Use DRH Tools Locally

The DRH Edge platform is designed for localized data handling and is ideal for the following scenarios:

Local Data Preview and Analysis

Allows users to preview study data, analyze metrics, and view charts directly on their local machines.
Ensures secure data exploration in a controlled, offline environment without the need for external data sharing.

Using DRH Edge Software

If you prefer to use the DRH Edge software for de-identification and anonymization, please follow these steps:

Meet the Edge Usage Prerequisites:
- DRH recommends using the suggested file structure for organizing your files. Alternatively, please provide the DRH technical team with detailed information about your file structure and column patterns in advance.
- This information is essential for developing the SQL scripts and creating a tailored package suitable for the user’s specific needs.
Custom Package Preparation:
- The DRH technical team will develop a bespoke package tailored to your organization’s file patterns.
- Share detailed requirements for de-identification columns with the technical team.
Verification and Upload:
- After testing the tailored package and generating the database, verify the database.
- After processing through DRH Edge, the generated database can be verified by edge user and then be uploaded to the SFTP folder for integrating in the cloud version.
- Once verified, upload the database to the designated SFTP folder.

DRH Edge UI Setup and Data Transformation

In this guide, we will explain how to set up the DRH Edge tool and perform data transformation using a tailored sample.

The DRH Edge tool allows you to securely convert your CSV files, perform de-identification, and conduct verification and validation (V&V) processes all within your own environment. You can view the results directly on your local system.

Requirements for Previewing the Edge UI:

Surveilr Tool (Use the latest version unless specified).
Note: The compatibility of the surveilr tool with the operating system (OS) should be periodically tested to ensure it continues to function as expected.
Deno Runtime (requires Deno 2.0)
Follow the Deno Installation Guide for step-by-step instructions.
If Deno is already installed, upgrade to Deno 2.0 by running the following command as an administrator:
```
deno upgrade
```

Getting Started

Step 1: Navigate to the Folder Containing the Files

Open the command prompt and navigate to the directory with your files.
Command: cd <folderpath>
Example: cd D:/DRH-Files

Step 2: Download Surveilr

Follow the installation instructions at the Surveilr Installation Guide.
Download the latest version of surveilr from Surveilr Releases and place it in the folder.

Step 3: Verify the Tool Version

Run the command surveilr --version in the command prompt or .\surveilr --version in PowerShell.
If the tool is installed correctly, it will display the version number.

The folder structure should look like this:

surveilr.exe
study-files/
  ├── cgm_file_metadata.csv
  ├── participant.csv
  ├── cgm_tracing_001
  ├── cgm_tracing_002
  ├── cgm_tracing_003
  ├── cgm_tracing_004
  ├── cgm_tracing_005
  ├── cgm_tracing_006
  └── ...

Step 4: Execute the Commands Below

Clear the cache by running the following command:

deno cache --reload https://raw.githubusercontent.com/surveilr/www.surveilr.com/main/lib/service/diabetes-research-hub/drhctl.ts

After clearing the cache, run the following single execution command:
```
deno run -A https://raw.githubusercontent.com/surveilr/www.surveilr.com/main/lib/service/diabetes-research-hub/drhctl.ts 'foldername'
```
- Replace foldername with the name of your folder containing all CSV files to be converted.
Example:
```
deno run -A https://raw.githubusercontent.com/surveilr/www.surveilr.com/main/lib/service/diabetes-research-hub/drhctl.ts study-files
```
- After the above command completes execution, launch your browser and go to
  http://localhost:9000/drh/index.sql.
This method provides a streamlined approach to complete the process and view the results quickly.

Step 5: Verify the Verification and Validation Results in the UI

Check the following section in the UI and follow the steps as shown in the second image.

vv-image

vv-steps-image

Data De-identification

As part of the DRH Edge Tool, we provide robust capabilities for de-identifying and anonymizing sensitive data, ensuring compliance with privacy regulations before data is shared externally. This is a critical step in protecting Personally Identifiable Information (PII) and Protected Health Information (PHI) while maintaining the data’s analytical value.

Why De-identification is Important?

Organizations handling Continuous Glucose Monitoring (CGM) data must comply with global privacy standards such as HIPAA (USA), GDPR (EU), and other data protection laws. De-identification ensures that sensitive information, such as participant demographics and CGM readings, remains secure before external sharing or publication.

De-identification Process in DRH

Our de-identification process focuses on removing or masking direct identifiers to protect privacy.

Masking or Removing Sensitive Data

Participant Name → Removed or replaced with a randomly generated identifier.
Date of Birth → Replaced with an age range or general birth year.
Email Addresses → Redacted or replaced with dummy values.
Device IDs → Hashed or replaced with a pseudonymized ID.

Example: Masking Investigator and Author Emails

For investigator and author files that contain email addresses, we apply email masking using a SQL command executed through the Surveilr tool.

Example Transformation:
📧 Original Email → 🔒 Masked Email

If additional columns require masking, we apply the same process based on customer requirements.

Example:

The following SQL perform the de-identification

Before masking the investigator file

After masking masked-investigator

Customization for Each Customer

Each customer’s file structure may vary. Customers can specify which columns need to be anonymized, and we will:
✔ Generate the necessary SQL scripts.
✔ Incorporate them into a custom de-identification package for the organization.

This ensures that every dataset is processed according to specific privacy requirements.

Tools for CSV Files Validation

We recommend the following third-party open-source tools to help validate whether your files adhere to the structure described below:

Data Curator

Data Curator is a lightweight desktop data editor designed to help describe, validate, and share open data. It offers a user-friendly graphical interface for editing tabular data while ensuring adherence to data standards.

Key Features:

Schema Editing: Easily modify Frictionless JSON schemas to suit your project’s requirements.
Load and Preview Data: Visualize and inspect CSV files in a user-friendly interface.
Validate Against Schema: Perform schema validation using the Frictionless JSON specification.
Edit and Save: Correct errors directly in the application and save validated data.
Metadata Management: Add metadata to improve data usability.
Export Options: Share validated datasets as clean CSV files or complete data packages.

Open Data Editor (ODE)

The Open Data Editor (ODE) is an online tool for non-technical data users to explore, validate, and detect errors in tabular datasets. It provides an intuitive web interface for identifying and correcting issues in open data.

Key Features:

Online Access: No installation required—access it directly through your browser.
Error Detection: Quickly identify errors in table formatting and data values.
Schema Validation: Validate datasets against predefined schemas, including Frictionless JSON schema.
Interactive Editing: Fix errors and inconsistencies in real-time.
Export Options: Save corrected files for further use or sharing.