NewAccess Mental Health Project: System Analysis and Design Report

This report provides a comprehensive system analysis and design for a cloud-based solution tailored to the NewAccess mental health intervention program. It begins with an introduction outlining the project's objectives, followed by a detailed examination of non-functional requirements, including system qualities and interfaces, and functional requirements, such as system constraints. The report then delves into the FURPS+ model, evaluating usability, reliability, performance, and security aspects. A review of cloud-based solutions is presented, highlighting their strengths such as multi-center collaboration and data privacy, and weaknesses such as bandwidth issues and limited control. The report proceeds to analyze different SDLC approaches, including adaptive and predictive models, discussing their respective pros and cons. The adaptive SDLC approach is recommended as the most suitable methodology for the NewAccess project, given its complexity and need for continuous patient information management. The report concludes with a list of references to support the analysis and recommendations.

System analysis and design
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Table of Contents
Introduction 1
Non-functional requirements 2
Functional requirements 3
FURPS+ - Usability, Reliability, Performance, and Security 4
Review of Cloud-based 5
Strengths of cloud-based solutions 6
Weaknesses of cloud-based solutions 7
SDLC Approach 8
Pros of adaptive SDLC 9
Cons of adaptive SDLC 10
Pros of predictive SDLC 11
Cons of predictive SDLC 12
Best SDLC for the health intervention program 13
References 14

Introduction
This paper deals with creating a system that ensures data analysis of NewAccess mental health.
A medical intervention project should be created through the use of cloud-based solutions and
SDLC approaches. It discusses the advantages of using the SDLC approaches like the
adaptability and predictive approaches as well as their disadvantages. It finally comes up with
the approach that is best-suited for the NewAccess intervention project.
Non-functional requirements
Critical system qualities: the system is user friendly. The system is also user focused and
technically object oriented. In this way it ensures that the client is actively involved in the design
so that the final results can consider all the requirements and specifications of the NewAccess
intervention. In doing so New Access intervention management team will be fully satisfied with
the outcomes of creating the software regarding data set of all the mental patients and staff
information.it considers all the past historical interventions in making sure there is maximum
diversification of information. It also utilizes high caliber materials and program tool sin order to
create high quality products for New Access health intervention program.
System interface: It is made up of cloud API serving as an interface that offer direct and indirect
cloud software survives to the clients. It is based on REST and SOAP frameworks. These
frameworks are incorporated in ensuring that the cloud based computing program offer a
permanent solution to the feeding of information system in the project involving New Access
health intervention.
It also have open cloud computing interface (OCCI)
Functional requirements

System Constraints
It is constrained by the huge data storage needed and low initial cost. It is also constrained by
long time requirements. Other challenges include customer dissatisfaction and lack of proper
training. This makes them to have lower understanding and knowledge regarding the working of
cloud based systems. This can lead to dilemmatic needs and high expectations.
FURPS
Usability-It is used to store patient data in NewAccess health center in a more secure manner
Reliable-it is more reliable since it enhances data ownership.NewAccess intervention becomes
the sole owner of the information.
Performance-It has limited control and increased flexibility hence produces high-quality results
Security-It ensures trust and dataset privacy. The patients’ confidentiality issues regarding
release of personal information from New Access health intervention is fully catered for.
Review of Cloud-based
Strengths of cloud-based solutions
The environment where you deploy Cloud-based
One can deploy the cloud-based in multi-center collaborative environments
Trust and data privacy
The outsourcing of various dataset records is a great solution which mainly depends on the kind
of information offered to the existing cloud providers. It is important in the privacy of medical
data as a key need. Privacy is quite a sensitive issue in a case where health digital images and

patient data are stored and transmitted across other public networks (Aljawarneh, Alawneh &
Jaradat,2017). NewAccess healthcare will insist on safeguarding the privacy of the key actors in
order to prevent data from being interfered with by the cloud-based service providers or
companies. It is evident that health image repositories often use outsized data volumes with an
increased list of important files. Apart from the health exams, the cloud-based systems support
database with textual information which conversely relates to the medical exams.
A health medical image repository which is mainly based in the cloud does not need a high
initial cost of investment as compared to the traditional solutions. This lowers the purchase and
maintenance costs of the data Centre.
The cloud-based computing is drastically changing and improving hence offering increased
resources at relatively lower costs.
In addition, the cloud-based solution is important since they can also facilitate a multi-center
collaborative environment such as sharing of health records across New Access institution.
The cloud base computing will also ensure interoperability with the needed health standards of
the current cloud provider's interfaces, improved communication, and data availability.
Weaknesses of cloud-based solutions
As a key weakness there exists challenges in outsourcing health medical images over the cloud
(Chauhan, & Saxena, 2013). Protecting the privacy of both the patients and physicians such as
protection against misuse of data become problematic.

The cloud-based computing has no redundancy nor a clear backup plan. It also has bandwidth
issues due to a large number of servers. The cloud-based approach has lower control hence the
client might not get access to all the required features.
SDLC Approach
SDLC is a significant procedure well utilized by software industry aimed at designing,
developing and testing the existing quality of the software. The SDLC approach to software
design in a certain project has the purpose of producing high-quality software's which are within
the limit budget and also in line with the expected time scales (Hendre, & Joshi, 2015)The
following are the advantages and disadvantages of both SDLC approaches.
Pros of adaptive SDLC
This particular methodology offers a great opportunity for various extensive stakeholders'
participation before the startup of the project at hand, mainly during and after each and every
stage of the projects lifecycle. Through this particular active engagement, this approach gives a
high interaction extent of client collaborating with the existing development project group.
This effective communication assists clients to clearly understand the working of the stages and
enables them to vividly share their perceptions and also add several other requirements
(Kalaiprasath, Elankavi, & Udayakumar, 2017). These active interactions enable the existing
client and the team to understand each other's requirements. Through efficient and early delivery,
this approach creates customers trust, active participation and encourages them to have a brand
image of goodwill in future endeavors.
Adaptive SDLC ensures increased transparency. Both the customer and the teamwork precisely
together explaining every detail so that the client can have a clear abstract and understanding of

