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Querying Healthcare Data Lake Using Llms Without Direct Data Exposure: A Feasibility Study.

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Querying Healthcare Data Lake Using Llms Without Direct Data Exposure: A Feasibility Study.

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Querying Healthcare Data Lake Using LLMs Without Direct Data Exposure: A Feasibility Study.

Frederic Ehrler¹, Florian Singer¹, Deniz Geçer¹

¹Direction of Digital Transformation and Augmented Intelligence, University Hospitals of Geneva.

Studies in Health Technology and Informatics

|May 23, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a secure method for querying healthcare data lakes using large language models (LLMs) indirectly. This approach enhances data accessibility for research while protecting patient privacy.

Keywords:

Artificial Intelligence Data Sharing Databases Factual Information Storage and Retrieval Medical Informatics Natural Language Processing

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Area of Science:

Health Informatics
Artificial Intelligence in Healthcare
Data Management

Background:

Healthcare data lakes integrate diverse information for research and clinical decisions.
Querying these data lakes, like MongoDB, is complex and requires specialized skills.
Direct use of large language models (LLMs) for querying poses significant privacy risks.

Purpose of the Study:

To present a method for indirect LLM-assisted querying of healthcare data lakes.
To ensure data confidentiality by preventing direct LLM access to sensitive information.
To explore the potential of LLMs in simplifying healthcare data access.

Main Methods:

A multistep workflow involving user intent clarification.
Schema and example-guided query generation.

Expert validation prior to query execution.

Indirect LLM assistance without direct data access.

Main Results:

The pipeline generated syntactically valid code aligned with user intent for test queries.
Qualitative assessment indicated potential for reducing technical barriers.
Limitations identified include hallucinated fields and missing lookups.

Conclusions:

Indirect LLM-assisted querying offers a promising, secure approach to accessing healthcare data lakes.
This method can improve usability for researchers and clinicians.
Further large-scale validation is necessary to confirm effectiveness and address limitations.