Healthcare

Healthcare

Learn about Secret Computing® applications for healthcare providers, payers, pharmaceutical companies and research institutions.

  1. Augmented Heart Disease Analysis

  2. Clinical Trials Patient Selection and Matching


Augmented Heart Disease Analysis

The One Sentence Summary: Healthcare providers and medical researchers access more features and more samples in its cardiovascular disease research initiatives, leading to more accurate disease prognosis.

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Case Study

AUGMENTED HEART DISEASE ANALYSIS

Industry: Healthcare, Medical Research

Application Area: Cardiovascular disease prognosis

Functions: Private Set Intersection, Logistic Regression

Situation: A large healthcare provider wants to facilitate early prognosis of cardiovascular diseases through increased private data utilization.

Complication: Privacy laws such as HIPAA and confidentiality concerns prevent contributors from sharing data outside of organizational firewalls.

Resolution: XOR enables researchers to privately compute across organizational data sources in order to increase both sample size and patient attributes, leading to improved model performance and disease prognosis.

Outcomes:

  • Increased sample size improves predictive model performance

  • Sample size increase >100% and growing with additional data contributors

  • Incremental feature set enables new predictive insights

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CURIOUS TO LEARN MORE ABOUT AUGMENTED HEART DISEASE ANALYSIS?

LET’S TALK!

Please contact our Business Development and Solutions team to learn more about Augmented Heart Disease Analysis.


Clinical Trials Patient Selection and Matching

The One Sentence Summary: Clinical trials researchers can securely access distributed, private EHR repositories for improved patient selection and matching while maintaining privacy and compliance.

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Customer Case Study

CLINICAL TRIALS PATIENT SELECTION

Industry: Healthcare, Medical Research

Application Area: Patient Selection and Matching

Functions: Oblivious Compare, Transfer Learning, LogReg, ‘Inpherence’

Situation: Clinical trials need sufficiently large sample sizes to be viable, so researchers need access to large patient populations in order to recruit candidates that meet the study eligibility criteria. Additionally, EHR data have rich textual features that can be extracted for efficient patient matching.

Complication: EHR data contains sensitive information and must remain HIPAA & GDPR-compliant, making it difficult for researchers to access sufficient patient populations for their trials. Also, descriptive textual features often go unused during the selection process because of their qualitative, language-based nature.

Resolution: Leverage XOR to run advanced matching and analysis across distributed, private EHR repositories for patient selection while maintaining privacy and compliance.

Outcomes:

  • HIPAA & data residency compliance is maintained during the computation

  • Additional textual features are leveraged in patient selection, increasing overall patient eligibility

  • Medical researchers fill trials more quickly, leading to more efficient drug development

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CURIOUS TO LEARN MORE ABOUT CLINICAL TRIALS PATIENT SELECTION?

LET’S TALK!

Please contact our Business Development and Solutions team to learn more about Clinical Trials Patient Selection and Matching.