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EHRs Data Harmonization Platform, an easy-to-use shiny app based on recodeflow for harmonizing and deriving clinical features

Arian Aminoleslami, Geoffrey M. Anderson, Davide Chicco

TL;DR

The EHRs Data Harmonization Platform can be used to extract, document, and harmonize variables from EHR and it can also be used to document and share research variables that have been derived from those EHR data.

Abstract

Electronic health records (EHRs) contain important longitudinal information on individuals who have received medical care. Traditionally, EHRs have been used to support a wide range of administrative activities such as billing and clinical workflow, but, given the depth and breadth of clinical and demographic data they contain, they are increasingly being used to provide real-world data for research. Although EHR data have enormous research potential, the full realization of that potential requires a data management strategy that extracts from large EHR databases, that are collected from a range of care settings and time periods, well-documented research-relevant data that can be used by different researchers. Having a common well-documented data management strategy for EHR will support reproducible research and sharing documentation on research variables that are derived from EHR variables is important to open science. In this short paper, we describe the EHRs Data Harmonization Platform. The platform is based on an easy to use web app a publicly available at https://poxotn-arian-aminoleslami.shinyapps.io/Arian/ and as a standalone software package at https://github.com/ArianAminoleslami/EHRs-Data Harmonization-Platform, that is linked to an existing R library for data harmonization called recodeflow. The platform can be used to extract, document, and harmonize variables from EHR and it can also be used to document and share research variables that have been derived from those EHR data.

EHRs Data Harmonization Platform, an easy-to-use shiny app based on recodeflow for harmonizing and deriving clinical features

TL;DR

The EHRs Data Harmonization Platform can be used to extract, document, and harmonize variables from EHR and it can also be used to document and share research variables that have been derived from those EHR data.

Abstract

Electronic health records (EHRs) contain important longitudinal information on individuals who have received medical care. Traditionally, EHRs have been used to support a wide range of administrative activities such as billing and clinical workflow, but, given the depth and breadth of clinical and demographic data they contain, they are increasingly being used to provide real-world data for research. Although EHR data have enormous research potential, the full realization of that potential requires a data management strategy that extracts from large EHR databases, that are collected from a range of care settings and time periods, well-documented research-relevant data that can be used by different researchers. Having a common well-documented data management strategy for EHR will support reproducible research and sharing documentation on research variables that are derived from EHR variables is important to open science. In this short paper, we describe the EHRs Data Harmonization Platform. The platform is based on an easy to use web app a publicly available at https://poxotn-arian-aminoleslami.shinyapps.io/Arian/ and as a standalone software package at https://github.com/ArianAminoleslami/EHRs-Data Harmonization-Platform, that is linked to an existing R library for data harmonization called recodeflow. The platform can be used to extract, document, and harmonize variables from EHR and it can also be used to document and share research variables that have been derived from those EHR data.

Paper Structure

This paper contains 19 sections, 10 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methods: Platform's application
  3. Use of the EHR Data Harmonization Platform in OHDP-Q for EHR data extraction and harmonization
  4. Use of the EHR Data Harmonization Platform in OHDP-Q for documentation of derived variables
  5. Results: use cases
  6. Launching the Shiny app
  7. An overview of the platform and its features
  8. Renaming a variable and recode the categories
  9. Derived variable from one variable, using built-in transformations: MMSE as category
  10. Derived variable from two variables that needs custom functions: MMSE and CEP
  11. Reproducibility of the work
  12. Discussion and conclusions
  13. List of abbreviations
  14. Conflict of interest
  15. Funding
  16. ...and 4 more sections

Figures (10)

  • Figure 1: Flowchart representing how we utilized EHRs data curation platform to create a standard approach for data curation in OHDP-Q
  • Figure 2: An example of a connected SQLite database that enables users to import, curate, and export data in chunks for the efficiency
  • Figure 3: The summary tab allows user to extract information about a variable they want to recode.
  • Figure 4: An example to rename and recode the categories of a variable
  • Figure 5: An example showing how to categorize a continuous variable and how they appear in the details sheet.
  • ...and 5 more figures