AI In Healthcare: Essential Knowledge For Nursing Instructors
Many nursing instructors began their careers in an era when artificial intelligence wasn’t a significant component of healthcare or nursing education. While AI is now extensively utilized across medical settings, its advancement in nursing has been hindered by insufficient understanding and expertise.
A groundbreaking research article in the Journal of Nursing Education by nursing education experts Tonya Schneidereith, Ph.D., MBA, and Joseph Thibault, MBA, MEd, outlined the fundamental framework for nursing institutions to incorporate AI into their educational programs in a secure, responsible, and ethical manner.
The researchers noted that while AI presents promising opportunities, it’s important to recognize its double-edged nature. “While the technology’s adoption brings numerous advantages, it also poses challenges to academic integrity and proper graduate preparation. Many current nursing educators belong to generations that typically lack comprehensive AI knowledge.”
Below are several fundamental concepts and definitions that nursing instructors should understand, which have broader implications in healthcare delivery.
Artificial Intelligence
AI encompasses any technological system capable of replicating aspects of human intelligence. These systems evolve independently through continuous data analysis, creating smart machines and developing algorithmic solutions. The technology processes information, learns from it, and makes reasoned decisions based on input data.
Machine Learning
Computers demonstrate the ability to learn without explicit programming. Machine learning aims to replicate intelligent human behavior and problem-solving approaches.
These algorithms generate predictions by identifying data patterns and trends. They essentially self-program through experiential learning. Common applications include chatbots, predictive text features, and personalized recommendations like those seen in Amazon’s shopping suggestions and Netflix’s viewing recommendations.
Deep Learning
Deep learning utilizes multiple layers of “neural” networks to process information patterns too complex for human analysis, such as image recognition. These neural networks mirror human brain structure and function to process vast data sets. During the learning process, connection strengths can be modified.
Healthcare applications of deep learning include streamlining processes like diagnostic image analysis and monitoring patient self-management and medication compliance.
Natural Language Processing
Natural language processing (NLP) enables computer systems to understand and respond to human communication. This technology powers customer service chatbots and virtual assistants like Google Translate, Siri, and Alexa. Search engines employ NLP to comprehend webpage content beyond simple keyword matching. It also enables text-to-speech conversion, language translation, and interactive chatbots. NLP’s ultimate aim is to comprehend human language and generate appropriate responses.
Prediction Models
Prediction models forecast outcomes using historical data analysis. These systems calculate probabilities based on previous similar events and underlying patterns to predict optimal outcomes. In nursing, these models assist with risk assessments, including fall prevention and pressure ulcer risk evaluation.
Expert Systems
Expert systems are designed to augment human expertise by emulating the decision-making processes of experienced professionals. These systems analyze information from their knowledge base to provide solutions tailored to specific user queries, effectively simulating human judgment and behavioral patterns.
One limitation is their dependence on manual data input and rule updates, as they lack autonomous learning capabilities. Additionally, expert systems operate without emotional intelligence or common sense and cannot provide detailed explanations for their decision-making processes.
In healthcare settings, medical professionals leverage expert systems for diagnostic support. For instance, in pressure ulcer management, these systems utilize predetermined rules to accurately identify and recommend treatment protocols based on ulcer staging.
Fuzzy Logic
Fuzzy logic systems excel at processing ambiguous or imprecise data, offering flexible reasoning capabilities. Unlike traditional binary logic, this approach accommodates varying degrees of truth and uncertainty in decision-making processes.
This technology proves particularly valuable in diabetes management, where multiple variables influence treatment decisions. These include factors such as pre- and post-meal glucose readings, carbohydrate consumption patterns, and individual insulin sensitivity levels.
Artificial Narrow Intelligence
Artificial Narrow Intelligence (ANI) specializes in specific tasks within defined parameters. Common applications include voice-activated digital assistants like Siri (Apple), Alexa (Amazon), and Cortana (Microsoft).
In medical applications, ANI demonstrates remarkable accuracy in disease diagnosis, particularly cancer detection, by analyzing human behavioral patterns, cognitive processes, and reasoning mechanisms.
Generative AI
Generative AI, exemplified by ChatGPT, represents an advanced deep learning application capable of creating original content. Drawing from extensive datasets encompassing literature, academic research, and online resources, it generates new text based on user prompts. However, it lacks critical analysis capabilities, requiring human verification of its outputs.
In nursing practice, while offering benefits like automated documentation assistance, it’s crucial that nurses exercise professional judgment when reviewing AI-generated content, particularly in patient care documentation.
Recommendations for Nurse Educators and AI
Evidence suggests that integrating various AI technologies into nursing education significantly enhances student preparation for professional practice, provided it’s implemented responsibly.
Nevertheless, some educators primarily associate AI technology with academic integrity concerns, such as unauthorized use of ChatGPT for assignments, rather than recognizing its educational potential. Developing a comprehensive understanding of AI applications and appropriate usage guidelines is essential for maximizing its benefits in nursing education.
According to recent research published in 2023, AI presents numerous opportunities to revolutionize healthcare education. Interactive chatbots enable personalized learning experiences and exam preparation through dynamic question-and-answer sessions. Advanced human simulation systems demonstrate remarkable capabilities, responding both verbally and physically to commands, including providing verbal responses and simulating pain expressions. Real-time avatar interactions foster critical thinking and problem-solving abilities among students. Additionally, AI technology facilitates telehealth services and remote patient monitoring while significantly reducing documentation workload.
Critical thinking remains a cornerstone of nursing education. Understanding and effectively utilizing AI technologies enhances nurses’ ability to deliver advanced patient care safely and efficiently. Ongoing research is vital to establish evidence-based guidelines for nurse educators, ensuring legal and ethical AI implementation in nursing practice.
In their comprehensive analysis, Schneidereith and Thibault emphasized the importance of nurse educators developing fundamental AI literacy, evaluating integration appropriateness, and identifying future implementation opportunities.
Their research highlighted the crucial role of nurses in technology development, stating that “nursing professionals should actively participate in patient care technology development to ensure practical usability and accurate clinical representation.”
The researchers concluded by asserting, “Our professional responsibility includes understanding AI fundamentals, evaluating integration appropriateness, and recognizing future application opportunities. We must navigate through both enthusiasm and skepticism to swiftly comprehend AI’s practical applications in nursing practice.”
Edited by Joelle Y. Jean, FNP-C, BSN, RN
Source of the article is https://nursejournal.org/
