Headspace NewAccess Project

This document provides a summary of the non-functional requirements, review of cloud-based solutions, and the SDLC approach for the Headspace NewAccess Project, which is a mental health intervention project targeting youth aged 17 to 25. The project aims to develop a cloud-based solution for gathering and accessing patient history and providing healthcare solutions.

Running head: HEADSPACE NEWACCESS PROJECT
Headspace NewAccess Project
Name of the Student:
Name of the University:

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Table of Contents
Introduction................................................................................................................................2
Non-Functional Requirements...................................................................................................2
Critical system qualities.........................................................................................................2
System interfaces...................................................................................................................2
User interface requirements...................................................................................................3
System constraints..................................................................................................................3
FURPS+ - Usability, Reliability, Performance and Security.....................................................3
Review of Cloud-based..............................................................................................................4
The environment where you deploy Cloud-based.................................................................4
Strengths of cloud-based solutions.........................................................................................5
Weaknesses of cloud-based solutions....................................................................................5
SDLC Approach.........................................................................................................................6
Pros to approach the project using the ‘Predictive’ SDLC....................................................6
Cons to approach the project using the ‘Predictive’ SDLC...................................................6
Pros to approach the project using the ‘Adaptive’ SDLC......................................................6
Cons to approach the project using the ‘Adaptive’ SDLC.....................................................7
Recommend to either Predictive SDLC or Adaptive SDLC..................................................7
Conclusion..................................................................................................................................7
References..................................................................................................................................9

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Introduction
Headspace NewAccess Project is the project based on a mental health interventions
which can target the youth which are aged 17 to 25. The Headspace is trailing this new
healthcare system which can record the story the mental patients as they are identified with
problems that young person with mental illness might see various professionals. NewAccess
is targeting people those have higher depression as well as anxiety. The project work is based
on developing a cloud based solutions for the organization which would gather the history of
the patients so that it would be easier to deploy, access as well as use of networked
information as well as applications.
This particular paper is providing a summary of the non-functional needs of the cloud
based solutions so that it is easier to understand the system’s requirements as well as
capabilities. The paper provides a proper review of the cloud base solutions so that
NewAccess can provide proper healthcare solutions to their patients. The SDLC approaches
are also provided in this paper with two of its approaches such as adaptive and predictive
approaches.
Non-Functional Requirements
Critical system qualities
The cloud based system can provide accurate clinical data such as patient’s
information so that there are no possibility of human errors while entering the data in the
system. It has capability to initiate the clinical documents by means of user interface (Zhang
et al., 2015).

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System interfaces
The system is designed to record the healthcare data of mental patients those are
visited the hospital. Due to their mental conditions, sometimes they are not able to tell their
problems to physicians, therefore in this case the cloud based solutions is used as a clinical
decision support system (Yue et al., 2016).
User interface requirements
There is required to have a user interface cloud based system which can provide
accurate as well as secured data to the healthcare providers. The confidential data of the
organization and patient’s data are kept secured in the system so that no third party person
can able to access it (Esposito et al., 2018). Therefore, it is implemented so that the data are
kept secured from unauthorized access, maintenance of the clinical data of the patients as
well as tracking the records if they are updated as per needs.
System constraints
The constraints which are raised behind development of the cloud based solutions are
lack of design which provide effect on the system performance and lead to failure of the
project. The proposed system is too complex to be implemented, therefore there is lack of
technical training lead to system constraints (Sajid, Abbas, &Saleem, 2016). It leads to
overload of the clinical data in the organization.
FURPS+ - Usability, Reliability, Performance and Security
Following are the non-functional requirements of the proposed cloud based solutions
for NewAccess:

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Functionality: The system users should have login ID in addition to password in order
to login into the system (Xu et al., 2017). The users can able to change the password and also
notified when he attempts to change the password.
Usability: The authorized access to the cloud based solutions of the healthcare
company, NewAccess is provided to the system users such as patient, doctors and system
administrator (Wang et al., 2016). The system can be used anytime and from anywhere but
the users should have authorized access to use it. The system can able to usable by the
program developers when they should complete a training of five weeks.
Reliability: The system can able to store larger amount of clinical data and it is easier
to access it by providing some details only (Castiglione et al., 2015). The transmission of data
process is being confirmed receiving terminal is ready state with prior to the start of the data
transmission.
Performance: The satisfaction level of system users are ensured by means to provide
them quick computing along with smooth import as well as export of data. The number of
transactions which are performed through the system is able to provide details about the
system performance (Abawajy& Hassan, 2017).
Security: The users are allowed to change the password after some period of time in
length of the time being established as password expiration duration (Xu et al., 2017). The
system administrator can only be able to change the access permissions of the system’s data.
Review of Cloud-based
The environment where you deploy Cloud-based
The use of the cloud based solutions is depedent on the SaaS solutions which are
accessed on demand by the end users and customers.

