Related Experiment Videos
A Framework for Transparent Reporting of Data Quality Analysis Across the Clinical Electronic Health Record Data
Melinda Wassell1, Kerryn Butler-Henderson2, Karin Verspoor1
1School of Computing Technologies, RMIT University, Melbourne, Australia.
Studies in Health Technology and Informatics
|July 16, 2026
Summary
This study introduces a framework for transparently reporting data quality assessments across the clinical electronic health record (EHR) data lifecycle. This enhances trust and aids AI model development by clarifying data provenance and fitness for reuse.
Area of Science:
- Health Informatics
- Artificial Intelligence in Medicine
- Data Management
Background:
- Data quality (DQ) and transparency are crucial for adopting clinical AI models and building clinician trust.
- Current DQ studies often lack clarity on when and where quality checks occur in the data lifecycle, creating uncertainty about data provenance and reusability.
- This ambiguity hinders the reliable development and implementation of clinical AI.
Purpose of the Study:
- To develop a framework for transparently reporting DQ assessments throughout the clinical electronic health record (EHR) data lifecycle.
- To address the uncertainty surrounding data provenance and fitness for reuse in secondary clinical data.
- To provide practical guidance for improving DQ and supporting AI development.
Main Methods:
- Iterative analysis to identify actors and phases within the clinical data lifecycle.
- Development of a reporting framework distinguishing between data-generating and data-receiving organizations.
- Definition of five key lifecycle phases and multiple actors within the framework.
- Application of the framework to a real-world dataset to assess its utility.
Main Results:
- The developed framework provides a structured approach for reporting DQ assessments across the EHR data lifecycle.
- The framework successfully demonstrated its applicability in identifying the origins of DQ issues within a real-world dataset.
- It enables mapping of DQ parameters to specific stages of the data lifecycle.
Conclusions:
- The framework enhances transparency regarding data fitness for reuse, crucial for reliable clinical research and AI model development.
- It offers practical guidance for researchers to understand data provenance and for organizations to target DQ improvement efforts.
- Implementing this framework can bolster clinician trust and accelerate the adoption of clinical AI.
Related Concept Videos
01:30Health Information Technology and Healthcare Information System
Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
01:20Introduction to Documentation and Reporting
Documentation is the systematic process of formally recording, maintaining, and communicating information.
Nursing documentation records essential information and details regarding a patient's care and treatment in written or electronic form. It is a critical aspect of nursing practice that involves documenting assessments, interventions, outcomes, and other relevant details about a patient's health status.
Documentation maps the patient's health journey by creating a comprehensive and precise...
Nursing documentation records essential information and details regarding a patient's care and treatment in written or electronic form. It is a critical aspect of nursing practice that involves documenting assessments, interventions, outcomes, and other relevant details about a patient's health status.
Documentation maps the patient's health journey by creating a comprehensive and precise...
01:11Purpose of Health Records I
The vital purpose of health records is to provide a complete and accurate account of a patient's medical history, including communication, diagnostic and therapeutic orders, care planning, research, and quality review.
Here's a breakdown of how health records serve these purposes:
Here's a breakdown of how health records serve these purposes:
01:24Data Reporting and Recording
Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
01:30Methods of Documentation VII: EMR
Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare settings,...
01:21Types of Reports II: Incident or Occurrence Report
An Incident or Occurrence Report in a healthcare setting is a crucial document used to record any unexpected occurrence that may or may not have affected a patient, employee, or visitor. Such reports are critical to improving patient safety and include all details leading up to and including the event.
Purposes:
In the healthcare industry, reports play a crucial role in documenting incidents within an agency. The primary objective of these reports is to ensure patient safety, uphold the...
Purposes:
In the healthcare industry, reports play a crucial role in documenting incidents within an agency. The primary objective of these reports is to ensure patient safety, uphold the...