It seems like almost every industry has embraced artificial intelligence (AI) and big data as fast as they possibly can, except for the pharmaceutical industry.

This isn’t because pharma companies don’t want to use accelerated learning algorithms to speed up their processes. Instead, there are valid concerns about security and accuracy that must be accounted for before pharmaceutical companies can turn their operations over to machines.

Now that many pharmaceutical companies have caught up to other industries and by embracing Big Data, they are starting to recognize its potential for everything from developing new drugs faster than ever to lowering costs for drug companies and consumers.

What is Big Data?

The term “big data” is an abstract concept. In general, the term is used to describe massive amounts of data collected from various sources. Big data needs to be organized in a way that provides helpful analysis.

It is incredibly time-consuming for a person to sift through huge datasets to determine what is and isn’t essential and organize it in a way that is easy to access and analyze.

Potentials for Big Data in the Pharmaceutical Sector

By adopting AI and machine learning (ML) to analyze big data, the pharmaceutical sector can make significant advances in the coming years. Here are a few examples of how.

Research and development

Without AI, new drugs take an average of 12 years to go from the research phase to a patient’s hands. One reason for a delay in drug discovery is the amount of time it takes human researchers to read and absorb information from the latest studies.

The drug company Pfizer has started using IBM’s AI Watson Health to speed up research and discovery. The result is better research done faster to get people the medications they need to live longer, healthier lives.

Clinical testing

Discovery is just one step in getting drugs from the lab into patients. The three stages of clinical testing traditionally take several years, with only 12 percent making it to patients’ hands.

AI can analyze data to make sure the right people are used in trials and then monitor them to speed up the data collection process. AI can also analyze the reported data, offering more accurate insights at a faster pace than human researchers.

Doctor collaboration

Big Data can help pharma companies make their treatments more effective for patients with improved lab data analysis. They can then advise doctors about new medications to help them determine which patients should use which medications for treatments.

Doctors can collect patient data using wearable devices that are connected to the internet to monitor patients. Then, AI can analyze that data to help doctors make informed decisions about treatment and mediation options.

Big Data is the Future of Pharma

This is only the beginning of how pharmaceutical companies will benefit from Big Data going forward. Taking a data-driven approach to researching, developing, and testing new treatments will lead to better patient outcomes.

References:

Drug Approvals – From Invention to Market…12 Years! MedicineNet. (n.d.). https://www.medicinenet.com/script/main/art.asp?articlekey=9877.

FDA. (n.d.). Step 3: Clinical Research. U.S. Food and Drug Administration. https://www.fda.gov/patients/drug-development-process/step-3-clinical-research.

Helfand, C. (2019, May 1). If pharma looks slow to adopt AI, it’s got good reason, expert says. FiercePharma. https://www.fiercepharma.com/marketing/if-pharma-looks-slow-to-adopt-ai-there-s-good-reason-expert.

Life Sciences Technology: Watson Health. IBM. (n.d.). https://www.ibm.com/watson-health/solutions/life-sciences-technology?cm_sp=Scheduler-_-CopyChng2-_-C.

Sullivan, T. (2019, March 21). A Tough Road: Cost To Develop One New Drug Is $2.6 Billion; Approval Rate for Drugs Entering Clinical Development is Less Than 12%. Policy & Medicine. https://www.policymed.com/2014/12/a-tough-road-cost-to-develop-one-new-drug-is-26-billion-approval-rate-for-drugs-entering-clinical-de.html.