Taming the Data Quality Dragon--A Theory and Method for Data Quality by Design.

Area of Science:

• Health Informatics
• Software Engineering
• Clinical Information Systems

Background:

• Data quality (DQ) is a major obstacle to achieving cost and quality improvements in clinical information systems (CIS).
• Current research on DQ in health informatics often lacks a holistic, 'by design' approach, relying on empirical studies and practitioner reports.
• Sustaining high DQ in CIS remains a complex and challenging objective.

Purpose of the Study:

• To present a general framework for clinical data quality.
• To integrate foundational engineering theories with health informatics concepts for DQ management.
• To introduce a novel method for monitoring and assuring DQ during system operation.

Main Methods:

• Developed a general framework for clinical data quality.
• Defined an architectural viewpoint for designing and reasoning about DQ.
• Introduced DQ Probes for real-time DQ monitoring and assurance.

Main Results:

• The proposed framework provides a structured approach to clinical DQ.
• DQ Probes were successfully validated in a real-world case study, demonstrating their effectiveness.
• The integration of engineering principles offers a more robust method for managing DQ in CIS.

Conclusions:

• The presented framework and DQ Probes offer a holistic, 'by design' approach to clinical data quality.
• This methodology can significantly improve DQ management in clinical information systems.
• The validated concepts provide a foundation for future research and implementation in health informatics.