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Methods of Documentation VII: EMR

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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...

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Related Experiment Video

Updated: Jun 25, 2025

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

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Published on: September 20, 2018

Extracting Electronic Health Record Neuroblastoma Treatment Data With High Fidelity Using the REDCap Clinical Data

Brian Furner¹, Alex Cheng², Ami V Desai¹

¹Department of Pediatrics, Section of Hematology/Oncology, The University of Chicago, Chicago, IL.

JCO Clinical Cancer Informatics

|May 30, 2024

Summary

This summary is machine-generated.

Extracting electronic health record treatment data using REDCap CDIS is feasible for the International Neuroblastoma Risk Group Data Commons. This method ensures high-fidelity data submission for neuroblastoma research.

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Area of Science:

Oncology
Health Informatics
Data Science

Background:

The International Neuroblastoma Risk Group Data Commons (INRGdc) is crucial for large-scale neuroblastoma studies.
Current research is hampered by the absence of real-world electronic health record (EHR) treatment data.
There is a need for efficient methods to collect and submit EHR treatment data for clinical research.

Purpose of the Study:

To assess the feasibility of extracting neuroblastoma treatment data directly from EHRs.
To evaluate the REDCap Clinical Data Interoperability Services (CDIS) module for data extraction.
To determine if extracted data can be submitted to the INRGdc.

Main Methods:

Identified neuroblastoma patients treated at University of Chicago and Vanderbilt University Medical Center after EHR go-live.
Extracted antineoplastic drug orders using the REDCap CDIS module via HL7-Fast Healthcare Interoperability Resources (FHIR).
Validated CDIS output against EHR relational databases and manual chart reviews.

Main Results:

The study included 41 patients from UChicago and 32 from VUMC.
EHR treatment data extraction using REDCap CDIS identified antineoplastic drug orders in 95.1% of UChicago patients and 81.3% of VUMC patients.
Validation confirmed high fidelity, with over 99% of drug orders from EHR databases identified in CDIS output, and manual review confirmed accuracy for missing/discontinued orders.

Conclusions:

The extraction of EHR treatment data using HL7-FHIR via REDCap CDIS is feasible.
This method provides high-fidelity data suitable for submission to the INRGdc.
This approach can enhance future large-scale neuroblastoma research by incorporating real-world treatment data.