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How Big Data can transform the Pharmaceutical Industry and Improve Performance

Introduction
For the past several decades, business men have been talking about Big Data. Big Data may be described as large volumes of data that pose challenges in the areas of data capture, storage, sharing, and analysis (The Economist, 2010). Solutions for processing large volumes of data have transformed business processes in virtually all industries. With particular reference to the pharmaceutical industry, research has focussed on data driven care. This research includes information about factors affecting diseases, their progression, potential treatment methods, the effect of certain drugs, and other such data. All this data has to be collected, stored and analysed (Menius & Rousculp, 2014). Since the data relates to human well being, big data assumes greater importance in the pharmaceutical industry as compared to other areas.

The need for big data

Medical research is most often focussed on causes of diseases and treatment options. However another area where the pharmaceutical industry conducts research is patient care. Collaborations are being entered into with health care institutions, practitioners, and community centres in an effort to provide better care to patients. Statistical data such as the prevalence of diseases, reactions to certain drugs, patients’ economic status, and ethnicity are being collected in an effort to understand patient needs and provide better care (Menius & Rousculp, 2014). As with any other business, pharmaceutical companies are also driven by profit. Medical research conducted by pharmaceutical companies is therefore, often motivated by the desire of these companies to manipulate finances (Brown, 2013). The data generated by clinical trials, research, and corporate statistics is considerable. All this data must be stored, categorized, analysed for good decisions that will lead to inventions of new medicines and better patient care that is affordable. Quality control is another area that generates large volumes of data. Pharmaceutical companies are at the crossroads between patient safety and profitability. Information from these two areas is often ambiguous and it is important to analyse and interpret it correctly (Brown, 2013).

In this paper we examine how big data will affect the Pharmaceutical industry and improve performance.

Best Practices for Data Driven Care
Menius & Rousculp (2014), present the areas of challenge for data driven care and the best practices to meet these challenges. The first area is analytics. Menius & Rousculp suggest that statisticians and analytic experts be involved in the research. Analytical experts are experienced in data manipulation. Their involvement will ensure that most of the ambiguity and false results are eliminated. It is also important to test the assumptions of the researchers empirically. A study that is designed to empirically evaluate the results will ensure that there is minimum bias involved.
Health care data is sensitive data and access to this should be limited to those who need it. There is an ongoing debate about who should maintain health care records of a patient. It is important for researchers to have access to patient data in order to study the effect of drugs. However the ethics of releasing patient records is questionable. Providing the right type of care to the patient at the right time is after all the primary goal of medical research. It is therefore important to resolve the issue of ownership (Menius & Rousculp, 2014).

Conclusion

In the field of medicine, information is gathered from both the medical as well as the financial field. This is because the companies that are involved with discovery and manufacture of new drugs are financial businesses that are driven by profit. Therefore there exist two contrasting perspectives, that of providing affordable care to patients at the right time and that of making a profit. It is necessary for these two opposing goals to meet in order that the medical research is meaningful. We examined these two views using two sources. Abigail Brown (2013) examines pharmaceutical research in the financial context while Menius & Rousculp (2014) suggest areas of challenge for pharmaceutical research, and how these challenges can be met. Ensuring that pharmaceutical products are safe, affordable, and yet effective requires large volumes of information from both the financial sector as well as the field of medicine. Medical data can be best understood by medical professionals while interpretation of financial data can be done best by experts from fields of finance and statistics. Menius & Rousculp (2014) suggest that financial experts and statisticians should be involved in medical research from the outset in order to ensure that the data is properly evaluated and any false results occurring as a result of bias on the part of medical experts are weeded out. Statisticians however are not medically qualified and therefore may not fully understand the importance or relevance of the data. This may result in misinterpretation of the data. It is therefore necessary for the experts from both fields to work in collaboration with one another for the best results to be achieved.
In conclusion of this research, we may state that big data assumes greater importance in the pharmaceutical industry because of the sensitivity of data, the involvement of a diverse mixture of experts, and the contrasting goals of these experts. A comprehensive program that addresses the needs of the research and allows access to data while protecting the rights of the patients and ensuring purity of data is the need of the hour.
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References

Brown (2013), Understanding Pharmaceutical Research Manipulation in the Context of Accounting Manipulation by Abigail Brown, Institutional corruption and the pharmaceutical industry, fall 2013.
Menius & Rousculp (2014), Growth in Health Care Data Causing an Evolution in the Pharmaceutical Industry by J. Alan Menius Jr, Matthew D. Rousculp, North Carolina Medical Journal vol. 75, no. 3 www.ncmedicaljournal.com, ©2014 by the North Carolina Institute of Medicine.
The Economist (2010), "Data, data everywhere" The Economist 25 February 2010 Retrieved 9 December 2012