The rapid advancement of artificial intelligence means computer systems are now capable of performing tasks that typically require human intelligence, such as visual perception, speech recognition, decision making and translation between languages.

But what do those capabilities mean for nurses? How should they approach AI in clinical practice? Should they have a voice in the selection and purchase of AI systems? How might they encourage fellow nurses who may have fears about AI? What other challenges does AI present for nurse leaders, and how can they overcome them?

Anna E. Schoenbaum, DNP, RN-BC, is a registered nurse and vice president of applications and digital health at Penn Medicine/University of Pennsylvania Health System. She oversees enterprise EHR and clinical systems, clinical imaging, predictive health, medical decision support, digital applications and training/education.

Healthcare IT News sat down with her to get answers to these and other questions.

Q. From your vantage point, what do nurse leaders think about when they hear the words artificial intelligence? Are there fears?

A. Nurse leaders, alongside other healthcare leaders, hold diverse perspectives on AI. Many recognize its transformative potential in healthcare, including benefits such as improved disease management, more efficient patient monitoring, precision healthcare plans, the mitigation of medical errors, and enhancing the patient experience.

However, these prospects are accompanied by genuine concerns and fears.

Nurse leaders are acutely aware of critical issues like data security, patient privacy, ethical considerations and the potential impact of AI on the nursing practice. Their perspective often emphasizes the need for a balanced approach, harnessing AI's advantages while addressing these apprehensions.

Amid ongoing workforce challenges and clinician burnout, coupled with the emergence of more robust AI models and heightened public awareness, there has been a surge in interest in applying AI solutions in clinical workflows. This growing interest underscores the importance of developing a comprehensive understanding of AI deployment, how to apply AI most effectively, and adapting operational models to accommodate this transformative technology.

For nurse leaders to effectively navigate this rapidly evolving landscape and gain a deep understanding of AI solutions, they must play an active role. This involves active participation in shaping a mission-driven strategic plan and gaining a firm grasp of key enabling factors.

These factors encompass governance, talent acquisition and development, agile delivery methods, rigorous technology testing, effective data management, comprehensive training programs, and the establishment of robust monitoring capabilities. Nurse leaders should also possess a thorough understanding of the support model, which entails the ongoing monitoring and maintenance of AI solutions to ensure their continued success.

Q. Should nurse leaders be involved in the purchase or creation of clinical and administrative systems with AI? If so, how?

A. Nurse leaders should unquestionably play an active role in the acquisition or development of clinical and administrative AI systems. Nurse leaders should work collaboratively with information services, informatics and operational leaders to implement artificial intelligence solutions, such as predictive models, generative AI, wearables and digital health applications, but it will take time for the technology to be tested for accuracy, reliability and scalability.

Penn Medicine pursues a range of AI-based technology development from implementing AI-driven decision support tools to using AI-based predictive analytics. We take a collaborative approach to ensure we have the input of key stakeholders.

If we are deploying a patient care AI solution, the input of nurse leaders is invaluable in these endeavors as they provide clinical insights, validate AI algorithms and ensure AI aligns with nursing best practices.

By sharing their experiences, nurse leaders can inspire their peers to effectively embrace AI. For example, when integrating predictive AI models into patient care, nurse leaders are instrumental in defining the problem that they are trying to address, the scope and the requirements needed.

For example, the nurses can ensure AI models accurately predict patient readmissions, ultimately benefiting patient care. Nurse leaders actively participate in the selection process, collaborate with IT teams, and advocate for AI solutions that prioritize patient safety, data integrity and usability, including accuracy, fairness, bias and equity.

Nurse leaders also are engaged in training and education efforts to prepare their teams for the seamless integration of AI.

Q. What are a couple of the challenges nurses face when confronting AI systems in their daily duties? And how do you think nurses can overcome these challenges?

A. Challenges in implementing AI technology are multifaceted. Nurse leaders, particularly, need to be aware of the maturity of the AI solutions they are adopting, and the prerequisites required for their successful integration.

This involves a deep understanding of data requirements, data collection and the accuracy of the data used to train AI models. Additionally, ethical and legal considerations such as fairness and bias in AI algorithms, data privacy, regulatory compliance and security must be carefully addressed when integrating AI into healthcare settings.

