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Scaling Up Infrastructure for the Analytics to Support Care Management: Molina Healthcare’s View

March 5, 2017
by Mark Hagland
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Molina Healthcare’s Ben Gordon shares his perspectives on infrastructure and data capabilities

What the IT leaders at managed healthcare plans are doing in terms of optimizing their organizations’ IT reliability, scalability, and flexibility is an area that IT leaders at patient care organizations would be wise to keep an eye on. While there are some IT fundamentals that are different at provider organizations and health plans, there are a surprising number of areas of similarity and overlap. One of these has to do with the struggles on the parts of both health plan and provider leaders to create greater usability, reliability, and scale to support their efforts at population health management, including both population health risk assessment and care management; as well as the acceleration in the ingress of massive amounts of raw data these days, with data coming from everywhere and needing to be managed intelligently.

It is in that context that the executives at the Long Beach, California-based Molina Healthcare—whose main areas of coverage and focus are Medicaid managed care, Medicare Advantage plans, and the coverage of dual-eligibles (those eligible for both Medicare and Medicaid coverage), made a major infrastructure-related announcement last month. On Feb. 21, during the annual HIMSS Conference, executives at Molina Healthcare, which covers 4.2 million individuals and families across the United States and Puerto Rico, announced that they were working with the San Francisco-based Splunk, to improve their capabilities. As the Feb. 21 press release stated, “Splunk Inc. (NASDAQ: SPLK), provider of the leading software platform for real-time Operational Intelligence, today announced Molina Healthcare Inc. uses Splunk® IT Service Intelligence (Splunk ITSI) to help provide faster, more reliable healthcare services for its customers. The analytics capabilities and visualizations in Splunk ITSI enable Molina to streamline stakeholder communication across the organization. Molina’s leadership team uses the insights provided by ITSI to make data-driven decisions about its IT services infrastructure, ensuring prioritization of innovative solution delivery and resulting in a reduction of IT incidents by 500 percent and mean time to resolution by 150 percent.”

The press release quoted Ben Gordon, Molina Healthcare’s vice president of enterprise infrastructure services, as stating that, “With Splunk ITSI, our IT team now effortlessly helps our members receive access to more customized services to better meet their health care needs. We can do this by quickly troubleshooting and collaborating to analyze actionable data that is easily visualized, classified and applied back to our members. The health care industry has experienced an explosion of data,” Gordon emphasized. “With the powerful analytics built into Splunk ITSI, we have more insight than ever before into our members’ preferences. Splunk ITSI helps simplify the way we run the organization.”

The press release went on to note that “Service outages and performance issues in health care have a significant negative impact on customer experience, service delivery and patient satisfaction. Molina Healthcare’s legacy monitoring tools lacked the capabilities to effectively monitor and troubleshoot its critical services, resulting in the team having to spend valuable time and resources dealing with issues. Molina Healthcare uses the innovative Glass Table feature in Splunk ITSI to visualize the flow of key business processes including user interactions as well as the real-time performance and health of critical systems. The Glass Table visualizations provide a custom view of Molina Healthcare’s business processes and IT infrastructure mapped to enterprise critical KPIs, enabling employees around the company to share the same view and use the same vocabulary for IT management and troubleshooting.”

Ben Gordon


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Last month, Gordon, who leads a team of about 250 IT professionals, spoke with Healthcare Informatics Editor-in-Chief Mark Hagland about this set of innovations, and its implications for healthcare IT more broadly. Below are excerpts from that interview.

Tell me a bit about what led you and your colleagues to purchase a commercial solution in order to help you ramp up on your core infrastructure and analytics capabilities going forward?

We’ve been growing rapidly for the last four or five years, and with that rapid growth, we’ve been trying to keep up with the business, and part of the strategy is to modernize our infrastructure, and put the right tools and strategy in place that allows us to scale. So we’ve built a metrics aggregation platform, where we’re talking all the tools we use to monitor all our technologies, and aggregate it into a central repository, and we use that repository to monitor how things are behaving and performing. Splunk was chosen to be that aggregator: it pulls in tools, and we’re building in end-to-end views of our services, such as member claims. It really helps with our operations center in terms of those resources understanding what’s important, and cuts down on the mean times to resolving the issues that pop up, as they pop up. Since Splunk has come in, they’re reduced dramatically the number of IT incidents.

How do you define IT “incidents”?

