Data science is definitely one of those fields of mechanism that is controlling almost all the major segments of technology starting from virtual reality to artificial intelligence.
It has far and wide potential to affect the pharmaceuticals industry which is an imperative branch of the healthcare industry.
Healthcare data science is a new career path chosen by many technology aspirants who have a keen interest in medical aspects as well.
Data science applications can be tweaked and used in almost all industries that are embarking on their digital transformation and upgrading to advancement.
Therefore, the presence of data science in pharmaceuticals is a matter of celebration as pharmaceuticals have large power to affect human lives.
In this article, I will take you through understanding data science and how it is used in the field of pharmaceuticals and other similar areas.
Ready?
Let’s begin.
What is Data Science?
Data science is a growing field as earlier mentioned.
It is an interdisciplinary field of study that focuses on the scientific methods used in gathering, analyzing, and applying data and statistics.
Data science is a unique field as it brings together many different types of areas of study to accomplish its end result.
For instance, advanced mathematics, computer science, statistics, and many other areas of study are all used in the data science profession.
How Data Science is Used in Big Pharma
The pharmaceutical industry is an area that is experiencing constant expansion.
As more technologically advanced prescription medications are being invented, the need for professionals who understand and make use of related data and statistics has been on a huge increase.
So, how is data science used in the pharma industry?
Let’s have a look at some of the many types of data required in this field on a daily basis.
Data Usage in the Pharma Industry
The statistics shared below show how often and to what extent data science and similar information is used in the creation of new drug therapies within the pharmaceutical industry.
- It takes approximately 8 years for a new drug to be approved by the FDA (little to no drug discovery goes on in Kenya)
- Each new drug approved costs an average of $500 million dollars
- 1 out of 10,000 compounds discovered makes it to the approval stage
- Only 3 out of 20 new drug therapies make enough profit to cover the losses experienced when the drug is undergoing testing
This set of data demonstrates the different areas where statistical information is gathered, studied, and then put to use within the pharmaceutical industry in order to discover newer drugs that will enhance treatment outcomes.
Collaboration Between Big Data and Big Pharma
In the past, there was hesitancy in using big data to further advance the development of new pharmaceutical molecules.
Nonetheless, in recent years, it has become clearer how dependent these two fields are on each other.
Whereas drug manufacturing companies used to heavily guard their privacy, they now understand how the use of big data can enhance their success.
Data Science is applied in the Pharma industry in the following ways:
1. Predictive Analytical Models
Using predictive analytical models simply means using current data to reliably predict future trends and outcomes. This offers the ability to be able to cater to future demands before they even arise.
A big part of the budget in big pharma is spent on the screening process before a drug even makes it to the clinical trial stage.
This ends up becoming a long and costly process, while patients are waiting for new molecules to be approved, which could improve their condition.
Data science is applied to shorten the screening process and lower the cost of drug development. This helps the companies to put primary focus on specific products and ingredients in therapies that have a high potential to be effective.
2. Better Clinical Trials
Clinical trials are one of the most intensive parts of drug development.
They can drag on for long periods and are also costly.
In theory, data science holds the key to the solution needed to shorten this process and make it cost-effective.
Here are a number of ways data science can make this possible:
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Selecting Patients
Companies can deploy a variety of data gathered from different sources to select suitable patients as candidates for clinical trials.
Data can be collected from social media, genetic testing profiles, and public health databases.
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Monitoring Progress in Real-Time
Monitoring every step of clinical trials is done with great care.
While conducting clinical trials, patient outcomes are carefully monitored.
Also, policies and guidelines on clinical trials have to be strictly adhered to.
Through data science, drug developers are able to monitor the trials in real-time.
This enables them to rapidly identify safety or operational signals requiring action to prevent significant and potentially costly issues such as adverse events and unnecessary delays.
3. Sales and Marketing
A few years back, most pharmaceutical companies used to perform sales and marketing primarily on foot by paid sales/medical representatives.
These reps would visit doctor’s offices and hospitals throughout the country to pitch for the sale of their products.
Data science is almost rendering this model of sales and marketing unnecessary.
Currently, at least 25% of marketing done by pharmaceutical companies is through digital marketing.
Also, almost all pharmaceutical companies heavily depend on targeted analytics to drive sales, improve spending, and enhance their overall bottom line.
Predictive analytics enables companies to determine which medical 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.
Additionally, modern medical representatives are equipped with smart electronic devices that have access to important real-time analytics designed to help them close sales. This makes them more productive and guarantees a higher degree of success.
4. Better Patient Follow-up
There is nothing as valuable to a pharmaceutical company as feedback from patients about their new molecule.
In time past, the process of getting such feedback has proven to be time-consume.
Nonetheless, with data science in place, pharmaceutical companies can now 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.
These are some of the ways data science is being deployed in the pharmaceutical industry. There are a dozen of other ways data science is being utilized.
