Taking Clinical Data Management into the Future with Big Data Integrations in the Cloud

By Temitope Keyes, VP of Business Development, ThoughtSphere

Temitope Keyes, VP of Business Development, ThoughtSphere

It is hard to think of an industry that has not, or could not, benefit from the scalability and ease of cloud technology. It is also hard to think of a field that has more to gain from these benefits than life sciences in delivering innovative treatments.

"To me, the keyword is “flexible.” It is crucial that clinical data solutions are designed as document-oriented databases, such as data lakes"

When it comes to accommodating the modern world, cloud-based solutions are better designed than traditional, on-premise data systems to accommodate the volume, variety, and velocity of today’s data-driven biotech and pharmaceutical companies. They allow users to access data from anywhere, on any device, thus making collaboration and analysis simpler and faster. The cloud also has a host of security advantages and makes it easier for regulated industries to stay in compliance by saving and storing data in a central place and better protecting sensitive data, like personal health and financial information.

Cloud solutions help businesses work faster and save resources. Often, organizations that move to the cloud can reduce spending on their own data centers and servers, and invest less in custom IT solutions. Cloud solutions can be used to solve complex data management challenges, freeing up team members’ time so they can focus on other things, such as actually using data rather than just figuring out how and where to store or retrieve it. It is also easier to keep software updated, since with Software as a Service (SaaS), programs are automatically updated when the vendor releases new versions.

The Unique Needs of Clinical Researchers

Clinical researchers are juggling ever-changing volumes of data from trial sites, patients, CROs and other vendors as well as from newer sources like EHR, social media and wearable technology. The eClinical technologies market is growing quickly and rapidly evolving. The industry is increasingly embracing cloud-based data solutions which allow sponsors and CROs to collect data digitally and store it centrally. These solutions allow every person touching a clinical trial to input feedback in one location, a single source of truth, making collaboration simpler and improving the accuracy of data. Cloud technology also improves sponsors and CROs’ approach to study monitoring and makes it easier to adopt risk-based monitoring strategies, as recommended by many global regulatory agencies, including the FDA.

But the cloud doesn’t alleviate every challenge. Most of the cloud-based clinical data solutions on the market aren’t flexible enough to accommodate the plethora of clinical development systems and sources that clinical researchers rely on. These can include EDC systems, Clinical Trial Management systems (CTMSs), IVRS, IXRS, ePRO/eCOA, labs, eTMF, safety systems (Argus, ARISg), clinical data warehouses, Excel spreadsheets, project management systems, contracts, EHR/EMR, and more. Researchers can’t just input all of their data, in its raw form, into the same system and allow data from disparate sources to “talk” to each other. Lacking that functionality makes it hard to manage all the information at their fingertips or to get a holistic view of the study.

Many clinical studies are forced to rely on a combination of digital and manual processes. IT teams often struggle to work with various in-house and vendor-provided solutions, which are either on-site or SaaS. Data is stored in silos, so it is difficult to cross-query, especially since different sources refer to data differently. Aggregating data using standard methods is difficult, and most companies do not have a sufficient Master Data Management (MDM) strategy and plan in place, nor do they have the proper technology and policies needed to support it.

Taking the Benefits of the Cloud One Step Farther

As global leader of products for Quintiles, ThoughtSphere’s CEO, Sudeep Pattnaik saw these clinical data challenges firsthand, and led a team tasked with trying to solve them. They built a $60M integrated healthcare data hub, but still the problem wasn’t solved, at least not to his satisfaction. My own experience from outsourcing clinical trials in big pharma and attempting to integrate with partners on the CRO side confirmed for me that until the problem of clinical data is solved, we will continue to fall short in trial execution.

Yes, clinical data can be stored in the cloud, but unless all clinical data can be easily input into the same place, managing a clinical trial is still difficult. It is still hard to make sure everyone involved in the research stays up-to-date, and that data is accurate. If you can’t keep up with the ever-changing nature of data, you may make faulty decisions, which in the pharma world means big bucks, lengthy delays, and even risks to patient safety.

To take the benefits of cloud computing one step farther, we need solutions that can integrate data from various source systems—a cloud-based, source-system agnostic data tool that makes it easier to manage and analyze data on a near real-time basis. This would ensure everyone involved in clinical research is working with the same data, no matter where they are located. We could run better, more-accurate reports based on a holistic view of site progress, more easily identify trends, and better handle the necessary regulatory submission processes.

To me, the keyword is “flexible.” It is crucial that clinical data solutions are designed as document-oriented databases, such as data lakes, that can accommodate structured or unstructured data, as opposed to relational databases, such as data warehouses, in which a scheme must be defined upfront. Most of the clinical data tools on the market are built on data warehouses, but data lakes are more flexible and the architecture of choice for some of the world’s most innovative technology companies. With this approach, data can be exported from the source system and stored in its raw format, whether it is structured, semi-structured, or unstructured. Then users can manage all of the different data sources involved in clinical research and perform ongoing and ad hoc analysis.

Thanks to cloud technology, this type of solution can be deployed quickly, in just a few days or weeks, whereas a custom data warehouse takes much longer to build. This tool can also be coupled with other cloud-deployed technologies, such as interactive visualization-based data discovery tools. These solutions can fuel significant gains in clinical data integrity, trial timelines, and in the effectiveness of Risk-based Monitoring (RBM) approaches.

Gains in efficiency and cost-savings are nice for every business, but for the pharma and life sciences industries, they can dramatically improve the R&D process and allow us to bring more innovative and effective solutions to market faster. It is not hyperbole to say that better clinical data saves lives and improves public health.

Cloud-based technology makes this type of innovation possible, but it is by coupling it with an improved, more flexible approach to data integration that we will unlock the most meaningful benefits for the world of clinical research.

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