Medical Device Integration: Building an AI Healthcare App That Brings Real Patient Data to Clinics

The client’s original app only showed raw data from their wearables collected via Bluetooth, with no long-term tracking or clinical integration. We had to replace it with a brand-new app.

From day one, we paid specific attention to writing secure, well-documented code fully aligned with HIPAA and FDA standards. Our careful design of data handling, user consent, and security protocols helped the client successfully obtain both certifications.

We built a cross-platform mobile app where users can monitor heart rate, blood oxygen, and blood pressure in real time. The app offers visualizations of historical trends, instant alerts when readings fall outside safe ranges, and full control over which clinics or doctors receive the data and how often it syncs.

We also ensured accessibility by using readable fonts, adjustable scaling, and high-contrast design for users with disabilities.

To help users make sense of their health data, we developed a machine learning model that detects abnormal trends in heart rate, blood oxygen, and blood pressure. Based on daily and nightly data, the app can detect early signs of heart issues, respiratory problems, or hypertension and prompts users to contact a doctor.

Users can also manually add symptoms or notes, allowing the AI to provide even more personalized insights. For example, if the model notices irregular sleep-time spikes, it can ask the user how they feel and suggest taking action.

The main challenge was to ensure full compliance. In healthcare, AI can’t give diagnoses or influence medical decisions. We designed the model strictly as a non-intrusive assistant: it flags patterns and invites users to follow up with professionals, never replacing a doctor.

We designed a secure architecture that ensures health data from the wearable devices gets to the cloud and ultimately to clinics without delays or security risks.

It starts with Bluetooth connectivity: we developed a certified pairing system that ensures only trusted devices can sync with the app. From there, the data moves securely to AWS IoT Core, then flows through Amazon Kinesis Data Streams for real-time processing.

Finally, it reaches our machine learning model on Amazon SageMaker, where it’s analyzed for anomalies and trends.

All this happens in seconds, with encryption, access control, and audit trails built into every layer to meet HIPAA and FDA requirements.

To make remote monitoring possible for clinics, the data had to reach doctors. That meant integrating the app with electronic health record (EHR) systems used by hospitals across the U.S.

We took full ownership of the integration, building connectivity with top EHR platforms in the US:

The biggest challenge was ensuring compliance with HL7, the U.S. standard for healthcare data exchange that exists in multiple versions. The systems we had to connect to used a mix of legacy and modern HL7 protocols. We developed a compatibility layer to normalize data formatting and transfer data across different systems.

Now, the users can transmit real-time data from their devices straight into doctors’ clinical dashboards.