> ## Documentation Index
> Fetch the complete documentation index at: https://docs.hana.health/llms.txt
> Use this file to discover all available pages before exploring further.

# Research

> Active research initiatives, clinical validation studies, and publications from HANA's healthcare AI research program.

## 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.

## Active Research

### 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

### 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

### 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

### 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

### 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

## 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.

## Research Partnerships

HANA collaborates with healthcare organizations and academic institutions on clinical AI research. Contact [research@hana.health](mailto:research@hana.health) for partnership inquiries.