DISCUSSION: Week 7 INNOVATIVE INFORMATICS TOOLS AND APPLICATIONS TO CLINICAL PRACTICE

Innovative Informatics Tools and Applications to Clinical Practice

With technology advancing so quickly, the use of AI, machine learning, genomics, precision health, and robotics in clinical practice has become integral in improving the delivery of healthcare. Not only can they help with producing more accurate diagnoses faster, but they can also advance personalized treatments tailored to a patient’s specific condition. Additionally, AI-powered systems are able to search through large amounts of data sets to monitor patient safety as well as identify patterns for further study – this could play an essential role in predicting medical outcomes and devising unique approaches to situations that might have gone undetected before.
The potential benefits of artificial intelligence in clinical practice are vast. They include, but are not limited to, accelerated diagnoses, improved accuracy of diagnoses, increased patient safety, more efficient treatment plans and earlier interventions (Conrad et al., 2020). However, there are also potential challenges that must be considered. These include the risk of human error being passed on to machines, data security and privacy concerns, and the possibility that AI will replace human doctors altogether. It is therefore important that any implementation of AI in the clinical setting be done thoughtfully and with caution.

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Machine Learning and Genomics are two remarkable innovations with incredible potential. Machine Learning can be applied to a wide array of fields, from data science to education and healthcare. Genomics, meanwhile, is providing a deep new understanding of our bodies, allowing for personalized treatments and advanced diagnostics. Both provide the potential to revolutionize the way we handle difficult tasks. With that said, it is important to understand the risks of using these technologies too quickly or when they are not mature enough. For instance, there are concerns over privacy violations in Machine Learning applications and ethical considerations around genomics. Taking these into account, both Machine Learning and Genomics should be applied carefully as we continue to innovate in this space.
Precision health and robotics are two of the most innovative technologies currently being applied to clinical practice. Precision health has ushered in an increased level of accuracy, allowing clinicians to personalize treatment plans and focus on preventative care. In addition, this technology allows healthcare providers to leverage genetic analysis through big data analysis and deep artificial intelligence computing in order to predict potential medical issues and recommend personalized intervention plans. Robotics has allowed for a more precise application of complex medical tools and procedures, as well as improved conductivity for medical examination protocols. The major challenges that come with robotics and precision health include the costs, lack of skilled personnel, as well as the possibility of data loss through scum.
The innovations including AI, machine learning, genomics, precision health, and robotics have a greater potential to improve healthcare practice and the related outcomes. The applications are more likely to reduce medication errors and enhance efficiency in the management of the entire healthcare system. In general, the applications are likely to improve diagnosis, care to patients, management of patient information, and overall improvement of home-based care.
AI, machine learning, genomics, precision health, and robotics in clinical practices integrate Big Data. By integrating AI, machine learning, genomics, precision health, and robotics into clinical practices, medical professionals can access vast databases of patient information for a comprehensive view of diseases and diagnoses. This enables doctors to tailor treatments that are precisely tailored for individual patients based on their particular traits and attributes. It also gives researchers valuable insights from patient data sets that can help shape new advances in treatments that benefit all. Big data provides an invaluable bridge between science and medicine in today’s highly competitive healthcare market.
According to the Bini (2018) article, Artificial Intelligence (AI) is a process of programming a computer to make decisions for itself. Machine Learning (ML) is a subset of AI where the computer is taught how to learn from experience, without being explicitly programmed. Data Mining is a method of discovering patterns and relationships in data, while Deep Learning is a subfield of ML that uses artificial neural networks with many layers to learn from data. In short, AI is the overarching umbrella term that encompasses all these other terms (ML, data mining, deep learning), while ML and deep learning are specific methods within AI that are used to analyze data and make predictions.
The differences among AI, machine learning, data mining and deep learning matter because they represent different levels of sophistication in terms of the ability of a computer program to learn from data. Data mining is the most basic level, followed by machine learning, then deep learning. And artificial intelligence represents the highest level of sophistication, as it involves machines that can learn on their own, without any help from humans (Kline et al., 2022). The relevance of these distinctions for big data matters because big data refers to datasets that are so large and complex that traditional data-processing techniques are no longer feasible. In order to deal with big data, it’s necessary to use sophisticated methods like machine learning and deep learning that can extract meaningful patterns from large datasets.

References
Bini, S. A. (2018). Artificial intelligence, machine learning, deep learning, and cognitive computing: what do these terms mean and how will they impact health care?. The Journal of arthroplasty, 33(8), 2358-2361. https://www.sciencedirect.com/science/article/abs/pii/S0883540318302158
Conrad, K., Shoenfeld, Y., & Fritzler, M. J. (2020). Precision health: A pragmatic approach to understanding and addressing key factors in autoimmune diseases. Autoimmunity Reviews, 19(5), 102508. https://www.sciencedirect.com/science/article/pii/S1568997220300604
Kline, A., Wang, H., Li, Y., Dennis, S., Hutch, M., Xu, Z., … & Luo, Y. (2022). Multimodal machine learning in precision health: A scoping review. npj Digital Medicine, 5(1), 1-14. https://www.nature.com/articles/s41746-022-00712-8

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New technology and tools will undoubtedly shape nursing practice. â€œResearch suggests that between 8% and 16% of nursing time is spent on non-nursing activities and tasks that should be delegated to othersâ€ (Robert, 2019). As a result, new innovations may minimize the time spent on these non-nursing activities and tasks to further support and strengthen patient care.

One such technology is the use of robots. While nursing robots are not yet readily available, researchers have earned millions in grants over the last decade researching and developing AI and robotic innovations to improve healthcare and nursing practice. From clinical practice to patient support, the future seems endless with possibilities.

For this Discussion, you will explore various topics associated with innovative technology and your healthcare organization or nursing practice. You will consider how you might utilize these advancements, as well as consider how these advancements might influence nursing informatics.

Reference:

Robert, N. (2019). How artificial intelligence is changing nursing. Nursing Management, 50(9), 30â€“39. doi:10.1097/01.NUMA.0000578988.56622.21

TO PREPARE
Review the Learning Resources associated with the topics: AI, Machine Learning, Genomics, Precision Health, and Robotics.
Consider the role of these technologies in your healthcare organization or nursing practice.
Analyze the differences of these technologies as they may impact healthcare delivery and nursing practice.
Reflect on the potential use of each of these topics and your personal experiences with their implementation into practice.
BY DAY 3 OF WEEK 7
Post a response to your blog for each of the following:

From the five topics: AI, Machine Learning, Genomics, Precision Health, and Robotics, assess the applications of the technology, noting the potential benefits and potential challenges of the innovations. Be specific.
Appraise the potential of the innovations to improve healthcare practice and related outcomes.
Explain whether these applications integrate Big Data? Why or why not?
Explain the difference between AI, Machine Learning, Data Mining and
Deep Learning as presented in the Bini (2018) article.
Why do these differences matter and how relevant are they for Big Data?