How Data Collection in Pharma Is Evolving

In 2020, Big Data is changing the way so many industries work. It’s disrupting the status quo and improving efficiencies and effectiveness in how companies do their business. Nowhere is this more evident than in the pharmaceutical industry. Data is being collected, tracked, and analyzed in every aspect of the industry. From the research being done to start the drug creation process to how it is used by the patient and every step in between, data is playing a large and ever-expanding role in how the industry functions. Here are three specific examples of how data collection in pharma is evolving.

 

Getting Products to Market Quicker 

Creating a new drug and getting to market is a long and expensive process. From discovery to development to clinical trials, it currently can take years – along with billions of dollars – to go from realizing there is a need for a particular drug to getting it in the hands of the patients who need it. Today, data collection is helping speed this process up in multiple ways. This benefits patients, who get the drugs they need sooner, and pharma companies who save millions or billions by cutting down the development cycle time.

There are three main phases that a drug must go through before it becomes a viable treatment for the general public. First, there is a discovery phase where pharma companies find what types of drugs are needed and what compounds can be developed to create that type of drug. Next, there is the development phase where the companies create the drug, and finally, there are the clinical trials where the makers of the drug test it on a range of subjects. Only then can the drug be made available to those who need it.

To speed up the discovery process, pharma companies use technology to gather data from an incredible number of sources. Clinical trials, scientific publications, and patient data sets are scanned and predictive analytics are used to help scientists discover new drugs much faster than past methods. The development process is also being moved along through data as witnessed in Project Data Sphere. This data-sharing initiative involves pharma giants partnering to share data and use the latest in analytic technology to help develop new cancer drugs.

Once a drug is discovered and developed, it is time to test the drug with clinical trials. These trials can take a very long time and cost a lot of money, but the data gained in these trials is of the utmost importance to learn about a drug’s safety and effectiveness. Big data is helping speed up these trails in several ways, including identifying qualifying participants faster and more accurately, monitoring patients remotely, and analyzing the data faster.

 

Better Safety and Product Visibility

While creating drugs more quickly is important to both patients and pharma companies, the top priority for all involved is safety. Pharmaceutical drugs are meant to heal, but they can also cause serious harm, especially if they are not handled properly at any point in the lifecycle of the product. Being able to use data to better monitor everything from the raw materials in the manufacturing process to the final product’s transportation and storage before going to the end-user is very important for drug safety.

In order to ensure the safety of these drugs, better product visibility is key. This means that the more the safety and compliance division of the company can monitor the conditions that are happening around the drug, the better they can guarantee that product’s safety and effectiveness. One way to do this is through remote environmental monitoring using data loggers.

Data loggers are devices that monitor, record, and report on environmental conditions in a given space such as a laboratory, a warehouse, or even a temperature-controlled vehicle. They allow companies to monitor and create precise conditions in the entire supply chain so that quality can be ensured. Companies such as Dickson offer data loggers supported by centralized cloud-based remote monitoring systems that can track complex systems via a single interface. Monitoring by fewer people in a centralized location saves companies money and allows them to exert more centralized control over conditions throughout their vast supply chains.

 

Deeper Insight to Patient Behavior

It makes sense that pharma companies are using data to create better outcomes for drugs in their pipeline. They use this data to improve everything from the very beginning of the process to the moment a doctor puts the drug in a patient’s hand. But can they also use data to help create better outcomes for patients once they are in control of the drug? The answer is yes. Big data’s effect on the pharma industry doesn’t stop at the door of the doctor’s office.

Remote monitoring of patient behavior, often through smartphone apps or newer inventions like smart pill bottles or even smart pills, allows pharma companies to gather information or provide solutions for patients in general or of a certain population or demographic. Collecting data about when and how people take their drugs allows manufacturers to come up with new and better ways to ensure their drugs are used correctly to create better patient outcomes.  

These data-driven solutions can help on both sides of the pharmaceutical medication spectrum. On the one side, solutions can be implemented for people who are forgetting to take their medications and thereby reducing the effectiveness of their treatment, to better remind them and track dosages. Data can also help provide more efficient tracking of drugs to help prevent abuse and unauthorized use of drugs.

 

Conclusion

These are just three of the ways that data collection is evolving the pharma industry. There are many more, and as the technology around data collection and usage evolves even further, the ways that the pharma industry uses it will grow as well. As this evolution continues, both pharma companies and the patients they serve can rest assured that Big Data is helping create better outcomes for all involved.

The views expressed in this article are those of the authors and do not necessarily reflect the views or policies of The World Financial Review.