Why is Big Data Decisive In The Pharmaceutical Industry’s Development?

Maintaining a consistent rate in introducing breakthrough treatments is crucial for pharma players to remain relevant in the market and attract investors. Besides conducting hundreds of clinical trials, drug discovery involves benefit assumptions, calculated risks, experiments, and integration of data obtained from various sources. Currently, in spite of facing challenges, the sector is looking at some potentially revolutionary advances. 

Here’s how big data analytics solutions have revolutionized drug manufacturing during recent years.  

Cross-industry Collaboration Can Help In Dealing With R&D’s Discouraging ROI 

Bringing a single drug in the market can cost as much as $2.6 billion, and of course, notoriously long research and development. The return on investment percentage recorded by most of the sector giants has been around 3.7 percent until 2016. The number of drugs approved by the US FDA is reducing each year.  

When it comes to the performance metric, the company’s returns on research and development are considered crucial by the investors who put their money on the pharmaceutical industry players. Thus, firms remain under constant pressure for improving productivity. 

Fortunately, data analytics can help industry players to take scientific partnerships and investment to the next level for accelerating drug development. 

Some countries are witnessing the cross-industry collaboration between insurance firms, healthcare players, and pharma companies. A shared data management platform helps firms to share information seamlessly and widen their database. 

Several small and large firms have formed partnerships, associations to collaborate and promote big AI push. The AAIH (Alliance for Artificial Intelligence in Healthcare) is perhaps one of the best examples.

Making Clinical Trials Convenient 

Advanced AI-powered sensors reduce the burden that the patient, their family members, as well as researchers face during the clinical trials. Even remotely located patients can be monitored, eliminating the need for them to visit the clinic daily. And of course, the systems also ensure the patients follow proper medicine schedule. 

Until the last decade, most of the clinical trial data was gathered only during the patient’s clinical visit or via self-reports submitted by the patient. That’s history. Now, an automated and constant flow of data from sensors helps pharmaceutical companies to ensure no clinical trials related stats are untracked or missed. Systems can be programmed to cancel the food and mood effects as well as fluctuations caused due to common factors. 

Algorithms and sensors ensure zero human errors in medical assessment. Efficient therapies and drugs can be designed due to accurate measurements of clinical endpoints and objective monitoring of disease progression.

Detecting Drug Reactions From Informal Sources Like Social Networking Sites  

Adverse drug reactions (ADRs) or harmful reactions of medicines are usually detected and documented during clinical trials. However, the drug may show additional consequences when consumed in real-world conditions. This is where AI and ML-powered social media scanning tools come into the picture. Such systems scan social media forums based on hashtags and gather data about side-effects and complaints about medicines. Put simply, they collect the data and create an ADR report as per the patient’s feedback. Such information is combined with data shared by pharmacists, clinicians, and lawyers. It further helps in ensuring that the drug manufacturer gets 100 percent accurate reports on the side-effects of their medicines available in the market. 

 Novartis’s head for global safety, David Lewis interacted with journalists and shared his opinion on the issue. He pointed out that ADR reports created with social media data offer more significant insights. There are some issues that the patient may not highlight in front of a doctor or nurse. However, he or she would prefer using a tweet on twitter to share it with the world.  

Booz Allen Hamilton’s health analytics subsidiary Epidemico happens to be one of the early adopters. Their ML algorithms help in retrieving ADR reports using social networking sites. Back in 2014, the firm conducted a study with the US FDA and found 4,401 tweets consisting of informal ADR feedback after scanning 6.9 million Twitter posts. The research work managed to put an end to all the debates questioning the accuracy and percentage of the availability of such feedback on social media. 

Pharma Companies Using Big Data For Effective Sales 

Data analytics tools have empowered pharma industry players to create sales and marketing strategies for targeting specific geographic areas. Such a targeted marketing strategy helps firms in saving efforts, time, and resources required for achieving sales goals. With more than 30% of marketing budgets dedicated to digital platforms, the role of big data is expected to be crucial during the coming months. 

Personalized Treatment With Predictive Analysis  

Another feather in the hat is predictive analysis software powered with big data. The system analyzes the patient’s existing medical condition, lifestyle, genetics, and other details to check if the selected drug can suit the person. Put simply; the algorithms help in delivering personalized treatment to patients. 

Drug Discovery And Patient Access 

Big data has the potential to improve various procedures related to drug discovery, clinical tests, and detecting the adverse drug reaction on test subjects. Pharmaceutical companies have finally found the ultimate tool that can help them accelerate drug development. 

The technology is helping pharma companies and government health agencies around the world. Data science education institutions across the globe are training data scientists to get the best out of big data. 

The society needs access to safe and effective medicines that are cost-effective. But, jumping into the project with unclear visions and high output expectations can prove to be of no use. AI projects need advanced planning for testing standards, hypothesis definition, and performance monitoring.

Remember, AI, big data, and ML won’t ever replace care providers and human scientists. These technologies work as tools to help professionals in increasing their productivity and accuracy.

If your firm is planning to invest in Big Data analytics solutions, you should consider discussing your project details with Smart Sight Innovations. The team at SSI Big Data development company has considerable experience in Big Data Application Development. Several of their mobile and web-based apps are in use around the world.