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Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
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Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
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Statistical Process Control (SPC) is a method used to monitor and control quality within processes, particularly in manufacturing and service delivery, by employing statistical methods. SPC aims to distinguish between natural (common cause) variation and variation due to specific changes or events (special cause), allowing for timely improvements and sustained quality. The control chart, a pivotal tool in SPC, visually displays data over time alongside a central line of upper and lower control...
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Updated: Nov 4, 2025

The Participant-Reported Implementation Update and Score PRIUS: A Novel Method for Capturing Implementation-Related Data Over Time
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Introduction to the EQIPD quality system.

Anton Bespalov1, René Bernard2,3,4, Anja Gilis5

  • 1PAASP, Heidelberg, Germany.

Elife
|May 24, 2021
PubMed
Summary
This summary is machine-generated.

The European Quality in Preclinical Data (EQIPD) consortium developed a free, flexible quality system to enhance the reliability of preclinical research data. This system aims to reduce innovation risks by ensuring data fitness for purpose across public and private sectors.

Keywords:
drug discoveryneurosciencenonregulated researchresearch rigor

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Area of Science:

  • Biomedical Research
  • Drug Development
  • Quality Management Systems

Background:

  • Innovative therapy development faces high failure rates, often due to insufficient research quality.
  • Adherence to evidence-based practices is crucial for reducing these risks.
  • Existing quality systems may not be suitable for the dynamic nature of preclinical research.

Purpose of the Study:

  • To introduce the European Quality in Preclinical Data (EQIPD) Quality System, a novel framework for enhancing preclinical research quality.
  • To ensure the generation of robust and reliable preclinical data, boosting innovation.
  • To provide a lean, effective, and freely accessible quality system for both public and private research sectors.

Main Methods:

  • Development of a quality system based on 18 core requirements, adaptable to user-specific needs.
  • Emphasis on data "fitness for intended use" as defined by diverse stakeholders.
  • Provision of guidance, process criteria, and implementation examples, without prescribing rigid solutions.

Main Results:

  • The EQIPD Quality System offers a flexible approach to quality management in non-regulated preclinical research.
  • Optional tools and assessment services are available to support system implementation and accreditation.
  • The system is designed to be non-burdensome, preserving scientific freedom.

Conclusions:

  • The EQIPD Quality System promotes the generation of reliable, fit-for-purpose preclinical data.
  • It supports innovation by improving data quality and reducing research failure risks.
  • The system aims for broad adoption and continuous improvement to serve the scientific community.