Like many other sectors, big data and real-time insights are positively impacting the pharma sector to benefit consumers and manufacturers alike.
Emerging technologies now make it possible for people to extract insights from huge quantities of data in minutes instead of settling for processes that could take days or even longer.
Furthermore, real-time insights aid proactive and well-informed decision-making. Big data and real-time insights are both positively impacting the pharmaceutical industry to benefit consumers and manufacturers alike.
1. Analyzing Data for Clinical Trials
A well-run clinical trial involves recruiting patients and determining the effects of drugs that are not yet available on the market.
Analysts say big data is useful for poring over geographical statistics that could indicate the city or state containing the largest number of potential trial candidates. Furthermore, data analysis could uncover correlations between various effects that humans wouldn’t otherwise notice.
See also: What is the role of standards, big data, and analytics in manufacturing?
The speed capabilities of big data make it an excellent complement to the statistics many pharmaceutical companies or other organizations gather during trials and additional research.
For example, the National Cancer Institute set up a prototype project to learn more about the role genetics plays in cancer. Scientists searched through a 4.5-million-cell matrix in 28 seconds. They also cross-referenced genes from 60 million patients.
2. Facilitating Tailored Communications Strategies
The process that occurs before new drugs hit the market can take years.
Pharmaceutical marketers know that coming up with a product that offers beneficial therapeutic solutions for patients in need is only part of the equation. It’s also necessary to ensure the promotional materials associated with those medications align with patient needs.
A globally established pharmaceutical company captured customer information in real-time in connection with the launch of its new product. It analyzed how people in the target audience engaged via social media and used that information to produce copy that directly addressed the shortcomings of competing products.
Additionally, the brand looked at social media material as a regulatory compliance measure. A tracking algorithm analyzed online conversations and gave notifications of thresholds that indicated possible adverse side effects.
Depending on the circumstances, communications specialists reached out to patients and did what was necessary to reduce the likelihood of negative opinions about the drug.
This process allowed the drug manufacturer to meet its initial prescription targets in 2015.
3. Reducing Waste From Logistics
Because many products manufactured by pharmaceutical companies are sensitive to temperature changes, it’s necessary to distribute them through cold chain transportation methods that keep the items in a chilled state during transit that typically involves a temperature range of 36-46 degrees Fahrenheit.
Merck uses sensors in its shipments that monitor temperatures as products move through supply chains, then informs the company if any of them were outside of the optimal range for too long. Eventually, they had 14 years’ worth of data that helped identify the factors that cause that undesirable outcome to happen.
However, the goal was to use predictive analytics in hopes of preventing the problems that lead to too many temperature-related fluctuations and make merchandise unfit for patient use.
Initially, the team responsible for this project produced static reports from the data. Next, they created an application that allows entering all the variables affecting a given shipment.
The technology then analyzes the input and gives the likelihood of temperature troubles. Company representatives can use that information to make changes and improve success rates.
4. Allowing Doctor-Pharma Collaboration
Analysts are confident that one way in which artificial intelligence (AI) will make improvements in healthcare is by predicting the best treatments for individual patients.
It can look through data faster than humans and find the interventions likely to cause the most significant advantages for ill patients and their caregivers.
With help from laboratory data, pharmaceutical representatives can reach out to physicians treating patients that fit appropriate criteria and advise on how a certain medication could and should fit within a person’s treatment plan.
Also, some physicians collect real-time data about whether treatments have intended effects, especially when their patients use Internet of Things (IoT)-enabled wearables. If an initial therapy fails, pharmaceutical company employees could suggest other courses of action to physicians dealing with certain kinds of ailments.
The combination of AI and real-time insights could speed up communications regarding data from labs and how it might illuminate ways to help patients get favorable results faster.
Without those technologies, too much time could pass before dialogue begins between a pharmaceutical representative and a doctor, potentially hurting health-related outcomes.
The use of big data and real-time analytics in the pharmaceutical sector is still emerging.
However, these usage examples clarify why both are such promising technologies.
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