The Future of Healthcare Technology Term Paper

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Role of HIT in Healthcare Planning

Healthcare information technology (HIT) is one of the greatest disruptive forces that will affect vision and planning over the next decade. However, regardless of the technology that is developed, it must be supported and executed by hospital leadership to be successful. Currently, research is underway for the use of artificial intelligence, supercomputing, and clinical support systems in the healthcare environment. This research will explore how these technologies are likely to change healthcare in the future, and the role of readership in making the integration of these technologies a success.


Currently, information technology is an integral part of the healthcare system. HIT refers to a variety of technologies that collect, transmit, store, and display patient data electronically (Hemmat, Ayatollahi, Maleki, and Safhafi, 2017). This makes it easy to send, review, and update patient information quickly and easily. The current list of technologies includes remote and mobile health technology, cloud-based services, medical devices, telemonitoring tools, sensor technologies, and electronic health records (EHR’s) (Hemmat, Ayatollahi, Maleki, and Safhafi, 2017). The use of technology in many different types of healthcare settings is already in daily practice. However, in the future, the use and types of technologies used in the healthcare setting are expected to expand exponentially. Countries continue to make extensive investment in the area of technology development for the healthcare industry (Hemmat, Ayatollahi, Maleki, and Safhafi, 2017). This suggests that there is considerable interest in the development of technology in the healthcare industry in the near and long-term future. Now, it is time to examine some of the different types of technology that can be expected to be seen in the future.

Artificial Intelligence

Artificial intelligence (AI) is technology that is designed to mimic human cognitive functions. One of the areas that it offers improvement in the healthcare setting is the ability to assist in analytics that can be applied to both structured and unstructured data (Jiang, Jiang, Zhi, Dong, Hao, & Ma, et al., 2017). In the future, it is expected to be able to help doctors make better clinical decisions, with many of the advances in this area in the field of radiology (Jiang, Jiang, Zhi, Dong, Hao, & Ma, et al., 2017). The fascinating feature of AI is that it can be trained through data to perform clinical activities including diagnosis and treatment assignment for patients (Jiang, Jiang, Zhi, Dong, Hao, & Ma, et al., 2017). Technology is being developed that can learn similar groups of subjects and the associations between features, such as race and demographic information, to increase the likelihood of the desired outcome.

One of the areas where AI is being developed to be used in future is in the field of cancer diagnosis. In a recent study, this technology was used to analyze abnormal genetic expression in non-coding RNA to diagnose gastric cancer in a sample population (Li, Liang, Yao, Sui, Shen, & Zhang, 2016).

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It also been applied to detect diabetic retinopathy through retinal fundus photographs with a sensitivity of over 90% (Gulsham, Peng, & Coram, 2016). These are only a few examples of the uses of this technology that are currently under study and development. However, an examination of the literature indicates that there is a significant amount of interest in the use of AI for diagnostic and treatment analysis. This technology is designed to assist physicians, and give them another tool, but it will not replace human intervention in the process. It is another set of eyes and gives the physician a level of information that they did not have access to in the past.


Supercomputing is another form of information technology that…

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…to support their application in the clinical setting.

There is no doubt that having the latest technology in a healthcare setting gives it a decided competitive advantage. Having the latest technology is an excellent branding and advertising point. Leaders must understand the importance of having the latest technology to improve their operational costs, staff efficiency, and reduce their rates of errors. It is responsibility of leadership to keep up with the advances in HIT and to find ways to implement these new technologies and their healthcare setting as quickly as possible. Of course, funding is always an issue when it comes to new technology, but the advantages in the ability to process through a higher number of patients and provide them with better care justifies the cost of this new technology.

In the healthcare industry, staff members and lower managers look to the attitudes and support of upper leadership in terms of whether to support the adoption of the latest advances in technology. Sometimes these new technologies can be intimidating because they are unfamiliar. However, it is up to the leaders to make certain that staff is trained and becomes familiar with the operation of the new technology and that they have a supportive attitude towards it on adoption. Leadership plays a key role in the adoption and use of new advances in technology in the real world.

Keeping up with the newest technology is paramount to the commitment to provide quality healthcare to patients. It is a leader’s responsibility to make sure that their patients have access to the most advanced technology available in order to improve overall outcomes. Technology is the key to leading a healthcare organization to excellence and sustaining a competitive advantage. Leaders must keep this in the forefront of their decision-making guidelines. The bottom line is not about controlling costs, it….....

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Centers for Medicare & Medicaid Services. (2014). Clinical decision support: more than just ‘alerts’ tipsheet. eHealthUniversity. Retrieved from Guidance/Legislation/EHRIncentivePrograms/Downloads/ClinicalDecisionSupport_Tipsheet-.pdf

Engel, K. (2019), Supercomputers are shaping future of humanity. Retrieved from

Gulshan, V., Peng, L. & Coram, M. (2016). Development and Validation of a Deep Learning Algorithm for detection of Diabetic
Retinopathy in retinal fundus photographs. JAMA, 316, 2402–10. Retrieved from:

Hemmat, M., Ayatollahi, H., Maleki, M., & Saghafi, F. (2017). Future research and health information technology: a review. Perspectives in Health Information Management, 14(1b), Retrieved from (2018). Clinical Decision Support. Retrieved from

Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Hao, L. & Ma, S. et al. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology, 2(4). Retrieved from:

Li, C., Liang, G., Yao, W., Sui, J., Shen, X., & Zhang, Y et al. (2016). Integrated analysis of long non-coding RNA competing interactions reveals the potential role in progression of human gastric Cancer. Int J Oncol 48, 1965–76. Retrieved from

O’Dowd, E. (2017). Cray Supercomputers, Microsoft Azure Aid Healthcare Analytics. HIT Infrastructure. Retrieved from

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