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Summary
This summary is machine-generated.

Ensuring high data quality is crucial, especially for sensitive healthcare information. This paper introduces a new approach and a cloud platform, MODELHealth, to improve data quality assurance in clinical settings.

Keywords:
data qualityehrhealth dataquality assurance

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Area of Science:

  • Data Science
  • Health Informatics

Background:

  • Data quality is a significant challenge for research and businesses.
  • Data quality is particularly critical for sensitive healthcare data.
  • Existing research on data quality assessment is reviewed.

Purpose of the Study:

  • To define data quality concepts and dimensions.
  • To introduce a novel approach for data quality assurance.
  • To present the MODELHealth cloud platform for healthcare data.

Main Methods:

  • Conceptual definitions and dimensions of data quality.
  • Review of existing data quality assessment research.
  • Development and implementation of a data quality assurance approach.

Main Results:

  • A framework for understanding data quality dimensions.
  • An introduced methodology for data quality assurance.
  • The MODELHealth platform designed for clinical and administrative support.

Conclusions:

  • The proposed approach enhances data quality assurance in healthcare.
  • The MODELHealth platform supports clinical decision-making.
  • Improved data quality is vital for the healthcare domain.