Clinical data managers are to clinical trials what Alfred is to Bruce Wayne! They are the unsung heroes in clinical trials because handling colossal amounts of data and curating high quality data is tiresome and arduous. Data managers deal with difficult problems and challenges on a regular basis in order to curate quality research data. As per a Global Market Report, 95% of clinical data managers said that a lot of manual efforts are involved in aggregating, cleaning, and transforming clinical trial data.
The constant demand to develop innovative treatments makes the job of data managers one of the most crucial aspects in clinical trials. It entails collecting, organizing, maintaining clinical data, and providing reports and analysis of trial data along with designing and executing data validation processes. In this blog, we will explore the challenges faced by data managers in clinical trials.
Challenges of Clinical Data Managers
Regulatory Compliance:
Being in compliance with regulations is always on the top of the mind of every clinical data manager. With several serious and negative risks that can crop up due to incomplete and inconsistent data, data governance issues make the job problematic for data managers. In fact, 81% of data managers say that data governance is the biggest challenge with trial data meeting regulatory compliance.
Fragmented Data:
One of the main challenges that clinical data managers face is fragmented data. An Oracle survey reported that 50% of clinical data managers use up to five different data sources. More often than not, trial data is complex, and it gets tough to come to grips with multiple sources of data across platforms. This makes it challenging for data managers to derive meaningful insights from the data.
Delayed Trials and Costs:
Another challenge that the clinical data managers struggle with is delays in starting clinical trials, which result in high overall costs. The setup time, complicated workflows, and transferring data from different systems are a few reasons that might lead to delays. So, it is imperative for data managers to keep in mind the fact that timeliness is a crucial factor in minimizing the cost of clinical trials.
Keeping Up with Advancements:
The demand for better treatments is perpetually on the rise and clinical data managers should be well aware of the latest data management and technologies so as to keep up with the growing need. Besides studying and understanding the advantages of such technologies, data managers should also analyze if adopting them would benefit the study/trial and make sure that they are being used appropriately.
Data Quality:
Perhaps the most significant of clinical data manager challenges is data quality. The ever-growing complexity of the clinical trials and the fragmented data poses a significant menace for clinical data managers in ensuring data quality. But as we already know, data quality is imperative to the success of a clinical trial. So, Clinical data managers must ensure that data is collected and stored accurately and that it meets the organizational standards.
Data Privacy/Security Concerns:
Among the challenges that data managers face in clinical trials is data privacy. Safeguarding sensitive patient information should be an utmost priority for clinical data managers and they should be transparent about how that particular data is being used in their trials. DMs should strictly stick to data security and privacy guidelines to protect the sensitive information.
Technology Integrations:
Clinical data managers often struggle with inconsistent processes and inefficient systems. This will give rise to complexities in implementing and handling data management systems and technologies. Data managers should do their homework on various software tools and make an informed decision on what tools will seamlessly work to capture, store, and analyze trial data efficiently.
Cost Management:
The challenges of clinical data managers include cost management. Budget constraints pose a significant problem for DMs and they should ensure that they allocate resources carefully for efficient data collection, validation, and analysis. Data managers should come up with efficient cost management strategies that optimizes workflows and implement economical technology solutions that will help contribute to the success of the clinical trial.
Data Integrity:
Maintaining the integrity of trial data present a sizable challenge for clinical data managers. However, ensuring integrity and accuracy of data all along the trial is pivotal for successful outcomes. This is exactly why data managers should monitor, review, and audit the trial data at regular intervals during the trial, in order to detect inconsistencies and maintain data integrity.
Why InductiveEDC is the Superpower that Clinical Data Managers Need?
InductiveEDC (IEDC) is the most advanced and robust data capture system developed by Inductive Quotient Analytics with an aim to help clinical data managers handle trial data with utmost efficiency. IEDC is infused with powerful features that can streamline data management processes, enhance data quality, and ensure regulatory compliance.
What more? For all the features it offers, IEDC is super affordable, and its intuitive UI makes it the best user-friendly data capture tool out there! The icing on the cake is that one does not need to be a technocrat to be able to use IEDC. It doesn’t just have simple drag/drop features, and the capability to integrate with any tool/software, but it can also be customized to any extent to fit the needs of the study!
Want to know more about how IEDC can help clinical data managers navigate the data complexities in clinical trials? Visit www.inductivequotient.com or reach out to our experts at hello@inductivequotient.com for a demo.