AI tools to enhance platform quality and speed while mitigating sponsor costs
EMA Wellness utilizes AI tools to enhance the quality and speed of configuration and
testing, as well as ongoing testing in the production environment to preclude in-study
issues.
EMAW utilizes LLMs on a per-study basis to independently score clinical
interviews. These scores are compared against rater scores, and in some cases, reviewer scores, and
any discordance is flagged at the item level. The LLM will include justification for each score, and
a clinician will review both measures and assess whether the discordance should be escalated to the
study team for potential remediation.
EMAW utilizes agent AI models for queries at the
study level, identifying anomalies, discrepancies, and outliers which require further review by the
study team. Architecture designed for large language models and advanced analytics.
Our
AI workflows support clinical interview quality review, endpoint consistency analysis, rater
surveillance, predefined flag generation, multivariate signal detection, composite endpoint
generation, and predictive treatment modeling.
Rather than layering AI onto disconnected
systems, EMA Wellness integrates capture, standardization, and analysis into a single operational
framework.
Two AI pillars
Large Language Models and Agentic AI
EMA Wellness uses two distinct AI layers across the clinical trial platform: LLMs for independent clinical quality review and Agentic AI for on-demand queries, anomaly detection, auditability, and multimodal analytics.
Large Language Models
LLMs support independent scoring and quality review across clinical assessments and interview content.
- Independent scoring
- Transcription review for qualitative assessment
- Discrepancy detection and flag generation
- Generates targeted clinical reviews
Agentic AI
Agentic AI queries across data modality and type to generate actionable insights from standardized multimodal trial data.
- Outlier and anomaly detection
- Insights based on specific requests
- Responses to all queries in less than a minute
- Multimodal analytics, visualization, and narrative
In practice
How LLM-driven quality review works during a trial
A simple view of how independent scoring and rater surveillance operate in real time without changing site workflows.
1. Assessment captured
A site completes a standard clinical assessment, such as MADRS or HAM-A, with audio or video recorded as part of the normal workflow.
2. Parallel LLM scoring
The LLM independently scores the assessment based on structured inputs and interview content, creating a second reference score.
3. Discrepancy detection
Differences between rater and LLM scores, as well as administration patterns, are evaluated to identify variability or potential quality issues. Predefined flags are generated when discrepancies exceed established thresholds.
4. Targeted independent clinical review
Flagged assessments are escalated for a clinical review, where a qualified clinician conducts an independent review of the clinical interview.
Production AI
LLMs provide a score and a qualitative assessment
LLMs can be utilized in trials with recorded interviews to provide both an independent score and a qualitative assessment of the interview quality to help study teams identify scoring variability, rater drift, and administration quality issues.
Independent scoring
LLMs generate parallel scoring outputs that can be compared against rater scores to support consistency, detect meaningful discrepancies, and prioritize clinical review.
- Parallel quality checks against clinical scale scoring
- Discrepancy detection and flag generation for targeted review
- Support for ratings confidence and endpoint integrity
- Human clinical oversight for final interpretation
Rater surveillance
Continuous review of scoring patterns, assessment administration, and rater behavior helps identify variability before it becomes a downstream data problem.
- Rater drift, repeated anomaly detection, and threshold-based flag generation
- Assessment administration quality review
- Protocol and interview-quality flags
- Site, visit, and rater-level visibility
Eligibility validation
Real-time LLM and data signals can support better screening and subject-fit decisions by improving visibility into ratings quality and participant profile consistency.
- Consolidated screening inputs
- Detection of anomalies and incongruent data
- Flags generate a deeper dive including clinical review of the diagnostic and screening interviews
Holistic study quality assurance
Structured LLMs review 100% of all recorded interviews in a study.
- 100% human reviews are cost-prohibitive, so LLMs provide coverage
- LLMs provide precision discordance insights to minimize the time required for adjudication
- Faster escalation of quality concerns at less cost
- Reduced retrospective cleanup burden
Agentic AI
AI agents provide insights on demand
The Agentic AI layer operates across multimodal data in the EMAW platform.
Outlier and anomaly detection
Detect outliers and anomalies across data, sites, raters, and subjects.
Insights on demand
Query across data modality and type to generate insights on demand.
Complete audit trail
Maintain a complete audit trail for queries and outputs for replication and report standardization.
Multimodal analytics
Analyze standardized multimodal data within the EMA Wellness platform.
What the AI layers support
AI-assisted platform configuration, testing, and production
Platform testing
AI tools are utilized to facilitate the speed and quality of study configuration, data ingestion, data transfers, and acceptance testing.
Avoid retrospective cleanup and data delays
Study teams can maintain data hygiene on an ongoing basis to avoid costly delays in database lock and final reporting.
Data access
Pristine data transfers in study and at study completion for precision insights and auditable results.
Platform performance
Continuous improvement loop in study to stay ahead of the need for unnecessary change requests and quality events.
Real-Time Clinical Detection
Move from retrospective cleanup to active clinical detection
The value is not based on data being labeled “real time.” It comes from clinical detection data reaching study teams early enough to guide quality assurance, oversight, eligibility validation, stratification, and follow-on study design.
Multivariate signal layer
Analyze patterns across endpoint scores, recordings, biomarkers, devices, visit timing, site behavior, and participant characteristics.
Treatment-effect prediction
Use cross-modal patterns to support earlier interpretation of emerging efficacy or safety signals.
Operational signals
Surface study execution risks quickly enough for teams to intervene during the trial, not after database lock.
Use AI as clinical trial infrastructure, not a bolt-on feature.
EMA Wellness applies AI to the data, quality, endpoint, and signal layers that determine whether a study can make faster, more confident decisions.
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