An IT incident is something that is either not behaving properly or has an issue. It’s alerting or alarming that something that needs to be addressed.

And that can refer to any kind of IT issue?

Yes, that’s correct?

Is this connected to data analytics?

It is. The platform views analytics. It isn’t being used for patient outcomes or insights at the moment. Think of the platform as, regardless of what you’re doing from a business standpoint, all of the applications and systems have to run properly to be able to provide those services. Splunk is providing the visibility and transparency into those systems, and allowing the teams to respond more rapidly, and be able to clear out the noise, and really focus on the optimal things, on things that matter.

What has been the single biggest challenge in optimizing your systems?

Systems are becoming more complex every day. We collect over 64 billion data points a month from all of our systems; it can really be overwhelming for our technology teams. So the challenge is figuring out what matters. So having a system like Splunk can help you filter out the noise from the important things—especially in this day and age of distributed systems, using the cloud and distributed architectures, you have to have systems like this to help you filter out the noise.

What kinds of qualities and characteristics were you looking for from a prospective vendor partner in this area?

Ease of integration with other tool sets and technologies is absolutely essential. Splunk is very easy to integrate; they have a robust user community, almost like an Apple store. People post widgets and things all the time. Also, the tool itself is very smart. In traditional tools, there’s a tremendous amount of configuration that you have to have in house. We wanted to adopt the next generation of tools, that don’t require the same amount of configuration to run them effectively. That’s what’s most important. We didn’t want to have to hire a bunch of people to manage our tools; we wanted the tools to do the work.

What should provider IT people understand about engaging in this kind of work?

It’s all about patient care and member outcomes, regardless of whether you’re a payer or provider. So making sure that the technologies across healthcare, the administrators and other folks helping members having good experiences with tools, that they’re always up and stable and performing well.

So the performance of your systems speaks to the quality of consumer experience that you can provide, then?

Yes, absolutely, there’s a cascading effect, in that regard Imagine if someone couldn’t send an email, or the technology were slow. The experience is a bad one, and the member suffers, or they go somewhere else.

What commonalities or differences might there be around scale, between health plans and providers?

Well, we’re certainly larger than a provider, but regardless of the scale, the technology is the same, regardless of the magnitude. The concepts, problems, and issues are the same. So tools like this can start small and scale up. This isn’t something applicable only to an extremely large set of data. It’s really about the tools to manage your data.

What data governance lessons have you and your colleagues learned so far, in all this?

One of the biggest things that’s come out of this exercise—most shops have an operations center that responds to issues and keeps systems running. When you’re hiring new people and bringing them in, there’s a learning curve. But you can build visual representations in Splunk. So if I say to you as a new staffer, go work on claims, you have to figure that out, and it can be very confusing. What we’ve built in Spelunk with the help for the network administrators and developers, we’ve built a visual view of claims, and it shows you the entire service, the flow of a claim. And imagine if you’re a new staff member here in IT, you can get a general view of how the claims process works, and that’s worth its weight in gold. And the mean time to resolving an IT issue has gone down. And regardless of whether you’re a provider or a payer, your operations center is such a key place in the organization. And data visualization has helped tremendously. New guys come in, and they can see how things work, and that’s big.

So the data visualization aspect of this is huge, then?

Yes, it absolutely is. And the real-time reporting. You can find out where a fax went that was sent from a doctor’s office, we can go in and find out where something a process, is.

Where is all this headed, do you think?

My personal feeling is that this is all about information in healthcare now. I think you’ll see more adoption of the cloud and third-party services. And that actually makes tracking of data and processes even harder. So I think you’ll see the tools becoming more intelligent, and there will be fewer of them. So tools like Splunk, data aggregation tools, will become more important. And there’s a lot of analyzing of patient outcomes, but are they really looking at processes form end to end? So instead of just looking at claims and outcomes, we’ll all be looking more and more at the entire process, and at the member experience as well.

Do you have any explicit advice, based on what we’ve talked about, for our readers, especially for CIOs, CMIOs, and CTOs in patient care organizations?

I would just say, for folks who are adopting the tool, that getting the involvement and collaboration across all tiers of IT as you work to visualize data and process, is critical. When we built these visual views, we had the developers, all the infrastructure people, too, in the room at the same time. So it really helped to have the developers and infrastructure people together. And in dev ops—developmental operations—a movement that’s been going on for speed of delivery and agile operations, dev ops bring the infrastructure guys c loser to the development cycles and have them work hand in hand together—that’s a key piece of this. In my past, you always had developers who had a view of their applications, and the infrastructure guys had their view, and you never had a common understanding. Now you have a common language now.

