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Rhino Health Solutions for the Public Sector

Introduction to the Rhino Federated Computing Platform

Rhino Health streamlines data collaborations through generative AI, federated learning and edge computing, making it possible to work with partners' data without requiring any data transfer. Data engineering, informatics, and AI/ML leaders at >50 leading hospitals, top 10 pharmacos, and public sector organizations are using Rhino's software to harmonize their data, do federated analytics, train AI models, and even deploy custom code - all while preserving patient privacy and data security.

Key Features:

  • The Harmonization Copilot
    • Generative AI-powered workflow that reduces data harmonization expenses by automating the mapping of idiosyncratic data into a target data model, leveraging Large Language Models that scale across clients without requiring any data transfer for model training or inference.
  • The Federated Computing app
    • Use edge computing to run compute on distributed data - be it for federated analytics (e.g. patient counts), preprocessing / annotation / transformation of data, or federated training and validating AI models. This is all enabled by several confidentiality and privacy-enhancing features such as role-based access control, differential privacy, k-anonymization, and encryption.
  • Federated Datasets app
    • Online database and visualization layer for multimodal datasets sitting behind the client’s firewall, allowing for rapid discovery and seamless linking into new analytics and development projects by internal and external viewers.
  • Federated Trusted Research Environment (fTRE)
    • Allows organizations to grant third parties controlled access to data sources, facilitating analysis including a full suite of biostatistical methods, AI model development, and deployment of commercial software packages on those data); but ensuring data always remain behind the site’s firewall.

End-to-End Solutions for the Public Sector:

Federated Consortia

(for academic AI collaborations, development of new quality & safety measures)
Quickly spin up collaborations across sites by facilitating data harmonization, data quality checks & preprocessing, and running federated analytics or federated learning across distributed data.

Public Health Surveillance Networks

Build a network of participating healthcare organizations or other data partners to provide real time disease-specific, syndromic, environmental, or behavioral signals - with the ability to complete federated joins on the same data subjects with records across multiple sources.

Model Monitoring

(to identify & address bias, ensure ongoing efficacy)
Validate the efficacy of AI models for different populations across a broad network of provider organizations to ensure AI models work for all patients, and identify model drift.