Applying big data in pharmaceutical industry : development, strategy and administration

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Μουσουλέας, Ιωάννης

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The pharmaceutical industry today has many challenges. Each company must promote research and be a pioneer in new drugs developments, minimize production costs, be able to control how its customers face their products. In order to do this, it should pay attention to every information by collecting from each data source that it can be useful to it. At the same time, data processing and management should be done at the minimum cost and maximize their value. This leads to an attempt by pharmaceutical companies to integrate new types of data and sources from around the globe leading to processes of data collection from unstructured data. Pharmacy companies led to procedures for collecting and processing Big Data, use procedures from artificial intelligence, data mining, text mining, sentiment analysis, etc. In our case, we have studied the introduction of big data in the pharmaceutical industry and the impact it may have on company decisions as well as on their strategy. We also studied – designed and developed three methods for the use of data mining tools that can drive industries into new decisions and policy. The tools relate to methodology for collecting and compiling data to control drug prices, use of sentiment analysis with social networking data to enable companies to design a marketing policy and research decisions, and use of classification algorithms, creating new methods for new efficient products. Thus, we put forward a method for Greek hospitals and pharmaceutical industries to collect information from different sources to facilitate their analysis. In this case data was collected by the Greek Ministry of Health, by assessing its official website, where files with drug prices are listed, and all relative information was collected on a single source. Our aim was to create a file containing only the useful information, such as the price for a selected drug, the hospital that made an order, the quantity of the order and the total cost. The selected drugs were atezolizumab, avelumab, ipilimumab for the two major hospitals of Patras, Agios Andreas and the Rio Hospital. This procedure could facilitate decision making on drug prices and transparency in relation to sales of medicines in hospitals. In the second case, we developed an application to determine the reputation of the pharmaceuticals based on public’s opinion, where companies through it can make decisions about improvements and the promotion of their products. The application uses sentiment analysis and text mining in an organized way for the factors the company wishes to study in relation to its products. Its data is derived from the big data coming from twitter. The application can lead to decisions that are significant in relation to the marketing of each company while improving product data. In the third case we present the use of classification tools, using well-known software such as WEKA, where their direct application to medical data can facilitate pharmaceutical companies develop new technologies and methods to help patients and produce more efficient products. The above three cases show the use of methodologies on data that can directly influence the decisions of pharmaceutical companies.



Big data, Pharmaceutical industry, Strategy