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.
De-identify provides an efficient and reliable solution to protect sensitive patient data while ensuring that healthcare providers, researchers, and other stakeholders can extract valuable insights from the data.
Why you'll love our tech
Protects patient privacy by ensuring all sensitive patient data is de-identified
Ensures compliance with HIPAA, GDPR and privacy acts regulatory requirements
Extract valuable insights from healthcare data while ensuring patient privacy
Save time and resources without a time-consuming individual patient consent process
Value our tech delivers
Advanced techniques with rich features
An assessment of the data set(s) in scope and de-identification configuration settings required to meet desired thresholds.
More than just masking or removing PHI. Our techniques include hashing, generalisation, k-anonymity, and differential privacy. This ensures compliant de-identification that retains as much data usefulness as possible.
Return of de-identified data with an accompanying detailed report.
De-identify datasets when released for external recipients for research or analytical activities.
It estimates the “contexts” of data recipients to produce de-identified datasets, optimally balancing between utility and protecting patient privacy.
De-identifying a database
Thoroughly testing a system in an environment that closely resembles the production environment is the best way to ensure that it performs as expected, especially when significant changes have been made.
De-identify can remove sensitive PHI information from the production database, while retaining most of the business transaction data, making it easier to test the system with realistic data.
De-identifying clinical free texts
Clinical free text medical data contains essential information that may not be available in structured records.
De-identify supports de-identifying clinical free text, which enables users to upload such data and generate pseudonymized surrogate clinical texts.
This process helps protect patient privacy while preserving the valuable information contained in the original clinical free text.
De-identifying HL7 messages
De-identify can parse batches of HL7 messages and display a user-friendly configuration screen that allows users to specify the appropriate de-identification actions for each field.
This module is particularly useful in reproducing messaging-related transactions that mimic the production environment during Healthcare IT projects.