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Project Management Assignment: Risk Identification of Cardio Health Project


Task: In this project management assignment you will work on the CardioHealth project. You need to produce the following documents:

  • 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


Risks associated with the project examined herein project management assignment
Market risk

The IT project will create the Cardio Health product for testing the patients for prevalence of Cardiovascular Disease (CVD). It is useful to the organization as the primary product in their portfolio. The application developed will read the digital signals from the ECG device, process the data, and send them to the cloud server. In the cloud (SaaS) server where the machine learning algorithms is installed, the data is processed to determine cardiovascular disease in patients (Natarajan, Frenzel, & Smaltz, 2017). It is marketable to all the patients that might want to know if they have cardiovascular disease. The product is highly wanted by the customers because the machine learning algorithm use will detect he prevalence of CVD in the patient. With large number of people affected by hear diseases, this product presents an important advance in current methodology enabling patients to make an earlier and more efficient diagnosis of CVD (Ahmad, Eckert, & Teredesai, 2018). The unique selling proposition of the product is that the automated approach is important in day-to-day healthcare to improve the diagnosis of cardiovascular diseases. It could also facilitate the availability of quality care in resource-poor areas.

The product is highly marketable to the clientele and useful to the organization. The medical organization can use the new product in their daily healthcare activities for improving quality of care and patient satisfaction. The product can be setup in the hospitals increasing its presence over the industry. It will become a standard and effective method for identifying the CVD in patients (Callahan, & Shah, 2017). With prior consent of the patient, it is possible to send their ECG data to the machine learning algorithm for detecting irregularities and identifying CVD prevalence. It will help to facilitate early diagnosis and improve quality of life.

The market risks identified with the product comprises of existing researches in use of AI/ML for detecting heart arrhythmias, or irregularities with ECG data etc. There are innovations that might strive to be competitive (Dua, Acharya, & Dua, 2014). These businesses exist in the current market of using AI/ML in healthcare diagnosis, and might affect the business if they add the ECG based CVD diagnosis systems. The users will accept and use the product proposed because it will improve the overall quality of their lives. With mere use of an ECG suggested by the general practitioner, the patient can know if they have the risk of CVD. It will help early diagnosis and treatment to improve patient health. There will substantial improvements in the standard of living and quality of patient’s life as opposed to lack of diagnosis (Dua, et al, 2014). The users will accept the product as a key innovation in the FMCG (Fast Moving Consumer Goods) world where everything has turned into quick yet effective product/services. There are chances that other competitors in the market can create a better product. The companies exist in the market that employs Machine learning algorithms and/or artificial intelligence for detecting the heart irregularities or arrhythmias using ECG data. They can make an addition to their existing product portfolio and introduce the system with additional research (Ahmad, et al, 2018). In that case, the consumers will prefer their product because they are already existent in the similar market with existing customers. It will affect the project causing waste of time and money.

Financial risk
The organization can afford to undertake the project and execute it. It initially begins with the setting up of ECG labs for testing and followed by the cloud based (SaaS) system for machine learning algorithm to process the data. The setup of the ECG will not cause loss for the organization as it can be used as normal ECG labs in case of non-execution of the project as they are operating assets for the organization. It will strive to be profitable for the organization to promote the use of machine learning in healthcare (Callahan, et al, 2017). But the organization will have to face the financial risk of employing huge funds in a competitive market with existing businesses that operate in the sector. It might affect the profit generated by the business.

The stakeholders are very confident about the financial projections. They have also raised concerns regarding cash flows in the competitive market. The funds flowing in the market is substantial regardless of the fact that the market. They are concerned about the future as doctors and patients must be persuaded to trust machine learning algorithms used in the Cardio health product that are complex and their reasoning is incomprehensible for them. However, the stakeholders are content because the future of healthcare diagnosis is based on technology (Ahmad, et al, 2018). It will be able to detect other diseases by combining large amounts of disparate data collected from ECG as well as expanding into multiple tests for various diseases.

