MedConceal: A Benchmark for Clinical Hidden-Concern Reasoning Under Partial Observability
MedConceal uses an interactive patient simulator to evaluate hidden-concern reasoning in medical dialogue, featuring 300 cases and 600 interactions.
Key Findings
Methodology
MedConceal evaluates hidden-concern reasoning in medical dialogue using an interactive patient simulator. Built from clinician-answered online health discussions, it comprises 300 curated cases and 600 clinician-LLM interactions. Each case pairs clinician-visible context with simulator-internal hidden concerns, structured using an expert-developed taxonomy.
Key Results
- Result 1: Human clinicians achieved the highest intervention success rate at 42.7%.
- Result 2: Doctor-R1 significantly improved intervention success from 15% to 42.7% in 20-turn dialogues.
- Result 3: Llama3-OpenBioLLM-8B achieved the highest reveal rate in confirmation tasks at 94.4%.
Significance
This study fills a gap in existing medical dialogue systems by introducing MedConceal, a new evaluation tool focused on reasoning under partial observability. It aids in developing more effective medical dialogue systems, improving patient-clinician communication, and enhancing treatment adherence and patient satisfaction.
Technical Contribution
MedConceal's technical contribution lies in its innovative patient simulator design, which enables interaction without directly exposing hidden concerns and evaluates dialogue processes through theory-grounded communication signals. This design allows for reasoning evaluation under partial observability.
Novelty
MedConceal is the first to focus on hidden-concern reasoning in medical dialogue systems, differing from traditional evaluation methods by introducing an interactive simulator that dynamically simulates patient hidden concerns.
Limitations
- Limitation 1: The simulator's behavior may not fully represent the complexity and diversity of real patients.
- Limitation 2: Current evaluation metrics may not capture all dimensions of dialogue quality.
Future Work
Future research could expand the simulator's complexity and diversity to better simulate real patient behavior. Additionally, more comprehensive evaluation metrics could be developed to more accurately assess dialogue system performance.
AI Executive Summary
Patient-clinician communication often faces the problem of asymmetric information, where patients are reluctant to disclose fears, misconceptions, or practical barriers. Existing medical dialogue benchmarks often overlook this challenge. MedConceal introduces a new evaluation tool focused on hidden-concern reasoning through an interactive patient simulator. Built from online health discussions, it comprises 300 curated cases and 600 clinician-LLM interactions. Experimental results show that human clinicians excel in intervention success, while different frontier models perform well in confirmation tasks. MedConceal provides a new evaluation tool for developing more effective medical dialogue systems, improving patient-clinician communication, and enhancing treatment adherence and patient satisfaction. Future research could expand the simulator's complexity and diversity to better simulate real patient behavior.
Deep Analysis
Background
In medical dialogue, the problem of asymmetric information between patients and clinicians has long existed. Patients are often reluctant to disclose their fears, misconceptions, or practical barriers, leading to poor treatment adherence and patient satisfaction. Existing medical dialogue benchmarks often assume full patient disclosure, ignoring the presence of hidden concerns.
Core Problem
The core problem is how to effectively reason about hidden concerns under partial observability. Clinicians need to guide patients to reveal their hidden concerns through dialogue and take appropriate intervention measures without fully understanding the patient's mental state.
Innovation
MedConceal's core innovation lies in its interactive patient simulator design. This simulator enables interaction without directly exposing hidden concerns and evaluates dialogue processes through theory-grounded communication signals. This design allows for reasoning evaluation under partial observability.
Methodology
- �� Interactive Patient Simulator: Built from online health discussions, comprising 300 cases.
- �� Structured Hidden Concerns: Using an expert-developed taxonomy.
- �� Theory-Grounded Communication Signals: Used to evaluate dialogue processes.
- �� Clinician Review: Ensures clinical plausibility.
Experiments
The experimental design includes 300 cases and 600 clinician-LLM interactions. Evaluation metrics include reveal rate in confirmation tasks and success rate in intervention tasks. Results show human clinicians excel in intervention success, while different frontier models perform well in confirmation tasks.
