Data Mining in the Pharmaceutical Industry
Industry Pharmaceutical companies are taking advantage of the growing use of technology in the healthcare arena by using data to enhance their marketing efforts and increase the quality of research and development. The process of data mining allows companies to extract useful information from large sets of individual data. This process provides a knowledge that is vital to a pharmaceutical company’s competitive position and organizational decision-making. “Data Mining enables firms and organizations to make calculated decisions by assembling, accumulating, analyzing and accessing corporate data.
It uses variety of tools like query and reporting tools, analytical processing tools, and Decision Support System (DSS) tools” (Rangan, 2007).
What is Data Mining and why is it used?
Data mining is the practice of automatically searching large stores of data to discover patterns and trends that go beyond simple analysis. Data mining uses sophisticated mathematical algorithms to segment the data and evaluate the probability of future events. Data mining is also known as Knowledge Discovery in Data (KDD)” (Oracle, 2008).
As stated, data mining is used to help find patterns and relationships stored within large sets of data, these patterns and relationships are then used to provide knowledge and value to the end user.
The data can help prove and support earlier predictions usually based on statistics or aid in uncovering new information about products and customers. It is usually used by business intelligence organizations, and financial analysts, but is increasingly being used in the sciences to extract information from the enormous data sets generated by modern experimental and observational methods.
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Data mining is being increasingly used in business to help identify trends that would have otherwise gone unnoticed.
There are several different opinions on the exact “steps” of data mining, but they all agree on these basics: planning, modeling and extracting information. Oracle defines 4 steps in the data mining process:
- problem definition,
- data gathering and preparation,
- model building and evaluation,
- knowledge deployment.
The first step of data mining is to understand the purpose, scope and requirements of the project . Once the project is specified from a business perspective, it can be formulated as a data mining problem and a preliminary implementation plan can be developed” (Oracle, 2008). The data gathering process takes a look at how well the data serves the purpose of the project. In this step many changes can be made to the attributes of the data so that they better serve the objective and requirements of the project. This process can play a large part in the value of the knowledge and information derived from the data. For example, you might transform a DATE_OF_BIRTH column to AGE; you might insert the average income in cases where the INCOME column is null” (Oracle, 2008).
The third step of data mining is to build and evaluate the model. The model should be tested and evaluated to make sure that it will answer the question and stay within the requirements of the business objectives stated in the first phase of the process. The final phase includes knowledge deployment which is where actual information and realization comes from the data.
Here is where the relationships and patterns are turned into something meaningful that meets the objective of the project. There are several techniques used for data mining, some of them have been used for decades prior to the information technology boom that has changed the system dramatically. According to (Alex Berson, 2000), these “classic” techniques include Statistics, Neighborhoods and Clustering while the “next generation” techniques include Trees, Networks and Rules.
In the end the purpose of each of these techniques is to explore data (usually large amounts of data – typically business or market related) in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data” (StatSoft, 2011). As stated above, data mining is often used to solve business decision problems, “it provides ways to quantitatively measure what business users should already know qualitatively” (Linoff, 2004). A growing number of industries are using data mining to become more competitive in their market by primarily focusing on the customers; increasing their customer relationships and increasing customer acquisition.
How is Data Mining Used in the Pharmaceutical Industry?
The pharmaceutical industry has copious uses for data mining which include increasing the efficiency of research and development, contributing to drug safety information and to increasing the effectiveness of their marketing efforts.
Understanding that the benefit of data mining is allowing for the extraction of useful information from large sets of individual data, it is evident that the pharmaceutical industry has a need for this process. The abundance of diseases prevalent in the world, the multitude of drugs available for each disease, and the variety of patients that take the products, produces massive amounts of information available in the industry. Pharmaceutical companies have begun to use this data to benefit patient safety, physician knowledge and their own marketing efforts.
Data mining can be used while companies are researching and testing new products. “Scientists run experiments to determine activity of potential drugs” (Rangan, 2007). They are able to use process that produce results and relationships much faster, they can quickly determine activity on “relevant genes or to find drug compounds that have desirable characteristics” (Rangan, 2007). “By relating the chemical structure of different compounds to their pharmacological activity, can bringing a degree of predictability to drug screening procedures that, until now, have tended to be a bit hit and miss” (Results, 2009).
