Ensure Data Quality while Optimizing a Clinical Trial Management System

Significance of Data Quality

Data Quality is of great significance for clinical research professionals when it comes to optimizing a current system or implementing a new clinical trial management system. It is essential to create processes that result in accurate, timely and complete data. As a result, it leads to more efficient workflows and minimized anxiety in reporting. However, quality data is not an impromptu thing, it is the outcome of a lot of planning and execution.

There are several characteristics of data quality that are kept in mind by clinical research professionals while capturing data. These are taught to students while they are enrolled in .

Characteristics of Data Quality

There are seven diverse characteristics that define data quality. They are:

I. Accuracy and Precision

II. Legitimacy and Validity

III. Reliability and Consistency

IV. Timeliness and Relevance

V. Completeness and Comprehensiveness

VI. Availability and Accessibility

VII. Granularity and Uniqueness

In addition to that, there are three areas that clinical research professionals keep in mind while starting a data quality initiative

I. Prioritize their needs first

Research Organizations often make one common mistake which is attempting to address all their data quality issues in one go. They often have the pressure to justify both costs and resources needed for the project. As a result, it leads to unrealistic expectations in that particular research organization. Instead of setting unrealistic expectations, a research organization should focus on prioritizing their biggest needs first. They could do so by doing a cost benefit analysis which determines whether certain data points are worth being tracked. While prioritizing specific data points, here are some questions that should be asked:

• Is the data relevant to multiple areas

• Does it support large organizational goals

• Does the data gathered require other data to be present.

When clinical research professionals ask the above questions, they will not only be able to prioritize goals first. Also they would be able to collect data accurately and consistently with ease.

II. Assessing data collecting and monitoring processes

After the process of prioritizing data points, clinical researchers should review their methods for data gathering and monitoring . Inferior data is more dangerous for clinical research organizations than no data at all. Therefore, it is vital for organizations to start these processes before they go live.

Monitoring needs to be clearly defined both from a personnel perspective that is who is responsible for data integrity and a process perspective i.e. What tools and the reports does the person use to make certain that processes are being followed.

This is also the perfect time to review the processes of other teams in the organization. Standardizing your collection and monitoring workflows throughout your organization isn't a necessity, it can help boost data consistency across teams, especially if those teams are using the same Clinical Trial Management System

Certain Tools that ensure ongoing success

The third step after prioritizing goals and reviewing processes is to give research staff the tools to efficiently maintain these processes. Tools could help identify missing data for both protocol's and subjects. These tools would help you fulfill the goal of prioritizing important needs and fixing those errors first.

To gain extensive knowledge on maintaining accurate data, get in touch with the best clinical research training institute in Pune.