Empowering Clinical Research: The Shift to Automated Workflows and Real-Time Data

For clinical researchers and healthcare professionals, the shift to automated workflows and real-time data in clinical research is a critical foundation to seamless clinical trial operations, error mitigation, and improved efficiency and clinical ROI. For clinical researchers and healthcare professionals, this shift is not just beneficial—it’s absolutely essential for advancing clinical trial efficiency and reliability, ultimately driving more reliable clinical research outcomes. 

Clinical trials, essential for advancing medical science and patient care, have long struggled with inefficiencies and data management challenges, particularly given the exponential increase in data points and commensurate data regulation and risk, making manual trial management inadvisable, and sometimes nearly impossible. Researchers and clinicians are frequently tasked with untenable manual administrative processes, repetitive tasks, and outdated workflows, limiting the time and attention they can devote to their clinical tasks. Fortunately, the introduction of automation and real-time data integration is poised to transform the clinical trial landscape, enabling researchers and clinicians to work at the top of their licenses and automate workflows and data and task management.

Traditionally, clinical trials involve duplicative manual data entry into multiple technology systems such as Electronic Medical Records (EMR), Electronic Data Capture (EDC), Interactive Response Technology (IRT), and electronic drug accountability software systems, or are managed entirely on paper. The multiple levels of manual data entry combined with the lack of standardization in sponsor and protocol requirements can lead to confusion and inconsistencies or, worst case, negative patient or research outcomes. Sites are Rigid study or regulatory requirements that do not consider a protocol or site’s unique perspectives, needs, or policies can further expose research to unnecessary risk, delays, and poor outcomes

Duplicative reporting requirements, including manual paper forms, add another layer of complexity and risk. The absence of streamlined consistent workflows for capturing data only exacerbates these issues. Manual processes are time-consuming and prone to human error, which can compromise the integrity and accuracy of trial data. A paper published in Applied Clinical Informatics notes that workflow automation offers opportunities to address the United States’ health care system’s challenges with quality, safety, and efficiency.

The volume and diversity of clinical trial data has significantly increased over the past decade. Late-stage protocols now collect an average of 3.6 million data points—three times more than 10 years ago. This increase in data volume, combined with diverse sources like EDC systems, Clinical Trial Management Systems (CTMS), lab data, imaging, Electronic Health Records (EHRs), and IoT devices, adds layers of complexity to data management.

Organizations often use multiple EDC solutions, with 73.9% using two or more. This fragmentation leads to common pain points, including managing mid-study updates and amendments, flexibility challenges, and customization issues. These challenges highlight the need for a more efficient and accurate data management approach.

Automated workflows offer a solution to the challenges posed by manual processes. By guiding users through standardized procedures, automation ensures consistency and reduces the likelihood of errors. This is particularly important in clinical trials, where inaccuracies in data entry can have significant consequences. For example, complex IP information such as medication IDs, lot numbers, and packaging labels must be accurately recorded to maintain trial integrity.

Automated workflows allow for templating, driving standardized data entry by various users and ensuring consistency and data integrity. By minimizing manual data entry, automation reduces the risk of errors and enhances the overall quality of trial data.

The repetitive and monotonous nature of entering the same data across multiple platforms can lead to staff burnout and dissatisfaction. This can contribute to errors and inefficiencies in clinical trial operations. Automated workflows alleviate this burden by handling routine tasks, allowing staff to focus on more critical and rewarding aspects of their work. By automating repetitive tasks, clinical trial teams can improve their productivity and job satisfaction. This, in turn, leads to better trial outcomes and a more efficient research process.

Automation ensures data integrity, reducing the risk of errors in critical trial data. Automated systems can speed up data collection, reconciliation, and database review with aspects such as remote monitoring and integrations. This enables faster decision-making and collaboration between various stakeholders in the clinical trial process.

Automated workflows also facilitate real-time data updates, ensuring that all stakeholders have access to the most current information. This improves the accuracy and reliability of trial data, ultimately driving innovative outcomes in medicine.

Integrating real-time data into clinical trial management systems (CTMS) and EMR can significantly enhance trial efficiency and accuracy. Real-time data provides up-to-date information, facilitating responsive and strategic decision-making.

CTMS integration enables a single source of truth, aligning other technology systems such as EMR and electronic accountability systems. This improves the accuracy of protocol and patient information, reduces time spent on duplicative data entry, and eliminates potential manual data entry errors.

Real-time data integration allows for up-to-date information to flow between systems, streamlining communications in aspects such as protocol start-up, patient enrollment, and respective status changes. This ultimately leads to improved Investigational Product (IP) management and patient care, ensuring timely and accurate per-protocol treatment.

