Clinical trials are meant to find answers to the study question by gathering data that can be used to prove or disprove a hypothesis. The study’s results heavily depend on the data’s accuracy. As the field of clinical data management evolves, so must the knowledge and Veristat research methods that modern data managers use, as seen on veristat.com. With the advent of new data sources, comes an influx of information on patients at an unprecedented rate of speed. However, data collection and analysis still present difficulties for managers.  The high speed and huge quantity of data from many sources make it impossible for standard data management methods to work anymore. Additionally, it is time-consuming and inefficient for data management teams to manually analyze, clean, and lock data.

Why is CDM important?

CDM is important in figuring out how safe and successful treatments (like drugs and medical devices) are. Effective CDM makes sure that the data gathered is complete, can be used for statistical analysis, is in the right format, and can be shared while still being a true reflection of the trial data collected. When managed correctly, it can speed up the development of treatment while simultaneously ensuring the quality of data and lowering costs. That’s why efficient CDM is among the most significant assets for drug manufacturers. It is crucial that clinical data collected and processed be of high quality and managed with integrity because data is so fundamental to the core function of research institutions like Veristat and pharmaceutical companies. The value it delivers and the work required to maintain its efficacy can be better understood with a deeper familiarity with the CDM process.

Clinical data management best practices assist in ensuring clinical data use and distribution compliance with regulatory requirements. Best practices in CDM include the following

Identify the data you need to collect and manage:

Identify all areas requiring data collection and proceed downward. Tables, graphs, spreadsheets, software, algorithms, text, audio, and video files, images, medical records, reference data from outside sources, models, patient records, and physical documents are all examples of information and file types that can be categorized as data.

Perform regular data audits and protect the confidentiality of sensitive information:

Given the speed of progress in technology, current information may quickly become out of date. To ensure that you have and are providing only the most relevant information, it is important to regularly audit your data. Data requests should specify measures taken to ensure the privacy of patient data and other sensitive data, such as health records.

Use the right tools and think beyond data storage:

The right tools are a crucial part of any data management strategy. Several CDMS (Clinical Data Management Systems) are available today to assist with data management needs. Examples include ORACLE CLINICAL, RAVE, and CLINTRIAL. Determine the numerous tasks for which you need equipment, and then pick the right tool for the job. Preserving data and anticipating problems like data loss, disk crashes, URL changes, or file deterioration is also an important part of data management. Make back-ups and archives of your info part of your plan.

Following the above best practices can assist researchers in managing data and ensuring that it is reliable, conveniently available, and confidential. Make sure you have the necessary help, resources, and technology to comply with data-sharing regulations.