This blog explores how OmniIndex’s semantic search can be used to boost productivity for companies without compromising the integrity or security of their files and data. Specifically, this blog explores a customer case study of Health Innovation Hub and their health app: Uniq. 

The Challenge: Connecting the Dots in a Sea of Personal Health Data

The Uniq app collects a vast array of data points for each user, from microbiome composition and blood biomarkers to daily logs of diet, exercise, and mood. The challenge lies in efficiently connecting and analyzing these disparate pieces of information to create a holistic picture of an individual’s health without exposing this sensitive and personal data to third-parties or putting it at risk of a breach. 

Traditional data management and search methods fall short when faced with the complexities of Uniq’s data landscape because they typically rely on keyword matching and require data to be decrypted before searching can occur. Owing to the regulated nature of the PII and health information Uniq are working with, as well as their commitments to user privacy and confidentiality, it was unacceptable for them to have to regularly decrypt all this data and risk its exposure. 

Keyword search also lacks the nuanced understanding of natural language and the ability to discern relationships between different data points. For example, it might identify instances of “exercise” and “mood” separately, but fail to recognize the correlation between regular physical activity and improved mental wellbeing. Furthermore, traditional search struggles with the inherent heterogeneity of the data, treating microbiome composition, blood biomarkers, and dietary logs as distinct entities rather than interconnected elements of a holistic health profile. This inability to understand context and relationships hinders the discovery of meaningful patterns and actionable insights crucial for personalized health recommendations.

The Solution: Semantic Search for Personalized Health Insights

OmniIndex’s semantic search technology offers a secure and customizable solution for gaining insights from regulated and sensitive data without decryption.

Traditional keyword search operates by matching literal strings of characters, requiring an exact (or partial) match between the search term and the indexed data. Semantic search, in contrast, leverages Natural Language Processing (NLP) and machine learning to understand the intent and meaning behind a query. It goes beyond simple keyword matching to identify synonyms, related concepts, and the relationships between different entities within the data. 

For example, a keyword search for “elevated blood sugar” might miss results containing “hyperglycemia” or “high glucose levels,” whereas semantic search can recognize these as related concepts and return all relevant results if its engine includes a medical lexicon. Furthermore, OmniIndex’s patented homomorphic encryption enables the data to remain encrypted at all times, even during indexing and searching, addressing critical security and privacy concerns associated with sensitive health information.

This specialization of the search is possible due to OmniIndex’s Small Language Model AI: Boudica. It can be tailored for specific customers by adding specialist thesauruses to enable the AI to understand technical jargon and the relationships between specialist words and concepts. 

For Uniq, this translates to the following potential benefits:

• Efficient Data Retrieval: Uniq’s team can quickly access and analyze specific user data, such as correlations between gut microbiome diversity and mood patterns, or the impact of dietary changes on blood sugar levels. This works because semantic search understands the relationships between these data points, surfacing relevant information without requiring complex queries.
• Personalized Insights: By understanding the context and relationships within the data, semantic search can help Uniq generate highly personalized health recommendations. For example, the system could identify specific dietary changes that are likely to improve a user’s gut health and overall well-being based on their individual microbiome profile and diary entries.
• Trend Identification: Semantic search can identify trends and patterns across user data, revealing valuable insights into the effectiveness of different health interventions. This allows Uniq to refine their recommendations and improve the overall user experience.

Streamlining Workflows and Ensuring Data Privacy

Beyond personalized insights, OmniIndex integrates seamlessly with existing workflows and ensures the highest levels of data privacy.

• Integration with the Uniq App: OmniIndex can integrate directly with Uniq’s app, allowing users to access personalized insights and recommendations directly within the platform. This creates a seamless and user-friendly experience.
• PII Redaction: Protecting sensitive user data is paramount. OmniIndex’s AI chatbot, Boudica, automatically identifies and redacts Personally Identifiable Information (PII), ensuring compliance with data privacy regulations.
• Threat Intelligence: Security is crucial in the healthcare space. OmniIndex enables real-time AI threat intelligence, protecting user data from potential security breaches. There is also built in compliance intelligence and a full immutable audit trail.

The Outcome: Empowering Users to Take Control of Their Health

In conclusion, semantic search offers a powerful solution for companies like Uniq, enabling them to unlock the full potential of their personal health data without exposing it through decryption. 

Click here to learn more about our work with Uniq Health.
Click here to learn more about our Semantic Search including a Deep Dive Podcast.