There’s a potential for acceleration of the optimization of systems, then, right?

Yes, that’s right. There really is great potential here going forward.


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Have CIOs’ Top Priorities for 2018 Become a Reality?

December 12, 2018
by Rajiv Leventhal, Managing Editor
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In comparing healthcare CIOs’ priorities at the end of 2017 to this current moment, new analysis has found that core clinical IT goals have shifted from focusing on EHR (electronic health record) integration to data analytics.

In December 2017, hospitals CIOs said they planned to mostly focus on EHR integration and mobile adoption and physician buy-in, according to a survey then-conducted by Springfield, Va.-based Spok, a clinical communications solutions company, of College of Healthcare Information Management Executives (CHIME) member CIOs.

The survey from one year ago found that across hospitals, 40 percent of CIO respondents said deploying an enterprise analytics platform is a top priority in 2018. Seventy-one percent of respondents cited integrating with the EHR is a top priority, and 62 percent said physician adoption and buy-in for securing messaging was a top priority in the next 18 months. What’s more, 38 percent said optimizing EHR integration with other hospital systems with a key focus for 2018.

Spok researchers were curious whether their predictions became reality, so they analyzed several industry reports and asked a handful of CIOs to recap their experiences from 2018. The most up-to-date responses revealed that compared to last year when just 40 percent of CIOs said they were deploying an enterprise analytics platform in 2018, harnessing data analytics looks to be a huge priority in 2019: 100 percent of the CIOs reported this as top of mind.

Further comparisons on 2018 predictions to realities included:

  • 62 percent of CIOs predicted 2018 as the year of EHR integration; 75 percent reported they are now integrating patient monitoring data
  • 79 percent said they were selecting and deploying technology primarily for secure messaging; now, 90 percent of hospitals have adopted mobile technology and report that it’s helping improve patient safety and outcomes
  • 54 percent said the top secure messaging challenge was adoption/buy in; now, 51 percent said they now involve clinicians in mobile policy and adoption

What’s more, regarding future predictions, 87 percent of CIOs said they expect to increase spending on cybersecurity in 2019, and in three years from now, 60 percent of respondents expect data to be stored in a hybrid/private cloud.

CIOs also expressed concern regarding big tech companies such as Apple, Amazon and Google disrupting the healthcare market; 70 percent said they were somewhat concerned.

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How One Community Hospital is Leveraging AI to Bolster Its Care Pathways Process

December 6, 2018
by Heather Landi, Associate Editor
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Managing clinical variation continues to be a significant challenge facing most hospitals and health systems today as unwarranted clinical variation often results in higher costs without improvements to patient experience or outcomes.

Like many other hospitals and health systems, Flagler Hospital, a 335-bed community hospital in St. Augustine, Florida, had a board-level mandate to address its unwarranted clinical variation with the goal of improving outcomes and lowering costs, says Michael Sanders, M.D., Flagler Hospital’s chief medical information officer (CMIO).

“Every hospital has been struggling with this for decades, managing clinical variation,” he says, noting that traditional methods of addressing clinical variation management have been inefficient, as developing care pathways, which involves identifying best practices for high-cost procedures, often takes up to six months or even years to develop and implement. “By the time you finish, it’s out of date,” Sanders says. “There wasn’t a good way of doing this, other than picking your spots periodically, doing analysis and trying to make sense of the data.”

What’s more, available analytics software is incapable of correlating all the variables within the clinical, billing, analytics and electronic health record (EHR) databases, he notes.

Another limitation is that care pathways are vulnerable to the biases of the clinicians involved, Sanders says. “In medicine, what we typically do is we’ll have an idea of what we want to study, design a protocol, and then run the trial and collect the data that we think is important and then we try to disprove or prove our hypothesis,” he says.


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Sanders says he was intrigued by advances in machine learning tools and artificial intelligence (AI) platforms capable of applying advanced analytics to identify hidden patterns in data.

Working with Palo Alto, Calif.-based machine intelligence software company Ayasdi, Flagler Hospital initiated a pilot project to use Ayasdi’s clinical variation management application to develop care pathways for both acute and non-acute conditions and then measure adherence to those pathways.

