Lin, H.C., Kuo, Y.C., & Liu, M.Y. (2020). A health informatics transformation model based on intelligent cloud computing – exemplified by type 2 diabetes mellitus with related cardiovascular diseases. Computer Methods and Programs in Biomedicine, 191. https://doi.org/10.1016/j.cmpb.2020.105409

Abstract

Cloud computing uses a universal database to store information in a given environment. In health informatics, traditional databases are used to store and transfer information in a local area. Cloud computing lowers the amount of physical hardware that is required and can potentially provide a more malleable database. The basis of this study was to see if cloud computing would be more efficient and be a better alternative to the traditional databases. By using Type 2 Diabetes Mellitus (DM2) with related cardiovascular diseases as a testing ground to see if the idea of cloud computing can be applied over wider areas of health environments.

Experimental or Nonexperimental:

This study was experimental. They had used two groups of DM2 (dependent variable). One used the regular databases and installed systems, and the other group used a cloud computing database. The different databases are the independent variables.

Qualitative or Quantitative:

This was a qualitative experiment. They measured the efficiency and performance of the cloud computing database and compared it to the traditional databases. However, there is a small part of this study that is in fact quantitative. This would be strictly how many more systems the cloud computing database can handle over the traditional database, but it does not play much of a role in the study.

Population Studied:

The population studied was patients within a health environment. This study isolated the DM2 area in a hospital area, but the database can apply to many systems. Not only can it handle the whole hospital, but it can be isolated into individual areas or clients. Practically any set of isolated data that is stored can be supported by this system.

Sample Used:

The sample used here was very large. The database system can handle hundreds of patient’s information and process it efficiently as needed.

Method of Measurement:

The method of measurement was a data chart. They would run the numbers of data and let the systems organize it and store it.

Method of Analysis:

The method of analysis was a comparison of how the two databases compared the same sets of data. The efficiency of the systems, time to process, storage compacity, and accessibility were all accounted for and compared based off the DM2 numbers.

Conclusion of the Study:

The conclusion of the study showed that the cloud computing database was more efficient than traditional systems. It allowed for more advantages and simpler implementation of new software and methods over the old systems.

Impact on Self:

This study has a minor impact on me. I just find this kind of technology cool and enjoy health informatics. I will have to be more familiar with cloud computing when I look for jobs, because it is becoming more of a common place technology.

Next Step:

The next step is to scale the cloud computing systems to handle more connected systems. There is also the possibility of incorporating VMware or other VM devices and splitting up the cloud computing database more for specific tasks.