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What 2021 Taught Us About Clinical Data Management Best Practices

2021 brought with it continued uncertainty and chaos that impacted almost every industry in the world. The clinical trial landscape was no exception. However, amidst all of the hardship brought on by Covid-19 came new innovations and creative processes that adapted to a shifting landscape.

Crises often force creative thinking, and during these unprecedented times,  life sciences companies worked to adjust their clinical trial data management processes and operations in order to streamline internal efforts and still deliver quality patient experiences.

For instance, the industry saw an increased adoption of decentralized clinical trial models, which enabled more patients to participate in studies and collect more types of data remotely. Additionally, as clinical trials began handling a greater volume of data, data management teams had to maximize their existing technologies and implement new ones in order to more effectively collect, analyze, and clean clinical data.

So, while clinical trial teams were primarily concerned with keeping trials on track this past year, 2022 will likely see the clinical trial data management field continue to evolve and enable researchers to do more with clinical data in a quicker, more cost-effective manner.

Let’s take a look back at what 2021 taught us about data management best practices within clinical trials and consider how it will inform the evolution of clinical trial data management as we head into the new year.

The Importance of True Clinical Research Data Management Professionals

It’s no secret that the rapid evolution of clinical data technology has enabled data managers, trial managers, and clinical operations professionals to better collect, analyze, and understand their data. But as the volume of data being generated continues to increase — with approximately 30% of the world’s data volume being generated by the healthcare industry — clinical trials still require human beings to own and control all of that data.

In the past year, we’ve seen just how important clinical research data management professionals are for efficient trial operations. While clinical and operational trial data management systems like elluminate® work to automate, centralize, simplify, and streamline data management processes, qualified individuals are still needed to maximize the value of these systems.

Today, data management professionals are a driving force behind how data management is carried out, acting more as technology consultants as opposed to behind-the-scenes data quality reviewers. From acquisition and analyses to data cleaning and statistical processes, data managers have increased influence over establishing clinical data strategies.

Needless to say, data management professionals will continue to be at the forefront of how clinical trial data management evolves in the coming years. This makes it all the more important to avoid data management churn within your organization and ensure your team members aren’t feeling burnt out or overburdened with their current responsibilities.

Patient-Centric, Decentralized Clinical Trials Take Center Stage

With the Covid-19 pandemic came the need for life science organizations to think differently about trial designs with many sites in hospital settings limiting access and availability of space and resources for research. Naturally, clinical and operational trial teams understood that embracing technologies to enable patients’ continued participation in trials was a must. But making this switch proved to be easier said than done.

Enabling remote patient participation meant trial administrators needed to use more data sources. The complexity and nuances of each data source coupled with the lack of technological infrastructure to support them meant clinical trial teams struggled to get a grip on effectively managing their data. Having to balance technological capabilities with the need to continually deliver satisfactory patient experiences left data and trial managers scrambling to implement solutions that would check all the boxes.

As decentralized and hybrid trials become more dependent on advanced technologies, organizations must take steps to embrace the transformation of traditional clinical processes. This new white paper walks through the key components of defining a data strategy that embraces digital transformation and helps companies prepare for the digitization of today’s clinical trials.

Read the white paper

Enter patient-centric, decentralized clinical trials powered by all-in-one data management systems. With the help of a modern clinical trial data management platform like elluminate, clinical trial teams were able to orchestrate their data management processes more efficiently, increasing productivity and improving submission quality and conformity. Working from a single source of truth fostered cohesion, minimized confusion amongst data management professionals, and enabled them to quickly access, analyze, and share clinical data as needed.

The advent of  technology enabled clinical trials to make better sense of their data and, in turn, deliver optimal patient experiences. As we head into 2022, clinical trial teams will continue to lean on these innovative systems to both power existing processes and make it possible for them to explore new initiatives.

More Data Led to More Disparate Systems

As previously mentioned, the volume of clinical trial data surged in 2021. But more data wasn’t the only challenge clinical trials grappled with last year. Research from the Tufts Center for the Study of Drug Development indicates that around four different data sources were used in clinical trials in 2019. That number is expected to grow to six data sources in the next few years. At eClinical Solutions, we already see our clients on average managing eight different data sources. So, not only must data managers collect, compile, clean, and report on more data than ever before, but they also must gather that data from mobile devices, tablets, watches, and other devices.

On top of that, clinical trials have relied on multiple electronic data capture (EDC) solutions to effectively store patient data. Additional research from Tufts indicates that 74% of clinical trials are using two or more EDC solutions to collect and store data. Having to manage multiple EDC solutions across trials makes it difficult for data professionals to seamlessly access and analyze clinical data through a centralized platform.

As 98% of clinical trials recently reported challenges with their data management systems, it’s no surprise that data management professionals sought to consolidate EDC solutions and disparate data sources so that clinical data could be easily accessible and digestible. With no signs of the volume of clinical trial data slowing down anytime soon, data managers are working today to implement centralized, simplified systems that enable easier collection and better analysis of patient data.

While 2021 forced clinical trial teams to reinvent the wheel to a certain extent, it also opened the door for more connected systems that minimize the chance of delayed trial outcomes and enable better collaboration and communication between data professionals and stakeholders.

What Does the Future Hold?

We’re still not out of the woods when it comes to the Covid-19 pandemic, but that isn’t stopping those working within clinical trials from making great strides towards building better ways to manage their data. 2021 forced clinical trial teams to embrace technologies and tools that digitized data management procedures. Now, with the industry at large understanding technology’s role in enabling improved data management processes, the focus in 2022 will turn towards the people powering these advancements.

The evolution of clinical trial data management is progressing quickly, and eClinical is personally excited about what the future holds for clinical trials. As data management professionals start to earn a better seat at the decision-making table and influence strategy decisions earlier on, clinical trials will likely see bolstered efficiency within their teams and improved accuracy in their final submissions.


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