That should help scientists and pharmaceutical companies identify more effective compounds to treat different diseases, allowing them to find drug leads in a fraction of the time and at a fraction of the cost of current methods. ” The earlier methods of experimentation was very time consuming and had to be done over and over again each time a new drug was being researched, none of the old information was every used to help with speedier development. Data mining allows the past research to be used when picking compounds as opposed to just randomly choosing and testing.
As a drug gets further into the development and into the clinical trial stages, data mining can help predict which diseases and patients will benefit from the drug. Based on past information data mining will provide a correlation between the new molecules, disease states and patients. For example, Pfizer is “turning to sophisticated data mining techniques to help improve the design of new trials, to better understand possible new uses for existing drugs, and to help examine how drugs are being used after they have been approved” (Salamone).
During trial phases they are able to “understand safety and efficacy profiles within the patient population by tackling the question of patient selection within the framework of demonstrating groups that are most responsive. Data mining framework enables specialists to create customized nodes that can be shared throughout the organization” (Rangan, 2007). Additionally, one of the greatest benefits of data mining in the pharmaceutical industry and the healthcare world is the discovery of adverse events and drug toxicity in patients.
It could help determine the adverse reactions associated with a specific drug and still go a step further to show if any specific condition aggravates the adverse reaction for eg age, sex, and obesity (Novartis Business Intelligence report, 2004). Data mining is useful in almost every stage of drug discovery and can aid in toxicity detection, side effect profiles and can work to uncover responsiveness in certain patients. “The patterns that emerge from data mining this information will not only improve our understanding of this disease, but could give practitioners new insights into prevention and treatment. (Rangan, 2007).
As addressed above, a limiting factor in past and current pharmaceutical data is the sheer amount of data and lack of information that exists in the industry. Knowledge and information is being slowed at even a physician and patient level, for example the FDA estimates that only 1% of serious adverse events are actually reported to the companies after they happen because most practicing healthcare providers do not have the time or means to report the adverse events and have no need for the data at a later time.
There is a strong need for data mining techniques within the pharmaceutical industry to understand and detect possible adverse events before they happen to patients. Outside of product research, development and safety, pharmaceutical organizations are using data mining techniques to increase their marketing efforts directly to the consumer as well as to the prescribing physician. They are able to see a better return on the investment of resources based on mining the prescription data released by pharmacies.
As discussed earlier, many businesses are using data mining to increase their customer relationships nd encourage product growth. They are able to use the data to gather knowledge and information in order to create more effective and efficient sales strategies. “Data mining can be used to supplement the pharmaceutical companies marketing efforts by market segmentation, measuring return on investment (ROI) and understanding profitable managed care formulary status. “A pharmaceutical company can analyze its recent sales force activity and their results to improve targeting of high-value physicians and determine which marketing activities will have the greatest impact in the next few months.
The data needs to include competitor market activity as well as information about the local health care systems. The results can be distributed to the sales force via a wide-area network that enables the representatives to review the recommendations from the perspective of the key attributes in the decision process. The ongoing, dynamic analysis of the data warehouse allows best practices from throughout the organization to be applied in specific sales situations. ” (Alex Berson, 2000) Market segmentation allows for tailored messaging and information to be given to appropriate customers where their need is specifically met.
Prescribing information allows the sales representatives to spend appropriate time and resources on customers that have the most need for individual products based on their patient population and historical prescribing trends. “Supplemented by survey data, patient and physician interviews, information gleaned from epidemiological studies and managed care organizations, questionnaires on web sites, and other market research, a quite detailed picture of a customer base can be identified, with marketing strategies devised accordingly” (Cohen).
This is critical at the launch of a product, in order to determine the “early adopters” that will drive a product use and share their success with professional peers. A successful product launch to the right market segment can allow a product to surpass its competitors in the field. Especially in the era of “me-too” products with similar efficacy and slightly lower side effects than earlier competitors, the effectiveness of a product launch is vital to the career of the product. Identifying the early adopters and focusing tailored promotional efforts on this segment (as opposed to broadcasting a general message to all physicians) can be crucial to the success of the product” (Cohen). Measuring the ROI of certain programs and resources the organizations use, can help save time and money by making sure the resources are being put in the most favorable places to produce the most amount of business and patient satisfaction. Data mining allows pharmaceutical companies to get an idea of how their field promotions and direct to consumer promotion programs are driving business results.
