All Required, In Order: Phase-Level Evaluation for AI-Human Dialogue in Healthcare and Beyond

2026-01-13

AI Summary

Current evaluation methods for conversational AI struggle to assess adherence to clinical obligations across full dialogues, particularly lacking support for the sequencing of information gathering and auditable evidence. This work proposes OIP-SCE, a novel stage-based framework for evaluating obligation compliance that translates clinical policies into shareable, executable dialogue steps. By integrating staged structural modeling, obligation-aware information tracking, and an evidence annotation mechanism, the approach operationalizes complex clinical rules while ensuring auditability. Validation in two real-world scenarios—respiratory history taking and insurance eligibility verification—demonstrates that OIP-SCE effectively aligns AI capabilities with clinical requirements, thereby enabling safe and routine deployment in regulated healthcare settings.

Technology Category

• Natural Language Processing: Conversational AI/Dialog Systems
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Conversational AI is starting to support real clinical work, but most evaluation methods miss how compliance depends on the full course of a conversation. We introduce Obligatory-Information Phase Structured Compliance Evaluation (OIP-SCE), an evaluation method that checks whether every required clinical obligation is met, in the right order, with clear evidence for clinicians to review. This makes complex rules practical and auditable, helping close the gap between technical progress and what healthcare actually needs. We demonstrate the method in two case studies (respiratory history, benefits verification) and show how phase-level evidence turns policy into shared, actionable steps. By giving clinicians control over what to check and engineers a clear specification to implement, OIP-SCE provides a single, auditable evaluation surface that aligns AI capability with clinical workflow and supports routine, safe use.

Problem

• AI-human dialogue
• clinical compliance
• phase-level evaluation
• obligatory information
• healthcare AI

Innovation

• Obligatory-Information Phase Structured Compliance Evaluation
• AI-Human Dialogue
• Clinical Workflow Alignment
• Auditable Evaluation
• Phase-Level Compliance