A step-by-step approach to achieving clinical data intelligence
In 1925, inventor Hugo Gernsbacher suggested a remote control device for doctors and called it “Teledactyl”, effectively predicting telemedicine almost a century ago. Since then, healthcare innovation has accelerated to the point where the industry has been able to deliver viable and safe COVID-19 vaccines in less than 12 months. How can intelligence from clinical data help build on the momentum gained during the pandemic?
Considering that it took over 50 years to deliver a polio vaccine, the fact that the industry has moved the COVID-19 vaccine from research and manufacturing to regulatory approval and distribution in a year is phenomenal. It is a demonstration of the innovation, collaboration and agility possible when the industry is faced with a crisis. Yet there are already some, including McKinsey, who say the COVID vaccine development process cannot be replicated for all future drug developments.
Either way, the focus on operational excellence and innovation experienced during the pandemic will continue. One way to ensure that companies are reviewing their drug development processes to bring innovation to everything they do.
Data: the biggest challenge in life sciences?
Data is the biggest challenge in achieving innovation at scale. This industry – like just about every other – produces more data in more formats on more channels than ever before. The numbers are astronomical; According to Transforming Healthcare Analytics, the amount of data in life sciences exceeded 2.3 zettabytes in 2020 – and is expected to grow by 48% per year.
To give a comparison, you would need 2.3 million 10TB hard drives to store this data, and you would add 1.1 million each year.
With new drugs taking an average of 12 years to develop – at a cost of $ 2.6 billion – better control of clinical trial data must be a priority for life science companies. Managing this data effectively improves and speeds up the drug development process in areas such as drug discovery, clinical trial design, patient engagement and post-market surveillance.
3 steps to transform the intelligence of clinical data
OpenText â¢ Clinical Data Intelligence for Life Sciences allows you to efficiently capture data from all available sources and add classification and categorization so that it can be efficiently stored, searched and retrieved by your clinical trial data management team. However, you don’t want to boil the ocean. This solution allows you to take a step-by-step approach to providing insight into clinical data.
Step 1: Capture documents from any channel
Start by capturing data from paper and digital documents. You can identify document types, extract relevant parts of documents, classify and categorize data, and validate extracted data through automated workflows or human oversight.
Step 2: Go beyond classification to enrich clinical data
Once the documents are classified, the next step is to add a range of AI capabilities – such as NLP and text mining – that will further accelerate the richness of your clinical data through enhanced metadata and extraction. automated text.
Step 3: Make clinical data drive insight and innovation
In the third step, you can extract all the value and information from the clinical data. The data is categorized and enriched so that it can quickly and easily feed into advanced analytics solutions to extract insights from the data to drive innovation at every step of the clinical trial process.
Learn more about accelerating complex clinical trials by reading this white paper from Proventa. And join us at OpenText World 2021 to learn about the latest trends in life science transformation. This free digital event takes place November 16-18. Register now.
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