Discuss about the System Analysis PG for Healthcare Industry.
In general, the healthcare industry has rapidly moved towards the digital platform over the past few years due to the amount of data it collects. In most cases, this data stems from patient’s records which include extended descriptions of the diagnostics and treatment procedures. Now, most of this data must be analyzed to yield conclusive results which necessitate the need for cloud storage facilities which collect, process and distribute meaningful information. Moreover, the same facilities enable the healthcare institutions to store their extensive records which are then readily accessed from any location and using any digital platform. Similarly, the Headspace project aims to promote the functionalities cloud computing into its existing IT infrastructure by linking its proposed information system to a cloud service provider (Reddy & Reddy, 2013). This report analyses the different system design parameters that will facilitate this collaboration including the non-functional requirements of the system itself. Moreover, the attributes of cloud solutions are given and so are the development methods.
These are attributes or characteristics and that define the system design thus constrain it from different functionalities across a wide range of operating platforms. Now, this definition is different as compared to that of functional requirements which outline the functionalities and operations of the system (Hassan, 2010). Therefore, these requirements define the system’s interaction with the end user which promotes the usability outcomes.
These elements facilitate and maintain the efficiency of the system thus ensuring that the overall user structure is satisfied. Furthermore, if they are not met, the system may fail to meet certain regulatory measures or standards set by the governing authority (Losavio & Chirinos, 2003). Now, they are:
Performance – the overall utilization of the system which is measured using the response time, static volumetric and throughput among many other factors.
Reliability and recoverability – consistency in operations and functionalities despite the changes in operation platform or occurrence of hardships.
Security – the property, more so the data must be protected against illegal access or exposure.
Usability – the most critical component that determines the overall system’s satisfaction levels. It is the system’s ability to facilitate operations through different practical functionalities (Microsoft, 2017).
These elements represent the overall structure that interacts with the end user i.e. the outline that delivers the results and allows users to give the system input. Its design generally dictates the system’s performance as the user’s appeal will determine its usability. Therefore, the developer must balance the technical prowess of the background structures with the overall system interface i.e. items such as colour, icons and images (E-cartouche, 2017). To this end, the following attributes are necessary:
Maintainability – the interface should live up to the time through update features i.e. patches that constantly engage the users.
Interoperability – especially with all platforms i.e. operating systems and deployment languages.
Accessibility and availability – despite the multiple functionalities, the interfaces must be size convenient for fast access regardless of the internet connection (Rahman, Safadi, & Basaula, 2015).
First, the major constraints, in this case, are the non-functional requirements themselves as they will restrict the development of the system itself. Furthermore, they will limit the deployment platforms which will include programming languages and operating systems. Moreover, they will affect the overall budget and time provision which will limit the system’s functionalities.
These are services that are offered to customers (subscribers) through internet connections or any other forms of public networks. Now, these services usually include IT resources such as networks, processing power and storage facilities among many others. A service providers (better known as CSP) will host these resources in foreign environments and lease them to willing subscribers. In the end, the subscriber will use a pay-as-you-use model to host their resources online under the CSP infrastructure (Council, 2017). Similarly, if adopted by the Headspace project, the proposed system will be hosted online, an outcome that will boost its availability and accessibility. Furthermore, due to its attributes, the cloud resources will have the following benefits and weaknesses.
Cost saving – a crucial component of any organization as it determines the overall expenditures and income returns. In this case, cloud computing eliminates the implementation and maintenance cost of IT resources.
Resource availability and accessibility – healthcare stakeholders would be able to access all resources hosted online so long as they have an internet connection.
Flexibility and redundancy adaptability – CSPs will host the same resource in multiple locations which improve the backup options available (Viswanathan, 2017).
Security and privacy – the CSP will operate in public platforms which raises the concerns of resource security and privacy. Moreover, since the resource occurs in an international platform (internet) the local Australian laws may not govern it. Therefore, the solution, in this case, will fall on the security measures implemented including data encryption and authentication where verification of the users will be done.
Loss of system control – cloud solutions lack the physical control of resources experienced by users when using the on-premise equipment. Furthermore, the end users (subscribers) cannot track or tag their resources as they are ferried online (Ward, 2017).
SDLC is a process that facilitates users to transform system’s theoretical ideas into practical operational systems. In essence, SDLC will involve an array of procedures and stages that will implement a software solution using methodological stages. Furthermore, since different systems have varying functionalities and characteristics, the process will change from time to time which outlines the different approaches associated with SDLC (Isaias & Issa, 2010). In all, some approaches will emphasize on some requirements and functionalities as compared to others. Therefore, the SDLC approach will generally determine the final solution depending on the deployment procedure used.
