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Digital Health Data Quality Issues: Systematic Review.

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Summary
This summary is machine-generated.

This study introduces a digital health data quality framework (DQ-DO) to address fragmented data quality dimensions. The framework reveals consistency as the most influential dimension, impacting all others and various outcomes.

Keywords:
data qualitydigital healtheHealthelectronic health recordsystematic reviews

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Area of Science:

  • Digital Health
  • Health Informatics
  • Data Quality Management

Background:

  • Digital health's promise hinges on data analysis for improved decision-making.
  • Effective data utilization is hindered by poor data quality (DQ).
  • A consolidated DQ taxonomy for digital health is lacking in existing literature.

Approach:

  • A systematic literature review following PRISMA guidelines was conducted.
  • 227 articles on digital health DQ in hospital settings were analyzed.
  • Inductive and computer-assisted analyses identified DQ dimensions, outcomes, and their interrelationships.

Key Points:

  • The developed DQ-DO framework includes 6 DQ dimensions: accessibility, accuracy, completeness, consistency, contextual validity, and currency.
  • Consistency is the most influential DQ dimension, impacting all others.
  • Five DQ outcomes (clinical, clinician, research, business, organizational) are identified, influenced by consistency and accessibility.

Conclusions:

  • The DQ-DO framework highlights the complexity of digital health DQ and the need for improvement.
  • It offers healthcare executives a holistic view of DQ issues and outcomes.
  • The framework aids in prioritizing DQ-related problem-solving.