Clinical Trial Workflow and Data Integration

This page describes the end-to-end workflow for alopecia severity assessment in a clinical trial using the Legit.Health platform, from protocol design through to EDC data delivery.

Workflow overview

The clinical trial workflow consists of eight stages:

Protocol design: Configure the study with Legit.Health
Site setup: Deploy the platform to investigator sites
Patient enrollment: Register patients in the platform
Image capture: Guided 4-quadrant scalp photography at the site
AI scoring: Automated ASALT computation
Report review: Investigator reviews the severity report
Severity tracking and alerts: Longitudinal monitoring with automated notifications
Data export: Structured delivery to EDC systems

1. Protocol design

Before the study begins, Legit.Health works with the sponsor or CRO to configure the study protocol:

Configuration item	Options	Example
Imaging perspectives	4-quadrant standard, custom	4 quadrants (left, right, top, back)
Endpoints	Total ASALT, regional ASALT, severity classification, SALT response (any combination)	Total ASALT + severity classification + SALT 50/75/90/100
Alert thresholds	Configurable percentage increase from baseline for automated notifications	≥25% increase from baseline triggers site notification
DIQA threshold	Strict (pivotal) or lenient (RWE)	Strict for Phase III
Data export format	API, CSV, CRF-mapped fields	CRF-mapped to sponsor’s EDC

A study-specific investigator manual is generated for each trial, providing site personnel with step-by-step instructions, example images, and a knowledge test to confirm training.

2. Site setup

The Legit.Health clinical trials platform is deployed as a web application accessible from any browser. Each investigator site receives:

Login credentials for all study personnel
Pre-configured protocol (perspectives, endpoints, alert thresholds)
Investigator manual (digital, accessible from the platform)
Training resources and knowledge test

No hardware installation is required; investigators use their existing smartphones for image capture.

3. Patient enrollment

After enrolling a patient in the study per the clinical protocol, the investigator creates the patient record in the Legit.Health platform:

Add new

Select the condition

Select the body area(s)

Select the most common

Scalp

Do you want to track this condition?

In follow up

Add condition

Each patient is identified by a study-specific pseudonymised identifier; no personal data (name, date of birth) is stored in the platform.

4. Image capture

The investigator captures photographs of the patient's scalp using the Legit.Health mobile application:

The application provides:

Perspective guidance: Visual silhouettes showing the required capture angle for each of the four scalp quadrants
Real-time DIQA quality check: Each image is evaluated for focus, lighting, framing, and resolution immediately after capture
Immediate feedback: If an image fails quality standards, the investigator is prompted to recapture with specific guidance
Confirmation: Once all four perspectives pass quality checks, the images are submitted for AI processing

For the standard 4-quadrant protocol, the entire capture process takes approximately 60–90 seconds.

5. AI scoring

Once images are submitted, the AI processes them automatically. Processing takes approximately ~2 seconds for 4 images combined. The system produces:

Per-quadrant hair loss percentage (left, right, top, back)
Per-quadrant ASALT score weighted by regional proportion
Total ASALT score (0–100)
Severity classification (None / Limited / Moderate / Severe / Very Severe)
SALT response flags (SALT 50/75/90/100 vs. baseline, when applicable)
DIQA quality score per image

The report is immediately available for the investigator to review.

6. Report review

The investigator reviews the severity report directly in the platform. The report includes:

Total ASALT score: Global SALT score (0–100) with severity classification
Per-quadrant breakdown: Regional hair loss percentages and ASALT contributions for each of the four quadrants
Image quality: DIQA score for each captured image

The investigator can verify that the AI's assessment aligns with their clinical observation. If the images are inadequate, the investigator can recapture and resubmit; only the latest submission for each visit is retained.

7. Severity tracking and automated alerts

For follow-up visits, the platform tracks severity evolution across all assessments for the patient.