what to expect. This provides the clients with the golden opportunity to offer alternatives and
options in order for them to make an informed decision regarding the progress of the project.
It is also important since it ensures early delivery (Mahmood 2013). Because of the existing
inactive involvement with the prospective customers during the entire time scale and delivery
stage of this project. Due to increased speed of accomplishing the task the work is done in an
absolute timely manner.it is focuses on the user most of the time in order to improve the
satisfaction level.it enhances clients full involvement with the progress of the work providing a
room to offer decisions, alternatives and options to make sure that it is designed according the
requirements and specifications of the New Access health intervention.
Adaptive SDLC approach has the strength of increased quality. It ensures the division of the
project into parts and evaluating the results and process from time to time. The quality outcomes
are also increased through testing and reviews carried out after every part is designed. During
this time, finding and fixing defects identified ensures that there arises no challenge of mismatch
in expectations. This is because the mismatch is well detected and hence corrected early enough
ensuring delivery of a quality services.
Cons
The approach is disadvantageous since it often demands a lot of user involvement which
consumers much of the clients time and their commitment during the project's lifecycle (Okezie,
Chidiebele, & Kennedy, 2012). A lot of the client's time is wasted since they are not well
engaged during testing and reviews sessions. They are thus often disengaged during completing
the development sessions.

This approach also needs a lot of requirement hence it become larger than it was visualized
initially. Scope creep challenge in this approach results in endless projects. The needed
deliverables are complex and predicting them is quite difficult before and after the development
time scale. This makes it more challenging to ensure continuous negotiation for fixed prices offer
and cost of the completion of the entire project.
Testing in this approach is a continuous process. In as much as testing enhances the delivery of
high-quality results, it leads to the increased cost of the project. According to researchers, the
extensive testing's accomplished in the most case brings about project failure in the long run.
Pros of predictive SDLC
This particular methodology is quite easy to comprehend. Therefore, this aspect makes it quite
simple for the developer team to increase the quality of their results.
Predictive SDLC is also important because prediction of final results, expected time frame and
the entire overall cost is possible.it improves the forecasting abilities of the developers.
Predictive SDLC approach makes it less complex for the team of developers. This prompts them
to easily come up with the anticipated budget and timeline required for the completion of the
project.it is also evident that project overruns are also rare cases in this approach.
It is also easy to make this approach (Rashmi, & Sahoo, 2012). It has the merit of reaching the
deliverable at a given time eascle.it is become easy to detect or identify the gaps existing the
software change. This makes it to become up to date with the modern era progress updated in the
software features.
This approach is crucial since it involves well priced system. It is also strong and quite simple to
understand and follow outlined procedures to the latter (Tuli, Hasteer, Sharma, & Bansal, 2014).

The end stage of each particular stage is well determined because it is outlined by the
establishment of anticipated deliverable factors and indicates the startup of other significant
level.
Cons of predictive SDLC
In this approach, some of the requirements are not adversely met. It enhances the success of the
efforts made in the entire procedure. They become wasteful since it involves repeated process so
as to accommodate all the missing requirements.
The predictive SDLC approach has no use for huge and objective focused work. Hence it is
utilized for lower level work projects as outlined at the startup levels and the anticipated
outcomes identified at the project initiation stage (Vijayakumar, & Arun, 2017). According to
this case study, it will not be well applicable to the health intervention program. Researchers
indicate that this approach is not best suited.
Best SDLC for the health intervention program
Therefore, I would recommend adaptive SDLC approach as the ideal methodology for the
project. This is because NewAccess heath intervention is quite complex project. It needs a
continuous process and involves various elements such as critical and divers information on the
patient’s mental and personal records and staff. Thus adaptive methodology is the best technique
to use in this intervention project.

References
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Baliyan, N., & Kumar, S. (2014, August). Towards software engineering paradigm for software
as a service. In 2014 Seventh International Conference on Contemporary Computing
(IC3) (pp. 329-333). IEEE.
Chauhan, N. S., & Saxena, A. (2013). A green software development life cycle for cloud
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Hendre, A., & Joshi, K. P. (2015, June). A semantic approach to cloud security and compliance.
In 2015 IEEE 8th International Conference on Cloud Computing (pp. 1081-1084). IEEE.
Kalaiprasath, R., Elankavi, R., & Udayakumar, D. R. (2017). Cloud. Security and Compliance-A
Semantic Approach in End to End Security. International Journal Of Mechanical
Engineering And Technology (Ijmet), 8(5), 987-994.
Mahmood, Z. (2013). Software engineering frameworks for the cloud computing paradigm. S.
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Okezie, C. C., Chidiebele, U. C., & Kennedy, O. C. (2012). Cloud computing: A cost effective
approach to enterprise web application implementation (A case for cloud ERP web
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Ramachandran, M. (2016). Software security requirements management as an emerging cloud
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Rashmi, M. S., & Sahoo, G. (2012). A five-phased approach for the cloud migration. Int J
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Tuli, A., Hasteer, N., Sharma, M., & Bansal, A. (2014). Empirical investigation of agile software
development: cloud perspective. ACM SIGSOFT Software Engineering Notes, 39(4), 1-6.
Vijayakumar, K., & Arun, C. (2017). Automated risk identification using NLP in cloud based
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