In their daily routines, nurses encounter unique challenges, and AI technology can serve as a valuable tool to assist them in their practice. For nurses to seamlessly adopt new technology, AI tools must be integrated into their workflows in a way that enhances efficiency, improves patient care and eliminates redundancy.

Take, for instance, the implementation of predictive AI models. While the introduction of a new column or dashboard may initially overwhelm nurses, if it is seamlessly incorporated into their workflow, nurses can more readily adapt to these innovative technologies.

To effectively overcome these challenges, nurses can benefit from well-structured education and training programs that introduce them to AI technologies within their applications and healthcare technology tools.

Nurse leaders play a pivotal role in advocating for such training and ensuring nurses receive continuous support to become proficient in using AI systems effectively. Additionally, fostering open communication and providing clear explanations about how AI enhances their roles can help with adoption.

Collaborative efforts between healthcare institutions, nurse leaders and AI developers are essential to address data-related challenges and ensure the quality and integrity of data used by AI systems. By proactively tackling these challenges, nurses can harness the potential of AI to improve patient care and outcomes without compromising their vital role in healthcare.

Q. As an IT leader and nurse leader, what experiences have you had with AI to date, and how have they gone? What was your input into these AI systems?

A. In my role as the vice president of application and digital health, I have gained extensive experience in implementing healthcare technology solutions, and in the last three years, I have increasingly been exposed to AI implementations.

Collaborating with the CIO, CMIO, CNE, CMO and other executives, I spend a significant amount of my time working to develop and execute on IS strategy, which includes cutting-edge AI technology deployment. I also oversee the AI solution selection process, negotiate contracts, manage resources, oversee the implementation, and establish support models, which includes monitoring our AI models.

I am also responsible for developing and managing a large operating and capital budget for new technology solutions, and AI solutions have increased within our application portfolio.

At Penn Medicine, I work with our C-suite to establish a multidisciplinary governance structure to collectively ensure the delivery of sustainable technology that provides value to our patients, clinicians and operations teams.

Participating members encompass a diverse set of roles, including application analysts, informatics, data scientists, software engineers, data engineers, human-computer interaction design experts, ethicists, legal/risk/compliance/regulatory professionals, and executive champions.

The AI committee must align incentives across clinical, operational, technical and research teams. It streamlines predictive concepts into operations, promotes education and the mastery of relevant skills, collaborates with external stakeholders, and shares experiences with healthcare AI.

A framework is essential for the operationalization of healthcare AI, including defining objectives and goals, selecting models, use cases, and metrics that align with these objectives, gaining governance approval, evaluating data processing and model training for ethical, legal and security issues, managing identified issues, training and validating models, ensuring transparency and interpretability, engaging stakeholders and end-users in design and implementation, and continuously monitoring and evaluating AI models.

I also spend time on the unit observing workflow and the use of technology. Recently, I observed our virtual nurses and walked the critical care unit to see how they are leveraging technology. These observations help me stay abreast of challenges and opportunities in delivering care and will help me apply possible solutions, whether it’s AI tools or a change in workflow.

Q. AI is evolving rapidly in healthcare. Where do you see AI and nurses in five years?

A. At Penn Medicine and other health systems across the country, the intersection of technology and healthcare continues to increase in transforming the way medicine is practiced, benefiting both patients and providers.

Our team is dedicated to the pursuit of using technology to achieve service excellence in patient care, education and research. As we begin to incorporate AI into the design and delivery of innovative technology, we need to be thoughtful in our deployment and support model.

Over the next five years, AI is set to play an even more significant role in healthcare, with healthcare leaders leading this transformation. AI will become increasingly integrated into clinical workflows, aiding nurses in tasks such as data analysis and predictive care.

For instance, the integration of AI-driven risk scores will allow leaders to assess patients' risk levels for specific health events, such as pressure ulcers, enabling the implementation of personalized care plans to prevent adverse events.

Healthcare leaders also will play a pivotal role in shaping AI-driven quality improvement initiatives, using AI to analyze patient data and identify trends that inform changes in nursing protocols, ultimately enhancing patient outcomes and nursing practice efficiency.

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