Unleashing Clinical Measurements

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.

Read: Applying the Data Distribution Service in an IoT Healthcare System

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.

If you want to learn more about the DDS standard you can refer to the OMG website where you can find the specification or take a look at the educational material available on SlideShare.

The Adoption of Operational Technologies in Simulation

Simulation and Operational Technologies have historically evolved in isolation and with very little cross-fertilization. This has led to a divergence of technologies and skills along with the necessity for ad hoc integration of simulation and operational systems.

This situation is rapidly changing due to a convergence of the simulation community towards operational technologies such as the Object Management Group’s (OMG) Data Distribution Service for Real-Time Systems (DDS). In this blog post I will summarize the motivations behind this convergence and provide quantitative indications, when data is available, of the induced benefits.

Performance and Scalability. Simulation technologies such as High Level Architecture (HLA), Distributed Interactive Simulation (DIS), Test and Training Enabling Architecture (TENA) have proven to be the bottleneck when building advanced real-time simulations or large-scale simulations. Performance and scalability is one of the top reasons why several simulation groups across large systems integrators, as well as companies specializing in simulation, have adopted DDS as their underlying data bus.  Performance improvements have been measured up to a factor 2x while through adopting the DDS fully distributed architecture they have achieved linear scalability!

Integration with Operational Systems. With the rising importance of co-simulation it becomes critical that simulations and operational systems can seamlessly interact. This means two things. First, it should be possible to  “inter-connect” the simulated and the real systems without having to develop specific adaptors. Second, the simulated system should  “keep the pace” of the operational system. As DDS is a mandated standard for several operational systems, the convergence towards DDS eliminates the need for bridging – thus eliminating the cost of integration. In addition this convergence provides users with a level of performance that is compatible with operational systems and a plug-and-play co-simulation architecture.

Market and Vendors. The operational technology market is bigger and more competitive than the simulation market, as such it provides higher quality technology at more modest prices when compared with the niche simulation market. In addition the number of vendors of DDS along with the number of Open Source offerings surpasses by several times that of a typical simulation standard or technology. This competitive scenario is thus more favorable to the user (please refer to the Porter Five Forces Scheme http://en.wikipedia.org/wiki/Porter_five_forces_analysis) as it increases bargaining power and lowers the overall cost of software procurement throughout its lifetime. In addition, the competitive landscape means that vendors need to continually innovate in order to gain market share thus providing end-users with top-notch technologies and tools.

Skills and Productivity. Another important reason why companies are converging toward Operational Technologies is because it is easier for them to find and retain competent engineers as well as to more easily reassign their workforce. In addition, operational technologies such as DDS have proven to improve the productivity, over technologies such as HLA, by 2-3x for just the development phase.

For those interested in some futher technical details, the following resources are are available:


DDS, MQTT and the Internet of Things

The commoditization of network connectivity is providing the foundation for the Internet of Things – a system in which data flows seamlessly, at Internet Scale, between network-connected devices, mobile devices, industrial and information systems.  Yet, network connectivity alone is not sufficient; another key building block needed for the Internet of Things are standards for interoperable data sharing – as without standardized open data sharing there is no Internet of Things.

The Object Management Group (OMG) Data Distribution Service for Real-Time Systems (DDS) and the upcoming OASIS Message Queuing Telemetry Transport (MQTT) provide two excellent examples of standards that address the Internet of Things.

Introduced in 2006, DDS has established itself as the standard for peer-to-peer real-time data sharing in Operational Systems , such as Air Traffic Management Systems, Medical Systems, and Combat Systems.  DDS has recently experienced rapid adoption as the foundation for an increasing number of Intelligent Systems in applications such as Smart Cities, Smart Grids, and m-Health.

MQTT was introduced in 1999 by IBM as a publish / subscribe, extremely simple and lightweight messaging protocol, designed for constrained devices and low-bandwidth, high-latency or unreliable networks.

DDS and MQTT share some common principles, such as parsimony and efficiency, temporal decoupling and anonymity, yet each technology has some unique features that make it most applicable for certain use cases.

For instance, MQTT is most suitable for sporadic messages and highly resource constrained devices whilst DDS is most suitable for those applications that require real-time data exchange – meaning applications in which data has an inherent temporal validity and in which stale data should never delay fresh data– and tight control over the Quality of Service (QoS).  In addition DDS supports peer-to-peer (infrastructure-less) communication, a feature that comes in handy for device-to-device communications.

In summary, DDS and MQTT are two very good standards for data sharing in the Internet of Things. DDS provides support for both Device-to-Cloud (Device-to-Data Center) communication as well as Device-to-Device.  MQTT provides very good support for Device-to-Data Center communication.

Finally, I have produced an ondemand webcast on Building the Internet of Things which you can access at: http://www.prismtech.com/opensplice/resources/webcast-archive.