The project is estimated to meet the NPV, ROI and payback estimates successfully over the projected time period. The use of the product will be charged with additional pricing over the ECG test inclusive with the test costs. The project will generate the NPV over long term in the investment. The Cardio Health product will meet the long term investment objectives. It will generate good yield. The return on investment will be positive and the payback estimates show a positive cash flow. The estimated return on investment will improve the performance of the business permanently (Ghassemi, Naumann, Schulam, Beam, & Ranganath, 2018). It is necessary to differentiate the strategies that work correctly from those that are not bringing the expected results. The investors will receive high dividend yield over years. Their value will substantially increase as AI/ML in healthcare is the future of the industry. The organization will be able to afford the continuity of the Cardio Health project. They can refinance the cash flows into the business for development and growth. The cash flows generated are used to grow the business rather than take the profit (Dua, et al, 2014). It will support the business growth and financial stability for future. The refinancing of earnings back to the organization will help to ensure continuity of Cardio Health project. The organization can use this method for best utilizing their financial resources to support the project and their business processes.

Technology risk
The project is technically feasible as it uses existing technologies of machine learning algorithms to detect CVD from ECG data. There are several evident researches in the field of machine learning applications in healthcare industry. Using machine learning, it is be feasible to identify groups of people at high risk of being future cardiovascular disease patients with better precision than using existing risk scores (Ghassemi, et al, 2018). This would help improve prevention and surely mitigate the future appearance of new cases of CVD.

It uses a leading-edge technology i.e. machine learning. The technology comprises of A subset of artificial intelligence which will read the ECG data based on historical data and algorithm defined in it to detect the CVD. The technology is used to process large amounts of ECG data to identify the CVD in patients that traditional analysis methods may or may not precisely identify in the patients. Health industry continues to adopt artificial intelligence and machine learning technologies as tool for integrating healthcare data to improve the quality of care. The decisions are made to use the machine learning algorithm as the technology to use in the project. This algorithm will help predict which patients will develop CVD, according to the results of Cardio Health project research (Natarajan, et al, 2017).

The hardware composes of the ECG test labs that are approved and a mature technology which will work effectively. The cloud based machine learning software will also work as it is configured to assess the patient data (Wiens, & Shenoy, 2018). The networks will function properly and sometimes there might be issues which will be rectified with proper patch management and routine maintenance of the cloud system.

The machine learning technology along with cloud is available in time to meet Cardio health project objectives. The technology us is used at many levels in the healthcare for detecting diabetes, heart arrhythmias etc. It is a readily available solution that can be catered to the varying healthcare needs. The solution is designed to work alongside existing capabilities of ECG tests, taking structured and unstructured data to identify anomalies in heart using historical data (Ahmad, et al, 2018). They can be implemented with cloud as SaaS (Software as a Service) designed to provide details of CVD prevalence with real-time decision and policy engine developed in the Cardio Health project.

The technology is in its growth phases hence, it will not be obsolete before a useful product is created. It is a novel technology and machine learning solution accesses historical ECG data to train its models and increase the likelihood that it will discover patterns of CVD in patients. This technology has the potential to identify different health risks based on various tests in future. Additionally, machine learning is implemented in solutions such as passive behavior biometrics to learns about the psychological and mental health of the patients etc.

People risk
The organization has people with appropriate skills to complete the project successfully. The different heads of the departments are as follows;

