Headspace NewAccess Project: Cloud Solutions and SDLC Analysis Report

This report provides a detailed analysis of the Headspace NewAccess project, focusing on the development of a computerized patient record system for mental health intervention. It begins by outlining the non-functional requirements of the system, including system qualities, interfaces, user interface requirements, and system constraints, and uses the FURPS+ framework to cover functionality, usability, reliability, performance, and security aspects. The report then reviews cloud-based solutions, evaluating their strengths (such as cost-effectiveness and accessibility) and weaknesses (like dependence on internet connectivity and customization limitations). Subsequently, it examines the System Development Life Cycle (SDLC) approaches, comparing the pros and cons of both Predictive and Adaptive SDLC methodologies. The report concludes by recommending the Adaptive SDLC approach as the most suitable for this project, due to its flexibility and user-centric focus. The report also mentions the importance of data security, including data ownership, highlighting that the selected approach can facilitate better user experience for Headspace NewAccess project.

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

1HEADSPACE NEWACCESS PROJECT
Table of Contents
1. Introduction..................................................................................................................................2
2. Non-functional requirements.......................................................................................................2
2.1 System qualities.....................................................................................................................2
2.2 System interfaces...................................................................................................................3
2.3 User interface requirements...................................................................................................3
2.4 System constraints.................................................................................................................3
2.5 FURPS+ Functionality, Usability, Reliability, Performance and Security...........................4
3. Review of cloud based.................................................................................................................5
3.1 Strengths of cloud based solutions........................................................................................5
3.2 Weaknesses of cloud based solutions....................................................................................5
4. SDLC approach...........................................................................................................................6
4.1 Pros to approach the project using the ‘Predictive’ SDLC....................................................6
4.2 Cons to approach the project using the ‘Predictive’ SDLC...................................................6
4.3 Pros to approach the project using the ‘Adaptive’ SDLC.....................................................7
4.4 Cons to approach the project using the ‘Adaptive’ SDLC....................................................8
4.5 Recommend to either Predictive SDLC or Adaptive SDLC.................................................8
5. Conclusion...................................................................................................................................9
References......................................................................................................................................10

2HEADSPACE NEWACCESS PROJECT
1. Introduction
The project is based on “Headspace NewAccess Project” where the company is provided
mental health intervention and its target age group is between 17-25 years. Headspace is in a way
to trail a new system for targeting the persons those has higher level of depression and anxiety.
The problem that the company faces is regarding that each time the mental patients are visited
the physicians, then they are required to tell their story (Moja et al., 2015). The analyst of the
company is implemented a new system which will capture the story when at the first time the
mental patient tells his/her story. The proposed system can aim to address health as well as well-
being of young people by offering them a holistic standard. In this study, the selected system is
computerized patient record system.
This paper summarizes the non-functional necessities of the computerized patient record
scheme and appraisal a cloud based solutions to the system so that it can provide require medical
as well as healthcare services to the Headspace NewAccess. System Development Lifecycle
(SDLC) approaches are used in the study so that the business can get benefited and it can able to
record all the medical history of the mental patients into the system.
2. Non-functional requirements
2.1 System qualities
Integration of the computerized patient record system is enabled with following system
qualities such as:
 The system has ability to initiate the documents scan from the user interface.

3HEADSPACE NEWACCESS PROJECT
 There is proper synchronization of the patient data as well as clinical documents where
the data are stored such as the information related to Patient ID, Patient Name and others
(Amato et al., 2017).
 It has ability to attach the hyperlinked documents towards the patient’s medical records.
2.2 System interfaces
The computer based patient record is such a system which can reside into the system
designed to support the users by offering them accessibility for completion of accurate data,
clinical decision supports system along with patient’s reminders.
2.3 User interface requirements
The user interface is required to achieve uniformity among the healthcare by collection of
validated system designs by means of description of the qualities as well as applicable contexts.
The user interface requirements of this proposed system are that it is secured as well as
convenient (Miller et al., 2015). There is required to protect authorization of the information
from the unauthorized access, maintenance of the patient’s clinical data along with tracking of
the payments into the progress.
2.4 System constraints
The system constraints are that the proposed system has complexity in its designing as
well as there is large number of user’s requirements. There is lack of practical standards as well
as overload of the information. Due to lack of system design, there are limitations of the system
performance (Wager, Lee, & Glaser, 2017). Some of the staffs are not having technical skills as
well as knowledge to develop and use the computerized patient record system so that they do not
understand the clinical workflows.

