There’s a reason early diagnosis gets so much airtime in healthcare — it works. Catching a condition early, whether it's an infection or something more chronic, can significantly shift patient outcomes.

One area where early diagnosis matters is infectious disease. Being able to quickly and accurately tell the difference between a viral and bacterial infection is essential. Take otitis media in kids: diagnostic error rates in differentiating viral from bacterial otitis media average 50% with traditional tools. 85% of patients get antibiotics, while only 15-20% truly need them. That’s not just overkill – antibiotic resistance becomes the next ‘cybersecurity breach’ of public health. No wonder the CDC flags it as one of the most pressing global health threats.

So, how do we fix that? If we get the diagnosis right early on, we don’t just save money on meds — we dodge side effects and help preserve antibiotic efficacy long-term.

Edge computing offers a path forward. Instead of sending every signal to a central server and waiting on round-trip latency, we bring the compute to the data. On-device data processing can handle noisy environments and intermittent connectivity. And if we’re being honest, it’s what separates guessing from evidence-based diagnosis.

What is Edge Data Processing?

Edge data processing, aka edge computing, is about moving computation closer to the source of data generation. Unlike traditional cloud computing, where data has to be transmitted to centralized servers for processing, edge computing places processing tasks on local devices, micro data centers, or even sensors themselves. This local-first approach is a practical necessity in healthcare, where low latency, bandwidth conservation, and data privacy are non-negotiable.

Compared to cloud-based systems, edge processing dramatically reduces the time between signal acquisition and actionable insight. It also offers advantages in maintaining patient confidentiality, keeping sensitive health information at the edge unless absolutely necessary to transmit.

Edge Computing Implementation Challenges in Medical Devices

Implementing edge data processing in medical devices isn’t just a matter of choosing the right microcontroller. It involves navigating a minefield of design trade-offs, engineering constraints, and clinical usability requirements.

A meditech company, OtoNexus, partnered with us to transition their handheld ultrasound device from an early prototype to a reliable clinical tool. The Novoscope aimed to improve the diagnosis of middle ear infections and reduce unnecessary antibiotic use. Our job was to turn that PoC into a production-ready tool that is robust enough to process diagnostic data directly on the device.

During our collaboration with OtoNexus, Janea Systems team encountered 5 common edge computing implementation challenges.

Challenge #1: Data Collection and Handling

Edge devices need to efficiently handle data ingress and egress despite limited processing power, memory, and connectivity. When the data in question is protected medical information, the bar is even higher. Manual entry opens the door to human error, while transmitting patient information without proper safeguards risks violates HIPAA.

Solution: We implemented QR code encoding and decoding for patient data collection and retrieval. Our engineers also integrated EMR for loading patient data from a QR code. All sensitive information is processed locally and transmitted using encrypted channels aligned with HIPAA compliance requirements.

Challenge #2: On-Device Data Analysis

Processing diagnostic signals directly on the device is one of the core promises of edge computing, but it's also among the hardest to get right. The algorithms must be clinically accurate and the implementation must fit within the device's resource envelope. In the case of Novoscope, this meant transforming research-grade code into embedded-grade production logic.

Solution: We built a custom script that converts Python algorithms into optimized C++ code. This way, the device runs diagnostic logic without external dependencies. The generated code was validated through unit and integration testing against OtoNexus research benchmarks to ensure clinical accuracy.

Challenge #3: Power Management

Edge computing can burn through power fast. Every background process, every signal analysis routine adds to the device’s energy footprint. In a tool like the Novoscope, thermal output also becomes a limiting factor.

Solution: We implemented power management protocols that extended battery life and transitioned the device between active, standby, and charging states. Our engineers added dynamic power controls based on thermal readings, optimizing energy use and extending device uptime.

Challenge #4: Network Configuration

Expecting clinicians to configure network settings via terminal commands or desktop apps is unrealistic. They need tools that work out of the box without a learning curve and IT support.

Solution: We created a captive portal that turns the device into a temporary access point. Clinicians use their phones to configure network settings through an HTML interface, with feedback shown on the device GUI. Additionally, users can enable and disable Wi-Fi both on the GUI and low-level sides.

Challenge #5: Video Recording & Playback Support

Visual documentation lets clinicians review and compare measurements over time, making early detection more evidence-based. Capturing video during a diagnostic session? Tricky, but doable. But playing those videos back on the device is a whole different beast.

Solution: We implemented on-device video capture and playback support tightly integrated with the diagnostic workflow. Videos can be reviewed directly on the device or transferred to the cloud for remote access and further analysis. The feature was optimized for minimal resource impact through efficient buffering and storage management.

Challenge #6: Resonant Frequency Detection

To ensure accurate signal acquisition, the device needs to operate at its optimal resonant frequency. That frequency isn’t fixed — it can shift slightly depending on hardware variation or environmental conditions. Detecting it automatically, in real time, and on a compact embedded system added complexity across the board.

Solution: We introduced a new diagnostic test state where the device is stimulated with a test pulse and applies FFT to detect resonant frequency and amplitude. This wasn’t just a backend tweak — it was implemented end-to-end: from low-level signal handling all the way up to the GUI. And because precision matters, we rigorously tested the feature against reference data from the OtoNexus research team.

Challenge #7: Low-Level Error Control

Hardware issues don’t always announce themselves with flashing lights — sometimes it’s a subtle glitch or a silent failure deep in the stack. In a clinical tool, those moments can erode user trust fast. The challenge was to detect and report driver or component malfunctions without disrupting the diagnostic process.

Solution: We implemented low-level monitoring that continuously checks for hardware and driver anomalies. If something fails, the system surfaces it immediately through the GUI with an error message.

Infrastructure Behind Edge Data Processing

Behind the scenes, we ramped up a testing and automation framework to support the fast-moving feature set. Functional and unit tests ran continuously, backed by a CI pipeline that enforced code quality thresholds and flagged regressions early. We integrated coverage tools and enabled automated checks before merges — because in embedded systems, catching one subtle bug early is worth a hundred fixes post-deployment. Even complex debugging across the full suite became manageable with the layered structure we put in place.

Edge projects are full of these sharp corners, but with the right tooling and clear communication with clinical stakeholders, they’re also incredibly rewarding to ship.

Janea Systems team specializes in navigating those sharp corners. With over two decades of experience in edge computing and embedded systems, we’re able to transform PoC into production-grade tools that meet clinical requirements. Whether it’s patient data handling, edge data processing, or operating within power constaints – our engineers are well-versed in solving complex technical challenges.

Have an edge computing project in mind? Get in touch — we’re ready to help.

Frequently Asked Questions (FAQ)

What are the top edge computing applications in healthcare?

Edge computing applications in healthcare include real-time diagnostics, remote patient monitoring, wearable biosensors, handheld ultrasound devices, and smart infusion pumps.

How does edge data processing improve clinical data management?

Edge data processing allows clinical data to be captured, analyzed, and acted upon at the point of care. This improves clinical data management by enabling immediate feedback, reducing transcription errors, and ensuring that healthcare data processing complies with privacy standards like HIPAA.

What is the lifecycle of data handling in edge-based medical systems?

The life cycle of data handling includes data collection and handling, local processing, secure transmission (if needed), and clinical feedback.