Michael Sanders, M.D.

Flagler targeted their treatment protocols for pneumonia as an initial care process model. “We kicked around the idea of doing sepsis first, because it’s a huge problem throughout the country. We decided to use pneumonia first to get our feet wet and figure out how to use the tool correctly,” he says.

The AI tools from Ayasdi revealed new, improved care pathways for pneumonia after analyzing thousands of patient records from the hospital and identifying the commonalities between those with the best outcomes. The application uses unsupervised machine learning and supervised prediction to optimally align the sequence and timing of care with the goal of optimizing for patient outcomes, cost, readmissions, mortality rate, provider adherence, and other variables.

The hospital quickly implemented the new pneumonia pathway by changing the order set in its Allscripts EHR system. As a result, for the pneumonia care path, Flagler Hospital saved $1,350 per patient and reduced the length of stay (LOS) for these patients by two days, on average. What’s more, the hospital reduced readmission by 7 times—the readmission rate dropped from 2.9 percent to 0.4 percent, hospital officials report. The initial work saved nearly $850,000 in unnecessary costs—the costs were trimmed by eliminating labs, X-rays and other processes that did not add value or resulted in a reduction in the lengths of stay or readmissions.

“Those results are pretty amazing,” Sanders says. “It’s taking our data and showing us what we need to pursue. That’s powerful.”

With the success of the pneumonia care pathway, Flagler Hospital leaders also deployed a new sepsis pathway. The hospital has expanded its plans for using Ayasdi to develop new care pathways, from the original plan of tackling 12 conditions over three years, to now tackling one condition per month. Future plans are to tackle heart failure, total hip replacement, chronic obstructive pulmonary disease (COPD), coronary artery bypass grafting (CABG), hysterectomy and diabetes, among other conditions. Flagler Hospital expects to save at least $20 million from this program in the next three years, according to officials.

Finding the “Goldilocks” group

Strong collaboration between IT and physician teams has been a critical factor in deploying the AI tool and to continue to successfully implement new care pathways, Sanders notes.

The effort to create the first pathway began with the IT staff writing structured query language (SQL) code to extract the necessary data from the hospital’s Allscripts EHR, enterprise data warehouse, surgical, financial and corporate performance systems. This data was brought into the clinical variation management application using the FHIR (Fast Healthcare Interoperability Resources) standard.

“That was a major effort, but some of us had been data scientists before we were physicians, and so we parameterized all these calls. The first pneumonia care path was completed in about nine weeks. We’ve turned around and did a second care path, for sepsis, which is much harder, and we’ve done that in two weeks. We’ve finished sepsis and have moved on to total hip and total knee replacements. We have about 18 or 19 care paths that we’re going to be doing over the next 18 months,” he says.

After being fed data of past pneumonia treatments, the software automatically created cohorts of patients who had similar outcomes accompanied by the treatments they received at particular times and in what sequence. The program also calculated the direct variable costs, average lengths of stay, readmission and mortality rates for each of those cohorts, along with the statistical significance of its conclusions. Each group had different comorbidities, such as diabetes, COPD and heart failure, which was factored into the application's calculations. At the push of a button, the application created a care path based on the treatment given to the patients in each cohort.

The findings were then reviewed with the physician IT group, or what Sanders calls the PIT crew, to select what they refer to as the “Goldilocks” cohort. “This is a group of patients that had the combination of low cost, short length of stay, low readmissions and almost zero mortality rate. We then can publish the care path and then monitor adherence to that care path across our physicians,” Sanders says.

The AI application uncovered relationships and patterns that physicians either would not have identified or would have taken much longer to identify, Sanders says. For instance, the analysis revealed that for patients with pneumonia and COPD, beginning nebulizer treatments early in their hospital stays improved outcomes tremendously, hospital leaders report.

The optimal events, sequence, and timing of care were presented to the physician team using an intuitive interface that allowed them to understand exactly why each step, and the timing of the action, was recommended. Upon approval, the team operationalized the new care path by revising the emergency-department and inpatient order sets in the hospital EHR.

