Incorporating Nursing Experience into Nursing Informatics

I am Hwayoung Cho, an assistant professor at the University of Florida College of Nursing. It’s an honor to share how my nursing experience sparked my interest in using technology — specifically artificial intelligence and nursing informatics — to reduce health care disparities and promote positive health outcomes.

Twenty years ago, as a registered nurse at an academic medical center, I was part of a team transitioning from paper-based charting to a new electronic health record system. While the goal of this new system was to improve care quality by providing timely, accurate, and up-to-date patient information, its information architecture, features, and function did not align well with clinical workflow. Nurses spent more time documenting data than providing direct care, which led to frustration. This experience prompted me to ask some critical questions: Who are the primary users of this system? Nurses. What is the system designed to do? Improve care quality. Were nurses involved in the system’s design? Probably not.

These questions led me to pursue a master’s program to learn the concepts of usability and evidence-based approaches for user-centered design and human-computer interaction. Further, during my PhD with a concentration in nursing informatics, I had opportunities to apply various usability evaluation methods to test the user interface and workflow design of several systems by considering the needs of our users in real-world workflow. I will, in upcoming blogs, provide a series of posts on these methodologies, so stay tuned for insights on selecting the right approach for your projects—before and after system development.

I remember when I was providing discharge education to my patients with chronic illnesses. I used to say, “I was glad to work with you, but I hope not to see you again here in the hospital.” Unfortunately, many were readmitted — often due to poor medication adherence, difficulty accessing health information, social determinants of health, and gaps in care continuity. In such cases, self-management is crucial for improved outcomes. I frequently asked myself, “How can we support these patients in managing their health in everyday life?” The answer lies in using technology in the right way.

By leveraging technology, I had an opportunity to develop a self-care intervention providing videos of symptom care strategies to help patients manage their health more effectively. My team incorporated electronic pill bottles capable of notifying patients if they missed their medication as prescribed, as well as fitness trackers, like Fitbit, to help them monitor physical activity. These tools were designed with the needs of vulnerable populations in mind — such as people living with HIV, who often don’t have access to computers but rely heavily on mobile phones. These insights helped us develop mobile health apps that directly addressed their needs.

My current work focuses mostly on HIV and aging. As HIV treatments advance, the life expectancy of people living with HIV also increases, but so does the prevalence of aging-related issues, like cognitive decline. In clinical practice, I observed that early intervention could significantly improve patient outcomes. This drove me to explore how AI, particularly machine learning, could harness the vast amounts of patient data collected through electronic health record systems to predict the risk of conditions like dementia in people living with HIV.

With health care data growing exponentially, AI has the potential to enhance our ability to diagnose, prevent, and treat medical conditions more effectively. By linking real-world electronic health records and claims data with social determinants of health, we are currently developing machine learning models to predict which individuals with HIV are at high risk for dementia. These predictive tools are designed to support clinicians in making the best clinical judgments and decisions by enhancing their ability to assess patient risks and intervene early. This reduces clinicians’ cognitive burden and improves decision-making, ultimately leading to more personalized care and better outcomes.

Most importantly, my nursing experience is the foundation of all my research. Even small ideas sparked by our curiosity can lead to meaningful change in nursing practice. Nurses provide most of the hands-on care for patients and often interact with technology; we can bring nursing experience into quality improvement projects or research. AI/technology is not going to replace nurses, but nurses using AI/technology are soon going to replace those not using it.

At UF Nursing, our informatics faculty are here to help you formulate research questions and acquire the technical expertise needed to address them. By combining clinical experience with technological innovations, we all together can create transformative solutions to improve patient care and address health disparities.

Dr. Hwayoung Cho’s research focuses on using technology to reduce health disparities, promote health outcomes and improve health-related quality of life for underserved populations. She teaches graduate nursing and nursing informatics courses for Doctor of Nursing Practice (DNP) programs.