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Innovative Informatics and Applicable Tools in Clinical Practices

Innovative Informatics and Applicable Tools in Clinical Practices

Healthcare organizations can deploy AI in multiple ways to deliver more impactful, efficient, and precise care interventions to ensure positive patient outcomes. As healthcare data continue to increase, AI is capable of fostering innovations and improvements in the sector for easy handling of patient information (Chen & Decary, 2019). In conjunction with ML algorithms, AI can deliver more intelligent and unique insights to help in clinical diagnostics and treatment decision-making. This technology benefits care providers and patients depending on the area of application, for example, if AI is employed in care improvements, management of chronic diseases, early risk identification, automation, and workflow optimization (Chen & Decary, 2019). AI is also primarily used in some surgical procedures through surgical robotics (Frazier et al., 2019). Some of the advantages of AI include; the ability to analyze data and improve diagnosis, carry out administrative and routine tasks, health monitoring, and digital consultations. AI also faces drawbacks like training complications and the challenge of adapting to change.

The Healthcare sector has faced several challenges in diagnosing some diseases, as many tend to present common symptoms. However, machine learning will assist by providing perfect diagnoses, recommending the right medications, and identifying high-risk patients. ML, in conjunction with deep learning, has been used in medical image diagnoses and smart health records (Kwon et al., 2019). Despite the benefits of ML in healthcare, challenges do exist in its employment. For example, ML creates medical complexity in stitching together patient information after IT systems change (Kwon et al., 2019). The other challenge is that ML is a costly technology that can’t be afforded by all health facilities. Genomics is the study of genes, which has posed its usefulness in the prediction, diagnosis, and treatment of diseases in a more precise and personalized manner. Genomics has been used in the development of oral plant vaccines and anti-malarial drugs. This genomics technology is advantageous as it has promoted the provision of personalized care and reduction of side effects of medications, and it has diversified clinical trials and research studies. One of the drawbacks of this technology is that it poses risks in case the patient data is not adequately managed.

The idea of precision health is a new technology that holds great promise and has shown a potential positive impact in the healthcare sector. It’s a kind of approach where one can protect their health. This helps in the prediction, treatment, and management of diseases for an individual as well as their family (Kwon et al., 2019). This promotes care efficiency and preventive care and limits the cost of care. However, it also increases the cost of care and infrastructure requirements which might be expensive. The sole goal of these technologies is to ensure the delivery of quality, safe patient care with guaranteed positive outcomes. Most of these technologies employ big data extensively. For example, AI works hand-in-hand with ML and other compatible technologies to process the vast data generated daily in the health sector (Bini, 2018). It is very challenging to process this data and draw viable conclusions, but with AI and ML, the whole process becomes easy. ML also utilizes big data in carrying out accurate clinical diagnostics (Bini, 2018). AI is a field of computer science where machines try to copy human reasoning through recognition and analysis of situations and make decisions. ML is simplifying, compresses, and optimizing a decision tree by eliminating irrelevant sections. Deep Learning incorporates training an inference algorithm by fine-tuning it to deliver the required level of accuracy and repeatability. All these aspects have great clinical significance in processing the enormous clinical data generated, as they complement each other.

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://doi.org/10.1016/j.arth.2018.02.067

Chen, M., & Decary, M. (2019). Artificial intelligence in healthcare: An essential guide for health leaders. Healthcare Management Forum, 33(1), 10-12 https://doi.org/10.1177/0840470419873123

Frazier, R. M., Carter-Templeton, H., Wyatt, T. H., & Wu, L. (2019). Current trends in robotics in nursing patents—A glimpse into emerging innovations. CIN: Computers, Informatics, Nursing, 37(6), 290-297. https://doi.org/10.1097/cin.0000000000000538

Kwon, J. Y., Karim, M. E., Topaz, M., & Currie, L. M. (2019). Nurses “Seeing Forest for the trees” in the Age of machine learning. CIN: Computers, Informatics, Nursing, 37(4), 203- 212. https://doi.org/10.1097/cin.0000000000000508

Newcomb, P., Behan, D., Sleutel, M., Walsh, J., Baldwin, K., & Lockwood, S. (2019). undefined. Nursing, 49(7), 54-60 https://doi.org/10.1097/01.nurse.0000554278.87676.ad

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Question 


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.

Innovative Informatics and Applicable Tools in Clinical Practices

Innovative Informatics and Applicable Tools in Clinical Practices

Explain whether these applications integrate Big Data. Why or why not?

Explain the difference between AI, Machine Learning, Data Mining and

Deep Learning is presented in the Bini (2018) article.

Why do these differences matter and how relevant are they for Big Data?

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 ArthroplastyLinks to an external site., 33(8), 2358–2361. doi:10.1016/j.arth.2018.02.067

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