Imaging IT’s Infrastructure Problem

A heated Twitter conversation revealed widespread discontent with imaging’s outdated and fragmented IT infrastructure, suggesting that it’s draining radiologist productivity and standing in the way of AI adoption.

This tweet by Memorial Sloan Kettering’s Anton Becker, MD, PhD got things started: “95% of radiology departments would do well to direct 100% of their AI efforts and budget towards upgrade and maintenance of PACS, RIS and dictation software for the next 5 years… Our field is plagued by legacy software.” 

And here’s what the ensuing replies and retweets revealed:

  • PACS Productivity – Nearly everyone agreed that their overall imaging IT setup was insufficient, with one rad estimating that a “supercharged PACS” would improve his productivity by 30%, and another noting that workflow customization would “at least double” her speed and accuracy.
  • Imaging IT Revolution – Some called upon the “legacy” PACS, RIS, and voice recognition vendors to make more “revolutionary changes,” rather than settling for tweaks to current setups. Others proposed government intervention.
  • IT Isn’t Flashy – One thing that might be holding some imaging IT overhauls back is “it’s not as flashy to boast” about high-quality infrastructure, and “the people who have authority to allocate resources are more motivated by flash than function.”
  • Holistic IT – Eventually the conversation led to several well received proposals that we “eliminate the idea of PACS as a category and start thinking more holistically about radiology IT.” In other words, this might be more of a “fragmentation problem” than a PACS/RIS/voice functionality problem (or an AI budget problem). 

The Takeaway

Even if RadTwitter tends to skew towards academic radiologists and often focuses on what’s going wrong, this conversation indicates widespread dissatisfaction with current imaging IT setups, and suggests that radiologist productivity (and perhaps accuracy and burnout) would improve significantly if imaging IT worked as they’d like it to work. 

It’s debatable whether this imaging IT problem is actually due to an unnecessary focus on AI (very little of the conversation actually focused on AI), but it does seem reasonable that rad teams with solid infrastructure would be more likely to embrace AI.

HIMSS 2022 Reflections

Two years after HIMSS became COVID’s first trade show casualty, healthcare’s leading IT conference returned to Orlando with a very post-COVID vibe and a surge in imaging activity. 

Hope you had a blast if you made it to HIMSS, and here’s some highlights in case you didn’t:

The HIMSS Crowd – Unlike the Delta-impacted HIMSS 2021 conference, this year’s event boasted a full exhibitor list and reportedly solid health IT leadership attendance. However, exhibitor staff often appeared to outnumber potential customers on the show floor, prompting conversations about whether HIMSS is evolving into a B2B event and causing some vendors to question where imaging sits on IT executives’ list of priorities. 

The Mixed Cloud – PACS and enterprise imaging vendors continued to ramp up their cloud capabilities and cloud leadership messaging, with nearly everyone agreeing that the future will bring far more cloud adoption. It was also clear that many radiology practices and hospital systems (and even some PACS vendors) are still taking it slow on their path towards the cloud. 

AI in the Aisles – Only a handful of imaging AI companies had booths this year, but it wasn’t hard to find folks from AI startups walking the show floor or in meeting rooms. That’s actually consistent with previous HIMSS conferences, and it makes a lot of sense given AI startups’ limited budgets and the low count of radiology leaders at the show.

AI in the Enterprise – Although we didn’t hear much about all those PACS-based AI platforms / marketplaces that were announced several years ago, AI was positioned at the center of quite a few PACS vendors’ future diagnostic workflow strategies. These strategies still largely focused on integrating third-party AI tools, but several major enterprise imaging players (e.g. Canon, Fujifilm, Siemens) also forecasted a greater future role for their own homegrown AI tools.

The Productivity Press – With imaging growing in volume / complexity at a much faster rate than imaging teams’ own headcounts / capabilities, just about every product message focused on improving productivity and efficiency. HIMSS 2022 saw imaging vendors address this in a wide variety of ways, including remote modality operation, ultrasound AI automation, automated scanner setup, and hanging protocol standardizing (to name a few).

