Case study • Healthcare / Medical Devices
BLE-Powered Smart Stethoscope Modernized for Reliable, High-Clarity Heart and Lung Readings
We rebuilt a digital smart stethoscope app for a prominent healthcare product manufacturer, fixing app crashes, inaccurate sound readings, and inconsistent data that made the device difficult for clinicians to trust. The goal was to give clinicians dependable digital stethoscope readings so they could diagnose confidently. Our new BLE-driven architecture improved heart-sound capture, waveform clarity, and session reliability, helping clinicians record, review, and manage patient data with confidence.
Success Highlights
Key Details
Industry: Healthcare / Medical Devices Geography: United States
Platform: BLE-based mobile diagnostic app
Business Challenge
Clinicians rely on accurate sound capture to detect abnormalities, but the legacy application slowed them down and produced inconsistent results. The unstable codebase also blocked the company from scaling or releasing new features.
Clinicians couldn’t store, search, or replay patient sessions.
The backend couldn’t support secure data exchange or compliance workflows.
Our Solution Approach
We rebuilt the app from the ground up with a stable BLE communication layer, reliable signal processing, and a structured data system. The goal was simple: give clinicians clean, dependable readings every time.
1 · Stabilize
Rebuilding the Foundation
We reverse-engineered the unusable code, replaced unstable modules, and created a clean build pipeline. BLE pairing and streaming were redesigned so sessions stayed connected and data didn’t drop mid-recording. This alone removed most of the reliability issues and provided crash free experience to the clinicians.
2 · Enhance
Accurate Waveform Plotting and Signal Handling
We added real-time ECG and PCG waveform plotting with refined filters and improved processing steps. Signals became clearer and easier to interpret, reducing the need to repeat recordings. The app now delivered consistent, clear visual heart and lung sound graphs without manual adjustment.
3 · Extend
Patient Records and Clinical Data Flow
We built a full patient record module so clinicians could store, retrieve, and review sessions whenever needed. This made it easier to compare past recordings, track changes, and manage cases more efficiently.
4 · Assure
Data Integrity and Compliance
We implemented algorithms that delivered reliable clinical readings and aligned the workflow with regulatory expectations. A murmur-detection feature improved early screening. The backend was redesigned to support secure data management and future EHR/EMR integration.
Technical Highlights
Stable BLE pairing and continuous data streaming
Cleaned and normalized signal processing for ECG/PCG
Real-time waveform plotting
Patient record management for session storage and retrieval
Algorithms tuned for clinical accuracy
Backend prepared for secure EHR/EMR exchange
function Signal Capture Flow
connect()
stream = getAudio()
clean = filter(stream)
plot(clean)
save(clean, patientId)
Business Outcomes
The platform shifted from rigid, keyword-dependent search to an intent-driven system.Recruiters could finally find People, Companies, and Jobs faster, with fewer refinements and far less friction.
55%
Accuracy gain
Cleaner ECG and PCG waveforms helped clinicians make faster, more confident decisions
3x
Stronger BLE stability
Device pairing and streaming became predictable, reducing interruptions during critical recordings.
60%
Fewer dropped sessions
Stability improvements cut data loss and re-recording time
Smoother onboarding for clinicians new to digital stethoscopes
A codebase the company can now scale without rewriting again
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