Risk Analysis Assignment Evaluating Cardio Health Project

Question

Task:
In this risk analysis assignment you will work on the CardioHealth project. You will need to produce documents and diagrams related to a few Project Management Knowledge areas.

• Identify project risks breaking them into categories:
  • Market risk: If the IT project will create a new product or service, will it be useful to the organization or marketable to others? Will users accept and use the product or service? Will someone else create a better product or service faster, making the project a waste of time and money?
  • Financial risk: Can the organization afford to undertake the project? How confident are stakeholders in the financial projections? Will the project meet NPV, ROI, and payback estimates? If not, can the organization afford to continue the project? Is this project the best way to use the organization’s financial resources?
  • Technology risk: Is the project technically feasible? Will it use mature, leading-edge, or bleeding-edge technologies? When will decisions be made on which technology to use? Will hardware, software, and networks function properly? Will the technology be available in time to meet project objectives? Could the technology be obsolete before a useful product can be created? You can also break down the technology risk category into hardware, software, and network technology, if desired.
  • People risk: Does the organization have people with appropriate skills to complete the project successfully? If not, can the organization find such people? Do people have the proper managerial and technical skills? Do they have enough experience? Does senior management support the project? Is there a project champion? Is the organization familiar with the sponsor or customer for the project? How good is the relationship with the sponsor or customer?
  • Structure/process risk: What degree of change will the new project introduce into user areas and business procedures? How many distinct user groups does the project need to satisfy? With how many other systems does the new project or system need to interact? Does the organization have processes in place to complete the project successfully?
• Stakeholder communication analysis

Answer

Market risk associated with the project undertaken in the risk analysis assignment
Cardio health project is created with the objective to test patients for risk of Cardiovascular Disease (CVD). The project is the primary product developed in the organization. It is the development of a cloud based machine learning algorithm that will read the ECG data, identify irregularities and assess risk of CVD in the patients. A digital application is developed to read the ECG digital signals that are processed with the machine learning algorithm to determine if patient has risk of CVD. It is a SaaS system which is based on the cloud server that contains the machine learning algorithm developed by John and Ben (Adamson, & Smith, 2018). The product will be highly demanded by health aware customers that would like to check and facilitate early diagnosis to treat CVD if detected). These are the consumer base that the product will be marketed to and sold.

The health-aware consumer base will demand the Cardio health project highly. The clinical information on CVD provided to the patient using the machine learning algorithm will contribute to the better development of the healthcare process. It will improve the relationship between the healthcare providers and patients improving quality of care therefore, influence the standard of the healthcare service. The product is marketable to the consumers as the product gives relevant information to these patients explaining purpose of intervention, risks and nature of the risk (Beam, & Kohane, 2018). Furthermore, it is understandable to them and appropriate to their personal and social circumstances. It will be largely demanded by these customers as they can learn about the risk of CVD.

There are currently firms that are operating in the sector using machine learning algorithms to find prevalence of various diseases. They can develop the machine learning system to diagnose diseases. They can develop competitive project that will use big data from medical institutions to identify CVD diseases. The algorithm not only makes a correct diagnosis, but also assesses the severity of each case and recommends the most appropriate treatment. If existing companies such develop the project that uses artificial intelligence to detect CVD, then it will have enormous potential to foster disease diagnosis and treatment (Char, Shah, & Magnus, 2018). It will instantly analyze and classify large amount of data that is very difficult or impossible for humans to handle.

These competitive machine learning systems can be develop with ML neural network models. These tools can affect the cardio health project and the long term feasibility as these can be a powerful addition to classify and affect vast amounts of biological data. One of the key branches of Artificial Intelligence is Machine Learning , it is increasingly necessary for machines to learn on their own, learning from their own experience, by classifying large amounts of data of a different nature and structure. The competitors will will try to advance in the application of techniques from Machine Learning that have demonstrated their efficiency in other health issue detection areas for transfer in the CVD. These can be implemented with combination of different sources of clinical information, such as history, genomic information, or graphic information on the patient, together with the development of ICT tools based on machine learning techniques, will allow the professional to make early diagnoses, improving the quality of patient life and reducing economic costs. However, these firms are already existing in the market it will affect the cardio health project in short term as well as long term (Adamson, et al, 2018). The cardio health project will be training the artificial intelligence system by teaching historical data. This form of learning makes the process hard and also expensive. Hence, competition in the industry affects the complete cardio project.