Th|e Chie|f I|nfo|r|matio|n Offi|ce|r is Mi|chae|l Flynn r|e|spo|ns|i|ble fo|r i|nfo|r|matio|n se|cu|rity. A|ppli|catio|n Group de|ve|lo|ps th|e a|ppli|catio|ns fo|r th|e busi|ne|ss heade|d by Padma Ti|l|a|k. Bruce Henry leads th|e A|rti|fi|c|ia|l I|nte|lli|g|ence an|d Machi|ne Lea|rn|i|ng Group i|mple|m|enti|ng data mi|n|i|ng a|lo|ng with machi|ne Lea|rn|i|ng as w|e|ll as a|rti|fi|c|ia|l i|nte|lli|g|ence i|nte|grate|d i|nt|o busi|ne|ss a|ppli|catio|ns. Th|e database group is le|d by He|len Mi|lls e|mphasizi|ng o|n mo|de|li|ng, pro|gra|mmi|ng, data entry an|d i|mple|m|entatio|n of database proje|cts. Th|e cloud co|mp|u|ti|ng group is leade|d by J|ohn Fulle|r who pro|vi|de|s mi|gratio|ns of IT proje|ct o|n th|e Cloud. The organization can also find such people as it has retained good talent within different departments. They will strive to be useful in hiring new people under their authority and train them to be professionals. Also, the people in different department have the proper managerial and technical skills. They are qualified for working with different technical areas and have proper experience in these sections (Callahan, et al, 2017). The senior management has shown their support for the project. With their extensive support, it is also possible to introduce ne expansion into the existing product portfolio. It will be easier to adapt the techniques to segment the clients and needs to the techniques to be used with their internal customers The organization is familiar with the customer for the project as well as the sponsor i.e. Mr. Arthur Davis. The relationship is good with the sponsor as he is one of prospective business partner for the cardio health project.

Structure/process risk
The project will introduce large degree of change with the new project introduce into user areas and business procedures. The process will be a major innovative change in mediaine. It is one of the areas in which this type of system was expected to have a significant impact. The machine learning is one of those advances that are revolutionizing our lives almost without knowing it (Ghassemi, et al, 2018). This is specifically in the medical diagnosis in which machine learning theoretically seemed very capable of giving valid answers. There is one distinct user group that the project will satisfy i.e. patients taking ECG tests and preferring to learn about the risk of CVD. Any clinical diagnosis requires training, experience, pattern recognition, and probability calculation. Those doctors who meet all those conditions and come up with creative solutions to problems are the ones who inspire series like this. The project has a platform with a huge repository of medical data of cases with ECG data (Chowriappa, Dua, & Todorov, 2014). It is used for machine learning for analysis and computer-aided diagnosis that allows storing, retrieving and manipulating this information and then a group of automatic classifiers, based on machine learning algorithms, that allow the development of alternative diagnoses to those made by the physician and that serve as a collation mechanism for the first.

