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A Comprehensive Ecosystem is Essential for Clinical Data Management

DM Webinar Blog Post

Co-authored by Lisa Carlson, Manager, Clinical Data Management at Bioclinica

Clinical data management (CDM) is essential for ensuring high-quality, reliable, statistically sound data, as outlined in the protocol and across all clinical trial phases. Because of this, the process is far more complex than just simply reviewing the data at the end of a study when all the data is available. Clinical data should be reviewed, cleaned and standardized throughout the study on a rolling cycle and then again in preparation for study closeout. 

Clinical trials benefit from a strong CDM team, clear communication and a comprehensive CDM ecosystem. An experienced Lead Data Manager and a strong supporting team build the foundation of the CDM ecosystem. Effective communication by the Clinical Data Manager with all study team members ensures a clear understanding of the needs and expectations. Communication should also continue throughout the study, to provide study updates, share questions and concerns and formulate potential solutions. In addition to providing the benefits of their knowledge and experience, a strong CDM team can facilitate the use of the entire ecosystem to simplify the process of clinical data management.

What is an ecosystem?
An ecosystem is any system or network of interconnecting and interacting complex interdependent parts; these relationships are important, and little changes can affect everything else in the ecosystem. Therefore, the way in which the people and parts interact has a huge impact.

And there are a lot of people and parts involved in CDM. An ecosystem ties together all the essential components, putting so much power at your fingertips. In addition to an increasing number of data sources and vendors involved in clinical trials, the process of preparing submission-ready data involves multiple data reviews, coding, external reconciliation, database lock, SAS datasets, Study Data Tabulation Model (SDTM) datasets and tables, listings, and figures (TLFs). Each step incorporates multiple components, or tools, that should be in every clinical data management toolbox.

Comprehensive clinical data management ecosystem toolbox
To efficiently achieve high-quality data, the toolbox for this CDM ecosystem should include an experienced team of subject matter experts (SMEs) in a wide variety of areas, standard templates, study dashboard, standard listings, customer/study-specific listings, external data handling capabilities, medical coding tools, integrations with external systems, macros, standard operating protocols and best-practice guidelines.

The following SMEs are needed: a dedicated project manager, SAS specialists, coding specialists, electronic data capture (EDC) database developers and integration specialists. Templates, medical coding tools and standard operating procedures help maintain consistency within and across studies and ensure readiness for compliance. At the same time, best practice guidelines ensure submission-ready data.

A study dashboard provides a wide variety of metrics to help monitor the pulse of the study throughout its conduct and while preparing for an interim analysis, data cut or database lock. Such a dashboard should ideally:

  • Provide a detailed status of each subject.
  • Identify outstanding tasks required to declare a subject data management clean at any point during a study.
  • Allow monitoring of study trends such as query response time, verification and electronic case report form (eCRF) entry.
  • Include key performance indicators (KPIs).
  • Allow for identification of clean patient groups based on a variety of sponsor-defined criteria – to help the study team focus on the priorities based on the criteria set.

To expedite data review, standard listings and customer/study-specific listings represent programmatic tools to perform complex data checks, and advanced macros can help efficiently review the listings. These programmatic listings improve accuracy and ensure consistency in terms of data review. External data handling enables the standard programmatic review of any external data reconciliation, such as central labs and SAE. Mapping of data external to the EDC, such as data from the interactive response technology (IRT), clinical trial management system (CTMS) and vendors, simplifies clinical data management.

What if you don’t have your own ecosystem and toolbox?

As you can see, a comprehensive CDM ecosystem includes quite an extensive toolbox of people, processes, documentation, programming and review. As Clinical Data Managers, we can attest to the tremendous role this ecosystem plays and how it increases our ability to seamlessly manage data. 

For many companies, a CDM ecosystem can be difficult to achieve, creating challenges in efficiently reaching submission-ready data. This is when an experienced partner with a ready-made comprehensive CDM ecosystem and toolbox can meet all your CDM needs or fill the gaps in your CDM ecosystem.

Watch our on-demand webinar recording to learn more about the components of a successful ecosystem and how an established ecosystem helped accelerate database lock and rescue a study for timely submission in our industry case studies.

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