mlsanityData to 14 Oct 2026

ClinQueryAgent: A Conversational Agent for Population Health Management

arXiv 2605.18768 ↗First posted 13 Apr 2026cs.IR cs.HC cs.MAcited by 0

On the map: LLM agents and tool use → Medical and scientific domain agents

Abstract (arXiv)

In this paper we introduce ClinQueryAgent, a system for translating natural language population health questions into executable database queries using agents with access to both local and external knowledge bases. Our novel architecture enables the use of powerful cloud-based language models whilst ensuring that no patient data leaves the secure environment. To combat inaccuracies over the course of longer dialogues due to context rot, information retrieval is delegated to a sub-agent. We deploy the system via a chat window embedded within an existing population health management platform where it has been used by 128 staff from 15 healthcare practices covering a total of 148,319 patients in the UK's National Health Service (NHS). We evaluate the system's capacity to autonomously handle a range of health informatics tasks on a constructed dataset and via a beta-testing phase. Our results show that both analysts and clinicians are able to easily generate actionable information from patient health records using natural language requests requiring no programming expertise to verify. We make a public demo of the system available at: https://demo-899965260288.europe-west1.run.app/

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use. Builds on 9 of its 23 references that are in the map. First posted on arXiv 13 Apr 2026.

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9 of 23 references.

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  1. 2025MedAgentBench: A Realistic Virtual EHR Environment to Benchmark Medical LLM Agents24 Jan

Other references · 8

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
  4. 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
  5. 2025LLMs Get Lost In Multi-Turn Conversationcited by 429
  6. 2024A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systemscited by 170
  7. 2024SM3-Text-to-Query: Synthetic Multi-Model Medical Text-to-Query Benchmarkcited by 14
  8. 2025Generating patient cohorts from electronic health records using two-step retrieval-augmented text-to-SQL generationcited by 4

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  6. 2026MedDialogRubrics: A Comprehensive Benchmark and Evaluation Framework for Multi-turn Medical Consultations in Large Language Models
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