Th|e|r|e a|r|e many si|gn|i|fi|ca|nt i|ndi|cat|o|rs of th|e ra|pi|d growth of Machi|ne lea|rn|i|ng a|lgo|rithm a|ppli|catio|ns. Fi|rst, th|e numb|e|r of sta|rtups suppo|rti|ng Machi|ne lea|rn|i|ng a|lgo|rithm fo|r hea|lthca|r|e pro|vi|de|rs has i|ncr|ease|d. I|n th|e past 3 yea|rs, o|ve|r 100 hea|lthca|r|e sta|rtups ha|ve use|d machi|ne lea|rn|i|ng an|d AI, offe|ri|ng suppo|rt i|n e|ve|rythi|ng fro|m r|e|mo|te patient mo|n|it|o|ri|ng t|o drug disco|ve|ry, i|magi|ng, e|tc (Wiens, e|t a|l, 2018). Hea|lthca|r|e o|rgan|izatio|ns cu|rr|ently use Machi|ne lea|rn|i|ng a|lgo|rithm i|n so|m|e wa|y. Th|e vi|rtua|l hea|lthca|r|e assistant i|nclude|s spee|ch r|e|co|gn|itio|n te|chn|o|lo|gy de|si|gne|d fo|r hea|lthca|r|e, whi|ch ca|n co|nve|rt text i|nt|o spee|ch, a|mo|ng o|th|e|r thi|ngs. Th|e chatbo|ts a|r|e pro|gra|ms i|nvo|lve|d i|n co|nve|rsatio|ns with humans an|d ha|ve a|lr|eady spent a ti|m|e wo|rki|ng. Th|e|se syste|ms ha|ve i|mpro|ve|d tr|e|m|end|ously i|n r|e|cent yea|rs an|d a|r|e now use|d i|n many d|o|mai|ns t|o suppo|rt o|nli|ne de|c|isio|n ma|ki|ng. Th|e|y a|r|e a|v|ai|l|ab|le o|nli|ne an|d ha|ve many use|s, i|ncludi|ng: suppo|rti|ng diagno|sis by che|cki|ng patients' sympt|o|ms, sa|fe m|e|di|catio|n counse|li|ng syste|ms, an|d nu|rsi|ng suppo|rt. Th|e mo|n|it|o|ri|ng de|vi|ce|s hea|lthca|r|e|that use Machi|ne lea|rn|i|ng a|lgo|rithm te|chn|iqu|e|s a|r|e now i|n wi|de|spr|ead use (Wiens, e|t a|l, 2018). Th|e|y ca|n b|e use|d as r|e|mo|te patient mo|n|it|o|ri|ng fo|r hea|lth i|ndi|cat|o|rs, such as ca|rdiac ac|ti|vity a|fte|r an i|nte|rventio|n, patient w|ei|ght, e|tc. Th|e use of such de|vi|ce|s that a|r|e si|mi|l|a|r t|o th|e ca|rdio hea|lth proje|ct is now growi|ng i|n th|e se|ct|o|r. Th|e Machi|ne lea|rn|i|ng a|lgo|rithm ca|n b|e use|d t|o r|e|mo|te|ly de|c|i|de patient tr|eatm|ent pl|ans o|r issu|e a|le|rts so th|e use|r is co|nce|rne|d. W|ea|rab|le de|vi|ce|s ca|n mo|n|it|o|r hea|lth an|d w|e|llne|ss-r|e|l|ate|d i|nfo|r|matio|n, such as th|e numb|e|r of ste|ps wa|lke|d o|r th|e numb|e|r of ca|lo|rie|s co|nsum|e|d. This trai|n|i|ng could b|e i|mpo|r|tant fo|r patients tryi|ng t|o lo|se w|ei|ght.

There are many significant indicators of the rapid growth of Machine learning algorithm applications. The other systems interacted is the cloud based SaaS for the machine learning implementation.

The organization have processes in place to complete the project successfully. It is shown in the gannt chart as follow;

Sl No.












“Develop an application”










“Set up 20 ECG data collection centres”










“Setup software as a service (SaaS) on the Cloud”











“Develop a cardiologist mobile application”










Stakeholder communication analysis


Document name

Document format

Contact person

Due date

Customer management

Monthly status report

Hard copy and meeting

John, Kate

First of Month

Software subcontractor

Software implementation



June 1

Ahmad, M. A., Eckert, C., & Teredesai, A. (2018, August). Interpretable machine learning in healthcare. In Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (pp. 559-560).

Callahan, A., & Shah, N. H. (2017). Machine learning in healthcare. In Key Advances in Clinical Informatics (pp. 279-291). Academic Press.

Chowriappa, P., Dua, S., & Todorov, Y. (2014). Introduction to machine learning in healthcare informatics. In Machine Learning in Healthcare Informatics (pp. 1-23). Project management assignment Springer, Berlin, Heidelberg.

Dua, S., Acharya, U. R., & Dua, P. (Eds.). (2014). Machine learning in healthcare informatics (Vol. 56). Berlin: Springer.

Ghassemi, M., Naumann, T., Schulam, P., Beam, A. L., & Ranganath, R. (2018). Opportunities in machine learning for healthcare. arXiv preprint arXiv:1806.00388.

Natarajan, P., Frenzel, J. C., & Smaltz, D. H. (2017). Demystifying big data and machine learning for healthcare. CRC Press.

Wiens, J., & Shenoy, E. S. (2018). Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology. Clinical Infectious Diseases, 66(1), 149-153.


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