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What is Clinical Data Management?

Clinical data management is the set of activities and processes that are responsible for generating and maintaining accurate and reliable clinical trial data. From inception to final submission, clinical data management touches every stage of a clinical trial to ensure data is being collected, integrated, and shared quickly and efficiently.

Clinical Data Management Orange Shapes

Why is Clinical Data Management Important?

Without clinical data management, teams have a difficult time ensuring the data they’re working with is accurate, reliable, and secure. This inevitably jeopardizes the integrity of clinical trial submissions and can drastically slow down processes throughout the clinical trial. Automation and artificial intelligence enablement in clinical data management allows teams to automate their time-consuming tasks, mitigate human errors, and maximize efficiency throughout every stage of the clinical trial.

Clinical Data Management Roles and Responsibilities

Clinical data management involves a wide range of functions and stakeholders across the clinical enterprise. Some roles are directly involved with the handling and management of clinical data, while others work parallel to data management to ensure its quality and accessibility. Here are some of the most important roles and responsibilities related to clinical data management that you should be cognizant of.

Data Manager

Data managers are primarily responsible for all data collection and distribution, making them an integral part of successful clinical data management. They are responsible for ensuring life sciences organizations get maximum utility out of trial data, from assuring its overall accuracy and integrity to facilitating easy access and analysis by data analysts. A good data manager will effectively oversee the entire clinical data management process and keep other teams running smoothly.

Medical Reviewer

Medical reviewers work to effectively understand the holistic patient experience and improve your speed-to-analytics timeline. They work to ensure medical records are accurate and traceable, while reporting any issues or errors regarding patient data. Proper clinical data management ensures medical reviewers have centralized access to all your varying data sources and systems. Additionally, medical reviewers should take an RBQM approach when handling clinical data to ensure quality and compliance in modern trials.

Clinical Operations

Clinical operations personnel typically handle the day-to-day responsibilities of running clinical trials in the field. They make sure data collected from trials is quickly and efficiently transferred into the appropriate systems and made available to the rest of the team. Their success depends on their ability to improve the end utility of trial information, and enable those who need access to data to spend more time analyzing it and less time searching for answers. In smaller organizations someone may assume the role of both head of clinical operations and data management.

Clinical Data Management Challenges

Clinical data management is an increasingly fast-paced environment that requires a multitude of players to keep on track. Unfortunately, many life sciences organizations are relying on outdated legacy systems and antiquated processes to carry out clinical data management, which in turn is slowing down speed-to-market and diminishing the value of clinical data. While there are a host of obstacles when it comes to clinical data management, these are three of the main challenges facing organizations today.

Largely Manual Processes

The time it takes to aggregate, clean, transform, and analyze clinical data takes a lot of time, especially when these processes have to be manually performed by team members. As organizations continue to handle more and more clinical trial data, they need to move away from manual management processes and embrace automation to streamline clinical data management.

Disparate Data Systems

With an increased reliance on CRO and the lack of integrated systems to manage the growing volume of clinical trial data, life sciences organizations are dealing with an increasing amount of disparate and manual data systems. Siloed clinical trial data is difficult to centralize and standardize, which can result in slower data acquisition and analysis, as well as final submission inaccuracies. To efficiently organize and consolidate clinical trial data, organizations need to achieve visibility across all data systems and sources  in order to effectively manage all clinical trial data.

Data Management Churn

Clinical trials are increasingly complex, and since many organizations are relying on manual processes for clinical data management, data managers are naturally becoming burnt out. When data managers leave their organization, it puts an increased burden on the rest of the team to pick up the slack. This results in additional burnout, creating a dead-end cycle that ultimately lessens the overall impact of your clinical trials. To avoid data management churn, organizations need to automate manual data management activities wherever possible so that data managers are able to work smarter, not harder and longer.

Clinical Data Management Regulations and Standards

To ensure clinical trial work is traceable, justifiable, and compliant, it’s important that your life sciences organizations familiarizes itself with CDM regulations and standards. Clinical trials should adhere to Good Clinical Practices (GCPs) as outlined by the FDA. These include:

Adherence to ethical principles

Patient’s rights, safety, and well-being

IRB/IEC review and approval and protocol adherence

Scientifically sound protocols

Adequate drug information

Risk minimization

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Through automation and sophisticated technological systems, clinical data management powered by AI and machine-learning models is more easily able to adhere to many of these GCPs and ensure their organization is complying with the necessary CDM regulations and standards.

Benefits of Clinical Data Management

Controlled Clinical Data Repository

A controlled clinical data repository, like elluminate® Data Central, enables clinical trial teams to work from a centralized source of truth in order to efficiently access and analyze data. With an increased reliance on a multitude of CROs to help manage datasets, life sciences organizations need to implement a centralized clinical data repository that expedites the data collection, cleaning, and analysis processes

Enhanced Trial Efficiencies

The ultimate goal of most life sciences organizations is to progress through clinical trials quickly and cost-efficiently in order to end up with final submissions that deliver real-world benefits to patients. Clinical data management optimizes and streamlines trial processes across the board, from planning and designing to analyzing and evaluating. When executed properly, clinical data management enables stakeholders to efficiently analyze and act upon your valuable trial data for shorter turnaround times. An effective clinical data management platform will automate manual sample tracking processes for increased accuracy and greater efficiencies, all from a centralized platform. See how elluminate Sample Management Analytics accomplishes just that.

Minimized Compliance Issues

If your trial submissions cannot be validated, traced, or otherwise fully audited, the final results will ultimately fall short of expectations. Proper clinical data management ensures the quality and integrity of your work, and makes it easier to demonstrate to auditors how you reached your conclusions. elluminate RBQM, for example, provides a single centralized platform for all trial data sources to ensure teams can monitor and mitigate risk with greater efficiency, accuracy, and ease. Acting from a centralized clinical data repository, your teams will be able to quickly and efficiently retrace their steps in order to extract the information needed to demonstrate compliance with regulatory standards.

Clinical Data Management Process Flow

Simplifying your clinical data management process flow can increase operational oversight, shorten data management cycle times, and reduce the manual burdens on data management teams. For starters, clinical teams need to develop a data management plan to outline all procedures and tasks before the trial begins. Next in the clinical data management lifecycle is the process of collecting patient data while also monitoring processes for potential risks or issues. Once trial data has been assured for accuracy and integrated into a consistent format, it can then be analyzed and acted on to report trends and formulate submissions.

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