Platform

EMAW’s GIANT™ platform collects and/or integrates all relevant subject, observer and clinician generated data, both actively and passively. This includes:

Types of Data Collected

The platform collects data through various methods, including:

eCOA

(ePRO, eCLINRO, eOBSERVRO)

Sensor and wearable data

EMAs, Surveys and diaries

EMR/EHR

Structured RWD

EMAW provides telehealth features for remote visits and all interviews may be recorded for quality assurance and further review.

Single Code Base enables higher quality data and data-based adjudication in real time

Our platform was built with a single code base and is accessible via a single downloadable application for use on any device, patient owned or provided. We offer the ultimate flexibility to sponsors looking to design traditional in traditional in-clinic, remote or hybrid trials, using BYOD or fulfilled devices, collecting and monitoring all patient and clinician generated data, active (ePRO, eCLINRO, EMA) or passively generated data from sensors.

Key Platform Benefits

The singular platform provides:

GIANT™ is 21 cfr part 11 and GDPR compliant and is currently active in over 25 countries.

Data Collection and Gathering

The EMA platform is designed to collect, validate, and report on large structured and unstructured data across various modalities.

The EMA platform is architected to collect, validate and report on large structured and unstructured data, across modalities. This includes more traditional primary, secondary and exploratory endpoints, such as ecological momentary assessment (EMA), sensor, eCOA and ePRO data. This data is collected via our application or portal, using single sign on across devices for ease of use by sites, clinicians and subjects; as well as via our API library of more than 200 APIs. In addition to measures, our platform offers telehealth capability which enables the collection of audio/video recordings of clinician interviews and site visits, as well as transcription of structured third part and EMR/EHR data.

Data Aggregation, Rules Based Data Insights / Triggers and Notifications

Data is aggregated for review and analysis across multiple levels, including: patient, site, country, arm and/or study. During configuration, we work with study teams to generate rules which are codified during user access testing (UAT) and which trigger flags. These flags may be in the form of a notification or an alert. Notifications may be thought of as snapshots providing longitudinal and momentary insights at the site, rater, subject and study level. Alerts require immediate review of the cause, and trigger and adjudication path. Examples include inclusion / exclusion, AESIs or ratings / scoring trends which are by definition highly unusual. These flags are generated across data modalities, which leverages the ability to gather data from a variety of sources and platforms. The benefits of having multimodal data collected and structured in one purpose built canonical environment are many, mostly around data speed, quality and cost.

Artificial Intelligence

GIANT™ uses artificial intelligence in several ways, including:

Software engineering and coding
Software quality assurance and testing
Transcription of third part data (e.g. pdf) files for integration of this data into the platform
Transcription of audio / video recordings for the purpose of creating an independent automated score of an interview which may flag an interview for further review

Quality Assurance, Rater Surveillance, Adjudication and Remediation

GIANT™ is at its core a data quality assurance platform. This includes:

Data checks (real time)
Data validation (real time)
Data quality reviews (real time)
Adjudication and remediation (in platform workstream)
Secondary clinical reviews / independent clinical ratings

Our single code base enables actual real time insights. We refresh our data intra daily to ensure the most timely, quality data is available.

In addition, we provide reviewers and stakeholders who are adjudicating flags or looking to dive deeper into the data full access to this data within the platform which precludes unnecessary loss of time effort or non auditable steps. This includes scheduling site calls, tier two reviews or independent ratings which have all been traditional pain points for clinicians and sites.

Integration of adjudication and remediation workstreams is another differentiating feature of the GIANT™ platform.

This integration of data, analytics and functionality makes EMA Wellness’ singular technology platform unique in clinical trials today.

Auditability

The EMAW platform is a ‘white box’ system which provides a complete audit trail for sponsor, regulatory and other quality assurance purposes. This includes all platform engineering, configuration, rules, flags, alerts, AI models and reports. The platform is GDPR and 21cfr compliant and has been validated in global phase 2 and 3 trials.

The platform is rights based and can be administered by sponsors, CROs or sites.

Features

The GIANT™ Platform includes:

Full eCOA capability
Traditional, virtual or hybrid trial design
Sensor, EMR, 3rd party data integration
Blinded data analytics
AI reviews of all audio/video files; file transcriptions
Integrated adjudication & remediation functions

The EMAW technology platform integrates the following functionality and provides dashboard visualization of each feature on a site, subject, rater and study basis:

eCOA, Central Ratings, Independent Reviews, Adjudication, Analytics and Reporting
Full integration with clinical services (highlight and link to services tab)
Data validation
- Error and logic checks
Analytical / model validation
- Full audit trails
Flags and alerts
- Data science and leading clinicians use ML to define rules, flags and alerts
- These codes are configured to action a workstream and/or adjudication
- Inter and intra subject, site, rater and study
Adjudication
- Clinical team members review alerts and undertake action (e.g. data errors, compliance, adherence, subject selection, other signals)
Signal detection
- Signals are detected and visualized to enhance multi modal insights
- Identify meaningful change
- AI used to generate models to drive signal detection for exploratory purposes
Multi Modal Data integration
- EMAW generates a digital measure which incorporates data automatically collected from select sources, including primary/secondary/exploratory endpoints, EMAs, sensors/wearables and EMRs
- multimodal measures and models for composite phenotyping
Scale library
- EMAW have validated and utilized all major neuropsychiatric scales on its platform
AI modeling
- Creation of AI models for more in depth analysis of larger data sets to detect signals for predictive and phenotypical insights
Web based
- The platform is device and OS agnostic
- BYOD and/or fulfilled device

Our Platform