Institute Privacy Preserving Data Collaborations Across Your Entire Healthcare Ecosystem

Fuel research, discovery, and better patient outcomes – privacy and compliance intact. 

Duality enables secure data collaboration on sensitive personal health information (PHI) that expedites every step of the healthcare innovation lifecycle: discovery, development, deployment and commercialization. 

With Duality, researchers, providers, and payers to query, analyze, and train machine learning (ML) models on the very best available patient data, without being encumbered by multiple expert determinations or resorting to methods which degrade data quality, like traditional de-identification.  

Use Cases

Easily overcome cross facility and interoperability challenges to combine diverse datasets and remain HIPAA compliant – without de-identifying PHI contained in Real World Data (RWD), and while improving the accuracy of the results.

Personalized Care and Coverage

Gain a complete view of patient health and risk by accessing multiple sources of sensitive data (provider, payer, pharmacies, fitness trackers, etc.) to deliver personalized healthcare and custom health insurance offers, while upholding data privacy expectations and regulations.

Nonadherence Prediction and Intervention

Use patient-level pharmaceutical  claim data to predict nonadherence and effectively intervene to reduce healthcare costs – without exposing or sharing PHI.

Omnichannel Care

Implement a shared data strategy to streamline patient engagement across all channels, digital and in-person, by combining consumer health data from providers, pharmaceuticals, payers, IoT devices, and more – to gain a comprehensive view  of patient and customers, across teams and divisions.

Healthcare Insurance Fraud

Streamline detection and investigation of abnormal patient claims by collaborating with other insurance companies on patient-level claims data. Use claims data to cross-check for duplicate claims and train and deploy AI fraud detection models- while keeping sensitive PHI and claims data hidden. 

Stage 4 Clinical Trials

Accelerate data collaborations  of patient populations and clinical data  – cross facilities to derive greater research discovery and outcomes  – without sharing data or exposing it to re-identification risk.

Genome-Wide Association Studies (GWAS)

Initiate multi-site research with ease by collaborating on encrypted genomic and clinical data – while increasing data insights without lengthy legal processes.

Health Economics and Outcomes Research (HEOR)

Gather real-world evidence from multiple sites and sources; healthcare providers, patient surveys and electronic health records and more to quickly gain a more complete and accurate understanding of a drug’s economic value and improve patient care.

Genetic Data Brokering

Genomic sequencing companies can now more easily grant pharma and biotech researchers secure access to a larger pool of genomic data, without degrading data quality with traditional de-identification – all while ensuring patient data privacy and regulatory compliance.


Dispersed Patient Data Inaccessible

Relevant data from clinical visits, lab results, pharmacy, insurance and additional sources is fragmented across organizations and cannot be accessed by partners seeking a holistic view of the patient to improve care.

PHI and PII Re-identification Risk Unmitigated

PHI and PII that is de-identified using traditional methods may be re-identified, exposing sensitive data. De-identification also can greatly reduce the value of the data.

Multicenter RWE Studies Delayed

Real World Evidence (RWE) studies are crucial to medical and pharmaceutical innovation but concerns around data privacy, security, regulation, and confidentiality make collaborative medical data analysis cumbersome.


Privacy Preserving Query

Duality enables queries on sensitive PHI and PII to answer questions and provide insights on patient behaviors and outcomes across data sets, –without exposing the search terms, moving the data from the endpoints, or exposing any personal information in the results.

Privacy Preserving Personalization

Leverage new data partnerships to gain a more comprehensive, accurate assessment of risk so they can offer personalized pricing and optimizing coverage plans for potential and current customers.

Reduce Time to Insight

Duality allows researchers to avoid lengthy data sharing BAA negotiations and IRB reviews by enabling every aspect of data collaboration, including linking and analyzing patient records - without sharing or exposing regulated PHI or PII.

Case Study

Related Content

Case Study: Privacy Preserving Genome-Wide Association Studies Powered by Duality

Dana Farber research project uses the Duality platform for GWAS studies.

Case Study: Leveraging Multisource Health Data to Personalize Health Plans

Using Duality, NTT DATA can suggest their clients with which to provide end users with more optimized health insurance plans.

Experience secure collaborative computing today. 

Maximize the value of sensitive, regulated, or confidential data.