What is Clinical Data Management

Clinical data management (CDM) manages the data protocols for clinical trials, including establishing the right systems, processes, procedures, and training to extract meaningful results and comply with local and federal regulations around the world.

Clinical data management ensures that data collected throughout - and resulting from - a clinical trial is accurate, reliable, and compliant with regulatory standards. It protects the integrity of the trial and lays a foundation on which innovation can begin to take place.

Managing data that arises out of a clinical trial is a big job. The number of data points for just Phase III trials tripled to 3.6 million data points over the course of 10 years. Connecting the dots between all this information and setting up structures that enable researchers to turn them into insights and innovation is at the heart of clinical data management.

You'll learn how to

Lay a foundation for effective clinical data management with collaboration and coordination
Understand the key skills required for clinical data management
Optimize clinical data management with a dedicated technology platform
Prepare for new efficiencies in clinical data management with emerging technologies

Lay a foundation for effective data management with collaboration and coordination

The key players in clinical data management need to work together closely to ensure successful outcomes. This includes the pharmaceutical companies and medical device makers who sponsor trials, the clinical research organizations who often run the trials, and the site personnel who collect and enter data into systems.

The primary objective of clinical data management processes is to provide high-quality data. Effective clinical data management keeps the number of errors and amount of missing data to a minimum. Accessible, high-quality data enables teams, researchers, and organizations to garner insights and draw important analyses. To do this properly, a variety of processes are put in place, namely clinical data management systems with embedded audit trails.

Though the scope of a clinical trial may be broad, the scope of clinical data management is highly defined to a few key tasks. These include:

When done correctly, these tasks set standards for how data is collected, stored, used, and shared. They make sure trials adhere to regulatory statutes and while keeping data and information secure.

Implementing advanced analytic tools enables teams to turn complex data sets into meaningful insights with the potential to greatly improve patient outcomes. Organizing data and making it accessible also enables organizations and trial teams to provide patients with easy access to their personal health data. This can help keep them engaged and encourage continued participation in the trial.

This is valuable, especially when you consider that recruiting patients can be one of the biggest challenges. Retention efforts claim 30% of the trial timeline and costs approximately $1.2 billion annually. In fact, 80% of trials fail to enroll on time, and worse, 11% of clinical sites fail to enroll any participants at all. These expensive delays cost pharmaceutical companies $600,000 to $8 million for every day of delay. (Back to top.)

The key skills required for clinical data management

Clinical data managers have a long list of responsibilities, many of them supervisory. One of their primary responsibilities revolves around designing a data management plan. This plan describes the activities around data acquisition, data review, data cleaning and preparation for data analysis, and submission. These processes make certain that the right data is being collected in the right way, ensuring the study questions can be answered.

Clinical data managers also oversee the creation of a clinical database (alongside database developers and programmers). They have an important role in developing the case report form (CRF) or electronic case report form (eCRF). Sponsors create these forms in conjunction with medical experts, statisticians, and regulatory authorities. Clinical data managers then implement them. The forms define the data fields, specify data types, outline the units of measurement. They also set the guidelines for form completion. Though CRFs can be paper or electronic the digital age has pushed heavily toward electronic data collection.

Other responsibilities include managing electronic data, standardizing that data, and locking the database at the right moment. They also review data and perform any necessary data cleaning activities. (Back to top.)

Optimize clinical data management with a dedicated technology platform

The pharmaceutical industry, and clinical trials in particular, notoriously struggle to manage data. This makes it hard to connect the dots and requires dedicated staff with the right expertise to manage it. However, even with such expertise, manual data management can present a huge challenge.

With so many moving parts, sponsors need a way to centralize data in one place. CDMs would allow them to collect, monitor, review, and clean data more easily, making it ready to analyze and submit. It would also enable them to automate data collection, organize that data , and gain insights in real-time. These capabilities will become even more important as clinical trials become more complex and the industry moves toward decentralized trials, which require more sophisticated monitoring.

Organizations understand the inherent data management challenges, with many turning to clinical data management tools to help streamline processes, minimize errors, and centralize data collection and related activities. These data management systems not only create the single source of truth, but they also provide an audit trail. When it comes time to submitting the data and results to the Food and Drug Administration for approval (in what can be a labor-intensive process), the audit trail can save time and resources.

While powerful on their own, integrating data management systems with other systems can deliver a variety of benefits across a pharmaceutical organization. It makes it possible for teams to benefit from associated technologies like artificial intelligence (AI) and automation that can deliver insights and flag things like quality issues that may occur with the data. (Back to top.)

Prepare for new efficiencies in clinical data management with emerging technologies

Data growth is explosive across the health and life sciences industry, as evidenced by the fact that health data is expected to account for 36% of the world's data by 2025-the most of any industry. Clinical trials are producing a substantial part of that data. In fact, between 2024 and 2030, clinical trials are expected to grow at an impressive compound annual rate of nearly 6.5%.

As with many other industries, professionals in clinical data management are looking to use AI to gain better insights into trials. This will require careful data governance to assure that data is HIPAA-compliant, secure, and in the right format for AI. This convergence of data and AI holds great potential for the health and life sciences industries. It spurs innovation, leads to better outcomes, and improves patient care.

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