White Paper

Metric-Based Quality Improvement Program Of GLP Data

Source: ABC Laboratories

Analytical Bio-Chemistry Laboratories is a GLP- and CGMP-compliant contract research laboratory that supports pharmaceutical and chemical product development. We understand that  ensuring data quality and on-time delivery are critical to customer satisfaction. The QIP is a systematic process for measuring predefined quality goals, quantify the quality of GLP data and reports produced by our scientists, provide a mechanism for reporting meaningful results to management, and identify specific areas for potential process and individual performance improvements. 

The QIP provided a mechanism that enabled us to measure the quality and timeliness of work performed between each of these major milestones. The process targeted by the QIP can be summarized into the following milestones:

  • Data generation
  • Quality Control (QC) review
  • QA Audit: Data or reports were submitted for a QA audit, and audit comments were recorded by the QA associate, then addressed by the Study Director/Principal Investigator (SD/PI) and reviewed by QA.
  • Reporting: Once all the comments were addresses, audit details were entered into the “QA to Management Report”

The metrics-based program was developed around eight, well-defined dimensions of quality:

  • Regulatory Compliance
  • Protocol deviation
  • SOP deviation
  • Documentation deficiency
  • Method deviation
  • Table error
  • Report error and
  • Other

To provide feedback, identify areas for improvement and quantify relative performance over time, numerical rankings
were tabulated. Results were reported to operations and senior management, providing a snapshot of performance and the impact of improvements.

The report was organized into definable groups, subgroups and sections, enabling ABC Laboratories to target specific investments in operational processes, training and individual performance. 

As the data indicates, a dramatic improvement was observed in the overall performance of the team and the quality of data and reports that it generated. 

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