To understand this methodology, we highlight the approach using a common example of the predictive SDLC method i.e. the waterfall model. Now, the waterfall model falls a sequential procedure during its implementation of system projects. The same model is followed by the overall predictive approach where design stages of system development are critically outlined before implementation and are then followed sequentially without any deviation (MIS, 2015). Therefore, the first step is always to identify the stages of development including their specific requirements and assumptions. From this step, the stages themselves are highlighted and documented for instance; requirements capture, system design, construction, requirement integration, testing and deployment. This outline follows a logical flow with each subsequent stage occurring after the successful completion of the previous one.
Again, following the same definition procedure, a common example of the approach is the Scrum model where agility and performance flexibility are usually met. Now, unlike the predictive approach, the adaptive approach will have a greater emphasis on user interactions as compared to system processes or tools. Moreover, the approach will also easily respond to changes which increase its adaptability functions (MIS, 2015). Nevertheless, the approach will also start by defining the system’s requirements and processes which are then split into logical implementation stages. These stages are then executed simultaneously which yields many initial and subsidiary solutions. From here, these subsidiary solutions are integrated to form the final solution using iterative techniques that maximize the system performance.
The adaptive approach seems to hold many benefits that the predictive approach cannot match. For one, the adaptive approach can adapt to changes which are inevitable in any modern system due to the advancements of technology. Secondly, the adaptive approach will optimize the resources including time, unlike the predictive approach which will require long timeline to implement the final solution (Isaias & Issa, 2010). Finally, the integration with cloud resources requires an agile method that will match any variations imposed by the technology, a functionality that can only be met by an agile approach.
Conclusion
Cloud computing is without a doubt the best solution for the problems facing the Headspace project which requires endless storage facilities to support its medical services. Moreover, with cloud-based solutions, the availability and accessibility of the project’s resources will be increased which will enhance the overall system’s performance. However, at the same time, the project must consider the security concerns of cloud computing where the control of the data is not guaranteed. For this concern, the project must implement proper security measures including data encryption and authentication.
References
Council, C. S. (2017). Impact of Cloud Computing on Healthcare. Version 2.0, Retrieved 02 October, 2017, from: https://www.cloud-council.org/deliverables/CSCC-Impact-of-Cloud-Computing-on-Healthcare.pdf.
E-cartouche. (2017). Types of User Interfaces. Cartography for Swiss Higher Education, Retrieved 02 October, 2017, from: https://www.e-cartouche.ch/content_reg/cartouche/ui_access/en/html/UnitGUI_UI.html.
Hassan, A. (2010). Software Architecture. CISC 322, Retrieved 02 October, 2017, from: https://research.cs.queensu.ca/~ahmed/home/teaching/CISC322/F09/slides/CISC322_02_Requirements.pdf.
Isaias, P., & Issa, T. (2010). Information System Development Life Cycle Models. Retrieved 28 September, 2017, from: https://www.springer.com/cda/content/document/cda_downloaddocument/9781461492535-c2.pdf?SGWID=0-0-45-1479416-p175478101.
Losavio, F., & Chirinos, L. (2003). Quality Characteristics for Software Architecture. JOURNAL OF OBJECT TECHNOLOGY, Retrieved 02 October, 2017, from: https://www.jot.fm/issues/issue_2003_03/article2.pdf.
Microsoft. (2017). Chapter 16: Quality Attributes. Design Fundamentals, Retrieved 02 October, 2017, from: https://msdn.microsoft.com/en-us/library/ee658094.aspx.
MIS. (2015). The System Development Life Cycle. Retrieved 02 October, 2017, from: https://utexas.instructure.com/courses/1166782/files/38198507/download.
Rahman, R., Safadi, W., & Basaula, A. (2015). Functional And Non-Functional Requirements. Retrieved 28 September, 2017, from: https://ami-2015.github.io/MyGuide/d2-final.pdf.
Reddy, G., & Reddy, U. (2013). Study of Cloud Computing in HealthCare Industry. International Journal of Scientific & Engineering Research, Retrieved 02 October, 2017, from: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.404.1483&rep=rep1&type=pdf.
Viswanathan, P. (2017). Cloud Computing and Is it Really All That Beneficial? Advantages and Disadvantages of Cloud Computing, Retrieved 02 October, 2017, from: https://www.lifewire.com/cloud-computing-explained-2373125.
Ward, S. (2017). 5 Disadvantages of Cloud Computing. The balance, Retrieved 02 October, 2017, from: https://www.thebalance.com/disadvantages-of-cloud-computing-4067218.
Warner, E. (2017). Adaptive vs. Predictive: Is the end clear? Idea, Retrieved 02 October, 2017, from: https://www.idea.org/blog/2005/12/02/adaptive-vs-predictive-is-the-end-clear/.
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