Clinical Production Eval

clinical-production-eval

Perfecting Human-AI Interaction at Clinical Scale. Turning Production Signals into Safer, More Human Conversations — Mukherjee et al. (2026) (arXiv:2603.29893, 2026)

What this evaluates

Evaluates the safety, accuracy, and interaction quality of a clinical AI voice agent using real-world production call data and clinician-validated simulations. It probes system-level reliability across clinical tasks, conversational dynamics, and operational performance to determine if the model handles noisy, multi-turn healthcare conversations safely.

Datasets

• Live Patient Calls — total ?; splits: test (-1)
• Clinician-Validated Simulations — total ?; splits: test (-1)
• HEART — total ?; splits: test (-1)

Metrics

• Error Rate (primary) — range: percent
  • Percentage of conversations containing clinically relevant errors, workflow failures, or ASR misrecognitions. Calculated as (number of failed calls / total calls) * 100. Lower values indicate better safety and accuracy.
• Win Rate — range: percent
  • Percentage of simulated conversations where the model's response is preferred over the baseline by clinician evaluators across dimensions like naturalness, empathy, and task-following. Calculated as (number of wins / total simulations) * 100.
• TTFT — range: ms
  • Time-to-first-token, measuring latency in milliseconds from call initiation to the first audio output. Treated as a safety constraint affecting patient trust and compliance.

Input / output format

Input: Real-world voice conversations (transcribed via clinical ASR) and simulated dialogue turns between patients and AI agents, including clinical context, scheduling requests, emotional cues, and system API states.

Output: AI agent responses (text/audio), system actions (API calls for scheduling/documentation), and clinician preference labels or error flags for each interaction turn.

Scoring recipe

def calculate_error_rate(live_calls):
    total = len(live_calls)
    errors = sum(1 for c in live_calls if c.has_clinical_error or c.workflow_failed)
    return (errors / total) * 100

def calculate_win_rate(sim_convs):
    total = len(sim_convs)
    wins = sum(1 for s in sim_convs if s.model_response > s.baseline_response)
    return (wins / total) * 100

def calculate_ttft(live_calls):
    return [c.time_to_first_token for c in live_calls]

Common pitfalls

• Static benchmark accuracy does not correlate with live call safety; production errors often stem from upstream ASR/input noise rather than downstream reasoning.
• Evaluating only single-turn correctness misses multi-turn repair, clarification, and longitudinal memory requirements critical for clinical safety.
• Latency (TTFT) is treated as a performance metric but is explicitly framed as a safety constraint affecting patient trust and compliance.

Evidence (verbatim from paper)

Evaluations on live patient interactions and simulated conversations with clinicians show that Polaris 4 reduces clinically relevant errors while improving interaction quality, longitudinal continuity, and responsiveness. The results illustrate the paper’s central claim: production-grade clinical intelligence is achieved by learning from real-world interaction signals and embedding them into system-level design, not by optimizing isolated model accuracy alone. Evaluating Error Rate (↓) on Human–AI Real Conversations ... Evaluating Win Rate (↑) on Simulated Conversations ... Main Model Latency Time-to-first-token (TTFT) ...

Citation

@misc{mukherjee2026perfecting,
  title={Perfecting Human-AI Interaction at Clinical Scale. Turning Production Signals into Safer, More Human Conversations},
  author={Mukherjee et al. (2026)},
  year={2026},
  note={arXiv:2603.29893}
}

• arXiv: 2603.29893