PROJECT PROPOSAL
Institution Affiliation
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Start-up Proposal: HEALTH-COP COMPANY
Predicting When and Where Lifestyle & Dietetic Related Health Issues Are Most Likely to Occur.
Introduction
Health-cop company is a data mining company that predicts health trends and possible illnesses that could be witnessed in the near future. The company will mainly focus on data mining and analytics to establish links between diet composition and health issues in society, (Larose, 2015). The data to be used in the predictive analytics will mainly be obtained from hospital databases, nutrition and dietetics websites, health journals as well as information shared through social media platforms. Health-cop company intends to predict such issues before they can become tough to manage.
Goals & Objectives
The main goal is to become a leader in health predictive analytics in the health sector, improve the level of preparedness for various health issues, and earn a profit from running the business. Health-cop’s main objective is to identify certain lifestyle and dietetic related illnesses that are most likely to be experienced within a certain region in the near future. The company will analyze purchases from food stores and groceries and also analyze the various meals ordered for from various food joints. The company also aims at providing consolidated reports on diet composition of various people from various regions based on data obtained from websites and social media platforms.
Organizational Structure
The company will be headed by a chief executive officer who will be in charge of overseeing all operations. A seven-member board of directors will be selected among data analytics professionals to undertake the duties of policy formulation and implementation. Health-cop will have a data mining division, analytics division, IT department, as well as a human resource and customer relations departments; each headed by a departmental manager. An independent division to deal with business modeling and statistical database creation will receive data from the analytics division. This division will create various projections that will be used to make predictions about specific illnesses.
Target Market
The company targets to sell its information to health departments at various levels of governments. The company will also provide its analysis to various hospitals for an agreed fee. Health-cop will also sell its findings to private health care institutions especially nutritionists and pharmaceutical organizations. The existing competitors in the market offer predictive analytics for chronic diseases unrelated to dietetics, (Sepah, et.al., 2015). Health-cop will majorly focus on lifestyle and dietetics related illnesses that are easily preventable thus the company will be unique in the market. The major illnesses that the company will analyze and report on are diabetics, obesity, and osteoporosis.
Budgetary Estimation
The start-up will require planning and preparation finances to facilitate sufficient research before launching the company. Costs will also be incurred to secure strategically positioned premises for the company. Acquisition of digital equipment such as computers and network cables as well as the installation of internet services will require sufficient funding, (Shah, et.al., 2018). Other operational expenses that are expected include salaries and wages for the company’s staff and marketing of the company and its services in the market.
Conclusion
In recent years, lifestyle-related illnesses have become an issue for many people in the world, (Peirson, et.al., 2015). The main factors that contribute to the increased incidence of such illnesses are changes in lifestyle and dietary behavior. The reported cases of diabetes, obesity, and osteoporosis have significantly shot up in recent times. This can all be attributed to the changes in diet behavior. A preventive analytical algorithm would be most suitable to manage these illnesses. A computer algorithm programmed to analyze what is being consumed in various regions and link the food substance to a certain lifestyle-related disease would be very important, (Razzak, et.al., 2019). This would facilitate early detection and application of preventive measures.
References
Larose, D. T. (2015). Data mining and predictive analytics. John Wiley & Sons.
Peirson, L., Fitzpatrick-Lewis, D., Morrison, K., Ciliska, D., Kenny, M., Ali, M. U., & Raina, P. (2015). Prevention of overweight and obesity in children and youth: a systematic review and meta-analysis. CMAJ open, 3(1), E23.
Razzak, M. I., Imran, M., & Xu, G. (2019). Big data analytics for preventive medicine. Neural Computing and Applications, 1-35.
Sepah, S. C., Jiang, L., & Peters, A. L. (2015). Long-term outcomes of a Web-based diabetes prevention program: 2-year results of a single-arm longitudinal study. Journal of medical Internet research, 17(4), e92.
Shah, N. D., Sternberg, E. W., & Kent, D. M. (2018). Big data and predictive analytics: recalibrating expectations. Jama, 320(1), 27-28.