Longitudinal tracking

The platform displays:

ASALT score evolution over time with visual comparison to baseline
Absolute and percentage change from baseline at each visit
SALT response classification (SALT 50/75/90/100) when applicable
Trend visualisation for identifying treatment response or progression

Automated alert system

The system can be configured to trigger automated email notifications when clinically significant changes are detected:

Configurable threshold: Default is >=25% increase in ASALT from baseline (adjustable per study protocol)
Automatic notification: Site receives an email alert when the threshold is met
Site action: The investigator confirms the finding through clinical assessment
Escalation: If confirmed (e.g., as an adverse event), enhanced monitoring can be initiated per the study protocol

Alert precision

The alert threshold is inclusive (>=25%, not >25%). This is a deliberate design choice to ensure that borderline cases are flagged for clinical review rather than missed.

8. Data export and EDC integration

Assessment results are structured for integration with the sponsor's EDC (Electronic Data Capture) system.

Exported data fields

Field	Description	Format
Patient identifier	Study-specific pseudonymised ID	String
Visit date	Date and timestamp of the assessment	ISO 8601
Total ASALT score	Global SALT score (0–100)	Float
Regional ASALT scores	ASALT score per quadrant (left, right, top, back)	Float (0–100) per region
Severity classification	None / Limited / Moderate / Severe / Very Severe	String
SALT response flags	Binary flags for SALT 50, SALT 75, SALT 90, SALT 100 response vs. baseline	Boolean per threshold
Change from baseline	Absolute and percentage change in ASALT from baseline	Float
DIQA scores	Image quality score per perspective	Float
Alert triggered	Whether the assessment triggered an automated notification based on threshold	Boolean

Integration methods

API-based: Automated data flow from the Legit.Health platform to the sponsor’s EDC system via RESTful API
CSV export: Structured CSV files for manual import into EDC systems
CRF field mapping: Data fields pre-mapped to the sponsor’s Case Report Form structure, configured during protocol design
QuantifiCare platform integration: For studies using the combined QuantifiCare + Legit.Health solution, scoring data flows through QuantifiCare’s platform to the sponsor’s data management system

All exported data is structured for direct mapping to CRF fields in standard EDC systems (Medidata Rave, Veeva Vault CDMS, Oracle InForm, etc.).

The integration is API-based and system-agnostic; it works with any EDC system (Medidata Rave, Oracle InForm, Veeva Vault EDC, and others). Export formats include RESTful API, CSV/Excel, and structured JSON. Legit.Health provides IQ/OQ validation documentation and data mapping specifications to support the sponsor's integration validation activities.

Decentralised and hybrid trial support

Hybrid designs

Some study designs combine investigator-captured and patient-captured images:

Investigator timepoints: Primary visits (e.g., baseline, month 3, month 6, month 12) where images are captured by trained site personnel at the clinic
Patient timepoints: Intermediate visits (e.g., monthly check-ins) where the patient captures images at home using the mobile application

The same AI scoring pipeline processes both types of images. DIQA quality control is applied identically, ensuring that patient-captured images meet the same quality standards as investigator-captured images.

Remote patient capture

For fully decentralised protocols:

Patients download the Legit.Health mobile application
The app guides them through the capture process with the same perspective silhouettes and DIQA quality checks used at investigator sites
Captured images are transmitted securely to the platform for AI scoring
The investigator can review scores remotely

Benefits for sponsors

Reduced site burden: Fewer in-clinic visits needed for severity assessment
Increased data frequency: More timepoints without increasing site workload
Patient convenience: Assessments from home reduce travel burden
Continuous monitoring: More frequent assessments enable finer-grained treatment response detection

For alopecia trials, decentralised capture is particularly relevant: patients can capture scalp photographs at home following the standardised protocol, enabling more frequent assessments without requiring in-clinic visits. The DIQA quality gate ensures that home-captured images meet the same quality standards as site-captured images.

Workflow overview​

1. Protocol design​

2. Site setup​

3. Patient enrollment​

4. Image capture​

5. AI scoring​

6. Report review​

7. Severity tracking and automated alerts​

Longitudinal tracking​

Automated alert system​

8. Data export and EDC integration​

Exported data fields​

Integration methods​

Decentralised and hybrid trial support​