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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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When the quality of water for concrete preparation is uncertain, its impact on the setting time of cement and compressive strength of mortar is assessed by comparison with de-ionized or distilled water benchmarks. American Society for Testing and Materials (ASTM) C1602 requires the setting times to be within 90 minutes of the control, British Standard (BS) 3146:1980 allows a 30-minute variance in the initial setting, while British Standards European Norm (BS EN) 1008 specifies initial setting...
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Preparation of High-Quality Fermented Fish Product
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Data Quality Improvement in Clinical Databases Using Statistical Quality Control: Review and Case Study.

Hassan Assareh1, Mary A Waterhouse2, Christina Moser3

  • 11 Simpson Centre for Health Services Research, Australian Institute of Health Innovation, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.

Therapeutic Innovation & Regulatory Science
|September 20, 2018
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Summary
This summary is machine-generated.

This study introduces acceptance sampling plans (ASPs) and statistical process control (SPC) as core mechanisms for improving clinical data quality. These methods enhance database accuracy and completeness, crucial for reliable medical research and patient care.

Keywords:
acceptance sampling plansclinical databasescontrol chartsdata qualitystatistical process control

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Area of Science:

  • Health Informatics
  • Biostatistics
  • Data Management

Background:

  • Data quality is paramount in clinical and medical settings.
  • Existing quality assurance frameworks, audits, and correction procedures aim to improve database accuracy and completeness.

Purpose of the Study:

  • To propose acceptance sampling plans (ASPs) and statistical process control (SPC) as a technical core for data quality improvement.
  • To review and discuss the implementation of ASP and SPC techniques in data quality evaluation.

Main Methods:

  • Overview of existing data quality approaches, focusing on statistical methods.
  • Review of acceptance sampling plans (ASPs) and statistical process control (SPC) tools (control charts, root cause analysis).
  • Application of selected techniques in two case studies on hospital databases.

Main Results:

  • Demonstrated the practical application of ASP and SPC techniques in real-world hospital data.
  • Provided evidence for the effectiveness of these statistical tools in enhancing data accuracy and completeness.

Conclusions:

  • ASPs and SPC tools are effective for data quality improvement in clinical contexts.
  • Guidelines are proposed for selecting appropriate techniques based on dataflow and database specifications.