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Research Origins

HANA’s technology originated from a clinical research project analyzing voice patterns in bipolar disorder patients. The original work focused on detecting transitions between depressive and manic episodes through vocal biomarkers — pitch contour, speech rate variability, energy patterns, and pause dynamics. Key findings from this research informed HANA’s vocal intelligence architecture:
  • Voice-based features can detect mood state transitions before patients self-report changes
  • Longitudinal tracking (baseline-deviation) significantly outperforms single-session emotion classification
  • Prosodic features are more reliable than lexical sentiment for detecting clinical state changes
  • Combining prosodic analysis with conversational content produces the strongest clinical signals
This work has since been extended to PTSD, chronic pain, palliative care, and geriatric populations — each with specific vocal patterns associated with clinical deterioration or improvement.

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Active Research

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Voice-Based Clinical Conversation Validation

  • Ongoing study measuring clinical accuracy of AI-conducted patient conversations compared to human staff
  • Evaluation across multiple clinical protocols: intake, chronic care management, behavioral health screening
  • Metrics: information extraction accuracy, protocol compliance, patient satisfaction, clinical outcome correlation

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Vocal Biomarker Detection

  • Longitudinal analysis of prosodic features as indicators of clinical state change across patient populations
  • Baseline-deviation methodology validation across demographics, languages, and clinical conditions
  • Integration of vocal intelligence with clinical conversation outcomes for predictive engagement models

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Deliberation Framework Validation

  • Systematic evaluation of multi-model deliberation vs. single-model clinical reasoning for conversation planning
  • Benchmarking against standard medical evaluation datasets
  • Focus on calibration quality, hallucination reduction, and clinical safety margins

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Patient Engagement and Retention

  • Longitudinal study tracking patient engagement metrics (completion rates, re-engagement, dropout rates) across voice AI vs. traditional outreach methods
  • Analysis of health coaching and accountability companion features on program retention
  • Multi-site study across primary care, behavioral health, and specialty organizations
  • Measuring downstream clinical outcomes: no-show reduction, care gap closure, medication adherence

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Voice AI Safety in Healthcare

  • Research into safety boundaries for autonomous voice-based patient interactions
  • Escalation threshold optimization: when should AI defer to humans?
  • Adversarial testing for rule-based safety agents, including compound risk signal detection
  • Assessment of text-prosody discrepancy detection as a clinical safety signal

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Publications

Research publications will be made available as studies are completed and peer-reviewed. Current work is in preparation for submission to healthcare informatics and clinical AI journals.

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Research Partnerships

HANA collaborates with healthcare organizations and academic institutions on clinical AI research. Contact [email protected] for partnership inquiries.