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Pharmacy

Application of Data Science in Pharmacy

Over the years, data science has grown to become a common conversation in any space that has technology. Starting from virtual reality to artificial intelligence, data science has proven to be an unignorable force in the technology space.

The impact of data science has not spared the pharmaceutical industry, an imperative branch of the healthcare industry. As such, healthcare data science is growing as a new and promising career path for medical professionals with a penchant for technology.

In this article, I walk you through how data science can be applied in the pharmaceutical industry and other closely related fields.

Are you ready?

Let’s begin.

What is Data Science?

So, what is data science?

Data science is an interdisciplinary field with a main focus on scientific methods used in gathering, analyzing, and applying data and statistics. It is a unique field as it brings together many different types of areas of study to accomplish its results.

For example, data science pools skills from advanced mathematics, computer science, statistics, and many other areas of study.

How Data Science is Used in Big Pharma

The pharmaceutical industry is experiencing constant expansion.

The invention of more technologically advanced prescription medications is on an all-time high record. As such, there is a growing need to have more professionals who understand and know how to use data in the pharmaceutical industry.

Here are several ways data science is applied in pharma:

1. Predictive Analytical Methods

Using predictive analytical models means using current data to predict future trends and outcomes reliably.

These models offer the ability to cater for future demands before they even arise.

A huge chunk of the budget in big pharma is consumed on the screening process before a drug makes it to the clinical trial stage. This is a long process and costly process.

But with predictive analytical models, you can shorten the new drug screening process and consequently lower the cost of drug development. Additionally, this helps the companies to focus on promising products that have a high potential to be effective therapies.

2. Better Clinical Trials

Clinical trials are an expensive part of the drug development process.

Moreover, they can drag on for long.

In theory, data science can offer the much-needed solution to shorten this process and make it cost-effective in the following ways:

  • Patient Selection

Pharmaceutical companies can deploy a variety of data gathered from different sources to select suitable patients as candidates for clinical trials.

These data can be collected from social media, genetic testing profiles, and public health databases.

  • Progress Monitoring

While conducting clinical trials, monitoring every step of clinical trials, including patient outcomes is done with great care.

Using data science, clinical trials can be monitored in real time, enhancing the accuracy of the clinical trial process.

This helps in rapidly identifying safety or operational signals that may require action to prevent costly issues such as adverse events and unnecessary delays.

3. Sales and Marketing

A couple of years ago, most pharmaceutical companies performed sales and marketing primarily on foot using paid sales/medical representatives.

Medical sales representatives would traverse the country to pitch for the sale of their products.

In recent years, data science has revolutionized the pharmaceutical sales space. At least 25% of marketing done by most pharmaceutical companies is through digital marketing.

Additionally, most pharmaceutical companies heavily depend on targeted analytics to drive sales, improve spending, and enhance their overall bottom line.

Through predictive analytics, pharmaceutical companies can determine which healthcare professionals are most likely to take an interest in a specific drug molecule based on data gathered and analyzed beforehand. This allows for the creation of a highly targeted marketing strategy positioned for a greater degree of success.

4. Better Patient Follow-up

One of the most valuable information in a pharmaceutical company is patient feedback on a new molecule.

In the past, the process of obtaining patient feedback has proven to be time-consuming.

However, data science has made it possible to gather this essential data in a format that is easy to read, analyze, and utilize. This has made it possible for companies to pick out adverse events, drug interactions, and side effects before they become serious and affect the lives of many people.

Ready to Be a Health Data Analyst?

These are some of the applications of data science in the pharmaceutical industry. I hope you now have a clue of what to expect, in the event you decide to pivot your medical career to healthcare data analytics.

All the best!

 

 

 

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