Study
Commercial ProductionHigh ImpactStrong effect

Standardized Data Quality Framework Enhances EHR Data Utility for Design Projects

A unified terminology and framework for assessing electronic health record (EHR) data quality are crucial for ensuring its 'fitness' for various applications, including operational analytics, quality improvement, and research.

eGEMs (Generating Evidence & Methods to improve patient outcomes) · 2016

01

Key Findings

  • 01A comprehensive, unified terminology for data quality assessment was developed.
  • 02A conceptual framework was established to organize data quality dimensions.
  • 03The framework aims to provide a common understanding of EHR data 'fitness' for specific uses.
02

Application

Design takeaway

Adopt a structured approach to assessing the quality of any data used in a design project, ensuring its suitability for the intended application.

How to apply

When sourcing data for a design project, use a checklist based on established data quality dimensions (e.g., completeness, accuracy, consistency) to evaluate its suitability.

Project actions

  • 01When gathering data for your design project, think about where it comes from and how accurate it is.
  • 02Consider creating your own simple checklist to assess the quality of your data sources.
03

Method & Evidence

AimHow can a harmonized data quality assessment framework improve the reliability and utility of electronic health record data for secondary use in design and research contexts?
MethodLiterature review and expert consensus building
ProcedureResearchers reviewed existing data quality publications, consulted with informatics and analytics experts, and analyzed operational manuals from EHR-based research networks. They then convened stakeholder meetings to refine and categorize data quality terms into a conceptual framework, leading to iterative improvements of the harmonized terminology.
ContextHealthcare data management and secondary data use

Variables

IVImplementation of a harmonized data quality assessment framework
DVUtility and reliability of EHR data for secondary use
CVType of EHR system, specific data use case, expertise of data analysts
04

Strengths & Limitations

Strengths

  • +Comprehensive review of existing literature and expert input.
  • +Development of a structured and organized framework.

Limitations

The specific terms and categories might need adaptation depending on the type of data and the design project's scope.

Reliability & validity

The reliability of the framework is supported by the iterative refinement process and expert consensus. Validity is established by its alignment with existing data quality principles and its practical applicability in assessing EHR data.

Think critically

How might a lack of standardized data quality assessment impact the scalability and generalizability of design solutions derived from data analysis?

05

Design Principles

"Data used in design must be rigorously assessed for quality and fitness for purpose."

In design practice, the reliability and accuracy of data directly impact the effectiveness of design decisions. By establishing a common language and structured approach to data quality, design teams can better understand the limitations and strengths of data sources, leading to more robust and evidence-based design outcomes.

06

What This Means for Your Design

To make good design choices, you need to know if the information you're using is good. This study created a way to check the quality of health data so people know if it's reliable for research or making improvements.

How to use in your project

  • 1.Reference this study when discussing the importance of data integrity and the methods used to ensure data quality in your design project's research phase.
07

Add to My Project

08

Quick Cite

(2016). A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data. eGEMs (Generating Evidence & Methods to improve patient outcomes). https://doi.org/10.13063/2327-9214.1244 Retrieved from https://designdex.org/study/1c4960ca-2ce9-406f-922f-b38b66a22ffd/standardized-data-quality-framework-enhances-ehr-data-utility-for-design-projects

Paragraph starter

The reliability of design decisions is contingent upon the quality of the data utilized. As demonstrated by Kahn et al. (2016), a harmonized data quality assessment framework, particularly for complex datasets such as electronic health records, is essential for establishing a common understanding of data strengths and limitations. This ensures data is 'fit for purpose,' thereby enhancing the validity of subsequent analysis and design interventions.

09

Source

eGEMs (Generating Evidence & Methods to improve patient outcomes)

A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data

journal · 2016

View source

Questions about this research

What does the research say about standardized data quality framework enhances ehr data utility for design projects?
Adopt a structured approach to assessing the quality of any data used in a design project, ensuring its suitability for the intended application. Evidence: eGEMs (Generating Evidence & Methods to improve patient outcomes) (2016).
Why does "Standardized Data Quality Framework Enhances EHR Data Utility for Design Projects" matter for design?
In design practice, the reliability and accuracy of data directly impact the effectiveness of design decisions. By establishing a common language and structured approach to data quality, design teams can better understand the limitations and strengths of data sources, leading to more robust and evidence-based design outcomes.
How can designers apply this research?
Adopt a structured approach to assessing the quality of any data used in a design project, ensuring its suitability for the intended application.
What were the main findings?
A comprehensive, unified terminology for data quality assessment was developed.. A conceptual framework was established to organize data quality dimensions.. The framework aims to provide a common understanding of EHR data 'fitness' for specific uses.
What research method was used?
Literature review and expert consensus building.
How strong is the evidence?
Evidence strength is rated Strong effect, based on a 2016 journal from eGEMs (Generating Evidence & Methods to improve patient outcomes).
What should I do differently in my next project?
When sourcing data for a design project, use a checklist based on established data quality dimensions (e.g., completeness, accuracy, consistency) to evaluate its suitability.
What are the limitations?
The framework's application may vary depending on the specific EHR system and the intended use of the data.
Is there evidence that data affects design outcomes?
The research successfully created a standardized set of terms and a framework to evaluate the quality of electronic health record data, making it easier to determine if the data is suitable for different purposes. In design practice, the reliability and accuracy of data directly impact the effectiveness of design decis Source: eGEMs (Generating Evidence & Methods to improve patient outcomes) (2016).
Where does this data quality research apply?
Healthcare data management and secondary data use It sits within commercial production research on designdex.org.

Related research topics

data design research · evidence on data · does data improve design outcomes · data quality studies for designers · data and data quality findings · commercial production research evidence