Overall AI such as machine-driven prediction and semantic technology aids nurses’ critical thinking process. It is a nonlinear practice that focuses nurses on clinical decision-making (Paul & Heaslip, 1995). AI Enhances Critical Thinking and the Nursing ProcessĬritical thinking applied in nursing is complex and multifaceted. AI programs have moved from merely searching for results to smart suggestion and recommendations machines (Polson & Scott, 2018), and for nurses’ benefit, they now guide decision-making by enhancing critical thinking and the nursing process which enables patterns and semantics detection that ultimately thinks more like humans and in clinical environments, nurses. Quality of the data for successful AI use is vital as they must be complete, structured, cleaned, unbiased, and more data is best as the volume of robust data sets help machines to learn, predict and process semantics languages better. The data used for effective decision making in AI feed programs for algorithms building, stems from many healthcare data sources, include EHRs, medical claims, voice/audio files, images, and workforce and hospital throughput data such as staffing and bed management. Examples of NLP and ASR are virtual assistants, chatbots, cell phone voice texting/messaging, and nursing applications include extracting EHR text from notes in non-discreet fields, vocal charting, and speech-activated paging devices. Combined with predictive analytics and ML, both NLP and ASR enable scientists to develop algorithms for language translation, semantic understanding, and text summarization, making it easier to understand and perform computations on volumes of text with less effort (Seif, 2018). Īnother AI program is natural language processing (NLP) coupled with Automated Speech Recognition (ASR), which help computers better understand and process human (unstructured) languages, to move smart machines closer to a human-level understanding of language (Seif, 2018). In nursing, EHR clinical decision support tools, radiology image recognition, and disease progression prediction are applications of this type of AI. ![]() Multiple industries use predictive analytics and ML in internet search engines, app streaming services– both audio and video – and in social media. It is a machine approach to refine those data, using knowledge to extract hidden value from newly discovered patterns, and dynamically informs data-driven decision-making to know what will happen, when and what to do about it (Carroll & Hofmeister, 2018). ![]() Predictive analytics is mathematical computations that analyze historical data from multiple sources to predict future events. One type of AI readily applied in healthcare today is predictive analytics and machine learning (ML). In healthcare it is “Clinical Intelligence”: machine algorithms designed for diagnostic and treatment processes utilized in the appropriate use cases for everyone (patients, health professionals and payors) as an extension of care for the right treatment, to the right person at the right time (Health Information and Management Systems Society, 2018). The transforming technology outputs suggestions requiring human judgment to apply recommendations, to be helpful and strike a balance with data-manipulated science and decision making. While AI is machine-driven, it is important to note that it is assistive. When many algorithms are put together and layered in applications, they become the backbone of AI (Polson & Scott, 2018). They are a set of well-constructed rules given to an AI program to help it learn on its own. Algorithms are sequential instructions that ensure particular task completion. ![]() To break down the intricacy of AI, nurses can begin to understand that at the technology’s core is algorithms. The foundation of AI can seem overwhelming and wildly complicated. It's to nursing's advantage as health systems, providers and payers look to health IT innovations, including AI, to disrupt the business model of healthcare and the products and solutions offered to meet the Quadruple Aim. The need for nurses’ comprehension of the foundations of AI and the symbiotic nature of it with nursing practice is essential with its increased use in practice in today’s value-based care environment. ![]() Understanding how AI and its use can enhance nurses’ decision making, by supporting critical thinking and positively impacting the nursing process is necessary. As we dive into the realm of emerging technologies in healthcare, we find artificial intelligence (AI) defined as the aptitude exhibited by smart machines broken down into perceiving, thinking, planning, learning, and the ability to manipulate objects (NITI Aayog, 2018), its applications and benefits to nurses in care delivery environments are still vague.
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