Paper on Cloud Computing in Healthcare Technology: Guidelines, Challenges, and Solutions

The thesis of this research is on the adoption of cloud computing in the medical technology advancement industry. It mainly gives out guidelines for individuals who try to bring cloud computing into healthcare in medical care advancement by using various processes and framework tools to overcome both the business and technical challenges.

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This statement’s primary focus is on exploring the current state of affairs in cloud computing applications in technological medical care advancements in industries. Its main aim is to give out all the guidelines for tackling computing advancements to people by using processes and framework tools to overcome both the business and technological challenges in the industry (Tyagi et al. 2016). The rise of technology in the health sector has been relatively slow because most enterprises have not fully adopted the cloud computing model as compared with others.

Research conducted by CDW 2011 showed that only 30% of the industries had adopted it in its maintenance and implementation of based systems. Among the 30% who have adopted this clouding system, only 18% used Google documents, 24% used Gmail, and 39% of this used live Meetings. As of recently, the clouding computing services are mainly commodity applications, as there is not yet the availability of other services on cloud-based, for example, storage (Yang et al. 2017). This thesis research was reviewed by interviewing several leaders, researchers, and professionals in cloud computing and the health information technology fields. They used different analytical tools in their management program and system of design, like business dynamics, system analysis, and getting to know the challenges and dynamics in its transformation from conventional computing to cloud computing in the industry of health care.

Many issues including and are not limited to privacy and security, government regulations and policies, and data handling are the main roadblocks to adopting rapid cloud computing. There is a literature description of these challenges, their consequences, and the urgency of solving them. However, few industries have looked into the root cause and have provided practical solutions (Sultan, 2014). This thesis mainly tries to bring together the main problems that have been unearthed in literature and in talking with persons in the field and provide pragmatic guidelines on ways of overcoming these roadblocks by the use of analytical tools. Although not all scenarios work, they always bring out the limelight by exposing the hidden blocks to enable others to go ahead in transformations.

The change in cloud computing has been coined as an umbrella term that explains a category of high service providers in its services, and mainly offered by Microsoft, Google, and Amazon. Its main aim is to deliver through a global network its computing resources as requested by a customer. Like other industries, health care has advanced much from paper-based and verbal descriptions of manual processes to modern information and database collection process systems. The health system information is susceptible to both physicians and parents who create it and the scientists who use it to conduct research. Hence, security and privacy are crucial concerns. Over the years, there has been an increase in demand for more advanced technology of health information to meet the established rules and regulations.

However, regulations and legislation have side effects associated with health workers. First is the problem of lack of consistency or enforcement when enforcing. The law’s primary goal is to maintain an appropriate balance between protecting people’s privacy and using de-identified information on health for a common purpose. At times, these laws might not go one in hand hence bringing many confusions (Kagadis et al., 2013). The HHS entrusted to the Medicaid, Centers for Medicare, and Administrator to investigate non-compliance and complaints. The other problems are the issues of the secondary use of the PHI. All persons transmitted or held by covered entities or by identifiable health information are protected by privacy. This regulation allows the use of de-identified or non-identifiable information for secondary administrative use and scientific research. Though, it does not supersede the FDA regulations or standard rules in the study. Another issue is the variability in interpreting the privacy rule of HIPAA. The covered entities are usually limited to a set of entities. Thus, if HIPAA does not cover them, they are not eligible to access information. Hence, it causes a limit on essential types of research, such as multi-center studies, population-based control research, and chart reviews.

In cloud computing, services are always offered in various ways, including infrastructure as a service. They use computing and storing resources as services. Its advantages are it can shut down and rapidly start up services; increases flexibility and lowers its cost; this service moderates capital expenditures (Meir, & Rubinsky, 2009). Also, it provides information as a service, software as a service, and provides a platform as a service. Each of the services has different business acceptance and awareness; software is the most common and popular model. Infrastructure and platforms as services make essential inroads in the cloud in its business usage to become sophisticated in its use.

As the federal government passes a law in research information, it provides incentives to institutions or individuals. This mainly obviously leads to implementations of technologies being introduced and promoted. The law acts as an enormous stride towards the adoption of technologies. The Hitech Act has led to the majority of organizations adopting the certification of EHR for incentive. However, healthcare or hospital facilities have different limitations, clinical concerns, and workflows of the existing infrastructure. Conclusively, knowing an organization’s requirements and needs and integrating with the cloud computing systems become secondary priorities.

References

Kagadis, G. C., Kloukinas, C., Moore, K., Philbin, J., Papadimitroulas, P., Alexakos, C., ... & Hendee, W. R. (2013). Cloud computing in medical imaging. Medical Physics, 40(7).

Meir, A., & Rubinsky, B. (2009). Distributed network, wireless, and cloud computing enabled 3-D ultrasound, a new medical technology paradigm. PloS one, 4(11), e7974.

Sultan, N. (2014). Making use of cloud computing for healthcare provision: Opportunities and challenges. International Journal of Information Management, 34(2), 177-184.

Tyagi, S., Agarwal, A., & Maheshwari, P. (2016, January). A conceptual framework for IoT-based healthcare systems using cloud computing. In 2016 6th International Conference-Cloud System and Big Data Engineering (Confluence) (pp. 503-507). IEEE.

Yang, C., Huang, Q., Li, Z., Liu, K., & Hu, F. (2017). Big Data and cloud computing: innovation opportunities and challenges. International Journal of Digital Earth, 10(1), 13-53.