4HEADSPACE NEWACCESS PROJECT
2.5 FURPS+ Functionality, Usability, Reliability, Performance and Security
Factors Details
Functionality  There should at least one documents scanning workstations on the
clinical floor.
 The proposed system has ability to connect to the centralized enterprise
database servers (Murphy et al., 2016).
 The propose system can support to run in higher availability mode.
Usability  The computerized patient record system must support the virtualization
each cloud server components.
 The system should use to perform documents search in proper amount
of time such as in 5 seconds (Rogers, Patterson, & Render, 2016).
Reliability  The proposed system interface is accessible via the web browser.
 The rate of failure occurrence per the proposed system is around 1/1000.
 The account update processes can roll back all clinical related updates of
the system.
Performance  The system has ability to store unlimited amount of clinical documents.
 Unless the system becomes non-operational, the system should present
and users with a notification inform that the system becomes unavailable
(Taieb-Maimon et al., 2018).
Security  The system users should require assigning login authentication password
to the system after their first login.
 Passwords are not viewable to others at the time of entry.

5HEADSPACE NEWACCESS PROJECT
3. Review of cloud based
3.1 Strengths of cloud based solutions
Due to usage of the cloud based solutions the workflow of the healthcare entities is being
improved and there is an increase into the patient’s access to the healthcare. The interaction
among organizations as well as stakeholders is being facilitated by application data plus
communication technologies. Following are the strengths of the cloud based solutions such as:
1. The core strength of the cloud computing into the healthcare system is cost dynamic. The
operational cost of the healthcare is being reduced because of absence of startup
expenditures (Miller et al., 2015).
2. The users and patients can access to the system anytime so that the patient’s mental
healthcare records are accessed anytime as well as anywhere.
3. The vendors should meet with HIPAA regulations as compared to the healthcare
practices.
4. The proposed computerized patient record system is easier to set up in case of the
disaster and it can provide better support to Headspace NewAccess Project.
3.2 Weaknesses of cloud based solutions
Following are the weaknesses of the cloud based solutions such as:
1. Adaptation of the cloud based solutions will increase percentage of dependable of the
project client as well as system users on the system only (Amato et al., 2017).
2. When the healthcare organization has low internet connection, then they are not able to
use the cloud based solutions, and therefore implementation of this system is really

6HEADSPACE NEWACCESS PROJECT
tough. Therefore, there is limitation of the bandwidth by means of low internet
connections.
3. There is limited customizable of the proposed system and the clinical information of
patient is compromised if there is co-mingled with clients.
4. SDLC approach
4.1 Pros to approach the project using the ‘Predictive’ SDLC
Predictive SDLC is easier to recognize as well as follow. Following are the advantages of
this proposed SDLC approach while implementation of computerized patient record system such
as:
i. It permits for easier forecast of the project finances as well as timelines. It is easier for the
project team to approach up with the financial plan as well as timeframe required to
finish the healthcare project work (Bajaj, Patel, & Patel, 2015).
ii. It is easier to manage as each of the stages of ‘Predictive’ SDLC so that the proposed
healthcare system can easier to monitor as well as handle the patient’s medical records.
4.2 Cons to approach the project using the ‘Predictive’ SDLC
Following are the disadvantages of this proposed SDLC approach while implementation
of computerized patient record system such as:
i. The software product is being produced at the last stage of software expansion process of
the healthcare organization (Shah, 2016). Testing as well as review of the healthcare
system is done at the later stage of the project lifecycle.

7HEADSPACE NEWACCESS PROJECT
ii. The predictive SDLC is not such exploitable for this complex healthcare project. This
particular methodology is not so much used into the smaller projects where all the project
related requirements are not properly defined at initiation stage. It is not properly defined
due to lack of time.
4.3 Pros to approach the project using the ‘Adaptive’ SDLC
Adaptive SDLC approach can provide an opportunity to the stakeholder’s engagement
into the healthcare project. Throughout the engagement, this approach can provide higher degree
of the collaboration of client with the system development team as it is involved with better
interaction as well as relationships at each project stages (Pradeep & Wijesekara, 2017).
Following are the advantages of this proposed SDLC approach while implementation of
computerized patient record system such as:
 Adaptive SDLC can allow for transparency of the proposed system. The system
developer can explain the implementation status to the project client before as well as
throughout the project stages.
 This approach gives earlier delivery of the system. Due to involvement of the project
client in the system development, there is faster development where the users can get the
delivery of the system on scheduled time (Kumar, 2017).
 This methodology is dependent on the users such as involvement of the project client in
contribution of their each system development stages (Wahyudi, Tileng, & Kurniawan,
2018). The client can provide feedback to development team of they require any changes
and translate their value to upcoming software.

