You cannot go on the internet or read an article nowadays without seeing at least something mentioned about the advancements and benefits of AI (which stands for artificial intelligence). Whether you see stories about ChatGPT or the progress of robotics, the world of technology is exploding with these developments. So what is AI, and most importantly, how is it shaping the future of nursing and health care?
A simple Google search will tell you that “AI stands for artificial intelligence, which is the ability of a computer or a robot controlled by a computer to do tasks that humans usually do because they require human intelligence and discernment. AI can involve perceiving, reasoning, learning, interacting with an environment, problem-solving, and exercising creativity.” AI can be applied to various domains, such as speech recognition, computer vision, natural language processing, robotics, etc. There are several ways this all fits into health care.
First, it’s important to note that using artificial intelligence (AI) in hospital and home health care settings isn’t a new phenomenon. It’s already currently used in many ways that are relevant to nurses. Anybody recently graduating from nursing school or currently employed by various health care facilities can attest to this fact. One of the first examples that come to mind is the mannequin “dummies” used in nursing simulation labs, which help students and experienced nurses learn and hone new or existing skills without using human subjects. Because the uses of AI in nursing are vast and naming all of them would be difficult, we’ll touch on some of the ones that stand out as nursing practice “game changers.”
Clinical decision support includes anything that enhances a nurse’s ability to make clinical decisions that impact client care or diagnosis. Some examples of this may be alerts appearing in a client’s electronic medical record or assistance with adapting clinical practice guidelines, order sets, or reporting. The use of AI could provide information to nurses as well as provide ideas for how to care for a client moving forward, all based on the data it collects. When using AI, the data collected for clinical decision support can offer predictions and suggestions for implementation with accuracy and specificity beyond human capacity, streamlining and making clinical decisions much easier for nurses.
With regard to AI augmenting client care for nurses, many more appealing advancements focus on actual client monitoring. Client monitoring is an essential component of nursing practice. Examples include manual vital signs monitoring such as palpation, auscultation, and visual inspection. Also, physical assessments of a client’s appearance and mobility are common forms of client monitoring. In addition, clinical observations, laboratory testing, and electronic monitoring, such as ECG and cardiac monitors, are also commonly used to monitor clients in critical care settings. Traditionally, these methods happen at fixed intervals (think every four hours, vital signs, or daily fall risk assessment), and they rely on human interpretation, which means they can be subjective or incomplete. They are also time-consuming and resource-intensive. But with the advancements in AI technology, new client monitoring methods have been developed to provide more detailed and accurate information. These developments include:
Accurate real-time data analysis: Analyzing client data in real time allows providers to monitor more frequently and quickly detect changes in a client’s health status.
Early detection of potential issues: AI in client monitoring can detect subtle changes in client data and analyze laboratory data to identify trends. This early detection could be highly beneficial in indicating the early onset of infections such as sepsis before they become life-threatening.
Personalized care: AI algorithms can analyze large data sets or medication data to identify potential drug interactions or side effects and recommend alternative medications or dosages.
Resource optimization and reduced nursing workload: AI can detect clients at high risk for readmission, recommend interventions such as home health visits or follow-up appointments, and make routine tasks such as data collection and analysis much more accessible by making them part of an automatic data collection.
The implementations above are the tip of the iceberg regarding how advancements in AI can assist in health care. However, there are some things to consider as technology moves forward. To help make the best decisions about AI tool use, actual nurses should be involved in all AI project development phases, from defining the problem to be solved by the tool being implemented to evaluating its impact on client care. AI application training should focus on why AI is needed and how it can improve client care. And while there may be challenges and potential drawbacks to using AI in health care, the key to unlocking its potential must include appropriate training and collaboration between health care professionals, technology experts, and policymakers. This critical step will ensure that AI is implemented responsibly and ethically, significantly advancing client care and drastically reducing nursing staff workloads.