Global Medical Company Safeguards Healthcare Data in Cross-Departmental Projects

Securing healthcare data for a cross-departmental project using machine learning to unearth novel disease insights.

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100%
compliance with data protection regulations
60%
increase in time-to-data
50%
faster time-to-insight for researchers
Case Study Summary
A global medical research institution was undertaking a cross-departmental project to discover novel disease insights, using machine learning and advanced analytics.

Given the senstitivity of the healthcare data involved, the project demanded a potent balance of data privacy, analytical precision, and regulatory compliance. Anonos Data Embassy emerged as the go-to solution to fulfill these requirements.
Challenge
The medical research project involved the use of sensitive healthcare data. The scope of the project meant the data would be shared across multiple departments. Therefore, ensuring data privacy, preventing unauthorized relinking, and complying with regulatory guidelines were paramount, all while maintaining the data's utility for machine learning and analytical purposes.
Solution
Anonos Data Embassy offered the company an arsenal of Privacy-Enhancing Technologies (PETs) to safeguard sensitive healthcare data while preserving its utility. The use of these techniques was carefully tailored to suit each stage of data processing and the specific needs of the project:

  • Statutory Pseudonymization: This advanced technique was introduced to align the project with external and internal data regulations. It effectively shielded both direct and indirect identifiers in the data, while deploying dynamic tokens to prevent unauthorized re-linking. Crucially, this approach maintained data's analytical utility and offered the potential to reconnect data to the original records, but only by authorized personnel under controlled conditions.
A diagram of Variant Twins technology, demonstrating the process of separating data value from personally-identifiable information through record-level dynamic pseudonymization to ensure insurance data privacy.
  • Synthetic data. A significant challenge in many research projects is the scarcity of sufficient data to formulate balanced and unbiased hypotheses. Synthetic data technology addressed this by producing artificial data records to complement and balance original pseudonymized datasets. This not only augmented the dataset with anonymized synthetic healthcare data but also empowered scientists to conduct comprehensive exploratory analyses without compromising privacy.
Synthetic Data Augmentation
Results
Regulatory Compliance Successfully met compliance with global and local data protection regulations, mitigating risks of non-compliance penalties.
Operational Efficiency Reduced healthcare data preparation and compliance validation times by approximately 60%, streamlining the overall research process.
Time-to-Insight Acceleration The streamlined data security and privacy procedures enabled a 50% faster time-to-insight for researchers, leading to quicker hypothesis validation.
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