Orchestral DeID

Orchestral DeID is the modern AI-driven engine for deidentifying sensitive healthcare data. Built specifically for the health sector, DeID allows you to remove personally identifiable information (PII) and protected health information (PHI) from any data source, enabling secure access for analysis, testing, research and innovation.

Whether you need to anonymize clinical documents, mask sensitive data, or prepare de-identified datasets for AI model training, DeID offers a scalable, standards-aligned solution that protects patients while powering progress.

DeID is an optional add-on to the Orchestral data platform. It integrates seamlessly with other Orchestral products to enable end-to-end, compliant data flows.

Features

• Deidentify any data types - removes identifiers from many different structured and unstructured health information and data types. Formats supported today (or planned) are database (RDBMS), CSV, HL7, X12, CCDA, PDF and simple text (with NLP extensions) and others.

• Pre-configured with healthcare knowledge - out-of-the-box support for healthcare standards including HL7 and CCDA. Pre-configured deidentification templates reduce deployment time.

• Learns your data - train the tool on your data to improve its understanding of any idiosyncrasies or special requirements.

• Enterprise scaling - works with hundreds or millions of records. It is built on the same robust infrastructure that underpins the rest of the Orchestral platform.

• Leverages established data privacy techniques - applies data privacy techniques such as k-anonymity, l-diversity, t-closeness, differential privacy, generalization & suppression, data masking / tokenization, and NLP for free text. The technique used depends on the data and what the data is used for - different thresholds for different use cases and how much risk is involved.

• Full or partial deidentification - it supports full or partial deidentification based on your governance and policy needs, helping you meet privacy regulations and ethical obligations with ease.

How it works

At its core, DeID works by applying automated deidentification, masking, and anonymization techniques to personally identifiable information (PII) and protected health information (PHI). It can handle both structured and unstructured data, including databases, CSVs, HL7, CCDA, PDFs, and free text such as clinical notes. Out-of-the-box, it comes pre-configured with healthcare standards knowledge, which means it can immediately recognize and strip identifiers from HL7 and CCDA formats. This reduces deployment time and ensures compliance from day one.

The system can be further trained on your organization’s data. By sampling real records, the tool learns idiosyncrasies and special requirements, then applies privacy techniques such as K-anonymity, which ensures that each record is indistinguishable from at least K-1 others. This minimizes re-identification risk, even when quasi-identifiers like age or ZIP code are present. DeID supports both full deidentification (removing all identifiers) and partial deidentification (masking only selected fields based on governance policies), giving flexibility depending on the use case.

The process follows two main steps:

1. Training DeID - the system is configured, samples are analyzed, and AI learns statistical patterns to fine-tune deidentification.

2. Deidentifying data - input datasets containing PHI/PII are processed, identifiers are removed or masked, and the cleaned data is output in the required format.

DeID operates at enterprise scale, capable of working across hundreds or millions of records. It leverages the same infrastructure as the broader Orchestral Health Intelligence Platform, ensuring reliability, compliance, and speed.

Use cases

• Preparing anonymized datasets for academic research.

• Removing PII/PHI for vendor testing and QA environments.

• Masking data for vendor testing and quality assurance environments.

• Sharing data with government or partner agencies securely.

• Enabling privacy-preserving AI/ML model development.

• Removing identifiers from free-text notes in clinical records.

• Meeting privacy regulations such as HIPAA, GDPR, and CCPA.

Benefits

Research, testing and innovation

Enables data scientists, analysts and researchers to access rich, deidentified datasets within the secure data governance boundary. This unlocks new opportunities for testing, model training, simulation and health innovation, without the risks typically associated with sensitive data exposure.

What makes DeID different?

DeID is healthcare-native, with pre-built support for HL7, CCDA, and free text deidentification, plus advanced features like synthetic data generation and NLP-based text handling.

Frequently asked questions