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Proactive Knowledge Inquiry in Doctor-Patient Dialogue: Stateful Extraction, Belief Updating, and Path-Aware Action Planning

Zhenhai Pan, Yan Liu, Jia You

Abstract

Most automated electronic medical record (EMR) pipelines remain output-oriented: they transcribe, extract, and summarize after the consultation, but they do not explicitly model what is already known, what is still missing, which uncertainty matters most, or what question or recommendation should come next. We formulate doctor-patient dialogue as a proactive knowledge-inquiry problem under partial observability. The proposed framework combines stateful extraction, sequential belief updating, gap-aware state modeling, hybrid retrieval over objectified medical knowledge, and a POMDP-lite action planner. Instead of treating the EMR as the only target artifact, the framework treats documentation as the structured projection of an ongoing inquiry loop. To make the formulation concrete, we report a controlled pilot evaluation on ten standardized multi-turn dialogues together with a 300-query retrieval benchmark aggregated across dialogues. On this pilot protocol, the full framework reaches 83.3% coverage, 80.0% risk recall, 81.4% structural completeness, and lower redundancy than the chunk-only and template-heavy interactive baselines. These pilot results do not establish clinical generalization; rather, they suggest that proactive inquiry may be methodologically interesting under tightly controlled conditions and can be viewed as a conceptually appealing formulation worth further investigation for dialogue-based EMR generation. This work should be read as a pilot concept demonstration under a controlled simulated setting rather than as evidence of clinical deployment readiness. No implication of clinical deployment readiness, clinical safety, or real-world clinical utility should be inferred from this pilot protocol.

Proactive Knowledge Inquiry in Doctor-Patient Dialogue: Stateful Extraction, Belief Updating, and Path-Aware Action Planning

Abstract

Most automated electronic medical record (EMR) pipelines remain output-oriented: they transcribe, extract, and summarize after the consultation, but they do not explicitly model what is already known, what is still missing, which uncertainty matters most, or what question or recommendation should come next. We formulate doctor-patient dialogue as a proactive knowledge-inquiry problem under partial observability. The proposed framework combines stateful extraction, sequential belief updating, gap-aware state modeling, hybrid retrieval over objectified medical knowledge, and a POMDP-lite action planner. Instead of treating the EMR as the only target artifact, the framework treats documentation as the structured projection of an ongoing inquiry loop. To make the formulation concrete, we report a controlled pilot evaluation on ten standardized multi-turn dialogues together with a 300-query retrieval benchmark aggregated across dialogues. On this pilot protocol, the full framework reaches 83.3% coverage, 80.0% risk recall, 81.4% structural completeness, and lower redundancy than the chunk-only and template-heavy interactive baselines. These pilot results do not establish clinical generalization; rather, they suggest that proactive inquiry may be methodologically interesting under tightly controlled conditions and can be viewed as a conceptually appealing formulation worth further investigation for dialogue-based EMR generation. This work should be read as a pilot concept demonstration under a controlled simulated setting rather than as evidence of clinical deployment readiness. No implication of clinical deployment readiness, clinical safety, or real-world clinical utility should be inferred from this pilot protocol.
Paper Structure (27 sections, 21 equations, 2 figures, 5 tables)

This paper contains 27 sections, 21 equations, 2 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Clinical note generation and dialogue summarization
  4. Clinical information extraction and dialogue state tracking
  5. Retrieval-augmented assistance and healthcare knowledge graphs
  6. Sequential decision making and uncertainty control
  7. Problem Formulation
  8. Dialogue as a partially observed inquiry process
  9. Stateful events
  10. CurrentState, GoalState, and GapSignals
  11. Belief state
  12. Action space and objective
  13. Method
  14. Stateful extraction
  15. Sequential belief updating
  16. ...and 12 more sections

Figures (2)

  • Figure 1: Conventional linear EMR generation versus the proposed proactive inquiry loop. The main control loop updates dialogue state and feeds back into the ongoing interaction, while the EMR is generated as a structured output branch from the evolving state. The figure emphasizes that the proposed framework intervenes before note finalization by querying unresolved clinically relevant gaps.
  • Figure 2: Hybrid retrieval in the proposed framework: vector coarse retrieval narrows the candidate set, object-level reranking aligns candidates with state structure, and path-aware reasoning prioritizes clinically actionable support.