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How can an optimized data strategy support your clinical program

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Data is at the heart of any clinical study and guides drug development decisions. A new therapy’s ability to receive approval from regulators such as the Food and Drug Administration, as well as other stakeholders, including payers and health technology assessment (HTA) organisations, is dependent on high-quality clinical data using clinical trial data management systems. Getting timely, high-quality clinical data can help you bring your therapy to market or licence it to another company.

What’s the big deal about clinical data management?

There are some reasons why a data strategy for your clinical program is critical. First, protecting the quality of your clinical data is the primary goal. Second, clinical data management can help you establish your approach to data collecting, storage, and analysis, such as selecting an appropriate electronic data capture (EDC) system and standardising data transformation processes. Third, it’s critical to proactively identify and fill information gaps in real-time to reduce delays and improve data quality. Finally, it’s also crucial to take a comprehensive approach. At Element Technologies, for example, our biostatisticians, programmers, and data managers cooperate to guarantee that the design, construction, and delivery are all up to bar. 


You must also be aware of the dangers of failing without having a strategic clinical data management system. Without it, the evidence package’s quality worsens, and decision-makers are less likely to approve the new therapy. These hazards can be mitigated and clinical development improved by implementing critical data quality strategies. It also raises the possibility of novel treatments reaching patients while increasing the likelihood of a return on investment.


Data Architectures in Healthcare: What Are the Dangers? 

What are the dangers and flaws in today’s healthcare data architectures? By examining typical healthcare data requirements, risks can be recognized. Is it, for instance, when analyzing the data and information of the organization:


  • Is it easily accessible to the organization’s relevant end-users? 
  • Is your data safe and secure from illegal access, use, or sources?
  • Can data be efficiently gathered, stored, mined, and analyzed to maximize utility?
  • Is it used to conduct vulnerability analysis and assess risk on the clinical and business side of operations?
  • Are you agile and adaptable to market shifts?
  • Patient-centric?


Updates to the organization’s overall data strategy should address any deficiencies mentioned above. Another data architecture risk issue to consider is the existing state of data-sharing inside the company, and which aspects of the data warehouse are fragmented. Interoperability is emphasized when data is shared. For example, it allows patients to transfer their medical records to other doctors or hospitals easily. On the other side, data fragmentation is non-uniform data that obstructs efficiency and useful insights. Data that isn’t segmented makes it difficult for the right departments to conduct effective analytics. The homogeneity of data is harmed by data fragmentation. 

Responsiveness of the Database System to Urgent Necessities

The COVID-19 outbreak has taught us a valuable lesson about the importance of efficient data streams and clinical data management. One example is the capacity to implement crisis planning per the goverment standards. Is there flexibility in the organization’s data strategy to change and add when there is a pressing demand for data and information? Executing and updating risk assessments, providing ongoing support for updating policies and procedures based on new evidence, responding and coordinating with state and local health departments, and incorporating training and testing programs are all important in crisis management.

Implementing a Data Strategy Has Several Key Advantages 

You’ll need to plan for all the data you’ll collect if you define a data strategy. However, because you’ll specify formats and processes in advance of necessity, it’ll take less time to integrate and reconcile the data.


A clinical trial data strategy has the following main advantages:

  • Data integration takes less time, both inside and between trials. 
  • Managing incoming data is easier and quicker.
  • Increased supervision compliance and lessened vendor oversight burden
  • Database locking and reporting have shorter timelines. 
  • Across all of your trials, data visualisation and analytics are simplified.


All of these must be able to be merged and reconciled to provide you with the comprehensive database you need to enable the clinical trial data reporting you want.

As the number of data sources grows, the time it takes to lock the database at the end of a trial grows in lockstep. As a result, the time it takes for individual study results to be delivered could be extended, and integrated reporting efforts to support regulatory filings could be increased. Increased data quality and access, shorter timelines, and lower costs are benefits of a clear, effective, and efficient clinical trial data strategy.


By entrusting your Clinical Data Management to Element Technologies, you can rest assured that your clinical study will be completed on schedule and with the greatest possible quality data. We employed a variety of industry-leading electronic data capture (EDC) systems tailored to your needs and configured for a decentralised/virtual trial approach.


Rapid database launch, integrated clinical data sources; professional medical coding, query management, and resolution, effective external vendor data management, database lock; and clean, high-quality clinical trial data are all part of our end-to-end clinical trial data management.


We provide clinical data management customised solutions, faster data access, allowing for speedier decision-making and ensuring patient safety.


We focus solely on data, assuring quality service and long-term collaborations. Data-focused CROs give attention and knowledge in all areas, particularly in data collection, management methods, and strategy.


Our data management professionals examine clinical trial protocols to determine which data-gathering methodology is most suited for your research and portfolio.


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