HealthTech12 months engagement

Clinical Wound Assessment App

QA for an FDA-regulated mobile application used by clinicians to photograph, measure, and track wound healing progress with AI-powered analysis. Nurses and wound care specialists in hospitals and home health settings relied on the app daily to document patient wounds and generate treatment reports. Because the AI measurements directly influenced clinical decisions, testing accuracy was not just a quality concern but a patient safety requirement.

iOSAndroidAPIMobile TestingSecurity TestingAPI Testing

30+

Devices validated

100%

HIPAA compliant

Production defects

The Challenge

Zero Error Tolerance

Incorrect wound measurements could lead to wrong treatment decisions. The FDA submission required documented evidence of accuracy across a wide range of wound types and sizes.

Device Fragmentation

Camera hardware differences between 30+ device models caused significant variation in image quality. The AI model had to produce consistent measurements regardless.

Offline Reliability

The app needed to work reliably in clinical settings with poor connectivity. Offline mode had to be bulletproof with zero data loss during sync.

FDA 510(k) Readiness

The client was preparing for their FDA submission and needed a comprehensive testing record that would withstand regulatory scrutiny.

Tools Used

Appium

BrowserStack

Postman

JIRA

XCUITest

Appium

BrowserStack

Postman

JIRA

XCUITest

Our Approach

How We Tested This Project

Regulatory Mapping

We mapped every FDA requirement to specific test cases, building a traceability matrix that connected each regulation to our test coverage. This gave the client a clear audit trail for their submission.

Device Lab Testing

Used BrowserStack and a physical device lab to test across 30+ Android and iOS devices. We focused on camera behavior differences, testing under controlled lighting conditions that simulated real clinical environments.

Offline Resilience Testing

Simulated network interruptions at every point in the clinical workflow to verify that no patient data was lost. We tested airplane mode, weak signal, and mid-sync disconnections to ensure data integrity.

Accuracy Validation

Worked with the client clinical team to validate AI measurements against a dataset of clinician-verified wound measurements. We built automated comparison scripts to flag any measurement that drifted outside the acceptable tolerance.

Scope

What We Tested

01AI wound measurement accuracy validation against clinician-verified ground truth data

02HIPAA compliance and data encryption at rest and in transit

03Offline mode data capture and sync reliability under interrupted connections

04Camera calibration across 30+ device models with varying sensor quality

05Clinical workflow end-to-end testing from wound capture to treatment report generation

06Accessibility compliance (WCAG 2.1 AA) for clinical users with varying technical ability

07Image compression impact on measurement accuracy at different quality levels

08Multi-patient session handling without data cross-contamination

09Integration with electronic health record (EHR) systems via HL7 FHIR

10Battery and memory usage profiling during extended clinical sessions

Impact

Results & Business Impact

FDA First-Pass Approval

The app passed its FDA 510(k) review on the first submission attempt, attributed directly to the thoroughness of our testing documentation.

99.7% Accuracy

Across 30+ validated devices, wound measurement accuracy stayed within the required 2mm tolerance in 99.7% of test cases.

Zero Production Defects

No production defects were reported in the first six months after launch.

12 Hospital Systems

The client onboarded 12 hospital systems in Q1, and clinician adoption rates exceeded projections by 35%.

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