Medical devices are an essential element of modern medicine as they provide accurate clinical measurements such as oxygen saturation, blood pressure and temperature, x-ray and ultrasound imaging, as well as automatically administer intravenous medications, and provide support of critical life functions. In spite of the advances toward improving medical devices’ accuracy, robustness and reducing their form factor, very little has been done to pursue interoperability – most medical devices communicate their measurements through proprietary data formats and protocols thus hindering the integration with other medical systems.
This lack of seamless integration has several negative clinical implications. As an example, the use of a single clinical measurement – oxygen saturation – makes it harder than it should to detect morphine narcolepsy false positives. As the oxygen saturation measurement is very sensible to the proper positioning of the probe, several false positive can be raised in patients that are restless, perhaps due to post-operatory pain.
If multiple clinical measurements, such as breath per seconds, oxygen saturation and perhaps heartbeats, were fused together to detect morphine narcolepsy, then the number of false positives could be dramatically reduced. In addition, the actual insurgence of a narcolepsy could be detected earlier.
Ideally, medical devices should make available their measurements using a standard data model and through an open and standard protocol. The good news is that we have been working for the past year with the MDPnP (Medical Device Plug-and-Play) – a consortium of hospitals, research centers and medical device manufacturers – to explore the use of the OMG Data Distribution Service as the standard for sharing clinical data. In addition, we along with a number of the MDPnP participants have been involved with the SmartAmerica Challenge, a White House Presidential Innovation Fellows project designed to showcase Internet of Things frameworks and benefits for a variety of environments including healthcare.
DDS enables seamless, efficient and secure information sharing across any device and at any scale. As a result, once the clinical data is made available over DDS, it can be consumed virtually anywhere, on anything, and by anybody who has the proper access rights. The simple fact of making data available through DDS enables a series of use cases that are very valuable in supporting both doctors as well as patients.
As an example, if we consider the image below, we see how a DDS-based platform, namely PrismTech’s Vortex, is currently being used to allow doctors to access patient data from anywhere as well as non-hospitalized patient to be continuously monitored.
Specifically, in this use case DDS makes it possible for doctors to enter a hospital room and discover the devices that are available. Doctors can then select one or more devices from which they would like to see live measurements. In case the doctors need to discuss the live data with colleagues from another hospital, the clinical data will be shipped in real-time to the remote doctors on whichever devices they have at hand–a mobile, a tablet or a notebook. In addition, data is continuously streamed to a private cloud where on-line as well as off-line analytics are executed on the various medical measurements.
Likewise, mobile personal medical devices can also make their data available seamlessly to doctors as well as to analytics applications. For instance, consider an elderly patient suffering from dyspnea. This patient can be continuously monitored while staying comfortably at home thanks to the use of a mobile oxymeter. This oxymeter would be sending data to the hospital’s private cloud via 3G/4G or WiFi where analytics applications interpret the data and respond as necessary. For example, if it’s detected that the patient needs some oxygen, a notification can go out to the patient while an alert is sent the doctor.
The beauty of DDS is that each of these use cases is seamlessly supported by its core abstraction: ubiquitous data sharing. Additionally, DDS supports for data modeling and QoS facilitates the definition of common data models for medical devices. The combination of standard data models and interoperable protocol are key elements to enable medical devices interoperability.