How Clinical Data Integrity and Quality Help Achieve GCP

posted on July 16th 2015 in Recommendations with 0 Comments /

Good clinical practice (GCP) is a foundational element in conducting clinical trials. According to the FDA, “GCP is an international ethical and scientific quality standard for designing, conducting, recording, and reporting trials that involve the participation of human subjects. Compliance with this sClinical Data Qualitytandard provides assurance that the rights, safety, and well-being of trial subjects are protected, consistent with the principles that have their origin in the Declaration of Helsinki, and that the clinical trial data are credible.”

The concepts of clinical trial data integrity and quality are important in reaching the standards of GCP. However, there is a lack of clear definitions and standards for these two very important concepts. Outlined below are important standards clinical data managers can use to ensure clinical trial data attains the standard of integrity and high-quality.

Ensuring clinical data integrity and quality

The FDA expects that data acquired during a clinical study follow the acronym ALCOA, which is attributable, legible, contemporaneous, original, and accurate. While these best practices are typically associated with paper records, they can be applied to electronic records as well:

  • Attributable–The record shows clearly who completed it, and audit trails identify who created or modified a record and limits authorization to make edits to qualified individuals.
  • Legible–Illegible handwriting makes it difficult to re-create events recorded during the study. With electronic records, data is presented in a clear and standard format.
  • Contemporaneous–Enter study data at the time the activity is performed, date/time stamps in electronic records collect time of entry.
  • Original–Original data may have been recorded on a scrap piece of paper. If so, keep the scrap piece of paper with the study record. With electronic records, audit trails identify when a record is created and modified.
  • Accurate–“minimize transcription” is a tip to reduce inaccurate data. System checks can ensure data meets the intent (ie, validation checks to determine when required data is missing, out of range information is entered, or data type is inconsistent).

Following the ALCOA recommendations is the path to ensuring clinical data with integrity and quality.

Detecting issues with clinical data quality and integrity

A critical role performed by clinical data managers at eClinical Solutions is to detect issues with data integrity and quality. It is critical that clinical data managers have confidence in the tools, systems, and processes used to collect, analyze, and store clinical trial data. Key indicators that the integrity or quality of clinical trial data is questionable are:

  • Data appears to have been manipulated. This indicates a data integrity issue.
  • Data doesn’t answer the study questions appropriately. This indicates a data quality issue.

Analytics reports such as data listings and reports to identify outliers, to query data, and to validate information, which are used to test the integrity and quality of clinical trial data. To assess the integrity of data clinical data managers will evaluate the tools used to collect study information, and confirm that the environment is controlled, that access restrictions are in place, and to determine what gets audited.

A clinical data repository (CDR) system can manage data in ways that ensure quality by building in validation systems, incorporating rules such as requiring number or text entry in specific fields, and including audit trails to note edits to data.

QA supports data integrity and quality

Your Quality Assurance (QA) team plays a key role in ensuring your clinical trial data integrity and quality. The QA role focuses on evaluating process, procedure, and oversight. Examples include:

  • Process–Are the right processes in place?
  • Procedure–Ensure teams have the right tools and they know how to use them.
  • Oversight–Is there proper oversight of the vendor? Are we are confident that the data is being collected, analyzed, and stored with integrity and quality.

Adopting GCP standards should be done in the planning phases for a clinical trial, so process and procedures are established from the beginning that ensure your data is collected, analyzed, and stored in an appropriate manner.

Do your data management groups have an appropriate level of understanding of these concepts, and are they using appropriate tools to perform these assessments? For more training in this area, here are two sessions scheduled for this fall. The first session is at the Society of Clinical Data Management (SCDM) annual meeting in September in Washington DC, and the second is at the Society of Quality Assurance (SQA) Quality College September 29 to October 2 in Cleveland, Ohio. Information on both of these workshops will be available soon. I hope to see you at one (or both) of these meetings.

Guest blog post submitted by Cheryl McCarthy

Photo by: tec_estromberg

about the author: ecs

Please let us know your thoughts...