Track 15: Artificial Intelligence

Artificial intelligence in nursing

Artificial intelligence (AI) encompasses a wide range of healthcare technologies that are transforming nurses‘ roles and improving patient care. In healthcare, AI typically refers to a computer’s ability to convert data into knowledge on its own to guide decisions or autonomous actions. However, precisely defining AI can be difficult due to its wide range of applications, which include risk prediction algorithms, robots, and speech recognition—all of which augment nursing practice and are rapidly changing healthcare as a whole.

Clinical decision support, mobile health and sensor-based technologies, voice assistants, and robotics are all examples of nursing AI tools. (Visit myamericannurse.com/how-artificial-intelligence-is-transforming-the-future-of-nursing for an introduction to AI, including definitions of machine learning, deep learning, and other related terms.

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Clinical decision support

Clinical decision support tools (such as EHR alerts, clinical practice guidelines, order sets, reports, and dashboards) improve nurses’ ability to make clinical decisions. They may provide information to the end user or actionable options based on the data. Clinical decision support may also be integrated into other tools, such as mobile health applications, in addition to the EHR. When combined with AI, clinical decision support can make predictions and recommendations with greater accuracy and specificity than humans. To prevent catheter-associated urinary tract infections, AI-based clinical decision support includes automatically generated nursing diagnoses, fall risk prediction, and guided decision trees.

These tools’ concepts are not novel. For example, fall risk prediction entails regular assessment and fall precaution implementation. Manual risk calculation, on the other hand, is time-consuming and prone to human error, resulting in inaccurate predictions. Over traditional methods, AI has three advantages:

• The ability to consider large amounts of data quickly in risk prediction
• increased intervention specificity (accurately flagging patients most at-risk)
• Variable selection and calculation are automated.
• AI correctly identifies at-risk patients by taking into account more diverse patient information from the EHR and other data sources.

Mobile health and sensor-based technologies

The COVID-19 pandemic altered patient care delivery by necessitating the retrieval of data from patients remotely and between clinic visits. Mobile health (health) and sensor-based technologies have the potential to reshape a nurse’s ability to deliver care and monitor patients, which account for more than 75% of healthcare spending in the United States.

Mobile health technologies (smartphones, smartphone apps, and wearable technologies) aid in the management of chronic illnesses by receiving and transmitting data directly between patients and providers, resulting in a comprehensive picture of a patient’s health in their daily environments.

Voice assistants and robotics

Voice assistants (such as Amazon Alexa and Google Assistant) may have a future in EHR applications, gathering patient data in the home and delivering interventions to supplement care. Consider the following scenario: a nurse uses Alexa to remind older adults to take their medications and to check their blood pressure. Alexa then enters patient information into the EHR for the nurse to review. Because of their voice-based interaction, these tools may be especially useful for older adults and patients with certain disabilities, such as poor eyesight. The value of voice assistants is dependent on nurse involvement in technology selection, implementation, and patient care.

Researchers have been using AI for several decades, but its application in practice is still relatively new. When nurses use artificial intelligence, such as clinical decision tools, they can quickly process large amounts of data to identify risks, recommend interventions, and streamline workflow. However, in order for AI to truly transform nursing practice, limitations must be addressed with nurse input.

Sub-tracks of Artificial intelligence in nursing

• Smartphones and wearables.
• At-home or portable diagnostics
• Smart or implantable drug delivery mechanisms
• Digital therapeutics and immersive technologies
• Genome sequencing
• Artificial Intelligence
• Robotics and automation
• The connected community

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