Alghatani and Rezgui (2019) present a framework for remote patient monitoring via cloud architecture. The primary intention is to reduce disparate data sources and walls between various data siloes, increasing cost effectiveness, response time, and quality of care. The cloud architecture involves the database itself, user interface(s), and artificial intelligence. This cloud is used by four primary groups: patients, hospitals, insurance companies, and controllers (system stewards).
The authors outline a number of advantages here. Telemedicine can be a great thing but has a number of barriers to overcome, not the least of which are cost, culture, political environment, and infrastructure. The cloud architecture seeks to mitigate the cost and infrastructure issues. IT resources can be extended dynamically based on need and the decentralized nature of the system allows for better scalability, flexibility, and reliability.
There are a number of challenges to be considered. The authors highlight seven:
- Data management
- Business continuity
An extensive discussion on data collection challenges is presented, outlining a number of possible methods for collection and synchronization. There must be an assumption that no device on this architecture will maintain constant contact with the cloud, and consistency models must be taken into consideration. One option is for each device to maintain local storage and upload to the cloud once a stable connection is available. Another option is a whisper network of its own, much like the early Amazon Kindle devices. A third and final option—also the authors’ proposal—is the utilization of fog computing as a layer between these devices and the cloud.
Privacy is always an issue and cloud architecture muddies the waters a bit, as there is no on-premise server locked down that holds the personally identifiable information. Banks and hospitals have typically been the slowest to adopt cloud computing, in my experience. As Alghatani and Rezgui (2019) note, governance and control are concerns here. The Health Insurance Portability and Accountability Act (HIPAA) requires confidentiality in all individually-identifiable health information; in 2013, this law was extended to genetic information by way of the Genetic Information Nondiscrimination Act (GINA). While the rules prohibit use of genetic information for underwriting purposes, there is no restriction on the sharing or use of genetic information that has been de-identified (National Human Genome Research Institute, 2015). De-identification is not entirely foolproof. There are cases in which the data can be re-identified (Rosenbaum, 2018).
Alghatani, K., & Abdelmounaam, R. (2019). A cloud-based intelligent remote patient monitoring architecture. Paper presented at the International Conference on Health Informatics & Medical Systems, HIMS’19, Las Vegas, NV.
National Human Genome Research Institute. (2015). Privacy in genomics. Retrieved from https://www.genome.gov/about-genomics/policy-issues/Privacy
Rosenbaum, E. (2018). Five biggest risks of sharing your DNA with consumer genetic-testing companies. Retrieved from https://www.cnbc.com/2018/06/16/5-biggest-risks-of-sharing-dna-with-consumer-genetic-testing-companies.htmlMost content also appears on my LinkedIn page.