How artificial intelligence is transforming the future of nursing
According to Merriam-Webster, artificial intelligence (AI) is defined as a machine’s capacity to imitate intelligent human behaviors, such as reasoning and problem-solving. In healthcare, AI frequently refers to computer software programs designed to interpret data (for example, patient records, administrative claims, medical imaging, and data from mobile devices), learn from that data, and inform clinical and operational decision-making. In 2018, Becker’s Health IT reported that healthcare AI was valued at more than $2 billion and projected to exceed $36 billion by 2025. Investment in AI is increasing as healthcare organizations seek to improve care and lower costs.
Healthcare AI isn’t the stuff of science fiction; it uses computational algorithms with the electronic health record (EHR) as the data source. Although work is being done to develop “robotic clinicians” to automate human activities, this AI application isn’t common, nor is it the primary focus of research and development. In fact, the National Academy of Sciences urges researchers and industry leaders not to prioritize developing task-automation AI; instead, AI should be developed to support tasks and reduce clinician burden.
In this article, we’ll clarify what AI means in the context of healthcare today and provide examples of how AI currently is used to support nurses and the care they provide.
AI can be difficult to define. The primary reason for this ambiguity is the breadth of what AI encompasses—different industries focus on widely different applications, and the completely contextual nature of technology complicates its definition. In other words, AI shifts depending on the person or organization that provides the definition. For example, would you consider a simple calculator to be AI? Probably not. But what if we presented such a technology to someone 100 years ago? In other words, context matters.
In healthcare, we typically define AI as tools (such as machine learning, deep learning, and other applications) that autonomously transform clinical data into knowledge used by patients, clinicians, and family members to make decisions that otherwise couldn’t be efficiently accomplished. (See AI definitions.)
Nurses deliver the best possible care by engaging in core practices such as assessment, planning, and outcome evaluation. Few nurses, however, possess an understanding of AI applications—including machine learning, deep learning, and natural language processing (NLP)—and their implications for nursing research and practice, as well as their potential role in improving patient care and health outcomes. (See AI safety and ethics.)
Much of the hype around AI in healthcare is due to the potential of machine learning. Simply put, machine learning refers to the use of a computer program to autonomously learn from data to perform a certain task. The “learning” refers to software self-adjustment that fine-tunes an algorithm over time to increase accuracy. The goal of the machine learning tool, and the data it has access to, is determined by its developer, but how the program uses the data isn’t known. This inherent uncertainty is called the “black box.”
Similar to any data-dependent tool, a machine learning tool’s function and use are only as good as its data sources. This is where nurses are needed. Nurses with a boots-on-the-ground perspective understand patient care and the information that’s required to make informed clinical decisions. Nurse input improves the applicability and accuracy of machine learning tools.
For example, Wang and colleagues developed a tool to predict fall severity to assist in preventing injury in high-risk patients. This algorithm used data points such as age, sex, race, bone density, procedural data, and diagnoses to develop a risk score for the likelihood of having a fall with severe injury. The researchers used retrospective data to train the model, allowing it to learn and create an accurate prediction score.
