Demand for high quality data is essential to researchers. De-identification ensures privacy protection when making PHI available for secondary use, but not all approaches offer the granularity needed for discovery and innovation. By leveraging risk-based de-identification, Academic Medical Centers and Health Registries are able to amalgamate the vast amounts of health data available into a database that offers high quality data with the lowest risk of re-identification.
Ensure Patient Privacy
Risk-based de-identification delivers high-quality, anonymous data that avoids consent bias and allows flexibility in responding to data requests for research. Many regulators and industry groups, like the European Medicines Agency, Institute of Medicine and HITRUST have already developed best practice that emphasize privacy-based standards for data sharing. Our methodology ensures patient privacy and compliance.
Quality data is at the core of risk-based de-identification. Privacy Analytics software and services built on HIPAA’s Expert Determination Method of the HIPAA Privacy Rule, retain nuance and granularity. Data masking techniques tend to limit dates to one year. By finding where risk lies in the data set, data managers can apply the appropriate level of de-identification, and allow dates to be retained to the week.
“It’s insurance that we’re not going to inadvertently do a disclosure that we shouldn’t.”- Dr. Ann Sprague, Better Outcomes Registry Network (BORN) Scientific Manager
Learn how BORN enabled the sharing of EMR and population-based birth data in order improve maternal health care through risk-based de-identification.READ THE BORN CASE STUDY
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