How to Improve Data Management in Clinical Trials

One of the main goals of a clinical trial is to gather as much high-quality and meaningful data as possible. This paves the way towards developing new and, above all, better therapies for patients. However, the actual data yield is often disappointingly thin. What are the reasons for this?

The path from the conception of a study to data capture and submission of the results to the authorities is long and arduous. Countless data points must be linked and brought into a meaningful relationship with each other. This, in turn, requires many processes and documents to be coordinated, and just as many rules and standards to be followed. Inefficient data flows or a confusing organization quickly have a negative impact on the collected data set. The prerequisite is therefore intelligent and efficient data management throughout the entire process. 

But how are data in clinical studies linked in the first place? From which sources do they originate, and when are patients and study sites involved in data collection? In this article, we will address questions surrounding data flow in clinical trials and present solutions for better data management.

What is it about?

1) What is clinical data management and where does the data come from?

2) At what stages are patients, clinics and study sites become involved in data capture?

3) How do hybrid study designs change the data flow?

1) What is data management and where does the data come from?

The term “data management” refers to all processes in clinical trials in which data are generated, stored and processed. This begins with the selection of the appropriate technologies for data capture and their connection to central databases. Both the potential participants and their clinical pictures as well as the treating medical staff and employees from the administration must be considered in the decision. Clinical data management is therefore the interface between investigators, monitoring and biometrics. The entire planning and preparation, the actual conduct of the study and the completion of the study depend to a large extent on the efficient organization of the data flow.

The data itself comes from a variety of sources and is collected in different formats. Sources include eCRFs and ePRO, as well as medical documents and records, drug and medical device tests, and data from wearables that patients wear in their daily lives. The collection of large amounts of raw data by participating physicians, clinics and trial sites is only the first step in this process chain. Consequently, they have to be processed, analyzed and interpreted in order to derive meaningful results.

It is important to put safeguards in place to protect patients’ sensitive health data . In particular, attention should be paid to security requirements in accordance with ISO 27001 (“Information Security Management System”) as well as HIPAA (Health Insurance Portability and Accountability Act) and an up-to-date SSL certificate. One benefit of digital data storage is the fast, secure set-up and constant availability, even in the event of server failures. 

2) At what stages are patients, clinics and study sites be involved in data capture?

Once a study has been designed and the optimal study design has been selected, it is time for the implementation. At this point, the actual data management begins. During the registration phase, patients are successively enrolled in the study. The study site then collects and documents the relevant data over a defined period of time. Afterwards, monitoring takes over the control of the collected data. Only when they have passed the initial quality check, the data will be sent to the data center, where they are collected in a database and verified.

Regardless of which collection methods have been deemed appropriate for the study, all data find their way into the central database, which forms the basis for analysis and evaluation. However, in order for the data to be available in a valid and meaningful form, timely and efficient communication between the individual stakeholders is crucial. For this reason, the relevant responsibilities are precisely defined in a data management plan. In this way, it is clear at all times which persons or institutions are responsible for the individual sub-areas of data management.

3) How do hybrid study designs change the data flow?

Today, around three times the amount of data is collected compared to ten years ago. But it’s not just the sheer number that has multiplied, it’s also the type of data collected and its sources. In addition to paper medical examinations and patient-reported outcomes (PROs), hybrid and decentralized studies also include data from laboratory results and digitally collected data from online diaries and ePROs, as well as those collected by home care services or during home visits by physicians. The data set is supplemented by automatically collected values from wearables that patients wear on their bodies over extended periods of time. In total, thousands of data points per day and per patient can be generated.

Hybrid and decentralized designs offer many opportunities to make clinical trials more efficient and accessible, while having a major impact on the flow of data. Thus, participants are in continuous exchange with the eConsent service they use. Moreover, they can stay informed about the study and all relevant topics in a self-determined and detailed way. They can also give their consent when certain aspects become relevant for them. In addition, data is collected by both the clinics and the patients themselves via electronic data capture systems (EDC), including eCOA and ePRO as well as wearables. In view of this extensive pool of data from a variety of sources and in different formats, the importance of data management in hybrid clinical trials continues to increase.

Conclusion: Innovative solutions and uniform standards improve data management

The goal of modern study designs is to improve efficiency and data quality. Innovative solutions, such as the mentioned use of medical wearables or the possibility for patients to use their own devices to collect data (BYOD), each make a contribution to this. In view of the many different data streams, however, there is a fine line between a rich and high-quality data pool and an unmanageable data swamp. Uniform standards offer the opportunity to make the data flow as efficient and profitable as possible.

The prospect of having to convert one’s own data capture processes to such standards can be daunting. This is because the process requires a lot of investment and fundamental change, which initially represents a cost and time factor. However, organizations that want to get the best results from their clinical research will quickly find that the effort is necessary and ultimately pays off in more ways than one. That’s because data collection and data management, while costly, are nonetheless crucial parts of a clinical trial. They are characterized by craftsmanship and effective collaboration between clinicians, biostatisticians, informaticians, monitors and organizers.

With our tools for electronic data capture, we support you in efficiently organizing your data streams and ensuring a permanently high data quality. Feel free to reach out to us or schedule a free software demo. We look forward to supporting you in optimizing your data management.