Introduction
Meeting the health needs of a diverse population has become a challenging task due to the wide variety of factors that must be considered carefully for each member of the target population. The introduction of the principles of 5Vs (patient volumes, a variety of sources, the velocity of data processing, the veracity thereof, and the subsequent increase in value) presumably allows for a significant improvement of the quality of health (Regmi & Gee, 2016). The reasons for the specified assumption include the fact that the use of the 5V framework helps determine and evaluate the health needs of the target population more accurately, carry out the demography processes in a more accurate manner, and boost health intelligence management by improving the understanding of the social determinants of health.
Health Needs Assessments: A 5-V Perspective
Applying the principles of the Theory of Change to the subject matter, one will realize that the use of the 5Vs framework helps improve both short- and long-term outcomes in assessing the needs of the target population. To be more accurate, the 5Vs strategy helps identify the immediate threats to the community and, thus, come up with a solution that will allow meeting the needs of the target population. Furthermore, the approach may serve as the foundation for an all-embracive data analysis and the further prevention of diseases and disorder development in the specified community. In other words, the research process is enhanced significantly, with a wide array of factors being taken into consideration (Breuer, Lee, Silva, & Lund, 2016).
Demography and 5Vs: Possible Improvements
The introduction of 5Vs into the healthcare environment has also expanded possibilities for research. Particularly, the opportunities for establishing a system in the management of data should be mentioned. Indeed, proper research needs to be systematic and rigorous, for which the use of the 5Vs framework allows. With the increase in the veracity and velocity of data processing, a system can be established and maintained during the research. Furthermore, the opportunities for repeatability and reproducibility need to be mentioned among the essential benefits of 5Vs in healthcare research since the tool helps synthesize the data in a manner as fast and efficient as possible (Wamba, Akter, Edwards, Chopin, & Gnanzou, 2015). Furthermore, opportunities for interdisciplinary studies are created so that the connection between different approaches toward needs management could become evident (Wong, Chiang, Choi, & Loke, 2016).
Health Intelligence Increase with the Help of 5Vs
Similarly, the levels of health intelligence can be increased significantly by using the 5Vs framework. Particularly, the approach sheds a lot of light on the social determinants of health. Thus, the strategies for addressing the needs of the target population can be identified (McDonald, Frazer, & Cowley, 2013).
Conclusion
Because of the large number of factors affecting the health levels among the members of the global community, there is a need to adopt the framework that could provide more efficient use of the relevant information. The application of the 5Vs framework is bound to lead to more impressive results than the current strategy since it helps shed more light on the issue of information management in the context of the contemporary healthcare environment. To be more specific, the framework creates premises for an in-depth study of the specifics of the target population and a comparatively fast delivery of the relevant results. Consequently, the needs of the target community are identified more precisely, and the tools for meeting them become more efficient.
References
Breuer, E., Lee, L., Silva, M. D., & Lund, C. (2016). Using theory of change to design and evaluate public health interventions: A systematic review. Implementation Science, 11(1), 63-79. Web.
McDonald, A., Frazer, K., & Cowley, S. A. (2013). Caseload management: An approach to making community needs visible. British Journal of Community Nursing, 18(3), 140-147. Web.
Regmi, K., & Gee, I. (2016). Public health intelligence issues of measure and method. New York City, NY: Springer International Publishing.
Wamba, F. S., Akter, S., Edwards, A., Chopin, D., & Gnanzou, G. (2015). How ‘Big Data’ can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production, Economics, 165, 234-246. Web.
Wong, H. T., Chiang, V. C. L., Choi, K. S., & Loke, A. (2016). The need for a definition of Big Data for nursing science: A case study of disaster preparedness. International Journal of Environmental Research and Public Health, 13(1), 1-13. Web.