Similar to many industries, the healthcare industry has been hit by digital waves bringing around a computing revolution (Behera, Bala and Dhir, 2019). Cognitive computing is one technology that has brought about the computing revolution. Cognitive computing makes it possible to process enormous amounts of data instantly to answer specific questions and provide customized intelligent recommendations enhancing decision-making in the healthcare sector.
Imagine a situation where a child with asthma starts to have trouble breathing at night (Voigt, 2017). An intelligent monitoring system, powered by cognitive intelligence, provides the child with proper medication via vents in the child’s room. The parents can sleep through the night and can analyze the episode via the system the next day. Cognitive computing has the potential of assisting medical professionals in faster and more informed decision-making, providing better treatment of diseases, improve patient outcomes and improve the overall quality of care (Behera, Bala and Dhir, 2019; Bhisey, 2021).
Cognitive computing can mimic the workings of the human brain by making use of neural network processes, deep learning algorithms, and self-learning algorithms that use data mining, pattern recognition, and natural language processes (Chen, Herrera and Hwang, 2018; Zhang et al., 2018). The human thought process is stimulated in a computerized model. By being able to mimic the human brain, cognitive computing can solve critical medical problems without constant human supervision similar to the previously mentioned example (Gupta et al., 2018; Sreedevi et al., 2022).
Cognitive computing can be applied in several ways in healthcare. Cognitive computing can help identify the most critical attributes of a patient case and then generate easy-to-consume summaries of the best treatment options by analyzing unstructured data, best practice data, published clinical studies, and clinical trial data (IEEE PULSE, 2017; Voigt, 2017). This also allows for the generation of personalized treatment plans improving patient experiences. Cognitive computing can also allow researchers to discover new insights into the relationships among, for example, genes, proteins, and diseases (IEEE PULSE, 2017). Finally, it can be used in clinical trial matching to optimize patient selection and recruitment.
Many argue that cognitive computing has the potential of bridging the gap between humans and technology allowing for computers and the human brain to overlap improving human decision-making and transforming healthcare on a global scale (IEEE PULSE, 2017). I agree with this statement, as IBM (2016) mentions, since cognitive computing is able to access many data sources, medical expertise is scaled and medical knowledge is democratized. This allows healthcare professionals to better make use of the data available to make the best decision for their patients. Do you agree with this statement? How do you think cognitive computing will effect the healthcare sector?
References
Behera, R. K., Bala, P. K. and Dhir, A. (2019) ‘The emerging role of cognitive computing in healthcare: A systematic literature review’, International Journal of Medical Informatics, 129(June), pp. 154–166. doi: 10.1016/j.ijmedinf.2019.04.024.
Bhisey, R. (2021) Improving Healthcare Using Cognitive Computing: An Application in Emergency Situation, Transparency Market Research. Available at: https://www.biospace.com/article/improving-healthcare-using-cognitive-computing-an-application-in-emergency-situation/ (Accessed: 13 September 2022).
Chen, M., Herrera, F. and Hwang, K. (2018) ‘Cognitive Computing: Architecture, Technologies and Intelligent Applications’, IEEE Access, 6, pp. 19774–19783. doi: 10.1109/ACCESS.2018.2791469.
Gupta, S. et al. (2018) ‘Big data with cognitive computing: A review for the future’, International Journal of Information Management, 42, pp. 78–89. doi: 10.1016/j.ijinfomgt.2018.06.005.
IBM (2016) The future of health is cognitive, IBM. Available at: https://www.ibm.com/downloads/cas/LQZ0O1WM (Accessed: 13 September 2022).
IEEE PULSE (2017) Cognitive Computing and the Future of Health Care, IEEE PULSE. Available at: https://www.embs.org/pulse/articles/cognitive-computing-and-the-future-of-health-care/ (Accessed: 13 September 2022).
Sreedevi, A. G. et al. (2022) ‘Application of cognitive computing in healthcare, cybersecurity, big data and IoT: A literature review’, Information Processing and Management, 59(2), p. 102888. doi: 10.1016/j.ipm.2022.102888.
Voigt, J. (2017) Cognitive Computing in Healthcare, Wharton Magazine. Available at: https://magazine.wharton.upenn.edu/digital/cognitive-computing-in-health-care/ (Accessed: 13 September 2022).
Zhang, Y. et al. (2018) ‘CrossRec: Cross-Domain Recommendations Based on Social Big Data and Cognitive Computing’, Mobile Networks and Applications, 23(6), pp. 1610–1623. doi: 10.1007/s11036-018-1112-1.
