Ready or Not, More AI Tools are Coming to Your Nursing Practice Setting by Christine Riley, MSN, RN
Introduction Every nurse remembers the day they took their licensure exam. If a nurse was licensed before 1994, the nurse may have even taken that exam with a pencil and test booklet and waited up to twelve weeks for the results! The National Council of State Boards of Nursing (NCSBN) began conducting studies with computer adaptive testing (CAT) in 1986 and administered the first National Council Licensure Examination (NCLEX) exam via CAT on April 1, 1994 (NCSBN, 2014). With CAT, the computer automatically selects questions from a pool and adapts subsequent questions based on previous responses. For many nurses, this exam may have been one of their first exposures to artificial intelligence (AI) in the field of nursing. The umbrella term AI describes the tools and techniques which are used to teach computers to mimic human-like cognitive functions, such as learning, reasoning, communicating and decision making (Robert, 2019; von Gerich, et al., 2022). Thoughts of AI may go straight to human-like robots, but AI is much broader than that. Think about when a cell phone application for driving directions automatically reroutes the driver based on traffic patterns, or asking a digital assistant such as “Alexa” or “Siri” what the latest
news headlines are. The examples are endless and continue into the healthcare and nursing realms, such as smart insulin pens, or automated alerts that pop up after analyzing data in electronic health records. The purpose of this article is to give a glimpse into the state of AI as it relates to healthcare and safe nursing practice. This snapshot is not a comprehensive review as AI in healthcare is accelerating at a rapid pace and contains numerous types of technology, not all of which are discussed here. However, nurses should be aware of the general terms related to AI, and how to incorporate these tools in nursing practice while followingTexas Board of Nursing (BON or Board) rules and regulations. The research process for this article started with a review of NCSBN Environmental Scans over the last five years and a search in PubMed of the following terms in “title/ abstract”: artificial intelligence, AI, healthcare, health, AND care, and the following terms in any field: nurse AND nursing. Results were limited to those written within the last five years and with free access so readers could refer to resources as well. It should also be mentioned that an AI tool, ChatGPT, was used to create an initial outline for this article, which was then modified by this author. ChatGPT 11
is an example of a large language model (LLM). This type of model is designed to process enormous amounts of text data to learn language patterns and mimic human intelligence. See Diagram 1 on page 10 for a visual representation of the AI terms used in this article. Current Applications of AI in Nursing A term that comes up frequently in relation to AI is machine learning (ML). Rong et al.(2022) defined machine learning (ML) as the computerized modeling of various components of the process of acquisition of knowledge. This means ML is a computer’s way of simulating human learning by processing data and algorithms, learning from the data processed to evolve and improve performance over time. The ability of algorithms to self-learn and develop via practice is one of the most important aims of machine learning (Rong, et al., 2022). In one study, utilizing data from electronic health records, this ML approach was applied to find and rank predictive factors of hospitalized patient falls. With the ability to forecast factors that influence patient outcomes, prediction models have the potential to support healthcare provider decision making and ultimately improve quality of care (Lindberg, et al., 2020). cont. on next page
