Description of Published Health Article
For purposes of this paper, we choose to use an article by Chirowa, Atwood and Van der Putten (2013) to demonstrate the use of big data in public health. The article explores the relationship between gender inequality, health expenditure, and maternal mortality in Sub-Saharan Africa. The authors use big data to explore this relationship by describing how different countries allocate their finances to the health sector, analyzing national data on gender inequality and explaining how these two metrics correlate with health outcomes.
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The article mostly focuses on investigating the relationship among the above-mentioned variables in seven Sub-Saharan countries – Angola, Botswana, Malawi, Mozambique, South Africa, Zambia and Zimbabwe. Broadly, by evaluating big data surrounding gender inequality statistics, health expenditure and maternal mortality numbers in these countries, the authors demonstrate that countries with high gender inequality and low health expenditures have high maternal mortality numbers (Chirowa et al., 2013).
Position on use of Big Data in Health
The availability of big data is generating a lot of hype in many fields of science and financial analytics. It has revolutionized how experts in these fields undertake their duties and solve problems affecting their disciplines (Shapiro, Mostashari, Hripcsak, Soulakis, & Kuperman, 2011). The health sector should not be left behind. Already, some players in this field are making positive strides in using big data to manage health issues.
Some of them are experimenting with big data, while others are using it to provide solutions in advanced research projects (Shi & Johnson, 2014). While these developments are welcome, we believe that the health care sector is still struggling to manage pedestrian problems associated with small data, such as complying with regulatory requirements and computing operational dashboard numbers. Unless such problems are effectively solved, it would be difficult to fully embrace the benefits of big data. Nevertheless, I believe the trend towards the adoption of big data is irreversible and it is only a matter of time before the health sector fully embraces its use to solve different health issues. Thus, the use of big data in health care is a welcome approach.
Can analyzing Big Data in Health Lead to Innovation and Breakthroughs?
Analyzing big data could lead to innovations and breakthroughs in the health sector because it gives practitioners a “big picture” understanding of health issues (Doolan & Froelicher, 2009). For example, we could come up with endless breakthroughs in predictive analytics by using big data. Indeed, if we undertake a national data review of socioeconomic factors associated with a specific population, we could come up with a lot of innovative solutions and breakthroughs in predictive analytics. For instance, if we find that a specific population sample, within a given area, is likely not to own cars, we could deduce that patients would have trouble attending their health appointments.
This situation could possibly lead to readmissions. A viable solution would be sending a taxi to pick up the patients because this strategy would be cheaper than letting them miss their appointments and increasing readmission rates. This is just one example, but the breakthroughs and innovations we could come up with by analyzing big data are endless. This is why we find an increased use of big data in the areas of genomics and prescriptive analytics in the health field (Doug, 2016).
To What Extent do Privacy and Data Security Concerns and the potential Misuse of Data Outweigh the Potential Advantages?
Although our capacity to use big data in the health care sector continues to increase, several issues associated with its use are disturbing. Privacy issues and data security concerns are at the top of the list (Smith et al., 2011). If we analyze privacy issues in isolation, we believe that privacy concerns and the potential misuse of data outweigh the potential advantages of big data use when they cause significant alterations of health data and when they lead to death, or injury.
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We also believe that data security concerns associated with the use of big data could outweigh their potential advantages if they create vulnerabilities that could cause colossal, or irreversible, damage to a person’s health, or to a nation’s health. Thus, it is important to make sure that privacy concerns, data security issues, and misuse of data possibilities are effectively checked when using big data.
Additional Insights on Big Data use
The use of big data in the health care sector presents fascinating possibilities in the management of health issues. However, many stakeholders do not fully understand these possibilities (Shi & Johnson, 2014). Consequently, we believe that awareness should be raised regarding the benefits of big data. For example, an agenda should be formulated at health forums to stimulate continuous and open dialogues among all stakeholders in the health sector regarding ways of adopting big data in solving health issues.
In these forums, stakeholders could exchange their experiences with the use of big data and brainstorm about new and better ways of improving its use. Currently, there are existing efforts by some quarters of the health sector to foster such debates (Smith et al., 2011). Such attempts should be encouraged because they will improve the future of the health sector.
Chirowa, F., Atwood, S., & Van der Putten, M. (2013). Gender inequality, health expenditure and maternal mortality in sub-Saharan Africa: A secondary data analysis. Web.
Doolan, D. M., & Froelicher, E. S. (2009). Using an existing data set to answer new research questions: A methodological review. Research and Theory for Nursing Practice, 23(3), 203–215.
Doug, A. (2016). Big data in healthcare made simple: Where it stands today and where it’s going. Web.
Shapiro, J. S., Mostashari, F., Hripcsak, G., Soulakis, N., & Kuperman, G. (2011). Using health information exchange to improve public health. American Journal of Public Health, 101(4), 616–623. Web.
Shi, L., & Johnson, J. A. (Eds.). (2014). Novick & Morrow’s public health administration: Principles for population-based management (3rd ed.). Burlington, MA: Jones & Bartlett.
Smith, A. K., Ayanian, J. Z., Covinsky, K. E., Landon, B. E., McCarthy, E. P., Wee C. C.,…Steinman, M. A. (2011). Conducting high-value secondary dataset analysis: An introductory guide and resources. J Gen Intern Med, 26(8), 920–929. Web.