Planning Ahead for Clinical Study Data Management

Effective clinical study data management is crucial to the success of a clinical study. Clinical studies require capturing, organizing and aligning vast amounts of diverse data, such as clinical, demographic, sample, and experimental data. This data needs to be accessible to current and future researchers while maintaining security and compliance. Without proper planning, data management for clinical studies can be inefficient, complex and in some cases nearly impossible to analyze and reproduce. 

Sharing access to clinical study datasets in ways that other researchers in the scientific community can easily verify and reproduce results is critically essential to advancing life science research. In fact, the National Institutes of Health (NIH) now requires grant applications and renewals to be submitted with a Data Management and Sharing Plan in order to help foster greater accessibility to NIH funded research. 

Ultimately, the efficiency and accuracy of data management practices are critical factors that determine the success of longitudinal clinical studies. In this blog post, we discuss the importance of robust data management strategies and explore best practices for optimizing the data management of clinical studies.

 

Contents: Challenges | Planning Ahead | Studies in Server SDMS

 

Challenges of Clinical Study Data Management

Regardless of the size or scope of the clinical study, common challenges exist that can be mitigated with proper planning and the right tools.  

• Disparate Sources: Clinical studies often require data from disparate sources to be centralized and aligned. This data includes patient surveys, assay results, EHR reports, and more, requiring robust mechanisms for data harmonization and interoperability.
• Manual Data Management Actions: Manually aggregating, cleaning, and transforming data creates the risk of critical errors and lapses in data integrity. Labor-intensive processes also consume significant time and resources.
• Data Quality and Consistency: Maintaining data of consistently high quality throughout the lifecycle of the study can be difficult. Quality must be upheld through compliance requirements, database development, data capture, cleaning, analysis and finally, publication.
• Compliance and Security: Upholding high standards for data security and compliance presents a multifaceted challenge in clinical study data management. Regulatory frameworks such as HIPAA, FISMA, and CFR Part 11 impose distinct requirements for safeguarding sensitive healthcare data, ensuring information integrity, and facilitating management of electronic health records. 

 

Planning Ahead for Study Data

Start thinking about data management early in the planning process of your clinical study. With proper planning, selecting the tools for your clinical study data management can be efficient.

• Data Management Plan (DMP): It’s essential to establish a plan for managing research data established prior to the start of the study, especially if your study is being funded by the NIH. This plan should cover data sources, collection methods, representation, storage and maintenance, as well as other factors like how access and reuse will be compliant with privacy and legal restrictions.
• Define Metadata and Establish Standards: Clearly define metadata attributes such as data source, collection method, variables, and units of measurement to facilitate data discovery, interpretation, and reuse. Following accepted standards for metadata, like those recommended by the Research Data Alliance (RDA), ensures consistency and compatibility across different research initiatives. Additionally, optimizing data management practices for later reuse aligns with the FAIR data principles—making data Findable, Accessible, Interoperable, and Reusable. Establishing these data standards preserves data integrity and protects reproducibility.
• Quality Control Procedures: Plan out your quality control procedures and audit logging for study data, including data collection, entry, validation, and analysis. Regular updates and adherence to these procedures are essential for maintaining data accuracy and integrity. 
• Long-Term Data Sharing and Storage: Plan for post-study data sharing and long-term archiving. Anticipating these data needs will help inform future research endeavors and comply with regulatory and funding requirements such as NIH data sharing policies. Incorporating provisions for data anonymization, and ensuring compatibility with data repositories where study data will be published, supports post-study publication and sharing.
• Technology and Software: Software solutions like Electronic Data Capture and CDMS exist to manage clinical study data through its entire lifecycle, from collection to analysis to publishing and archiving. Leveraging available data management solutions enhances productivity, data integrity, and collaboration among research team members. This might look like choosing a CDMS that integrates with an EDC like REDCap. For those with IT resources and technical skills, a segmented approach utilizing different software packages for study management might make sense. However, many researchers prefer a comprehensive, less technically intensive solution like a Clinical Data Management System (CDMS), which streamlines data management for the study’s entire lifespan. Beyond streamlining processes, implementing robust security measures like encryption, access and audit trails is essential to mitigate the risk of data breaches and much easier to implement for a single technical system. 

 

LabKey CDMS

LabKey CDMS is a toolset that forms a Clinical Data Management System (CDMS), designed to organize and manage study data in a central, secure environment for data curation, analysis, collaboration and publishing. LabKey CDMS aims to address the specific needs of clinical study data management, from data harmonization to secure compliance to convenient collaboration.

Contact us if you are interested in discussing CDMS for your trials.