peer’s response

Cognitive computing is a phenomenon involving the synthesis of large quantities scientific information to make intelligent and informed decisions. Cognitive computing involves data mining to pull from information. Then, the computer mimics a human brain by assessing the data, looking for patterns, and formulating a response. In healthcare, cognitive computing could be used to assess symptoms and recommend specific diagnostic or treatment options (Behera et al., 2019).
Cognitive computing in healthcare has the potential to improve the quality of patient care. Healthcare providers are very much respected, but they are still human. A provider makes informed decisions based on his or her education, experience, and expertise. However, cognitive computing makes decisions based on vast quantities of information that one human brain would not be able to retain. It is possible this strategy could make more informed healthcare decisions, simply because cognitive computing can assess much more information. Cognitive computing could also provide much more efficient  and accessible healthcare. If a patient can tell a machine their presenting issue and symptoms, their concern could be addressed immediately. The patient would not have to make an appointment, get transportation to the appointment, and meet with one or more providers Coccoli, , & Maresca, 2018).
Cognitive computing may have negative effects on the quality of patient care. There is value in human interaction and seeing a patient face-to-face. I work in mental health, so most of the information I assess is subjective. Patients have to trust me enough to express thoughts of suicide and symptoms of depression. That trust may be lost when speaking to a machine instead of a human (Coccoli, , & Maresca, 2018).
Cognitive computing has the potential to decrease the cost of healthcare. If cognitive computing can assist providers in making informed decisions, then there is less guess work and problem solving. Research shows that 80% of healthcare costs are due to physician decisions about testing, procedures, pharmaceuticals, and other treatments. Cognitive computing may reduce the amount of repeated or unnecessary tests and procedures. It may also ease the provider’s need to rule out particular diseases. Insurance companies must adapt to a changing healthcare system using cognitive computing (Emanue & Wachter, 2019).
It’s important to note that there is a wide spectrum involving the use of cognitive computing.  Some research focuses on the replacement of healthcare professional with cognitive computing, while others focus on how cognitive computing can assist healthcare workers and improve the quality of care (Chen et al, 2017). Nursing could be impacted by using cognitive computing as a tool to make informed medical decisions. This could potentially help with making decisions about testing, procedures, medications, and when to make consults (Behera et al., 2019).
There are some barriers to preventing widespread usage of cognitive computing. This includes patient privacy issues, patient distrust of technology, technology problems, accessibility of devices to use the technology, and the technical skills required to use such technology. To maintain privacy, programs must be protected from outside sources viewing and using patient information. This includes the use of encryptions, passwords, and secure clouds. Patient education will play a large role in the implementation of cognitive computing, especially if patients are expected to use the technology and dispel any feelings of paranoia or fear. Nurses will need to make sure the patient feels comfortable using this technology. The nurse must also assess to see if the patient has the technical skills needed to effectively use the technology and a device that allows them access. There must be some training involved and a way to get assistance when needed (Coccoli, , & Maresca, 2018).
Behera, R.K., Bala, P.K., & Dhir, A. (2019). The emerging role of cognitive computing in healthcare: A systematic literature review, International Journal of Medical Informatics, 129(2019), 154-166. (Links to an external site.).
Chen, M. Yang, J., Hao, Y., Mao, S. & Hwang. K. (2017). A 5G cognitive system for healthcare. Big Data and Cognitive Computing, 1(1). (Links to an external site.)
Coccoli, M., & Maresca, P. (2018). Adopting cognitive computing solutions in healthcare. Je-LKS, 14(1). (Links to an external site.)
Emanue, E.J., & Wachter, R.M. (2019). Artificial intelligence in health care: Will the value match the hype? JAMA Network, 321(23), 2281–2282. doi:10.1001/jama.2019.4914

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