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Emerging Trends and Risks in Clinical Data Management
Hybrid and agile models are reshaping the landscape for both clinical trials and clinical data management.
As they continue to evolve, the industry is witnessing the creation of an evolving body of best practices, as well as new risks and pitfalls for data management.
In the wake of the pandemic, 95% of companies in the life sciences sphere have said they want to leverage clinical trials moving forward.
To reconcile with the huge wave of changes making themselves felt across the industry at present, here are some of the major trends anticipated in the next few years.
Integrating Data from Multiple Sources Using Smart Data Reconciliation
New technologies and tools can help to automatically interpret and construe data, offering reconciliatory approaches in instances where multiple datasets from different sources are being input synchronously.
AI and machine learning can be applied to drive data harmonisation and map data to the standards required by the Clinical Data Interchange Standards Consortium (CDISC), such as the Study Data Tabulation Model (SDTM).
The same tools can also be used to enhance signal detection in certain study approaches.
Standardising Data Using Tools Powered by AI and ML
New AI- and ML-powered tools can standardise the formatting of disparate information, encompassing everything from digitised protocols to case report form (CSF) design.
This is also relevant for the setup of electronic data capture (ECD) approaches, as data can be stored centrally and passed to systems through application programming interfaces (APIs).
As an added benefit, the information captured by ECDs can be reused throughout the clinical development lifecycle for data management.
This ensures the leveraging of knowledge extracted from previous studies, with any data discrepancies or disparities ironed out before being carried over.
Substituting EDC with DDC to Reduce Site Burden
Although it has immense utility, EDC and data management remains a significant pain point in many clinical trials as it forces site personnel to enter handwritten data, which is both tedious and time-consuming.
This also means that even as investigators in the act of assessing patients, they’re also occupied with and distracted by computer screens.
Substituting EDC for direct data capture (DDC) could help to smooth the clinical data management process.
With DDC, information captured via a diagnostic tool or device is then automatically stored and recorded, removing a step from the diagnostic pipeline.
From a patient perspective, DDC allows patients to directly capture data into an electronic tool, facilitating remote monitoring and greater utility in home care.
Interpolating Clinical Data Management Approaches
Change is a constant in the pharmaceutical industry as in all vocations, and new and agile models come with distinct, occasionally unseen challenges that companies will need to prepare for in order to navigate them properly.
Quirks of the industry such as siloed data storage may exacerbate existing challenges, as can a lack of access to raw and harmonised data.
At its core, this may boil down to constraints on human capital, including expertise, interactivity, and project management.
Developing an appropriate and expedient strategy for clinical data management at the first opportunity can help to circumvent some of these pitfalls.
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