Short answer

Prioritize the integration of comprehensive data quality management strategies and tools within the design and implementation of any system handling clinical data, ensuring its reliability for future analysis and application.

Field: Commercial Production
Source: Journal of Medical Internet Research (2025)
Method: Systematic Review
Sample: 105 papers
Evidence: Strong effect

Ensuring data quality across the entire clinical data lifecycle, from planning to utilization, is essential for the effective secondary use of electronic health records (EHRs) in healthcare innovation and policy. This commercial production research insight is drawn from a 2025 study published in Journal of Medical Internet Research. Using Systematic review with 105 papers, researchers explored how this design variable affects real-world outcomes. The key design takeaway: Prioritize the integration of comprehensive data quality management strategies and tools within the design and implementation of any system handling clinical data, ensuring its reliability for future analysis and application.

Study

Commercial ProductionNew This WeekStrong effect

Data quality management in clinical data life cycles is crucial for reliable secondary use.

Journal of Medical Internet Research · 2025

Key Findings

01A four-stage clinical data quality management life cycle exists: planning, construction, operation, and utilization.
02Key data quality dimensions frequently assessed include completeness, plausibility, concordance, security, currency, and interoperability.
03Standardized quality control methods and automation are necessary for the secondary use of EHR data.

Application

Design takeaway

How to apply

When designing or evaluating systems that collect and manage clinical data, explicitly map out the data quality checks and management processes for each stage of the data's life cycle, from input to archival and secondary use.

Project actions

01When designing a system that collects data, consider how you will ensure the quality of that data throughout its life.
02Think about what 'quality' means for your specific data and how you will measure it.

Method & Evidence

AimWhat are the key challenges and established processes for managing data quality throughout the clinical data life cycle, particularly for secondary data utilization?

MethodSystematic Review

ProcedureA systematic review of scientific literature was conducted, screening over 80,000 articles to identify those focusing on clinical data quality management, assessment methods, and tools. The review synthesized findings from 105 selected papers that described a four-stage data quality management process (planning, construction, operation, utilization).

Sample105 papers

ContextClinical data management, Electronic Health Records (EHRs), Healthcare Informatics

Variables

IVData quality management processes and dimensions

DVReliability and utility of clinical data for secondary use

CVData source (EHRs), Time period of literature review

Strengths & Limitations

Strengths

+Comprehensive systematic review methodology.
+Identification of a structured data quality management process.

Limitations

The scope of the review might not cover all emerging data quality issues or specific technological solutions. The effectiveness of proposed quality management processes can depend heavily on implementation context.

Reliability & validity

The reliability of the findings is supported by the systematic review methodology. Validity is enhanced by the broad scope of the literature search and the focus on established data quality dimensions.

Think critically

How can the proposed four-stage data quality management process be adapted for non-clinical data sets, and what new challenges might arise?

Design Principles

"Data quality is not an afterthought but an integral component of the entire data life cycle, from initial design to final utilization."

The increasing reliance on EHR data for health policy and medical technology advancement necessitates robust data quality management. Without standardized processes and automated checks, the insights derived from this data may be flawed, leading to suboptimal decision-making and hindering progress in healthcare.

What This Means for Your Design

To make sure health data from computers is useful for new research or decisions, we need to check its quality at every step, from when it's first collected to when it's used later on. This involves making sure the data is complete, accurate, and secure.

How to use in your project

1.Reference this study when discussing the importance of data quality in your design process, especially if your project involves data collection or analysis.

Add to My Project

Quick Cite

Paragraph starter

This research highlights the critical need for robust data quality management throughout the clinical data life cycle. By adopting a systematic approach that includes planning, construction, operation, and utilization stages, and focusing on dimensions such as completeness, plausibility, and security, designers can ensure the reliability of data for secondary use, thereby supporting informed decision-making in healthcare.

Source

Journal of Medical Internet Research

Challenges for Data Quality in the Clinical Data Life Cycle: Systematic Review

journal · 2025

View source

Questions About This Research

What does the research say about data quality management in clinical data life cycles is crucial for reliable secondary use?: Prioritize the integration of comprehensive data quality management strategies and tools within the design and implementation of any system handling clinical data, ensuring its reliability for future analysis and application. Evidence: Journal of Medical Internet Research (2025).
Why does "Data quality management in clinical data life cycles is crucial for reliable secondary use." matter for design?: The increasing reliance on EHR data for health policy and medical technology advancement necessitates robust data quality management. Without standardized processes and automated checks, the insights derived from this data may be flawed, leading to suboptimal decision-making and hindering progress in healthcare.
How can designers apply this research?: Prioritize the integration of comprehensive data quality management strategies and tools within the design and implementation of any system handling clinical data, ensuring its reliability for future analysis and application.
What were the main findings?: A four-stage clinical data quality management life cycle exists: planning, construction, operation, and utilization.. Key data quality dimensions frequently assessed include completeness, plausibility, concordance, security, currency, and interoperability.. Standardized quality control methods and automation are necessary for the secondary use of EHR data.
What research method was used?: Systematic Review with 105 papers.
How strong is the evidence?: Evidence strength is rated Strong effect, based on a 2025 journal from Journal of Medical Internet Research.
What should I do differently in my next project?: When designing or evaluating systems that collect and manage clinical data, explicitly map out the data quality checks and management processes for each stage of the data's life cycle, from input to archival and secondary use.
What are the limitations?: The review focused on literature published between 2010 and October 2023, potentially missing newer developments. The specific context of data quality challenges might vary significantly across different healthcare systems and regions.