ Ongoing education and involvement in AI-related conferences or workshops are important to keeping nurses and midwives updated with technological advances.¹⁵

Collaboration between academic and healthcare institutions is important for developing effective AI training for nurses and midwives. Joint efforts could include creating curriculum guidelines, simulation labs, and clinical placements in AI-focused environments. Collaborative research projects can explore AI’s impact on nursing, midwifery, and patient outcomes, ensuring education aligns with emerging healthcare needs and advancements in AI.¹⁶

Healthcare professionals must be mindful of the potential risks associated with over-reliance on AI in their personal academic and professional development. While AI can assist with research and writing tasks, excessive dependence on these tools for academic progression, such as writing assignments, journal articles, or other scholarly work, can lead to a loss of critical thinking and originality, and pose a significant risk of plagiarism.

AI-generated content may not always be fully accurate or appropriately cited, as AI systems rely on patterns and data from large datasets rather than first-hand knowledge or critical evaluation of sources. These tools may produce information that appears plausible, but could be factually incorrect, outdated, or incomplete. Maintaining personal accountability in the production of academic work is important for upholding the integrity and ethical standards of the healthcare profession.

Healthcare professionals must ensure that they continue to engage deeply with their subject matter, applying their own expertise, critical analysis, and creativity, rather than relying on AI as a shortcut.¹⁷

Continuous professional development in AI and emerging technologies

To stay current with the fast-evolving field of AI, nurses and midwives need to commit to ongoing professional development. This can involve attending conferences, webinars, and workshops, as well as pursuing self-guided learning through AI-related courses and digital education platforms.¹⁸

Engaging with AI experts and interdisciplinary teams, alongside active involvement in professional networks and scholarly journals, enables nurses and midwives to stay informed about emerging technologies and best practices, ensuring they are well-prepared to integrate AI effectively into clinical care.¹⁹ Structured training programmes, developed with support from universities, industry, and professional associations should cover AI fundamentals, clinical applications, and hands-on tool use.²⁰

For AI tools to be successfully implemented and accepted in healthcare, thoughtful planning and strategy are essential. This can involve running pilot projects, involving early adopters, and collecting feedback to refine AI algorithms and processes. Collaboration among IT teams, clinicians, and other stakeholders is important to ensure that the technology meets user needs and aligns with the goals of the organisation.³

AI is no longer a distant concept, but a present reality in healthcare, and whether we like it or not, it is here to stay. It would be naive to assume that this technology will not continue to develop and expand its capabilities. It is important to view AI as a tool that enhances, rather than replaces, human intervention and intelligence. ANPs/AMPs must harness its potential to support clinical decision-making, improve patient care, and optimise healthcare delivery.

However, this must be done with a focus on ethics, protecting patient data, ensuring transparency in AI-generated insights, and addressing potential biases in the system. By using AI responsibly and ethically, ANPs and AMPs can ensure that it complements their expertise, enhancing their ability to provide high-quality, compassionate care. AI should not replace the human element of healthcare and advanced practice, but act as a valuable tool to empower practitioners to deliver more personalised, efficient, and accurate care.