A staged approach to achieving clinical data intelligence

In 1925, inventor Hugo Gernsbacher suggested a remote control device for physicians and called it 'Teledactyl,' effectively predicting telemedicine nearly a century ago. Since then, healthcare innovation has accelerated to the point where the industry was able to deliver viable and safe COVID-19 vaccines in less than 12 months. How can clinical data intelligence help build on the momentum gained during the pandemic?

When you consider it took more than 50 years to deliver a vaccine for Polio, the fact that the industry brought the COVID-19 vaccine from research to manufacturing to regulatory approval and distribution in a year is phenomenal. It's a demonstration of the innovation, collaboration and agility that's possible when the sector is faced with a crisis. Yet, there are already some, including McKinsey, who say the COVID vaccine development process can't be replicated for all future drug development.

In any case, the focus on operational excellence and innovation experienced during the pandemic will endure. One way to ensure this is for companies to revisit their drug development processes to bring innovation to everything they do.

Data: The biggest challenge to Life Sciences?

The biggest challenge to achieving innovation at scale is data. This sector - like just about every other one - is producing more data in more formats across more channels than ever before. The figures are astronomical; according to Transforming Healthcare Analytics, the amount of data in Life Sciences exceeded 2.3 zettabytes in 2020 - and is predicted to grow at 48% annually.

To give some comparison, you'd need 2.3 million 10TB hard disks to store that data, and you'd be adding another 1.1 million each year.

With new drugs taking an average of 12 years to develop - at a cost of $2.6 billion - taking better control of clinical trial data must be a priority for Life Sciences companies. Effectively managing this data improves and accelerates the drug development process in areas such as drug discovery, clinical trial design, patient engagement and post-market surveillance.

3 stages to transforming clinical data intelligence

Clinical Data Intelligence for Life Sciences from OpenText™ allows you to efficiently capture data from all available sources and add classification and categorization so it can be effectively 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 staged approach to delivering clinical data intelligence.

Stage 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 via automated workflows or human supervision.

Stage 2: Move beyond classification to enrich clinical data

Once documents are classified, the next stage 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 automated text extraction.

Stage 3: Make clinical data drive insight and innovation

In the third stage you can extract full value and insight from clinical data. The data is classified and enriched so that it can quickly and easily feed advanced analytics solutions to derive insight from the data to drive innovation at each step in 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 Sciences transformation. This free, digital event takes place between November16-18. Register now.

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