8HEADSPACE NEWACCESS PROJECT
4.4 Cons to approach the project using the ‘Adaptive’ SDLC
From the previous section, it is analysed that the adaptive SDLC is required extensive
involvement of the system users throughout the project lifecycle. Following are disadvantages of
this methodology on the system implementation phase such as:
 Scope creep is occurred due to usage of the adaptive SDLC, and therefore it can lead to
never-ending of the project plan (Matharu et al., 2015). The project deliverables are not
able to forecast before as well as throughout the project development.
 Testing is being integrated through entire project lifecycle and it helps to deliver quality
products. While there is increase in the quality products, there is an increase in the cost
throughout the long term.
4.5 Recommend to either Predictive SDLC or Adaptive SDLC
Among two SDLC methodologies, adaptive SDLC is the best methodology for
implementation into the advance work of the computerized patient record system. As this
particular project is based on various users such as caseworker of the company, medical workers,
General Practitioner, psychiatrist and others users of the proposed system and this method is also
based on user’s requirements, therefore it is suitable. This method is used as the system
requirements are properly recorded from project initiation to completion phase. This particular
project is based on various components and it is also object oriented in the nature, therefore
predictive SDLC methodology is not applied. With adaptive methodology, there is proper
implementation into design in addition to development of the proposed system.

9HEADSPACE NEWACCESS PROJECT
5. Conclusion
It is concluded that from two of the SDLC methodologies, adaptive SDLC methodologies
is best suitable for the healthcare project as it is implemented into the software projects due to
specified as well as known project outcomes. It is allowed with flexible to direct the project path
as well as course of the project plan. It is involved with breaking the particular healthcare project
in various components so that it is easier to understand the system requirements. In this particular
system, there is needed to protect authorization of the information from the illegal access,
protection of the patient’s clinical data along with trailing of the payments into the progress.

10HEADSPACE NEWACCESS PROJECT
References
Amato, M. G., Salazar, A., Hickman, T. T. T., Quist, A. J., Volk, L. A., Wright, A., ... &
Adelman, J. (2017). Computerized prescriber order entry–related patient safety reports:
analysis of 2522 medication errors. Journal of the American Medical Informatics
Association, 24(2), 316-322.
Bajaj, K., Patel, H., & Patel, J. (2015, October). Evolutionary software development using Test
Driven approach. In 2015 International Conference and Workshop on Computing and
Communication (IEMCON) (pp. 1-6). IEEE.
Kumar, L. (2017). Predicting Software Quality Parameters using Artificial Intelligence
Techniques and Source Code Metrics (Doctoral dissertation).
Matharu, G. S., Mishra, A., Singh, H., & Upadhyay, P. (2015). Empirical study of agile software
development methodologies: A comparative analysis. ACM SIGSOFT Software
Engineering Notes, 40(1), 1-6.
Miller, A., Moon, B., Anders, S., Walden, R., Brown, S., & Montella, D. (2015). Integrating
computerized clinical decision support systems into clinical work: a meta-synthesis of
qualitative research. International journal of medical informatics, 84(12), 1009-1018.
Moja, L., Friz, H. P., Capobussi, M., Kwag, K., Banzi, R., Ruggiero, F., ... & Kunnamo, I.
(2015). Implementing an evidence-based computerized decision support system to
improve patient care in a general hospital: the CODES study protocol for a randomized
controlled trial. Implementation Science, 11(1), 89.

11HEADSPACE NEWACCESS PROJECT
Murphy, D. R., Meyer, A. N., Bhise, V., Russo, E., Sittig, D. F., Wei, L., ... & Singh, H. (2016).
Computerized triggers of big data to detect delays in follow-up of chest imaging
results. Chest, 150(3), 613-620.
Pradeep, R. M. M., & Wijesekara, N. T. S. (2017). Predictive cum Adaptive Systems
Development Methodology for HydroGIS Tool Development.
Rogers, M. L., Patterson, E. S., & Render, M. L. (2016). Cognitive work analysis in health care.
In Handbook of human factors and ergonomics in health care and patient safety (pp.
494-503). CRC Press.
Shah, U. S. (2016). An excursion to software development life cycle models: an old to ever-
growing models. ACM SIGSOFT Software Engineering Notes, 41(1), 1-6.
Taieb-Maimon, M., Plaisant, C., Hettinger, A. Z., & Shneiderman, B. (2018). Increasing
Recognition of Wrong-Patient Errors through Improved Interface Design of a
Computerized Provider Order Entry System. International Journal of Human–Computer
Interaction, 34(5), 383-398.
Wager, K. A., Lee, F. W., & Glaser, J. P. (2017). Health care information systems: a practical
approach for health care management. John Wiley & Sons.
Wahyudi, S. E., Tileng, K. G., & Kurniawan, I. B. (2018). Enhancing Students’
Technopreneurship Projects with Mobile Collaboration and Communication
Application. Journal of Telecommunication, Electronic and Computer Engineering
(JTEC), 10(2-3), 129-134.