Sanders says having the data generated by the AI software is critical to getting physicians on board with the project. “When we deployed the tool for the pneumonia care pathway, our physicians were saying, ‘Oh no, not another tool’,” Sanders says. “I brought in a PIT Crew (physician IT crew) and we went through our data with them. I had physicians in the group going through the analysis and they saw that the data was real. We went into the EMR to make sure the data was in fact valid, and after they realized that, then they began to look at the outcomes, the length of stay, the drop in readmissions and how the costs dropped, and they were on board right away.”

The majority of Flagler physicians are adhering to the new care path, according to reports generated by the AI software's adherence application. The care paths effectively sourced the best practices from the hospital’s best doctors using the hospital’s own patient groups, and that is key, Sanders notes.

“When we had conversations with physicians about the data, some would say, ‘My patient is sicker than yours,’ or ‘I have a different patient population.’ However, we can drill down to the physician’s patients and show the physician where things are. It’s not based on an ivory tower analysis, it’s based on our own data. And, yes, our patients, and our community, are unique—a little older than most, and we have a lot of Europeans here visiting. We have some challenges, but this tool is taking our data and showing us what we need to pursue. That’s pretty powerful.”

He adds, “It’s been amazing to see physicians rally around this. We just never had the tool before that could do this.”

While Flagler Hospital is a small community hospital with fewer resources than academic medical centers or larger health systems—for example, the hospital doesn’t have a dedicated data scientist but rather uses its in-house informatics staff for this project—the hospital is progressive in its use of advanced analytics, according to Sanders.

“We’ve been able to do a lot of querying ourselves, and we have some sepsis predictive models that we’ve created and put into place. We do a lot of real-time monitoring for sepsis and central line-associated bloodstream infections,” he says. “Central line-associated bloodstream infections are a bane for all hospitals. In the past year and a half, since we’ve put in our predictive model, we’ve had zero bloodstream infections, and that’s just unheard of.”

Sanders and his team plan to continue to use the AI tool to analyze new data and adjust the care paths according to new discoveries. As the algorithms find more effective and efficient ways to deliver care that result in better outcomes, Flagler will continue to improve its care paths and measure the adherence of its providers.

There continues to be growing interest, and also some hype, around AI tools, but Sanders notes that AI and machine learning are simply another tool. “Historically, what we’ve done is that we had an idea of what we wanted to do, conducted a clinical trial and then proved or disproved the hypothesis, based on the data that we collected. We have a tool with AI which can basically show us relationships that we didn’t know even existed and answer questions that we didn’t know to ask. I think it’s going to open up a tremendous pathway in medicine for us to both reduce cost, improve care and really take better care of our patients,” he says, adding, “When you can say that to physicians, they are on board. They respond to the data.”


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At RSNA 2018, An Intense Focus on Artificial Intelligence

November 29, 2018
by Mark Hagland, Editor-in-Chief
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Artificial intelligence solutions—and discussions—were everywhere at RSNA 2018 this week

Artificial intelligence solutions—and certainly, the promotion of such solutions—were everywhere this year at the RSNA Conference, held this week at Chicago’s vast McCormick Place, where nearly 49,000 attendees attended clinical education sessions, viewed nearly 700 vendor exhibits. And AI and machine learning promotions, and discussions were everywhere.

Scanning the exhibit floor on Monday, Glenn Galloway, CIO of the Center for Diagnostic Imaging, an ambulatory imaging center in the Minneapolis suburb of St. Louis Park, Minn., noted that “There’s a lot of focus on AI this year. We’re still trying to figure out exactly what it is; I think a lot of people are doing the same, with AI.” In terms of whether what’s being pitched is authentic solutions, vaporware, or something in between, Galloway said, “I think it’s all that. I think there will be some solutions that live and survive. There are some interesting concepts of how to deliver it. We’ve been talking to a few folks. But the successful solutions are going to be very focused; not just AI for a lung, but for a lung and some very specific diagnoses, for example.” And what will be most useful? According to Galloway, “Two things: AI for the workflow and the quality. And there’ll be some interesting things for what it will do for the quality and the workflow.”

“Certainly, this is another year where machine learning is absolutely dominating the conversation,” said James Whitfill, M.D., CMO at Innovation Care Partners in Scottsdale, Ariz., on Monday. “In radiology, we continue to be aware of how the hype of machine learning is giving way to the reality; that it’s not a wholesale replacement of physicians. There have already been tremendous advances in, for example, interpreting chest x-rays; some of the work that Stanford’s done. They’ve got algorithms that can diagnose 15 different pathological findings. So there is true material advancement taking place.”

