Understanding the Role of Big Data in Pharma
What does drug research, clinical trials, and even pharmaceutical sales and marketing have to do with big data? Actually, a lot. There are so many moving parts in the success of the pharmaceutical industry and the emergence of new drugs that it’s now essential to harness big data in pharma to produce the most effective drug solutions in a timely manner. Allerin states that investors in the pharma and healthcare industries are already investing almost $4.7 billion into utilizing big data. Big data plays an important role in several specific areas of the pharmaceutical industry.
Research and Development
Research and development is normally the first step in finding new products or improving existing ones. This phase of the process often continues throughout the development, testing, and marketing of new medications. According to Toptal, pharmaceutical research and development is normally a long process. It can sometimes take over $2 billion and 10 years to bring a new medication to the market.
The use of big data can improve research efficiency and dramatically reduce costs and the time to develop new drugs. During the R&D phase, researchers need to be able to link different sets of data together. This will enable them to view the “big picture” and see new insights into drug possibilities. This is often possible because of the data researchers, scientists, and industry leaders have access to. Shortening the time-frame and saving money will more quickly bring life-saving drugs to the market.
Drug discovery is also a very time-consuming component in the process of putting new drugs on the market. Big data is behind the predictive modeling necessary to speed up the discovery process. Using large amounts of data, you can more easily predict toxicity and potential drug interactions. These predictive models include complex mathematical simulations and models that make predictions regarding drug compounds and potential reactions in the human body.
Big data can also tie in data from previous medical trials and even data from previous marketing strategies. Using all of this together, you’re more likely to accurately predict patient outcomes and if and when FDA approval might occur. Having access to large amounts of data will accelerate the overall process that leads to new drug discoveries. Searching and using big data might include studying sets of patients, data from previous clinical trials, and scientific publications.
Many individuals are on more than one type of medication, making this an area in which precise research is crucial. Medications that are extremely safe taken by themselves may illicit dangerous reactions when in combination with other drugs. The CDC states that adverse drug reactions lead to about 1.3 million emergency room visits every year. Adverse drug reactions depend on a reporting system that is up-to-date with the assistance of pharmacists, clinicians, and lawyers. Using big data to search and organize information regarding adverse reactions not only saves time and money, but potentially saves lives.
These types of drug reactions are also found on social networks. Patients will often list side-effects and complaints on Twitter, Facebook, and a variety of online forums. You can use big data to mine these forums and platforms for patient reviews. Using systems such as natural language processing, you can secure large amounts of data. Big data analytics will organize and analyze this information to formulate useful conclusions regarding adverse drug reactions.
Clinical trials involve different types of testing to find out if a particular drug is safe and effective for human use. There are normally several phases before reaching FDA approval and introducing a new drug on the market. There are several specific areas that use big data throughout the clinical trials process.
- Recruiting Patients – It’s important to find the right group of individuals to test the drug. Using big data, you can find the right combination of people by looking for the right genetic traits and disease history. Certain types of patients are more easily found through specific databases when using big data.
- Providing Anonymous Information – Becker’s Health IT reports that Pfizer is using anonymous patient data to pinpoint important aspects of disease during clinical trials. This particular database is an open source platform and provides hundreds of millions of records for pharmaceutical and healthcare professionals to use.
- Using Electronic Records – Big data analytics can reduce the number of click-and-type tasks that physicians and researchers spend time doing. It can also reduce the number of data entry errors and improve storage and utilization of electronic records.
The successful roll out of any new drug is dependent on the collaboration of several industries. This often includes healthcare institutions, insurance companies, and scientists working outside of their organization. A pharmaceutical company that gathers and shares a variety of information can increase their database for future research and clinical trials. When data is in the cloud, it makes it much easier for a variety of individuals and organizations to access and use large amounts of information.
PharmaVoice.com states that the days of safeguarding all elements of development within a single organization are gone. Collaboration across several industries leads to fewer drug trials and higher levels of trial efficiency. Organizations are now joining online portals that enable the sharing of certain types of information in a responsible manner. A variety of individuals not only need access to large amounts of data, but there is also a need for high performance computing that includes both high speed and extraordinary amounts of storage. Big data is increasingly providing the essential components to seamlessly bring together the necessary people and organizations.
Sales and Marketing
Big data in pharma can provide the necessary analytics to correctly analyze customer behavior. There are several ways big data can successfully impact sales and marketing.
- Extract Geographical Information – Big data can help you find out which geographical areas sell the most of any promoted medications. This information will help pharma reps pinpoint their focus on a smaller group of physicians. Data analytics can even improve the return on investment for each drug representative visit.
- Review Past Marketing Campaigns – Data from past projects and campaigns is crucial to understanding what will and won’t work in future campaigns. This will require centralizing large swaths of data and possibly using machine learning solutions to organize, understand, and effectively use the data.
- Create New Marketing Campaigns – Using predictive analysis, big data can help you create the most efficient and effective marketing campaign for your products. Data can find and interpret patterns in your existing sales and make more accurate predictions regarding future trends.
Drug companies can save time and money by sending their pharmaceutical reps to only those physicians that require a visit. As much as 25 percent of marketing is now accomplished on a digital platform. Although drug rep visits are not obsolete yet, companies are finding that big data analytics can improve their return on investment.
According to the Alliance of Advanced BioMedical Engineering, big data will likely play a profound role in the future of drug development. Creating designer drugs for individuals instead of mass production is likely where the market is heading. This is just one of many exciting trends the pharmaceutical industry is exploring. Big data in pharma will continue to work in conjunction with each phase of the drug process as technology rapidly advances.