Cloud Platform and Data Storage Systems in the Healthcare Ecosystem

1. Introduction

An important component for any sector is “Data”. Proper management is important in terms of factors such as timely access to information, their relevance, and accessibility. This process is simply called Data Management. Whether it is the Business sector (Especially in Business management data sets play an important role) or construction, education (Khang & Muthmainnah et al., 2023), military or healthcare, the proper management of data sets in such sectors is an important issue and a separate group of experts specifically deals with it.

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  The Actuality: In terms of the fact that data management is important for any sector, the topic is constantly maintaining its relevance. Thus, in the healthcare sector, various proposals are being made for patient data management, and through these proposals, the healthcare sector is developing both in terms of management and security.

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  The Purpose: Here, the process of data storage systems and data management in the Healthcare sector is reviewed. By showing the structure of the systems, attention is paid to future perspectives and cloud computing and other cloud storage possibilities, especially applied in the Healthcare sector.

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  The Methodology: Platforms of cloud computing and data storage systems and the working principle of the general methodology have been reviewed. What methods are needed for future prospects and innovations of improved shared platforms using these methodologies are also considered separately. Data collection, storage, transfer, processing, delivery to the user (it can be any organization or enterprise) - each of them are processes that need to be carried out in a separate and detailed form. These are the stages of data management in general. In other words, it is the life cycle of management.

It is known that there are different models of designing. Cascade model, iteration model, spiral model known as classical model. It should also be noted that models designed for many different areas can be adapted to each other. For example, we can adapt the design models to the data management process. At its simplest, the data management lifecycle resembles the Design Cascade model. Thus, it is not possible to move to another stage without completing one stage. Even if it is possible, it will eventually lead to certain errors, which will lead to misdirection of management. For example, the model mentioned as Figure 1. It is a cascade model and shows the step-by-step implementation of data management processes. So that,

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  Data is collected – which is done by a human or an automated machine (or computer) depending on the situation.

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  Data is stored – which is the stage of preparing the collected data for the next process (i.e., processing). Basically, the collected data is stored as raw (initial) data in a certain database.

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  Data is transferred – It is the process of transferring data for processing. Sending data for analysis and processing is also important in terms of time. Which has an impact on management in general.

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  Data is processed – certain operations are performed on the initial data to obtain the information necessary for the user, organization or enterprise. It is the process of converting primary data into working data, being a data processing process. At this time, the data is analyzed.

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  Data is delivered to the user - the complete completion of events such as a certain decision-making process, any request, etc., and the life cycle of the user's access to data ends with the delivery of the data to the user.

Figure 1.

Cascade model of the “data management process” life cycle

(Khang, 2021)