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Inefficiencies in Clinical Data Management: A Growing Concern

In the world of clinical trials, ensuring that data is accurate and reliable is paramount. A recent study conducted by Veeva Systems highlights a pressing concern among clinical data managers and clinical research associates (CRAs) regarding the efficiency of current practices. The findings reveal that nearly two-thirds of these professionals believe that inefficiencies in manual data reconciliation and related tasks pose a significant risk to the quality of clinical data in the future.

Key Findings

The research indicates that completing manual data reconciliation, review, and cleaning tasks is no small feat. On average, each data manager spends over 12 hours per week on these activities for each study. Such extensive manual workloads are primarily driven by several factors:

• - Redundant Manual Steps: A staggering 68% of respondents pointed to the necessity of repetitive data re-entry as a key issue.
• - Inefficient Workflows: About 58% recognized that their current workflows were not optimized for efficiency.
• - Disconnection Between Systems: 59% of participants indicated that operating multiple unconnected systems hampers productivity and increases the likelihood of errors.

These elements contribute to a risk-laden environment where data quality is at stake, affecting not just the studies at hand but potentially influencing regulatory submissions in the longer term. Manny Vazquez, a senior director at Veeva, stressed the gravity of this situation: “The risk of poor data quality spans far beyond just a monitoring visit or listing review, potentially impacting regulatory submission success.”

The Push for Automation

Automation emerged as the primary focus for data managers aiming to address these inefficiencies. A significant 71% expressed optimism about their roles evolving to incorporate more automated processes over the next two years. Such automated technologies not only promise to reduce manual drudgery but also enable teams to focus on strategic initiatives, such as risk-based data management.

Despite this enthusiasm for automation, challenges remain. Protocol complexity (58%), insufficient budgets and resources (57%), and resistance to change (48%) were frequently cited as obstacles. These challenges underscore the need for clinical leadership to advocate for innovative changes that can facilitate more effective working methods.

CRAs Demand Better Solutions

Alongside data managers, CRAs also expressed their struggles with the manual nature of their work. Nearly 44% of them emphasized the importance of improving documentation and tracking processes. A lack of connectivity across clinical systems necessitates time-consuming manual validations during monitoring visits, hindering overall efficiency.

Connected Systems Are Key

The survey results suggest a significant belief in the potential benefits of connected systems. A robust 81% of participants believe that linking clinical systems could streamline study execution dramatically. However, a disconnect remains: while 75% of data managers report their teams are upgrading their systems, only 57% of CRAs feel the same. Furthermore, many professionals feel that current Standard Operating Procedures (SOPs) do not optimally utilize available tools nor align with real-world workflows, presenting gaps that must be addressed.

Conclusion

As the clinical research landscape evolves, addressing these inefficiencies becomes crucial. By embracing automation and streamlining processes, organizations can improve productivity and data integrity. The research clearly indicates a strong desire among data managers and CRAs for change, and it is incumbent upon clinical leaders to facilitate this transition. By focusing on technological advancement and fostering collaboration, the future of clinical data management can be secured, ultimately benefiting patients and the broader healthcare community.

This comprehensive study included insights from over 85 data managers and CRAs from various sponsors and Clinical Research Organizations (CROs), shedding light on the current state of clinical data management and identifying the necessary steps for progress.