Expanding Ologies – HIMSS also revealed more multi-ology progress as enterprise imaging players better connected their solutions, added new ology-expanding partnerships, and integrated their acquired companies. That said, it seems like the majority of “enterprise” imaging engagements are still limited to radiology, or at least starting there.

Looking Beyond Imaging – A walk around the show floor suggests that healthcare tech is evolving at a much faster pace outside of imaging, with major adoption and technology advances in telemedicine, patient monitoring, at-home and hybrid care, and patient engagement. Although most of these solutions have little to do with radiology right now, these efforts could change how and where many patients get their care, which would have an impact on nearly all specialties. By the way, we have an excellent newsletter about this space for those looking to keep up with these trends. 

The Takeaway

After one year of digital conferences and another year of minimally-attended hybrid events, the bar has been set pretty low for 2022 trade shows. That said, HIMSS had everything that you would expect from a successful post-COVID trade show (plenty of vendors, exciting tech, strong attendance, good vibes), which is a good sign for future events as long as the pandemic cooperates.

Although HIMSS 2022 didn’t necessarily reveal any major focus changes for imaging IT, it did showcase some solid progress advancing the major imaging trends that we’ve seen over the last few years (cloud, AI, productivity, enterprise-expansion), and we’re excited to see what else this year has in store.

Sirona Medical Acquires Nines AI, Talent

Sirona Medical announced its acquisition of Nines’ AI assets and personnel, representing notable milestones for Sirona’s integrated RadOS platform and the quickly-changing imaging AI landscape.

Acquisition Details – Sirona acquired Nines’ AI portfolio (data pipeline, ML engines, workflow/analytics tools, AI models) and key team members (CRO, Direct of Product, AI engineers), while Nines’ teleradiology practice was reportedly absorbed by one of its telerad customers. Terms of the acquisition weren’t disclosed, although this wasn’t a traditional acquisition considering that Sirona and Nines had the same VC investor.

Sirona’s Nines Strategy – Sirona’s mission is to streamline radiologists’ overly-siloed workflows with its RadOS radiology operating system (unifies: worklist, viewer, reporting, AI, etc.), and it’s a safe bet that any acquisition or investment Sirona makes is intended to advance this mission. With that…

  • Nine’s most tangible contributions to Sirona’s strategy are its FDA-cleared AI models: NinesMeasure (chest CT-based lung nodule measurements) and NinesAI Emergent Triage (head CT-based intracranial hemorrhage and mass effect triage). The AI models will be integrated into the RadOS platform, bolstering Sirona’s strategy to allow truly-integrated AI workflows. 
  • Nine’s personnel might have the most immediate impact at Sirona, given the value/scarcity of experienced imaging software engineers and the fact that Nines’ product team arguably has more hands-on experience with radiologist workflows than any other imaging AI firm (at least AI firms available for acquisition).
  • Nine’s other AI and imaging workflow assets should also help support Sirona’s future RadOS and AI development, although it’s harder to assess their impact for now.

The AI Shakeup Angle – This acquisition has largely been covered as another example of 2022’s AI shakeup, which isn’t too surprising given how active this year has been (MaxQ’s shutdown, RadNet’s Aidence/Quantib acquisitions, IBM shedding Watson Health). However, Nines’ strategy to combine a telerad practice with in-house AI development was quite unique and its decision to sell might say more about its specific business model (at its scale) than it does about the overall AI market.

The Takeaway

Since the day Sirona emerged from stealth, it’s done a masterful job articulating its mission to solve radiology’s workflow problems by unifying its IT infrastructure. Acquiring Nines’ AI assets certainly supports Sirona’s unified platform messaging, while giving it more technology and personnel resources to try to turn that message into a reality.

Meanwhile, Nines becomes the latest of surely many imaging AI startups to be acquired, pivoted, or shut down, as AI adoption evolves at a slower pace than some VC runways. Nines’ strategy was really interesting, they had some big-name founders and advisors, and now their work and teams will live on through Sirona.

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-- The Imaging Wire team