The proper use of data science and predictive analytics can improve any online business, in or out of the medical field, but plays a particularly unique role in the life sciences.
Science seeks truth through empirical evidence and scientific analysis. Clinical studies are heavily regulated to ensure that results are as accurate, reliable and unbiased as possible. The data from these studies often contain personal patient information that requires a high level of safeguarding.
Data science allows the life sciences industry – including pharmaceutical companies, biotechnology, and medical device organizations – to safely and effectively collect patient data and derive meaningful conclusions from that data. Determining the overall effectiveness of new medication, determining if a drug or device is safe to use, and comparing a drug to other approved treatments are all examples of clinical data sciences at work.
Clinical data science operations allow medical professionals to collect, clean, store, analyze and share information faster than ever before. If you haven’t already incorporated data science into your business here are six benefits of implementing data science for your life sciences company.
It should come as no surprise that data scientists need to follow strict regulatory guidelines for collecting, storing, analyzing, and publishing clinical study data and results. By having a rigorous data science operation, including a set of standard operating procedures and qualified software, you’ll be confident that your organization is “audit-ready” at all times and that you are ultimately protecting the safety of the patients.
Pharmaceutical companies need large pools of trial data to release and market a drug safely, and they’ll spend the money to get that data. In 2019 alone, R&D spending by pharmaceutical companies was $186 billion. Having strong data science operations, which leverages solid and validated technologies, can help you reach decisions quicker, getting the drug to market faster and reducing the overall development costs.
Collecting data can be costly. It is important the right amount of data is collected based on a statistical calculation of the sample size necessary to come to accurate and reliable conclusions. A large amount of data can reveal subtle trends that might have otherwise gone unnoticed, which is why sample sizes are so important to the field of life sciences. Data science is necessary to ensure there is a balance between cost and accuracy.
The repeated production of interesting and accurate medical information can quickly make you a trusted source of information. As long as you’re conducting proper data analysis and following guidelines during collection, it’s hard to argue with data.
Data science technologies like EDC systems or Data Warehousing helps organize and store data for future use. By storing data in a common platform using industry standards, it reduces the costs and time needed for cross-study analyses, submission, and publications. For example, perhaps you noticed an interesting correlation between data points in two different studies. With all that data stored away, you might be able to conduct a meta-analysis that uses previous studies’ findings.
Clinical data science has drastically changed and improved in the 21st century. With the help of the internet, clinicians can reach a vast audience from which they can extract valuable data.
ProPharma Group helps businesses struggling with data collection, software development, machine learning, and more and offers R&D technology services that can help your business thrive with data science.
Whether you’re looking for help with data management or data analysis, process engineering or identification and implementation of the right tools that make sense for your business, our team specializes in consulting solutions and resourcing support in the life sciences space.
If you’re curious about a place for data science operations in your pharmaceutical, biotech, or medical device company, contact us to learn more.
October 7, 2020
In the era of technological disruption, data science is a disruptor for the books. Today’s data scientists develop processes, algorithms, and systems to mine structured and unstructured data with the...
April 13, 2023
Key messages from the mid-point achievements report To strengthen regulatory and scientific support for innovative medicines and diagnostics development, in March 2020 EMA published its Regulatory...