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Record Requirement Headspace Organization

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“My Health Record” is a record developed by the Headspace organization for maintaining the electronic summary of mental illness of youths. This record can be shared across various geographical regions, healthcare providers, and doctors. The patient details ranging from medicine prescriptions, impervious status of the patients, health checkup record, lab results, age, and height to weight are contained in this record. According to most of the healthcare executives, the proper utilization of patient’s data allows them to offer superior quality and cost effective care to the patients.

The patients residing in pastoral areas must have to go for longer distances to the hospitals by holding their paper medical reports. The independent health record also does not contain previous references for the patient reports. But “My Health Record” provides enhanced prognosis, quicker healthcare by the doctors across the globe, and efficient treatment through the deployment of cloud. But it also suffers from other aspects like security and reliability that are discussed in the accompanying sections.


Functional and Non-Functional Requirements

The requirements are either functional or non-functional that are required for developing any project. Here described are the functional and non-functional requirements for developing the Headspace project.

Functional Requirements

“My Health Record” Design Considerations

  • Cloud-Based Electronic Health Report: An assortment of healthcare technology reserves are consorted to outsized clouds for sharing the patient records easily (Ahuja, Sindhu & Jesus, 2012).
  • User Validation: “My Health Record” has given the owner rights to both the patients and doctors. They can provide authentications based on their preferences by using passwords.

System Architecture

The system framework for the cloud-based organization health report system is shown in the figure below:

Cloud-Based System Requirements

The cloud-based organization comprises middleware, authorization server, and significant database.


A common environment that is offered for all the health record system is middleware. The middleware functions as an interface used for facilitating the interactions amid the healthcare providers and central database server (Cristina, 2010). In addition to that, it is connected to several hospitals for the purpose of sharing the patient information in the cloud environment.


Database Server

The Infrastructure as a Service (IaaS) cloud service model is deployed in the Headspace organization due to its flexibility. In IaaS, a database server acts as the information store for saving the electronic health reports of the patients and for recovering the patient details.

Authorization Server

The authentication process is controlled by means of this server. The entity (either patient of any medical representative) is verified that they have the permission to access and modify the health records.

Non Functional Requirements

The non-functional aspects like usability, reliability, performance, and security for Headspace cloud model are depicted below:


Reliability is the crucial facet of cloud computing notably in healthcare organizations. The patient details and medical assessment support should be accessed by the providers without any disruptions. According to Greg Lord, “The conflicting occurrence can be experienced by the users of Electronic health records for implementing cloud computing technology”. Hence in healthcare, the clinician must have access to sign in to the electronic health record system without affecting the reliability.


The health care contributors have confronted some performance issues associated with the electronic record. So, the “My health record” system created in Headspace must be faster enough to be accessed by the physicians when they are in face to face interaction with the patients. In addition to that the performance can be affected by means of network traffic congestions (Padhy, Patra & Satapathy, 2012). So, the reputation of electronic record supplier will be affected due to the inadequate performance of the system. By the utilization of cloud-based deployment environment, the network innate drawbacks can be overwhelmed and thus the reliability and performance of the cloud based system will be enhanced.


Information security is the top concern in the cloud based healthcare organizations. The clinical information is more critical than any other financial data (Saif, Wani & Khan, 2010). Hence, the aspects of security and confidentiality are ultimately imperative.

The security policies and standards defined by the healthcare contributor are important for guaranteeing the safety of patient data contained in “My health record”. The storage and conveyance of secured health details creates the ambiguity after the employment of cloud-based constituent to the healthcare administration.

Hence the cloud service providers make ensure that they provide required security to the confidential patient’s information. The security and privacy needs are not only required in the remote information center but also at the point where the information is conveyed between remote information center and the cloud user.


The incorporation of electronic health records is highly resisted due to the deficiency in usability (Csiszar, 2014). The attributes of usability are users’ identification and tasks determination. The usability measures are evaluated by assessing the task performed by the contributor’s team on the system of cloud supplier.


Strengths and Weakness of Cloud Based Solution

The strengths and weakness associated with the cloud based solution in Headspace is provided in the table below:



1. The software can be accessed anywhere by using the gadgets connected with the network.

2. The necessity of utilizing single motionless computer by the user is avoided.

3. The information can be commuted to other cloud systems.

4. The software with similar versions is provided to all the users.

5. Performing upgrades to the earlier versions are simplified.

6. The management of the software is abridged and not complicated.

7. The security of the data in electronic health record is enhanced.

8. The technological assistance is provided by the system itself.

9. The additional investment for managing the database is not necessary.

1. The source of data is not known since the control of data, accessibility, safety, and management are handed over to a third-party cloud contributor.

