eClinical technology has transformed the clinical trial landscape, maximized data collection efficiency, and streamlined trial operations. Innovations in this space, such as electronic trial master files (eTMF), clinical trial management systems (CTMS), and electronic patient-reported outcomes (ePRO), have also contributed to a more cost-effective, holistic, and accurate representation of patient data.
However, many organizations face several challenges that can impede the successful rollout of new technological resources. The main challenges include harmonizing technological initiatives with a team of people trained to manage the system and entering the appropriate data points. Without the human component, a robust clinical tool will continue to fall short of its intended goal and fail to provide valuable insights, impacting optimal clinical management decision-making.
Other factors impeding the successful implementation of clinical technologies include user acceptance, data security, and regulatory compliance. Once these potential setbacks are identified and planned for, teams can adequately prepare to launch new technologies as part of their research initiatives with minimal interference to cost, timelines, and data quality.
Let’s assess five common pitfalls trial teams face when incorporating new technology and identify potential solutions to mitigate the common risks.
#1 Data Input and Quality
One of the key benefits of eClinical technologies is the ability to collect and manage data in real time. This simplifies clinical trial monitoring and identification of potential issues. However, data quality may be jeopardized, especially where data is entered or stored inaccurately.
To ensure high-quality data, it is critical to train staff thoroughly on data input, management, and quality. In conjunction with an eTMF, for example, adequate human intervention is needed to run thorough quality control checks, index documents, and maintain the expected document list (EDL).
As part of a robust initiation, ensure you establish clear guidelines for data entry and maintenance in accordance with quality standards. In addition, it is important to monitor data quality throughout the trial and leverage data clean-up procedures to proactively identify and correct errors before they are compounded further into the trial.
#2 Resistance to Change
One of the most significant challenges of eClinical technologies is the resistance to change from the traditional paper-based systems that have been used in clinical trials for decades. Moving from paper-based systems to electronic ones requires significant changes in how clinical trials are conducted and can be challenging for those who are accustomed to traditional methods.
To prepare and ensure a smooth transition, develop a clear plan for implementing eClinical technologies that includes extensive training and support for clinical research staff. In addition, it is important to involve stakeholders from the beginning of the project to gain buy-in, understand and address preliminary concerns, and build a culture of collaboration and cooperation.
#3 Data Security
Collecting and managing sensitive patient data is a pivotal component of clinical trials, commanding tight security systems to protect it. Data breaches and security lapses may result in reputational damage, regulatory penalties, and even legal action.
To combat these, create robust security measures compliant with relevant regulations and standards, such as the General Data Protection Regulation (GDPR), CFR Part 11, and the Health Insurance Portability and Accountability Act (HIPAA). Furthermore, it is important to train clinical research staff on data security best practices and monitor systems for potential vulnerabilities.
#4 Integration with Existing Systems
Each clinical trial involves multiple stakeholders, including sponsors, contract research organizations (CROs), investigators, and study sites, and each may have different technology systems in place. Successful integration with these systems can be complex and time-consuming, especially where there are compatibility issues and roadblocks.
To mitigate this, it is critical to develop an airtight integration strategy, encompassing the identification of systems requiring integration and any data exchanges. In this stage, it is also important to involve all stakeholders to ensure that the integration can be successfully implemented without the loss of data or creating complications with individual systems.
#5 Regulatory Compliance
With a rapidly evolving regulatory landscape on both a local and global scale, keeping in lockstep with changes to guidelines and requirements can be complex. The use of electronic systems as part of clinical trials is subject to stringent regulatory requirements, including those set out by the FDA and the European Medicines Agency (EMA).
Based on their understanding of the regulatory landscape, clinical research teams should develop a comprehensive regulatory strategy that outlines the regulatory requirements and the steps they will take to ensure compliance. A quality management system (QMS) can also ensure consistency and facilitate visibility into processes to ensure compliance protocols are being adhered to. The QMS should be designed to ensure that the technology is developed, implemented, and used in compliance with regulatory frameworks.
With the introduction of new technologies to a trial, research teams should implement a strong framework to roll out and train staff effectively. Although eClinical technologies offer unique advantages in data collection and streamlined trial monitoring, data quality can be compromised if not managed effectively. Proper training of staff on data input, management, and quality is crucial to maximizing eClinical capabilities. Through continuous monitoring of data quality throughout the trial and implementing data clean-up procedures, these tools can be leveraged to their full potential.
Furthermore, by developing robust security measures alongside an airtight integration strategy and training programs, the risk of delays, poor data quality, security breaches, and compliance issues are significantly reduced. This maximizes technological capabilities to enhance the quality and reliability of the data, ultimately leading to improved patient outcomes and advancing the development of life-saving treatments.
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