The promotional efforts of these organizations are tremendous and indlude field promotions: representative sales calls, peer-to-peer dinner meetings, exhibits at conventions, promotional samples, and direct to consumer advertising which include: commercials, websites, patient education materials the companies spend billions of dollars on the promotion of a single product. Data mining can help stream line the customers that are targeted for these promotional events and help make more accurate decisions on where to spend their resources so that they make sense for the physician, patient and the organization.
The formulary status of a specific drug is very dependent on the location and area in question. The managed care market dynamics are very critical to effective targeting and marketing of pharmaceutical companies. Physicians are generally unaware of specific prescription coverage on certain health care plans, especially if a pharmacy benefits manager (PBM) is used in lieu of the actual healthcare benefits to manage prescriptions.
For this reason it is important that each organization have the data to support the needs of the various customers and plans to help physicians overcome the obstacles they encounter while prescribing certain drugs to their specific environment. There are many facets of the pharmaceutical industry, including patient care and marketing that can widely benefit from utilizing decision support systems and data mining. The process is revolutionizing early drug discovery and increasing the speed and effectiveness that scientists have in uncovering new molecules to treat various disease states.
It has a place in patient safety by providing early detection of drug-on-drug interactions, toxicity and adverse reactions. Finally it is widely used to supplement the marketing efforts in the field and increase the business acumen and accuracy of the promotional side of the industry.
Recent debate in the legality of Data Mining and the Pharmaceutical Industry
In the past 10 years, the pharmaceutical industry has been scrutinized for various activities that have lawmakers on the watch and uncertain about the agenda these organizations have when promoting their products.
With the blatant need for healthcare reform and a slow demise of the American healthcare industry this scrutiny of the pharmaceutical industry has steadily increased and the reputation of these organizations has increasingly plummeted. Within this scrutiny, authorities have begun to question the lawfulness of data mining and the use of prescription-drug records used in promotional efforts. Some argue that the data-mining is purely to grow market share for money-hungry companies and has little relation to the care or need of patients and physicians.
As recently as 2011 the Supreme Court heard a case assessing the legality of prescription-drug records being used to promote pharmaceutical products. After a patient fills a prescription “pharmacies can sell the other information in those prescriptions to data-mining companies (they cannot sell patient identification information), who sift through all this information, spot trends and patterns, and then sell that to, as in this case, drug companies, who can then have their sales representatives do targeted marketing of brand-name drugs to doctors” (Coyle, 2011).
Drug makers buy prescription records that reveal the prescribing practices of individual doctors from data mining companies and, based on the information, practice a type of marketing called “detailing,” in which sales representatives, who already know which doctors prescribe certain kinds of medications, pitch information about new drugs they think will be of interest to the doctor” (Lewis, 2011).
The discrepancy existed in the State of Vermont where lawmakers made it unlawful to sell this information without the prescribing physicians consent, however this law was ruled unconstitutional in the lower federal appellate court, bringing the decision to the Supreme Court. The following is an excerpt from an interview done after the hearing. Vermont’s purpose in enacting the law was to protect the privacy of the doctors’ information, to encourage prescription of generic drugs, which would help lower health costs in the state, and also to protect the public health, which it felt could be endangered by drug companies’ sales representatives presenting one-sided information to the doctors. Then, on the other side, you have the drug companies and other businesses concerned that if the court restricts access to this kind of information then, that they won’t get the kind of information they say they need to make important business decisions, ot just marketing decisions, research decisions, other decisions that they think could be beneficial to consumers” (Coyle, 2011).
The Supreme Court ended up over-ruling this decision based on the First Amendment right and gave pharmaceutical industries a big victory in their use of Data Mining. “The Supreme Court handed down a 6-3 majority decision and ruled that the law interfered with the pharmaceutical industry’s First Amendment right to market its products (Lewis, 2011). ” Despite the controversy, it is evident that there is a wealth of knowledge and information to be gained by the use of data mining in the pharmaceutical industry.
Data Mining in the Pharmaceutical Industry. (2018, Jan 17). Retrieved from https://paperap.com/paper-on-data-mining-in-the-pharmaceutical-industry-4571/