Financial risk
The organization has ability to undertake and execute the Cardio Health project. However, financial risks of the cardio health project have a negative impact on the provision of ECG tests services. It causes a higher administrative cost that involves the protection and quality provision of health project to combat factor mitigation and the high-cost of the machine learning implementation in the cardio health project. The organization can undertake the project and it is feasible in financial terms (Beam, et al, 2018). There is adequate capacity for the organization to ensure continuity of the project.

Stakeholders are satisfied with the project and financial projections. The stakeholders are satisfied in accordance with the analysis based on their positions and their ability to influence the Cardio Health Project. The key stakeholders are John, Ben and Arthur Davis. Stakeholder approval is gained after providing adequate assessment of the project management processes. With proper understanding for stakeholders on life cycle of the project, the approval is gained with satisfaction on the CardioHealth project (Char, et al, 2018). Stakeholder satisfaction is important as they influence the project, deliverables, and team members of the CardioHealth project. In the Cardio health project, the satisfaction was ensured for both internal and external stakeholders to be clear about the project's expectations and requirements.

The project estimates on NPV, ROI and payback estimates are ascertained to be successfully attained over projected duration. The project depicts that there will be good net present value generated for the project as it is a large project with good potential. The Cardio health project investment will generate substantial value in the future considering that the real value of money changes over time. The growth of NPV is ascertained to be substantial in the future as the growth of machine learning is very large and had huge potential. The application can also be extended to applications like using different healthcare data for detecting prevalence or risk of various diseases (Mohr, Zhang, & Schueller, 2017). With higher NPV, the return on investment will also be high. There will be good return on investment from the project as the NPV is ascertained to growth substantially over the life of the Cardio Health project. The positive ROI depicts that financial gains are obtained with each action implemented. It is a good measure as obtained from the investment and depicts a good value for the return obtained from the CardioHealth project.

The payback estimates for the project are good and it is short period for starting the dividend issue. The project will be quickly in demand as ECGs are recommended by general practitioners and the test offered by CardioHealth will complement the service for additional charge in the test pricing. The project payback estimates allows determining time for paying back the investors through dividend yield as the benefits of the CardioHealth operation. This is part of the financial indicators that we have at our disposal for the economic evaluation of our project and decision-making (Mohr, et al, 2017). It is also possible to ensure continuity of the Cardio Health project as organization has sufficient resource for covering the project.

Technology risk
There is technical feasibility for the CardioHealth project. The new proposed machine learning technology is technical feasible and is applied into the healthcare technology for detecting CVD. The algorithm is developed by developing the algorithm with large amounts of high-quality available of patient undergoing ECG tests which is used to refine algorithms. The machine learning algorithm of the CardioHealth is cost effective, easy to implement in hospitals as the have existing ECG labs (Char, et al, 2018). They can use it to assess the systems and data flows, differentiating automation-ready areas from those that require more investment.

The CardioHealth project is cutting edge technology. This technology is used to assess the inconsistency in the ECG test data flows obtained on the patients. The advances of this cutting edge technology have sparked the idea among John and Ben to design the cloud based machine leaning for performing diagnosis activities to find prevalence of CVD. They offer the promise of machines diagnosing the patient in future for various diseases based on different test results. Hardware requirements include 20 ECG collection centers, laptops, internet devices etc. They will be working effectively with the project implementation. Software implementation includes cloud based machine learning software. It will be configured relative to the data and the proposed CVD detection algorithm with the data. The network requirement is the internet needed for transmitting the data into the cloud to be tested by the machine learning algorithm (Farrar, & Worden, 2012). Any issues identified in network or hardware is addressed by the respective personnel assigned for the tasks.

The cloud based machine learning technology is readily available to be implemented in the Cardio Health project. The implementation of the system also maintains constant learning and make decisions taking into account unforeseen situations. This will improve the detection of CVD in the patients as the algorithm will keep learning over time. The CardioHealth project teams would learn slowly and progressively, and the model would become more flexible as the system matures and gains experience with detecting CVD with the use pf ECG test data. The implementation will not take time but the design process will consume time as it is catered to specific need of creating technology to detect CVD with ECG test data (Mohr, et al, 2017). The machine learning implementation will learn in the cloud sever and on the basis of what they find without having to be disconnected to be reprogrammed or retrained to adjust to other conditions. For instance, it can be remodeled to find other diseases with different tests.