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Figure 1: Cloud based solutions for healthcare
(Source: Created by author)
Strengths of cloud-based solutions
By means of the cloud based healthcare solutions, the users can get limited access to
the system by means of internet, therefore there is low possibility of the clinical data to be
stolen. The data can able to share with other systems so that it can become easier to transfer
clinical data from one place to other (Hossain& Muhammad, 2016). There is maintenance as
well as upgrades to new versions are also easier. The system is totally based on the IT
support, therefore reduces the operational cost and human errors. Those potencies of cloud
based solutions can lead to price savings solutions to the company.
Weaknesses of cloud-based solutions
Apart from the strengths, there are some weaknesses of the cloud based solutions such
as the company can face risk of turning over the data, availability of security issues as well as

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control over the third party. The company has no control over the data where it is being
stored (Hassanalieragh et al., 2016). Therefore, security as well as privacy of data is main
weakness in the healthcare company as the data are being stolen and hacked.
SDLC Approach
Pros to approach the project using the ‘Predictive’ SDLC
Predictive SDLC approach is used in NewAccess so that it becomes quicker to predict
the budget and time period required to finish the planned work. Each of the project SDLC
phases of the healthcare work regarding development of cloud based solutions are managed
properly, so that it is easier to verify and also handle the medical records of the patients.
Cons to approach the project using the ‘Predictive’ SDLC
At the final stage of expansion of software, the software product for the cloud based
solutions are generated. At the later stage, the system testing and system review is being
performed as per the predictive SDLC approach (Muhammad et al., 2017). This approach has
cons that at the initiation stage, the system requirements are not defined properly due to
shortage of time.
Pros to approach the project using the ‘Adaptive’ SDLC
Adaptive SDLC is such an approach where is allowed for transparency of the cloud
based solutions. The system developer can implement the system as per the requirements of
the client. The client can get the solutions delivered on scheduled time. Using this approach,
the client can get earlier delivery of the system. Based on the demands of system users, and
system client, this cloud based solutions are implemented (Sajid, Abbas, &Saleem, 2016).
The users can provide their own response on the system after their usage to ensure if the
system is developed as per requirement or not.

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Cons to approach the project using the ‘Adaptive’ SDLC
Using this adaptive SDLC approach, scope creep is caused as the scope of the system
is not properly defined in SDLC phases. The plan of the project is not determined earlier
before the system progress. Through entire project lifecycle phase, the testing is done
properly so that it causes a higher quality of the product at the time and after development of
the cloud based solutions (Castiglione et al., 2015). When there is an increase into the system
quality, then the cost of the scheme development is also increased for long term.
Recommend to either Predictive SDLC or Adaptive SDLC
From two of the SDLC approaches, adaptive and predictive SDLC methodologies, for
this healthcare project plan, adaptive SDLC approach is suitable as it is based on the system
user’s demands and based on their feedback after using the system, the changes are done. The
proposed cloud based solutions are object oriented in the nature, therefore predictive SDLC
methodology is not used for this particular project work. The selected method helps in
suitable implementation of the project work with implementation in project design and
development of the cloud based system. The approach provides huge collaboration
opportunities with the clients and system development team. There is required to have better
interaction plus relations with the system users.
Conclusion
From the entire study, it is analyzed that the healthcare organization should
implement a cloud based solutions so that the proposed cloud system is implemented for
easier access to the system users without causing any security and privacy issues. Adaptive
SDLC approaches are used for easier to understand the needs of the proposed healthcare
system. The system is used to protect the clinical data of the patients and trail the system. The
cloud based solutions secured the clinical information so that it is easier to access and no

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unauthorized person can access those data. The system provides with better approaches of
SDLC to be used in the implementation work.

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References
Abawajy, J. H., & Hassan, M. M. (2017). Federated internet of things and cloud computing
pervasive patient health monitoring system. IEEE Communications Magazine, 55(1),
48-53.
Castiglione, A., Pizzolante, R., De Santis, A., Carpentieri, B., Castiglione, A., & Palmieri, F.
(2015). Cloud-based adaptive compression and secure management services for 3D
healthcare data. Future Generation Computer Systems, 43, 120-134.
Esposito, C., De Santis, A., Tortora, G., Chang, H., & Choo, K. K. R. (2018). Blockchain: A
panacea for healthcare cloud-based data security and privacy?. IEEE Cloud
Computing, 5(1), 31-37.
Hassanalieragh, M., Page, A., Soyata, T., Sharma, G., Aktas, M., Mateos, G., ...&Andreescu,
S. (2015, June). Health monitoring and management using Internet-of-Things (IoT)
sensing with cloud-based processing: Opportunities and challenges. In 2015 IEEE
International Conference on Services Computing (pp. 285-292). IEEE.
Hossain, M. S., & Muhammad, G. (2016). Cloud-assisted industrial internet of things (iiot)–
enabled framework for health monitoring. Computer Networks, 101, 192-202.
Muhammad, G., Rahman, S. M. M., Alelaiwi, A., &Alamri, A. (2017). Smart health solution
integrating IoT and cloud: A case study of voice pathology monitoring. IEEE
Communications Magazine, 55(1), 69-73.
Sajid, A., Abbas, H., &Saleem, K. (2016). Cloud-assisted IoT-based SCADA systems
security: A review of the state of the art and future challenges. IEEE Access, 4, 1375-
1384.

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Wang, S. L., Chen, Y. L., Kuo, A. M. H., Chen, H. M., &Shiu, Y. S. (2016). Design and
evaluation of a cloud-based Mobile Health Information Recommendation system on
wireless sensor networks. Computers & Electrical Engineering, 49, 221-235.
Xu, B., Xu, L., Cai, H., Jiang, L., Luo, Y., &Gu, Y. (2017). The design of an m-Health
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Yue, X., Wang, H., Jin, D., Li, M., & Jiang, W. (2016). Healthcare data gateways: found
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Zhang, Y., Qiu, M., Tsai, C. W., Hassan, M. M., &Alamri, A. (2015). Health-CPS:
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