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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,...

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Related Experiment Video

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Reduced Procedure Time and Variability with Active Esophageal Cooling During Radiofrequency Ablation for Atrial Fibrillation

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Concept mapping to develop a framework for characterizing Electronic Data Capture (EDC) Systems.

Alicia F Guidry¹, James F Brinkley, Nicholas R Anderson

¹Division of Biomedical & Health Informatics and Institute for Translational Health Sciences, University of Washington, Seattle, WA, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|November 13, 2008

Summary

Clinical and Translational Science Awards (CTSAs) drive the need for improved electronic data capture (EDC) systems. A concept mapping framework was developed to evaluate EDCs for translational research data management needs.

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Area of Science:

Biomedical Informatics
Translational Science

Background:

Clinical and Translational Science Awards (CTSAs) have spurred demand for enhanced electronic data capture (EDC) systems.
Effective EDC systems are crucial for facilitating translational research.

Purpose of the Study:

To develop a framework for evaluating EDC systems based on specific clinical/translational research lab data management needs.
To characterize EDC systems currently utilized across CTSA sites nationally.

Main Methods:

Concept mapping was employed to create an EDC evaluation framework.
A spiral model approach was used for refinement, incorporating consultations with the University of Wisconsin CTSA.
A survey of other CTSAs was conducted to gather information on their EDC systems.

Main Results:

A validated framework for EDC system evaluation was established.
The study characterized the landscape of EDC systems used within CTSA institutions.
Identified key features and functionalities relevant to translational research data management.

Conclusions:

The developed framework provides a structured approach to assessing EDC systems for translational research.
Understanding current EDC usage across CTSAs can inform future system development and selection.
Optimizing EDC systems is essential for advancing translational research efficiency and data integrity.