The machine learning is a cutting edge technology and there are no chances of it being obsolete. It will sustain for longer periods of time as the machine learning and AI based systems are the future of technology. This futuristic algorithm will to find a function that assigns a suitable output tag from the input data of the ECG tests (Farrar, & Worden, 2012). Because they train with a data history, they learn to assign the appropriate output tag to a new value for detecting the CVD, performing the equivalent of a prediction of the output value.

People risk
There are sufficient personnel in the organization with vast expertise and good skills completing the projects successfully. The personnel, their assigned roles, and duties are elaborated as follows;

The employees have the talent and skills to work in the CardioHealth project. The different depoartments headed by the respective sernios employees are targeted to achieve certain objectives of the project collectively contributing to the CardioHealth project completion (Crown, 2015).

They align with the machine learning methods to use enables computers developing it to learn autonomously using these methods. The personnel work along with these methods for analyzing large volume of data, helps attain achieve employee objectives faster, reducing errors and ultimately improving workflow. It is used by these skilled personnel to lighen their the workload, improving responsiveness, boosting efficiency (Mohr, et al, 2017). With skilled and experienced personnel it is possible to liberalize workers from time-consuming tasks with the machine learning implementations.

The personnel will look into diversification of the CardioHealth project for certain diseases. They will develop the systems to obtain different types of images such as ultrasound, radiography, magnetic resonance imaging, computerized axial tomography, positron emission tomography, among others. These images can provide valuable information to detect. Proper treatment of these images by applying Machine Learning techniques, such as machine learning and pattern and image recognition, will allow early diagnosis of breast cancer , non-invasive detection of endometriosis , and prediction of the effects of leukemia treatment in a personalized way. In this way, CardioHealth project will facilitate early diagnosis of these diseases, as well as a more personalized and effective treatment for patients.

Structure/process risk
The CardioHealth project has substantial change introduced will have a substantial change implemeted in the healthcare. These systems are applied in various uses. Systems for corporate infrastructures, which provide electronic connectivity and advanced supports for general and administrative purposes, although medical data is also used. For example, th|e Electronic Medical Records , or what is the same, the digital administration of medical records, which facilitates their archiving, consultation, editing and exchange among health professionals (Holzinger, 2016). The electronic medical record is much more than a data storage and recovery system, it contributes to the increase of the resolving capacity and the quality of patient care in daily clinical practice and is an element of relationship between different professionals and between these and patients throughout the healthcare process.

Applications of information services for professionals and patients, access to databases and knowledge (Holzinger, 2016). In futrure, CardioHealth project will facilitate emotional support, the exchange of information, experiences and self-help advice, and even healthcare, and favor the change from the current paradigm focused on institutions to a model focused on patients who play an increasingly relevant role.

Gannt chart shows the different taks sof the project as follows.

It can expand into applications oriented to support communication in medical, clinical and surgical tasks. They are perhaps the most genuinely representative of communications capabilities (Crown, 2015). The continuous advance of computer science has caused new trends to favor highly innovative ventures into the field of health. These algorithms will be based on prescribed set of well-defined, ordered, and finite rules or instructions. They allow an activity to be carried out through successive steps that do not raise doubts as to who should do the activity. What differentiates the new technologies based on Machine learning, from the traditional technologies used in the field of health. It is the ability to collect information, process it, and provide the end user with a well-defined diagnosis. Machine learning achieves this goal through machine learning algorithms. that they are able to recognize patterns of behavior and extract their own logic. To reduce the margin of error, algorithms based on Machine learning need to undergo continuous evaluations. Given an initial state and an input, following the successive steps a final state is reached and a solution is obtained with the Cardio heath project.

References;
Adamson, A. S., & Smith, A. (2018). Machine learning and health care disparities in dermatology. JAMA dermatology, 154(11), 1247-1248.

Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. Jama, 319(13), 1317-1318.

Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care—addressing ethical challenges. The New England journal of medicine, 378(11), 981.

Crown, W. H. (2015). Potential application of machine learning in health outcomes research and some statistical cautions. Risk analysis assignment Value in health, 18(2), 137-140.

Farrar, C. R., & Worden, K. (2012). Structural health monitoring: a machine learning perspective. John Wiley & Sons.

Holzinger, A. (2016). Machine learning for health informatics. In Machine Learning for Health Informatics (pp. 1-24). Springer, Cham.

Mohr, D. C., Zhang, M., & Schueller, S. M. (2017). Personal sensing: understanding mental health using ubiquitous sensors and machine learning. Annual review of clinical psychology, 13, 23-47.