Meanwhile, Dr. Whitfill said, “At the same time, people are realizing that coming up with the algorithm is one piece, but that there are surprising complications. So you develop an algorithm on Siemens equipment, but when you to Fuji, the algorithm fails—it no longer reliably identifies pathology, because it turns out you have to train the algorithm not just on examples form just one manufacturer, but form lots of manufacturers. We continue to find that these algorithms are not as consistent as identifying yourself on Facebook, for example. It’s turning out that radiology is way more complex. We take images on lots of different machines. So huge strides are being made,” he said. “But it’s very clear that human and machine learning together will create the breakthroughs. We talk about physician burnout, and even physicians leaving. I think that machine learning offers a good chance of removing a lot of the drudgery in healthcare. If we can automate some processes, then it will free up our time for quality judgment, and also to spend time talking to patients, not just staring at the screen.”


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Looking at the hype cycle around AI

Of course, inevitably, there was talk around the talk of the hype cycle involving artificial intelligence. One of those engaging in that discussion was Paul Chang, M.D.., a practicing radiologist and medical director of enterprise imaging at the University of Chicago. Dr. Chang gave a presentation on Tuesday about AI. According a report by Michael Walter in Radiology Business, Dr. Chang said, “AI is not new or spooky. It’s been around for decades. So why the hype?” He described computer-aided detection (CAD) as a form of artificial intelligence, one that radiologists have been making use of for years.

Meanwhile, with regard to the new form of AI, and the inevitable hype cycle around emerging technologies, Dr. Chang said during his presentation that “When you’re going up the ride, you get excited. But then right at the top, before you are about to go down, you have that moment of clarity—‘What am I getting myself into?’—and that’s where we are now. We are upon that crest of magical hype and we are about to get the trench of disillusionment.” Still, he told his audience, “It is worth the rollercoaster of hype. But I’m here to tell you that it’s going to take longer than you think.”

So, which artificial intelligence-based solutions will end up going the distance? On a certain level, the answer to that question is simple, said Joe Marion, a principal in the Waukesha, Wis.-based Healthcare Integration Strategies LLC, and one of the imaging informatics industry’s most respected observers. “I think it’s going to be the value of the product,” said Marion, who has participated in 42 RSNA conferences; “and also the extent to which the vendors will make their products flexible in terms of being interfaced with others, so there’s this integration aspect, folding into vendor A, vendor B, vendor C, etc. So for a third party, the more they reach out and create relationships, the more successful they’ll be. A lot of it will come down to clinical value, though. Watson has had problems in that people have said, it’s great, but where’s the clinical value? So the ones that succeed will be the ones that find the most clinical value.”

Still, Marion noted, even the concept of AI, as applied to imaging informatics, remains an area with some areas lacking in clarity. “The reality, he said, “is that I think it means different things to different people. The difference between last year and this year is that some things are coming to fruition; it’s more real. And so some vendors are offering viable solutions. The message I’m hearing from vendors this year is, I have this platform, and if a third party wants to develop an application or I develop an application, or even an academic institution develops a solution, I can run it on my platform. They’re trying to become as vendor-agnostic as possible.”

Marion expressed surprise at the seemingly all-encompassing focus on artificial intelligence this year, given the steady march towards value-based healthcare-driven mandates. “Outside of one vendor, I’m not really seeing a whole lot of emphasis this year on value-based care; that’s disappointing,” Marion said. “I don’t know whether people don’t get it or not about value-based care, but the vendors are clearly more focused on AI right now.”

Might next year prove to be different? Yes, absolutely, especially given the coming mandates coming out of the Protecting Access to Medicare Act (PAMA), which will require referring providers to consult appropriate use criteria (AUC) prior to ordering advanced diagnostic imaging services—CT, MR, nuclear medicine and PET—for Medicare patients. The federal Centers for Medicare and Medicaid Services (CMS) will progress with a phased rollout of the CDS mandate, as the American College of Radiology (ACR) explains on its website, with voluntary reporting of the use of AUC taking place until December 2019, and mandatory reporting beginning in January 2020.

But for now, this certainly was the year of the artificial intelligence focus at the RSNA Conference. Only time will tell how that focus plays out in the imaging and imaging informatics vendor space within the coming 12 months, before RSNA 2019 kicks off one year from now, at the conference’s perennial location, McCormick Place.



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