A Framework for Transparent Reporting of Data Quality Analysis Across the Clinical Electronic Health Record Data Lifecycle

TL;DR

This paper introduces a comprehensive framework for transparent reporting of data quality assessments throughout the clinical EHR data lifecycle, aiming to improve trust, provenance understanding, and data reuse for clinical AI.

Contribution

It develops a structured reporting framework that maps data quality checks to specific lifecycle phases and actors, enhancing transparency and guiding quality improvements.

Findings

Framework effectively reveals data quality issues origin

Applicable to real-world clinical datasets

Supports better data provenance understanding

Abstract

Data quality (DQ) and transparency of secondary data are critical factors that delay the adoption of clinical AI models and affect clinician trust in them. Many DQ studies fail to clarify where, along the lifecycle, quality checks occur, leading to uncertainty about provenance and fitness for reuse. This study develops a framework for transparent reporting of DQ assessments across the clinical electronic health record (EHR) data lifecycle. The reporting framework was developed through iterative analysis to identify actors and phases of the clinical data lifecycle. The framework distinguishes between data-generating organizations and data-receiving organizations to allow users to map DQ parameters to stages across the data lifecycle. The framework defines 5 key lifecycle phases and multiple actors. When applied to the real-world dataset, the framework demonstrated applicability in revealing where DQ issues may originate. The framework provides a structured approach for reporting DQ assessments, which can enhance transparency regarding data fitness for reuse, supporting reliable clinical research, AI model development, and internal organisational governance. This work provides practical guidance for researchers to understand data provenance and for organisations to target DQ improvement efforts across the data lifecycle.