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Clinical Research Courses helps enhance the quality of Data in Clinical Trials

Posted by divya shetty on December 13th, 2019

Characteristics of Data Quality

There are a wide variety of clinical trials conducted by the country. In order to ensure that the clinical trial is conducted in an efficient manner, data has to be captured in a proper way. Many organizations may differ on the opinion of what is defined as good clinical data. However, judging the quality of data requires an examination of its characteristics according to what is essential to the organization and the and the various applications which are used in the organization. Therefore, seven characteristics that define data quality are:

        I.            Accuracy and Precision

      II.            Accuracy and Precision

    III.            Legitimacy and Validity

    IV.            Reliability and Consistency

      V.            Timeliness and Relevance

    VI.            Completeness and Comprehensiveness

  VII.            Availability and Accessibility

  1. Granularity and Uniqueness

Accuracy and Precision

This characteristic denotes the exactness of the data.  Clinical data cannot have any incorrect elements and must convey the correct message.  The data should have a constituent that is connected to its intended use.. Without that, it would be difficult to tell how the data will be consumed. As a result, ensuring accuracy and precision could be off-target or more costly than necessary. For an instance, accuracy in healthcare might be more important than in another industry and, therefore, justifiably worth higher levels of investment.

Legitimacy and Validity: Surveys would set certain requirements and some boundaries of this characteristic. For an instance in surveys, items  such as gender, ethnicity and nationality would be generally restricted to an array of options .Any answers other than these would not be considered valid based on the requirements of the survey. Moreover, this should be carefully considered when determining its quality. The people in each department in an organization have an excellent understanding of what data is valuable to them or not .Therefore, the professionals in a team should use the requirements to full advantage so that they could carry out the evaluation of the data in a better way.

Reliability and Consistency: Many systems in today’s environments utilize and gather the same source data. There may be different sources from which the data has been gathered by  the system.  However, it cannot reverse a value residing in a different source or collected by a different system. There must be a stable and steady mechanism that collects and stores the data without contradiction.

Timeliness and Relevance: Clinical research organizations must be a valid reason to collect the data to justify the effort required, which also means it has to be collected at the right moment in time.  Professionals who are in the clinical data management field have to ensure that data collected too soon or too late could misrepresent a situation and drive inaccurate decisions in clinical trials .Therefore, it is important for professionals to collect the data on a timely basis.

Completeness and Comprehensiveness: In the industry, incomplete data is as dangerous as inaccurate data. The loopholes in data collection lead to a partial depiction of the picture. As a result of this, there would be partial information on the running of operations .Therefore, it’s important to understand the complete set of requirements that constitute a comprehensive set of data to determine whether the requirements are being fulfilled or not.

Availability and Accessibility: This characteristic can be tricky at times due to legal and regulatory constraints. Regardless of the challenge, though, individuals need the right level of access to the data in order to perform their jobs. This is based on the presumption that the data exists and is accessible to all.

Granularity and Uniqueness: The level of detail at which data is collected is significant so that confusion and inaccurate decisions can be avoided. Aggregated, summarized and manipulated collections of data could offer a different meaning than the data implied at a lower level. An appropriate level of granularity must be defined to provide sufficient uniqueness and distinctive properties to become visible. This is a requirement for operations to function effectively.

To conclude, data has to be recorded in such a way that the pharmaceutical companies benefit from possessing this data. Therefore, the clinical research industry requires trained professionals who have enrolled in clinical research courses to work in the field of c

Also See: Clinical Research, Research Courses, Clinical Trials, Clinical Data, Data, Clinical, Therefore

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