Transforming clinical data management systems for biotechnology trials

In the dynamic environment of biotechnology trials, the significance of Clinical Data Management systems (CDMs) cannot be overstated. As we navigate the complexities of these trials and the pivotal role these systems play, it’s evident that industry-changing advancements are underway, driven by emerging technologies and a commitment to precision, efficiency, and innovation.

The evolution of technology in clinical data management has driven a remarkable and much needed shift from traditional, paper-based methods to an agile and integrated approach, allowing for easier storage, retrieval, and analysis of clinical data.

Data acquisition and analysis has since become more sophisticated and integrated with various digital technologies now available for usage in trials. This has also enabled more efficient recruitment of participants, shortened timelines, and reduced costs. As a result, technology used within clinical trials is now more than ever under the microscope of regulatory bodies. It has become an increasingly important component of transforming clinical trials and it's expected to continue to revolutionise the field of biotechnology in the years to come.

Within this article, we shall explore some of the technologies that are transforming CDMs in biotechnology trials more closely.

Improved EDC systems allow for centralised data management systems

EDC technology advancements have given biotechnology trials the ability to establish a centralised approach to Clinical Data Management systems. This is critical for biotech studies, where the volume of data is likely to be larger, with multiple data sources/vendors. The enhancements in EDC technology have helped this transformation to improved data systems because they reduce duplication of data and errors that arise from the use of disparate data sources, i.e., data collected for participant randomisation and Patient Reported Outcome Assessments should be integrated and, with EDC, stored within a centralised CDM workbench environment.

Centralised CDM systems also ensure that data is consistent and standardised across the study. This approach promotes data sharing, facilitates prompt decision-making, and enhances the accuracy and completeness of the data collected. The EDC system eliminates the need for manual data entry, which can be time-consuming and prone to errors. EDC also allows for timely data corrections, reduces the potential for missing data, and improves data quality.

Decentralised trials driving new forms of data capture and source data verification

The integration of a decentralised trial approach has played a crucial role in the advancement of biotechnology trials, particularly in a post-COVID-19 landscape. It has significantly enhanced real world evidence (RWE) trials by revolutionising patient participation and data collection.

Offering enhanced accessibility, decentralised allows diverse and geographically dispersed populations to engage in trials without the constraints of in-person visits. This not only promotes inclusivity, but also improves patient engagement and adherence to study protocols. The continuous remote monitoring facilitated by decentralised trials, coupled with the flexibility in data collection methods, provides timely and detailed insights into participants' health experiences. Moreover, the cost efficiency and reduced burden on participants contribute to the scalability and sustainability of RWE trials.

Creation of data handling plans and data flows is necessary

Trials utilising a decentralised study approach have transformed how much data is possible to capture. In addition, biotechnology trials involve the use of various other technologies, including genetic engineering, molecular biology, and genomics to develop and test new treatments for a range of diseases and illnesses. In these trials, and due to the decentralised trial design, we tend to see larger amounts of data being generated and collected from various sources, including participant medical histories, laboratory test results, and clinical assessments. An effective data integration and handling process is essential for successful trial management and to ensure accurate and reliable results.

The first step to ensuring good management and oversight of data handling is the creation of data handling plans and data flows specific to each data source/vendor from which the data will be transferred/integrated and reconciled. Data integration involves combining data from multiple sources and platforms to create a comprehensive dataset that can be analysed and interpreted. Data handling procedures must adhere to strict quality standards and regulatory requirements to ensure the integrity of the data throughout the trial. This involves the use of rigorous quality control processes, efficient data entry, and ongoing monitoring of data quality. The frequent reconciliation of data coming from external sources is vital in adhering to these standards and achieving high quality data.

Machine learning

The power of machine learning (ML) is expediting clinical trial study onboarding processes, with ML algorithms, especially in medical coding, offering efficiency and accuracy in managing terminologies. By utilising the advancements in ML technologies for insightful data analysis and identifying hidden patterns and trends, it facilitates proactive analysis, cleaning, and reporting, contributing to the overall success of biotechnology trials. Recognising the criticality of risk-benefit analysis and leveraging ML to detect signals from clinical research data ensures timely reporting of adverse events, a crucial aspect of ensuring patient safety.

Automation-Based Reconciliation

Introducing Robotic Process Automation (RPA) for data entry, validation, and quality control, reduces the risk of manual errors, improves overall efficiency, and nullifies the requirement for manual and resource-intensive tasks related to designing electronic case report forms (eCRF) and edit checks. Seamlessly integrating non-eCRF data with Electronic Data Capture (EDC) data, offers efficient solutions and a streamlined reconciliation process for data management.

Unstructured data

In the vast landscape of biotechnology trials, data comes in various forms, including unstructured data like images, scans, and patient records, such as progress notes, pathology reports, and clinical narratives. Embracing Natural Language Processing (NLP) to build clinical data collection models in multiple languages enables the ability to extract valuable insights from unstructured data and allows researchers to gain deeper insights into patient experiences, treatment responses, and adverse events, contributing to a more comprehensive understanding of the trial outcomes.

Risk management

As biotechnology trials undergo significant transformation, the role of risk management becomes paramount in ensuring a seamless and effective transition. By addressing potential challenges through calculated measures and foresight, as well as putting thorough assessments and mitigation plans in place, the industry can navigate the complexities of this transformation with resilience and adaptability. Identifying potential challenges early in the process allows for timely and effective solutions, ensuring the resilience of the clinical data management system. Here, we delve into strategic risk management considerations that can guide the process of transforming clinical data management systems.

Rigid data security protocols

When adopting new tech advances, it’s important to strengthen data security protocols, implement encryption, and adopt advanced authentication methods to protect sensitive clinical information. This involves staying at the forefront of encryption protocols and advanced authentication methods. This approach mitigates the risks associated with evolving technologies, ensuring the confidentiality and integrity of sensitive clinical information.

Design for scalability

Designing the system to be scalable is crucial, anticipating and accommodating growth in data volume and user base without compromising performance. This ensures that the clinical data management system can expand seamlessly without compromising performance, minimising the risk of scalability issues.

Next generation technologies: A collaborative approach

As the industry continues evolving to accommodate the advances of technology and the increasing availability and size of clinical trial data, the integration of innovative data visualisation techniques emerges as a cornerstone. A collaborative approach and commitment to precision, efficiency, and compliance will resonate with the industry's need for advanced solutions in the dynamic landscape of biotechnology trials and allows organisations the freedom to focus on high-value tasks.

As we propel ourselves into 2024, it is the synergy of technology and clinical data management that will continue to define the success of biotechnology trials.