Results
Human clinicians achieved the highest intervention success rate at 42.7%. Doctor-R1 significantly improved intervention success from 15% to 42.7% in 20-turn dialogues. Llama3-OpenBioLLM-8B achieved the highest reveal rate in confirmation tasks at 94.4%.
Applications
MedConceal can be used to evaluate and improve medical dialogue systems, helping develop more effective patient communication strategies, enhancing treatment adherence and patient satisfaction.
Limitations & Outlook
The simulator's behavior may not fully represent the complexity and diversity of real patients. Current evaluation metrics may not capture all dimensions of dialogue quality. Future research could expand the simulator's complexity and diversity to better simulate real patient behavior.
Plain Language Accessible to non-experts
Imagine a doctor and a patient in a room, where the patient has hidden concerns like misunderstandings about medication or fear of treatment. The doctor needs to discover these hidden concerns through dialogue, like finding hidden objects in a dark room. MedConceal is like a tool that helps the doctor find these objects in the dark. It simulates patient responses, helping doctors practice better communication to find their true concerns.
ELI14 Explained like you're 14
Imagine you're playing a game where you're the doctor, and your task is to find the patient's hidden concerns. The patient might not tell you directly because they're scared or embarrassed. You need to ask questions to discover these hidden details. MedConceal is like a game helper, helping you practice asking better questions to find out what the patient really thinks. This way, you can help them get better treatment.
Glossary
Hidden Concern
Concerns or misconceptions a patient does not voluntarily disclose to a clinician.
In the paper, hidden concerns are the core content to be revealed through dialogue skills.
Partial Observability
Refers to a dialogue setting where the clinician cannot fully understand the patient's mental state.
In the study, partial observability is a key challenge in evaluating dialogue systems.
Patient Simulator
A system used to simulate patient responses, aiding in the evaluation of dialogue strategies.
MedConceal uses a patient simulator to evaluate hidden-concern reasoning.
Intervention Success
The proportion of dialogues where the clinician successfully addresses the primary concern.
Experimental results show human clinicians excel in intervention success.
Reveal Rate
The proportion of hidden concerns successfully revealed during dialogue.
Llama3-OpenBioLLM-8B achieved the highest reveal rate in confirmation tasks.
Open Questions Unanswered questions from this research
- 1 How to maintain simulator effectiveness in more complex patient scenarios?
- 2 Are current evaluation metrics sufficient to capture all dimensions of dialogue quality?
- 3 How to further improve dialogue system intervention success rates?
Applications
Immediate Applications
Medical Dialogue System Evaluation
MedConceal can be used to evaluate the performance of existing medical dialogue systems, helping identify areas for improvement.
Long-term Vision
Patient Communication Strategy Optimization
By continuously improving the simulator and evaluation metrics, it helps develop more effective patient communication strategies.
Abstract
Patient-clinician communication is an asymmetric-information problem: patients often do not disclose fears, misconceptions, or practical barriers unless clinicians elicit them skillfully. Effective medical dialogue therefore requires reasoning under partial observability: clinicians must elicit latent concerns, confirm them through interaction, and respond in ways that guide patients toward appropriate care. However, existing medical dialogue benchmarks largely sidestep this challenge by exposing hidden patient state, collapsing elicitation into extraction, or evaluating responses without modeling what remains hidden. We present MedConceal, a benchmark with an interactive patient simulator for evaluating hidden-concern reasoning in medical dialogue, comprising 300 curated cases and 600 clinician-LLM interactions. Built from clinician-answered online health discussions, each case pairing clinician-visible context with simulator-internal hidden concerns derived from prior literature and structured using an expert-developed taxonomy. The simulator withholds these concerns from the dialogue agent, tracks whether they have been revealed and addressed via theory-grounded turn-level communication signals, and is clinician-reviewed for clinical plausibility. This enables process-aware evaluation of both task success and the interaction process that leads to it. We study two abilities: confirmation, surfacing hidden concerns through multi-turn dialogue, and intervention, addressing the primary concern and guiding the patient toward a target plan. Results show that no single system dominates: frontier models lead on different confirmation metrics, while human clinicians (N=159) remain strongest on intervention success. Together, these results identify hidden-concern reasoning under partial observability as a key unresolved challenge for medical dialogue systems.