Ensuring Error-Free and High-Quality Clinical Data: Key Attributes of Next-Generation CDMS

In any clinical trial, data is a critical component that can significantly impact the entire project in terms of end-to-end turnaround time, costs, and efficiency. Today, with technological advancements powering clinical data management, it may in fact even have a role in determining the overall success of a project. For pharmaceutical companies, contract research organizations and other medical services-related organizations with a considerable investment in trials, efficient clinical data management can thus significantly improve their return on investment and ultimately help them roll out drugs faster.

Until a few years back, most solutions for clinical data management entailed large amounts of capital expenditure, apart from being extremely complex and inflexible. Clinical data managers were constantly challenged to keep the project on track, raise queries on time, and communicate effectively with fellow stakeholders. With the evolution of Electronic Data Capture (EDC) and more recently cloud-based Clinical Data Management systems (CDMS) that are available as Software-as-a-Service (SaaS), managing high-quality clinical data has now become simpler and less arduous. Providing a scalable and flexible solution to manage study data efficiently, CDMS SaaS solutions also eliminate the need to install and manage the application in-house.

Also Read: EDC for Clinical Trials

However, not all CDMS systems fulfill the rigorous needs of a clinical data manager to produce reliable, credible data at the most optimal costs and least amount of time. Additionally, many such setups do not provide sufficient features and capabilities to ensure the data collected complies completely with relevant protocols and regulations, has the lowest number of deviations, and contains the least amount of variations. As a result, clinical data managers are burdened with the responsibilities of addressing these requirements manually to meet various standards pertaining to studies. But this can be avoided if CDMS systems are redesigned with an objective to relieve the pain points of clinical data managers.

So, what should be some of the main capabilities of a CDMS that can help clinical data managers maintain error-free and high-quality clinical data? Let us explore.

Data Entry and Validation

One of the most important features of a well-designed CDMS is its ease of use and flexibility. An intuitive interface that is capable of providing users with automatic guides for steps or workflows–for instance, creating new entries, participants or scheduling visits–is crucial. An accurate validation system that can be applied to both paper as well as electronic data capture is indispensable.

Data Review, Reconciliation and Discrepancy Management

As clinical data managers closely interact with multiple stakeholders, they are required to routinely review data and manage discrepancies. An easy data review functionality and associated query resolution procedures to manage and record deviations thus become must-have features. Accurate, in-built workflows to manage data reconciliation requirements, avoid data redundancy and duplication are also essential.

Data Reporting and Audit Trails

Many times, clinical data managers need to extract trial reports to review and share with their sponsors and other stakeholders. These reports have to be flexible and customizable in order to meet the varying needs of different users. Both automated, timely reports as well as ad-hoc reporting capabilities with an ability to generate outputs in multiple formats–including text, CSV and Excel–becomes vital.

While these characteristics represent some of the core data-related functionalities of a well-designed CDMS, companies will also need to look out for the ability of the solution to provide custom features. This will not only help them meet the standard needs of clinical trials but also help them mould the solution to their specific organizational requirements.