NeuAge Institute (NAI)

Clinical Data Management: 3 Tips for Those in Clinical Research Training

clinical research courses
When it comes to data, precise and effective management is key in order to properly assess and evaluate results. Without proper management, data can go missing or end up duplicated, resulting in inaccurate findings.

At the NeuAge Institute, we have developed a Drug Development, Clinical Research, Drug Safety, and Pharmacovigilance Certification program that provides insight on the latest developments in FDA and global regulations. Students finish this program comfortable with the practical and regulatory aspects of clinical data entry and overall management. Continue reading to learn key tips for optimal clinical data management.

1. Data Entry Tips for Clinical Research Training

Entering data can be tedious, but when using the right process, it can be quick and efficient. Students at the NAI focus on Electronic Data Capture (EDC) systems that direct clinical trial data collection electronically, as opposed to paper forms.

Tips for clear data entry include recording all metadata related to the entry, including the individual who made the entry, the date and time of entry, and the details of any changes made. Additionally, students in clinical research training need to remember to always include units of measurement to create clear and concise entries every time.

clinical research training
Clinical research has moved toward electronic data capture to store, sort, manage, and verify data virtually

2. Optimizing the Data Collection Processes

Avoiding error in the data collection process ensures that all of the data collected can be used in the research. When collection errors occur, the data is deemed inaccurate, rendering it unusable. Students enrolled in a clinical research program at the NAI will learn proper organization and management of studies, focusing on Good Clinical Practices (GCP) and Good Pharmaceutical Practices (GVP).

Knowing these industry guidelines helps students understand what it takes to collect accurate, complete data. The applied and practical approach to training at the NAI gives students hands-on experience with career-specific duties. In regards to data collection, it is important to make sure each data is up to quality standards, complete in nature, and as accurate as possible.

3. Understanding Data Validation and Integrity

Creating a data validation plan is important for any clinical research project. Data validation involves resolving database queries and inconsistencies by checking the data for accuracy, quality, and completeness. If anything was missed during the collection phase, it should be addressed in the validation phase of clinical data management. Validation includes analyzing data to create tables, lists, and graphs that explain the findings of the data and preserve the integrity of the database.

 

clinical research program
Validating the data allows analysts to lock in the data and verify that it is accurate

Students thrive on this organizational approach to clinical data management, and are often inspired to pursue a similar role upon program completion. To achieve this, the NAI partners with a wide network of companies and businesses within the pharmaceutical industry, offering co-op and job placement opportunities to help students enter their desired role quickly. With the extensive experience gained in this 3-month program, students can kick start their careers in the industry.

Interested in enrolling in clinical research courses?

Contact the NAI for more information!

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