Orion Health Orchestral De-Identify

Extract insights while maintaining the security and privacy of patient data

Orchestral De-Identify is software for end-to-end de-identification of health data. It is specifically designed for the healthcare industry, with a focus on patient privacy and compliance with regulatory requirements.

Discover

How can De-Identify help?

Discover how your data can become a powerful strategic asset in this video with Fiona McPherson Grant, VP of Data at Orion Health.

Learn how de-identification tools within our platform make it easy to securely share and repurpose your data for research, commercial use, or other valuable applications.

Key Features

Why you'll love our user experience

Guided optimization process

The user interface guides you through the process of creating de-identification profiles, balancing risk and data utility with real-time feedback on information loss and bias.

Pre-built health data standard profiles

The platform saves you time with template de-identification configurations for different health data types and projects.

Key features

Versatile data handling

The De-Identify platform supports de-identification across a broad range of health data types and formats with flexible deployment options.

  • Types of data we can de-identify:

  • Databases

  • Datasets

  • Clinical free text

  • HL7 Messages

  • CCDs

  • AI & ML models trained on clinical notes

  • KEY FEATURES

    Maximize data utility by using a combination of the latest de-identification techniques to minimize data loss.

    Supression

    Involves selectively removing specific identifiers—like patient IDs or exact locations—from a vast range of healthcare data.

    Randomization

    Shuffles data elements to obscure their original order, maintaining data integrity for analysis while preventing re-identification.

    Generalization

    Organizes data of high individual specificity into broad groups.

    Perturbation

    Adds or alters data points slightly, like adding background noise to a conversation.

    Masking, Hashing, and Encryption

    Obscure data with masking or hashing. Encrypt it for later re-identification.

    Pseudonymization

    Replacing identifying fields within a data record with artificial data, or pseudonyms.

    Frequently
    Asked Questions

    While many healthcare organizations can improve their handling of Private Health Information (PHI) with some form of de-identification, we commonly talk to healthcare systems and healthcare technology or service vendors.  

    Healthcare systems use de-identification tools to securely handle data and comply with HIPAA for data movement outside their organizations. De-Identify can remove sensitive data, allowing it to be used safely for research or analysis. 

    Technology vendors or health companies with offshore teams can use De-Identify to enable safe data sharing for testing, troubleshooting, or upgrade preparation. 

    Our product stands out in the de-identification market in four key ways: 

    • Guided Optimization Process: The user interface facilitates the creation of de-identification profiles for different projects, providing real-time feedback to help balance data utility and risk while minimizing information loss and bias. 
    • Versatile Data Handling: It supports de-identification across various data types, including databases, datasets, clinical free-text notes, CCD documents, HL7 messages and AI & ML models trained on clinical notes. 
    • Multiple De-Identification Methods: It offers flexible options, including data removal, anonymization, pseudonymization, and data transformation. This flexibility allows users to apply different de-identification methods within the same data set. 
    • Pre-built and Configurable Project Profiles: The platform streamlines your workflow with pre-built de-identification templates tailored to various health data types and projects. It also allows users to create, save, and reuse project profiles that capture custom de-identification configurations.  

    De-Identify supports both manual and automated workflows. Initially, users configure the fields to de-identify and save the settings in a project profile. This profile can then be reused automatically, triggered by data events, such as new data uploads, through an API integration. This allows the de-identification process to run in the background without manual intervention each time.

    The original data remains intact. De-Identify creates a new, separate de-identified version of the dataset, which can then be used as needed for testing, research, or sharing. The de-identified data can be integrated with the original dataset if desired, or kept separate, based on the specific use case. 

    Onboarding involves setup and “train-the-trainer” sessions, tailored to each organization’s technical maturity. Initial setup may require some project hours and services to fully equip clients to run their de-identification processes independently. This setup also varies depending on whether the organization chooses cloud-hosted or on-premises deployment. 

    Our team is happy to talk you through the tool and provide a demo for your specific use case. You can contact us here. We also have a Buyer’s Guide to De-Identification Solutions which you can read and download below.

    NEW EBOOK

    Learn more in our Buyer's Guide to De-Identification Solutions

    What you’ll learn:

    • Assess your requirements
    • Understand your challenges
    • Articulate the ROI of purpose-built de-identification solutions.

    Authored by Fiona McPherson Grant, Orion Health VP of Data

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