2. Information accessibility, disaster recovery, quicker response times, and failure restrictions are difficult to achieve.

3. The service providers possess more administration capacities when compared to the end users.

4. The revelation of information without any authorization leads to severe impacts in the organizations.

5. When an error or complicated system failure is encountered, it is difficult to recover and restore the data. In addition to that, difficulties arise in notifying the exaggerated users.

6. The data recovery costs are higher when compared to in-house premises.

7. Pricing firmness and availability is crucial.

8. The entire data in the organization will be affected if there are any problems with the data or software.

Hence in order to alleviate the weaknesses, a contract should be signed between the organization and the cloud service contributors (Hitachi, 2012). Since the cloud suppliers have more control over the data and the software, the costs for recovering the data during any software or system failure should be included in the contract.

Pros and Cons of Predictive and Adaptive SDLC

The software development life cycle describes a technique for enhancing the software development and overall quality of the software. SDLC containing various software development models comprises 6 stages ranging from plan, requirements definition, design, development, test, to deployment of the software (Dinh, Lee, Niyato & Wang, 2013).

Predictive Analysis

When the management of technology is concerned, the business planning, data set extraction, and decision making are the essential processes. One of the important business contrivances to perform these activities is Predictive analysis. The prevailing trends and business data can be examined by the predictive prototypes for identifying the destined challenges and benefits.

Adaptive Analysis

The Adaptive analysis method is utilized in the Agile SDLC model. In this adaptive approach, detailed proposal is not provided and the lucidity on the destined tasks is not demonstrated. Feature driven development is the main aspect of this approach.


SDLC Predictive Analysis

SDLC Adaptive Analysis


·         The analysis can be implemented for various number of business policies. Hence, it is widely used for reference engines.

·         It provides decision-making support to business managers and executives for improving the sales and business productivity

·         The modifying needs of the customers can be met by this approach

·         Investment of time and efforts are not required since the customer requirements are changed frequently

·         Presumption is not needed since there is face to face interaction with the customers

·         The software with superior quality can be developed in minimum time and convinced consumers


·         Extraction of big data sets for utilizing the data focused decision making process is complicated

·         Sometimes the algorithms fail to impact the customer variables like buying patterns of consumers

·         The sudation of the model is required when the needs of the customers are changed over time

·         The assessment of the initial labors of the software development is difficult

·         There is less prominence for the purpose of documentation and design

·         The technically savvy individuals alone are able to take this sort of business decision needed during the software development process


The teams for managing technology in the sectors of finance, utility, healthcare, etc. depend on the predictive analysis for planning the destination and for enhancing the customer satisfaction. For example, Amazon and Netflix rely on predictive analytics. Netflix are able to analyze the viewer’s savor and provide multimedia services according to that. The Amazon predicts the product choice of the customers and transmits that packages to the destined region of the customers before they purchases the products.

So, based on the advantages provided by predictive analytics, the Headspace organization can utilize it for ensuring data safety and for improving the patient’s satisfaction.



In this report, the functional and non-functional requirements for developing “My Health Record” system are discussed. The management of the patient’s health records can be accomplished by adopting the cloud technology. In addition to that, the reduction of management drawbacks can be reduced thereby lowering the healthcare costs. Moreover, the electronic health reports, pharmacy reports, physician’s notes, and digital medical representation can be acquired in real time. Headspace customizes the healthcare industry by executing the analytics on the constituted cloud data.



Ahuja, P., Sindhu, M. & Jesus, Z. (2012).A Survey of the State of Cloud Computing in Healthcare. Network and Communication Technologies, 1(2), 12-19.

Cristina, C. (2010). Electronic Health Record Adoption: Perceived Barriers and Facilitators-A Literature Review. Centre for Military and Veterans’ Health, 1-53.

Dinh, H. T., Lee, C., Niyato, D. & Wang, P. (2013).A survey of mobile cloud computing: architecture, applications, and approaches. Wireless Communications and mobile computing, 13(8), 1580-1611.

Hitachi (2012). How to Improve Healthcare with Cloud Computing, Hitachi Data Systems, 1-20.

 Csiszar, J. (2014, February 8). Paper-Based Medical Records vs.  Electronic Medical Records. Retrieved from 8591569.html

Padhy, R., Patra, M. &Satapathy, S. (2012). Design and Implementation of a Cloud based Rural Healthcare Information System Model. UNIASCIT, 2 (1), 149-157. 

Saif, S., Wani, S., & Khan, S. (2010). A Network Engineering Solution for Data Sharing Across Healthcare Providers and Protecting Patients’ Health Data Privacy Using EHR System. Journal of Global Research in Computer Science,2 (8).


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