JMIR Publications

ABSTRACT

Background:

Integrated health data is foundational for secondary use, research, and policy making. However, data quality issues – such as missing values and inconsistencies – are common due to the heterogeneity of health data sources. Existing frameworks often apply static, one-time assessments, limiting their ability to address quality problems across evolving data pipelines.

Objective:

This study evaluates the AIDAVA data quality framework, which introduces dynamic, lifecycle-based validation of health data using knowledge graph technologies and SHACL-based rules. The framework is assessed for its ability to detect and manage data quality issues – specifically, completeness and consistency – during integration.

Methods:

Using the MIMIC-III dataset, we simulated real-world data quality challenges by introducing structured noise, including missing values and logical inconsistencies. The data was transformed into Source Knowledge Graphs (SKGs) and integrated into a unified Personal Health Knowledge Graph (PHKG). SHACL validation rules were applied iteratively during the integration process, and data quality was assessed under varying noise levels and integration orders.

Results:

The AIDAVA framework effectively detected completeness and consistency issues across all scenarios. Completeness was shown to influence the interpretability of consistency scores, and domain-specific attributes (e.g., diagnoses, procedures) were more sensitive to integration order and data gaps.

Conclusions:

AIDAVA supports dynamic, rule-based validation throughout the data lifecycle. By addressing both dimension-specific vulnerabilities and cross-dimensional effects, it lays the groundwork for scalable, high-quality health data integration. Future work should explore deployment in live clinical settings and expand to additional quality dimensions.