Enhancing Trial Efficiency

Real-time data provides clinical trial teams with the information they need to make informed decisions quickly. By having access to the most current data, teams can identify and address issues quickly, reducing delays and improving trial outcomes. Integrating real-time data into clinical trial operations also enhances collaboration among stakeholders. With a single source of truth, all team members can work from the same set of data, improving communication and coordination.

Supporting Strategic Decisions

Real-time data supports responsive and strategic decision-making by providing timely insights into trial progress and performance. This enables clinical trial teams to make adjustments as needed, ensuring that trials stay on track and meet their objectives. By leveraging real-time data, clinical trial teams can identify trends and patterns that inform future research efforts. This data-driven approach enhances the overall quality and impact of clinical trials.

Integrating medication orders with EMR for faster and more accurate processing is a critical advancement in clinical trials.  Direct EMR/eSOURCE to EDC connectivity remains the top concern for research sites, according to Florence’s 2024 State of Industry Report. This number has increased for the fifth year in a row, with 30.16% of sites identifying it as their foremost challenge in 2024. This highlights the persistent pain point of duplicate data entry and disparate data systems. Integration of EMR and EDC systems improves efficiency by eliminating the need for manual transcription of data. This not only reduces the risk of errors but also enhances the accuracy and safety of clinical trials by linking medication orders and proactively identifying potential errors.

Improving Regulatory Compliance

Integration of EMR and EDC systems improves regulatory compliance by ensuring that all data is accurately recorded and easily accessible for auditing purposes. Automated systems can track and log all data entries, providing a comprehensive audit trail that supports regulatory reviews. This improves the overall quality and integrity of clinical trial data, ensuring compliance with regulatory requirements and industry standards.

Enhancing Data Security

Automated EMR and EDC systems enhance data security by providing secure, encrypted data transfer between systems. This ensures that all data is protected from unauthorized access and tampering, maintaining the confidentiality and integrity of trial data. By improving data security, automated systems help ensure that clinical trials comply with regulatory requirements and maintain the highest standards of data protection.

Automated metrics assist in staffing and budgetary decisions by providing insights into resource utilization and trial performance. By analyzing time-based metrics, clinical trial teams can identify areas where resources are underutilized or overextended, enabling more strategic resource allocation.

This improves operational efficiency and reduces staff turnover by ensuring that team members are assigned to tasks that align with their strengths and interests.

Improving Budgeting and Expense Tracking

Automated metrics enable cost efficiency and improved clinical outcomes by providing real-time insights into trial expenses and resource utilization. By tracking and analyzing time-based metrics, clinical trial teams can identify areas where costs can be reduced, and resources can be more effectively allocated.

This enhances the overall efficiency and effectiveness of clinical trials, ensuring that they are conducted within budget and achieve their desired outcomes.

Proactive Workforce Planning

Automated metrics support proactive workforce planning by providing insights into staff performance and resource utilization. By analyzing time-based metrics, clinical trial teams can identify areas where additional training or resources are needed, ensuring that all team members are equipped to perform their tasks effectively. This enhances the overall efficiency and effectiveness of clinical trials, ensuring that they are conducted with the highest standards of quality and integrity.

Automated workflows and real-time data access empower clinical research sites the opportunity to achieve greater levels of efficiency and accuracy. By addressing inefficiencies, minimizing repetitive tasks, and data challenges of traditional clinical trial processes, automation and real-time data integration streamline operations, enhance data accuracy, and support informed decision-making.

With continued concerns regarding quality, effectiveness, efficiency, safety, and patient-centeredness of health care, automation of workflows that leverage modern computing capabilities offers an opportunity to address relevant concerns while benefiting from pertinent advancements from outside of health care.

For clinical researchers and healthcare professionals, adopting these technologies is essential for advancing clinical trial efficiency and reliability. By leveraging the power of automation and real-time data, clinical trial teams can improve the quality and impact of their research, ultimately driving innovative outcomes in medicine.

Ready to transform your clinical trial operations? Explore how Vestigo can help you integrate automation and real-time data into your clinical trials. Sign up for a demo today and experience the benefits of automated workflows and real-time data for yourself.

– Reviewed by Sara Brueck Nichols, MHA

References:

Florence. (2024). 2024 State of Tech-Enabled Clinical Trials Report. Retrieved from https://florencehc.com/downloads/2024-state-of-tech-enabled-clinical-trials-report/

Shen, L., Zhai, Y., Pan, A., Zhao, Q., Zhou, M., & Liu, J. (2023). Development of an integrated and comprehensive clinical trial process management system. Journal of Translational Medicine, 21, Article 100. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10078087/.

Smith, H., & Korfhage, J. (2021). Identifying Opportunities for Workflow Automation in Health Care: Lessons Learned from Other Industries. Journal of Medical Internet Research, 23(8), e25743. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8318703/.