In a previous blog post, we discussed Service-oriented Device Connectivity (SDC), a protocol that enhances interoperability, security, and real-time data management in medical devices, aiming to revolutionize healthcare through automated operating rooms, smart hospitals, and personalized medicine.
In an effort to apply our theoretical understanding of SDC, we utilized an existing reference implementation in Python, which was not intended for medical use. However it served as a foundational framework for integrating SDC standards into various medical devices, ensuring secure and standardized communication. Specifically, we applied this framework to an oximetry use case, which was showcased at the HIMSS conference in Rome.
Our SDC Demo Use-Case
Our demonstration featured an ‘SDC box’ powered by a Raspberry Pi. This box served as the data producer and ran our implementation for SDC conversion. The ‘patient monitor’, represented by an iPad, acted as the data consumer. The oximeter was connected to the SDC box via USB cable, while the monitor was linked through Wi-Fi, with the option to also connect via Bluetooth.
The main goal was to show how our implementation translates proprietary protocols to SDC. When the oximeter takes a reading, it sends data through its own proprietary protocol. Then the data producer translates it to the SDC protocol, which is shown on the data consumer, our quasi-patient monitor. The oximeter and patient monitor are then seamlessly connected, displaying real-time data.
To provide a broader perspective, if our SDC implementation were applied in an ICU, connecting devices such as ventilators and monitors, communication between them could automate several critical care tasks.
Let’s imagine a use case like this: The monitor continuously tracks the patient’s SpO2 levels, and if a significant drop is detected, it sends the data to the ventilator via the SDC protocol. The ventilator then automatically adjusts its settings to increase oxygen delivery, and if the patient's condition still doesn’t improve, the system alerts healthcare staff for manual intervention.
This practical example highlights how SDC can create seamless, automated communication between medical devices, eliminating the need for constant manual input from healthcare staff and enhancing patient safety.
Visualizing Technical Foundations of SDC Implementation
To better understand the implementation workflow, we present a detailed process diagram using BPMN 2.0 notation. In our example, the BPMN 2.0 diagram visually outlines each step of the implementation, from the initial setup and configuration of the oximeter to the data exchange between the provider and consumer. This structured approach highlights the critical components and interactions necessary for achieving effective device connectivity and interoperability in a healthcare setting.
The reference implementation sets up a system that allows different devices, such as medical equipment, to connect and communicate with each other over a network. It uses a standard way to locate and identify devices on the network. The system uses multicasting to broadcast device information across the network, making it easier for other devices to locate and connect to it (see the “SDC Consumer” pool in the diagram). This setup supports both Windows and other operating systems, enabling the devices to communicate through network addresses.
To manage requests, an internal server is set up to receive and process information in two steps: first, it handles the incoming request, then it sends it to the correct part of the system for processing and dispatching.
Security is a top priority, especially in clinical settings. The system supports secure TLS connections, which are mandatory in healthcare environments to protect patient data. Device-specific security certificates are used to ensure these connections are safe and compliant with medical standards.
One major component, the BICEPS subscription manager, organizes the exchange of information between medical devices. From a user's perspective, different types of information are available, including patient data such as waveforms and device location within a healthcare setting.
For example, in a pulse oximeter setup, the device is given a specific reference so it can be easily identified. When it's ready to be used, it shares an internal "medical device information base" (MDIB), which contains essential details about the device, like its metrics, location, and model information, such as the manufacturer and firmware version. This is visible in the diagram, where the “Update MDIB state” task is executed.
Once the device is ready, it becomes visible on the network, allowing other devices or monitoring systems to find it. The oximeter begins to read and update patient data like oxygen levels and pulse, sharing this in real-time through the MDIB (represented in the “Module for physical device” lane in the diagram). The monitoring system can detect and display these updates as they happen.
This setup allows clinicians to subscribe to specific updates on the monitor, so they're immediately notified of any important changes. The system is designed to make it easier to track and display the health information needed for patient care.
By enabling seamless device interoperability, SDC has the potential to reduce manual intervention, and improve patient outcomes through real-time data sharing. As healthcare systems adopt SDC, they open doors to more responsive and efficient patient care environments. This progress not only enhances safety and precision but also lays a foundation for future innovations in medical technology.
Ultimately, SDC is a powerful step towards a fully connected, intelligent healthcare ecosystem.
Co-author: Mitja Lakič
Co-author: Eva Bajželj
