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The Internet of Medical Imaging Things is Here - Diagnostic Imaging PDF Print E-mail
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Thursday, 17 March 2016 09:47

When GE Healthcare takes a year or two to react to visionary innovation by Siemens Healthcare, this usually means the company is up to something “big.” Now, barely four months after launching the GE Health Cloud at RSNA 2015, “big” is already an understatement to qualify this game-changing initiative by GE.

Taking the time at HIMSS 2016 to understand the IoT strategy of the Big Three in medical imaging was a captivating exercise; not just because all three vendors are moving full steam ahead with their IoT strategy in health care, but because each vendor seems to be approaching medical imaging “things” in its own way.

The Medical Imaging IoT 1.0 Can be Traced Back Two Decades Ago
The concept of connecting and monitoring medical imaging equipment via remote servers over the Internet is not new. In fact, it has been the cornerstone of the remote servicing capabilities that medical imaging vendors started to offer many years ago. This can be dubbed the 1.0 version of IoT in medical imaging.

This remote connectivity has allowed vendors to gain tremendous efficiencies in their maintenance and support functions by moving from an after-the-fact, break-and-fix service model to a proactive, preventative service model. Other than in military health care facilities, where IT security is extremely stringent, the majority of medical imaging customers have signed up for remote services by vendors. Imaging providers have recognized this as an efficient way of spotting early symptoms of a breakdown, and therefore minimize downtime.

The Road to the Medical Imaging IoT 2.0: Three Vendors, Three Approaches
In 2015, the Big Three medical imaging vendors took major steps toward the next generation of the Internet of Medical Imaging Things. This kickoff can be tied to Siemens Healthcare’s introduction of teamplayat RSNA 2014, with GE Healthcare jumping on board toward the end of 2015, and Philips Healthcare ramping up its game quickly.

GE Healthcare: Leaping Forward to an All-in-one Health Cloud
GE Healthcare made a bold statement at RSNA 2015 by sharing publicly its intention to network 500,000 devices on the new Health Cloud. This would include the global GE installed base of patient monitoring, imaging equipment, etc. Going from bold to bolder, at HIMSS 2016, GE stepped up its ambition, stating it intends to connect “2 million imaging machines worldwide, including 500,000 GE Healthcare devices.” One can argue there is no technical obstacle for bringing in competitor devices as part of one’s own IoT.

While GE is starting off these ambitious plans from scratch, the pace at which it has started operationalizing the Health Cloud over the past few months is impressive. GE is acting on its promise to develop Health Cloud as an open “ecosystem,” where independent software vendors (ISVs) can “plug in” their best-of-breed applications. Two major health care IT integrators, Capgemini and Tata Consultancy Services, have already signed up to accelerate the development of new analytics solutions on Health Cloud, which might be customized by these companies to individual customers.

GE is already acting on the vision that computer-intensive image processing can effectively be shifted from local processors to the cloud. This can be a huge game-changer, both for imaging equipment and workstation designs. A good example is the cardiac MR application, ViosWorks, from Arterys, one of the ISVs now in the Health Cloud ecosystem. This advanced application performs the data heavy-lifting in the cloud while applying deep-learning algorithms; traditionally this would be performed on the MR workstation or the advanced visualization workstation.

Three other applications, among many that have already found a home on the Health Cloud, offer interesting views on the operational use cases into which the platform will develop. By taking in Candescent Health, GE will be able to deliver enterprise workflow orchestration, an area where GE has placed little investment since its legacy departmental radiology information system (RIS). The “Radiology Insights” application developed in-house to track X-ray repeat or reject rates is a perfect example of IoT applied to radiography. Similarly, the same application applied to “Modality Utilization” can be considered an IoT-based fleet management application.The Internet of Medical Imaging Things is Here - Diagnostic Imaging Nadim Michel Daher, Industry Principal, Medical Imaging and Imaging Informatics, Frost & Sullivan

Siemens Healthcare: Slowly but Surely Operationalizingteamplay
Leveraging technology it uses for the remote monitoring of equipment such as Lifenet and Siemens Remote Services, as well as a partnership with Microsoft Azure, Siemens has made some significant strides in developing teamplayas its go-forward IoT platform for medical imaging. Notably, teamplayalready provides two solid use cases and is about to release a third, all of which are being used and validated by some very large Siemens customers.

The first use case is dose optimization in CT imaging, allowing Siemens (and in the future non-Siemens) customers to benchmark their dose efficiency for each type of procedure, and to collaborate amongst themselves around their dose reduction initiatives. The second use case is asset optimization, allowing Siemens (and in the future non-Siemens) CT and MR customers to apply various asset management, fleet management, and financial analytics. Previously only available in the context of value-add customer services, these functions are crucial in assisting department managers with their return on investment (ROI) and total cost of ownership (TCO) modeling of capital equipment.

The upcoming use case of protocol sharing, which will help boost the market adoption of teamplay,allows CT and MR customers to share protocols and exchange best practices around their image acquisition protocols. Some large Siemens accounts have already jumped on this significant new opportunity to leverage teamplayas an internal enterprise tool for the standardization and benchmarking of quality, protocols, dose, productivity, etc.

Philips Healthcare: An Outside-In Move into the Imaging IoT
Early signs seem to be pointing to Philips Healthcare’s HealthSuite Digital Platform becoming the way forward for the company’s medical imaging IoT strategy. As an early indication and a case in point, the latest ultra-mobile ultrasound device Lumify launched at the RSNA 2015 will be making its image data accessible on HealthSuite. This makes Lumify the first truly IoT-connected imaging device by Philips, in the same way that many personal devices have become “things” in the IoT over the past few years.

In the first development phase, Philips is building the HealthSuite as an anchor to its “continuum of care” strategy, that is, a way to connect all the various touchpoints along the care continuum; meaning HealthSuite is supporting the company’s overarching strategy for health care. As such, it makes sense that HealthSuite would start in areas outside of acute care settings and diagnostic settings, in areas such as home care or personal health, before gradually moving into medical imaging settings. In developing the Health Suite cloud, Philips is leveraging a number of high-profile technology partnerships, including Salesforce and Amazon. The most recent partnership with Hitachi Data Systems, though more directly related to data management and vendor-neutral archiving, may have positive implications on HealthSuite as well.

Unlike Siemens and GE, Philips may not have “productized” its medical imaging IoT capabilities on the Big Iron side just yet, but this is not to say that these are not already at play for the vendor. Indeed, Philips is already putting machine-to-machine connectivity to good use at some of its large customer sites. MacKenzie Health in Ontario, for example, which entered into an 18-year enterprise managed service partnership with Philips last December, is a site that has developed an “enterprise IoT” network of their imaging fleet for the purpose of advanced use cases such as analytics, collaboration, or standardization.

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