Clinical research requires collecting and handling data from various sources or from individual patients to arrive at a valuable analysis.
Accurate data are imperative in every clinical study.
Clinical research requires collecting and handling data from various sources or from individual patients to arrive at a valuable analysis. Thus, accurate data are imperative in every clinical study.
Research team members must ensure that data gathering, access, and sharing are seamless to achieve more precise results. To this end, electronic data capture, or EDC, is one of the most valuable automation tools used in clinical research. Here's what you need to know about EDC and how data administrators can use it efficiently.
What Is EDC?
An EDC application, such as ryze automation software, significantly cuts the time spent entering data by converting the entries from a paper-based document. called case report form (CRF). into the system. Research data becomes an electronic CRF, which is necessary for developing researchers' analysis and conclusions.
Because clinical studies aim to address a wide range of issues, EDC software products also come in different features to record and manage research data. Often, the software has templates that the team can modify based on their needs or in standard workflows to assist data entry. An EDC software can also be used for data validation and reporting research progress.
5 Tips To Properly Manage EDC
Because it is fully automated and can be customized based on research requirements, the administrator is responsible for optimizing the use of an EDC system. The following points ensure the optimization of this valuable research tool:
1. Ensuring That The CRF Is Complete
EDC, the primary tool for research data conversion, relies heavily on the soundness of a clinical study's CRF. Thus, proper data collection depends on the CRF fields and how they are filled up.
Similarly, an EDC that's not customized to cater to the research team's needs may cause further delays and frustration by facilitating the entry of erroneous and irrelevant data into the system. This is unacceptable, particularly in clinical studies involving public health, such as experimental treatments for the coronavirus pandemic.
2. Making It Easy To Use
EDC is mainly used in clinical trials to collect individual patient data and medical history, and track symptoms and outcomes for every treatment received, including adverse reactions. Hence, researchers must keep track of several forms for one subject. A sound EDC system must streamline data from each case, but ensure its validity and integrity.
An EDC system must be designed in a way that reduces, if not eliminates, the likelihood of collecting invalid or irrelevant data. Additionally, administrators must develop the system to facilitate minimal data entry mistakes. A good rule of thumb is to make EDC as comprehensive yet straightforward as possible by avoiding complicated routes that open in multiple screens and sub-menus.
3. Balancing Accessibility With Data Privacy Rules
EDCs are often web or cloud-based, which means authorized users on any internet-connected device can access them. While this can be beneficial to members of the research team, the administrator must ensure that access restriction levels are in place for each individual.
Like in an office setting, the device and user must be authorized before allowing access to research data. Likewise, not all team members must be allowed to view, edit, print, or e-mail the data in compliance with privacy rules, such as the Health Insurance Portability and Accountability Act.
4. Ensuring Security
Apart from ensuring compliance with the privacy rules applicable to the research industry, EDC system management also requires robust security to prevent unauthorized access and cyber hacking. These days, cybercriminals have been employing sophisticated techniques in their attempts to break through barriers set up by the health sector. Your EDC software must come with stringent defense mechanisms to thwart these attacks. At the same time, the system must provide options to safely back up and recover research data in the event of disruptions.
5. Enabling Frequent Data Checks And Validation
An intuitive CRF is not the end-all and be-all of successful data management. As mentioned, a well-developed EDC system enables researchers to spend less data entry time with higher accuracy levels. Moreover, an EDC can be programmed to perform regular checks to ensure that the data inputs are valid and complete.
Some EDC software comes with machine learning or artificial intelligence systems capable of detecting errors or anomalies, making it easier to clean and correct data gathered from various clinical trial stages.
Managing EDC is crucial in ensuring the success of a clinical study. With this automation tool, team members can get rid of repetitive tasks and focus on the subject at hand. Proper management of EDC also leads to appropriate data entry and reliable data gathering that's valuable in arriving at an accurate conclusion. Ultimately, all stakeholders, including the public, will benefit from this outcome.