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At the HIT Summit in Raleigh, a Probing Discussion of the Value of Data Analytics

September 27, 2018
by Mark Hagland, Editor-in-Chief
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At the Health IT Summit in Raleigh on Thursday, a panel of industry leaders considered carefully some of the value-add issues around data analytics

On Thursday at the Health IT Summit in Raleigh, sponsored by Healthcare Informatics, a probing discussion took place among industry leaders of some of the challenges and opportunities facing patient care organization leaders around leveraging data analytics to achieve strategic goals.

The Health IT Summit is being held at the Washington Duke Inn & Golf Club, on the Duke University campus, in Durham. On Thursday morning, following an opening address by Stephen Blackwelder, Ph.D., chief analytics officer at the Duke University Health System, based in Durham, Dr. Blackwelder led a panel entitled “Advancing Analytics and Data Best Practices in Your Health System.” He was joined by Tricia Nguyen, M.D., CEO of the Commonwealth Health Network, part of the Falls Church, Va.-based Inova Health System; Walter Kwiatek, chief academic information officer at Duke Health Technology Services; and Mark Pitts, a healthcare analytics executive at the Cary, N.C.-based SAS.

Panelists discuss analytics issues on Thursday at the Summit (l. to r.): Blackwelder, Nguyen, Kwiatek, Pitts

Early on in the discussion, Blackwelder, who had just introduced the audience to some of the advances he’s been helping to lead at Duke University Health, asked his panelists, “What sorts of challenges are you struggling with, top of mind challenges in your organizations?”

“I’ve been in multiple organizations from payers to delivery systems,” Dr. Nguyen responded, “and what I find is, the business understands what they need to do their transactional, daily business. When I meet the network team, they’re only concerned with the transactional activities of recruiting physicians into the network. But what they fail to understand is, who are the physicians we need? Their goal is to recruit as many PCPs and specialists as they can, without data and analytics to identify high-value providers. When I arrived at Inova almost two years ago, that was my first question. It turns out that the team was relying on Aetna to tell them who the high-value providers were in the network. Yet even Aetna didn’t know, because they didn’t have real data miners working in their business. In health plans, network is separate from the clinical division, and care management, and finance. And they do not coordinate. Now, they collaborate much more, because everybody is talking about high-value networks. So, one of the biggest challenges is getting the business to think about data and make high-value decisions.”


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Duke HTS’s Kwiatek said, “One of the interesting things to me is teaching the business of research to researchers here at Duke. Typically, data assets are tied to a specific brand or type of user. Figuring out how to address our assets differently or uniquely, is driving our culture change.”

“I have a unique perspective because my role at SAS is to support the largest payers, providers, and life sciences in the world,” Pitts said. “I’m also married to a provider, who’s seeing patients right now, so I hear about the challenges of using data in patient care every day. And the core problems are not technical; the challenges are really human. The challenges I’ve had throughout this industry are almost entirely human; it’s human resistance to change, a lack of understanding among folks about how to get things done. It’s about, don’t ask me for another report, let’s figure out how to change processes. So for me, it’s a human challenge: it’s how you educate people, get them to collaborate across different elements of an organization, and drive collaboration across IT, business, and clinical folks, and pursue change.”

Blackwelder continued the discussion, saying, “I had been in a health plan and spent some time in a multispecialty ACO, done some work for a state government. Duke was the first organization I’d been in that had really unleashed the idea of empowering people to create change. That’s partly because it’s a research university. And those of you who’ve been in vertically integrated organizations that are top-down, this may sound a little strange or that it involves in political backbiting. But at Duke, I really have the opportunity: someone could propose an idea for a project, go to a bunch of folks and develop a proof of concept, and if that proof of concept is accepted, it can become a part of the organization, and we can see what happens. In terms of that sort of “Shark Tank”-y type of activity, if I’m able to be helpful to that person, we’ll collaborate; if not, he’ll go to Tricia, because she can be helpful. And my job is about making sure there’s a solid foundation under everybody’s feet; and that means that I’m really motivated to collaborate with that person. I’d rather that we created tools that played into a scaling fashion. We talk a lot about collaboration in an enterprise organization.”

Kwiatek noted that “There are things we do that create value today, and things that create value tomorrow. There’s this hurdle in the middle, this chasm, around scaling up. Creating a partnership around the scale-up function is critical to us: how do you take something from a model that might work in an isolated area, and scale it up to create value organization-wide?”

“In a large integrated health system that’s not just a provider but also an ACO [accountable care organization]—it started with the CMO and CIO, agreeing that there needed to be some form of structure around data management and governance, so they put together a structure; and they were relatively mature when I arrived, in that they had governance structure around the EHR,” Nguyen said. “But the claims and lab data weren’t integrated. So we asked, could it be integrated; so they expanded the governance to include the team outside the clinical data we’d been governing. And what worked well was they had data storers—what you called data janitors or custodians. And then they could explain what the business needs were and explain it in technical terms that the data storers and data miners could understand, so that if there was a question that came up, they could explain it to them. But it all revolves around data governance and structure. But it has to come from the top down.”

“You’ve seen the CVS-Aetna news,” Blackwelder continued. “Some of us are struck by how familiar some of these new ideas sound. The way in which the CVS-Aetna partnership plays out could be interesting; they each own some direct-to-consumer elements that wasn’t the case in the 1990s. And outside healthcare, you look at Lyft and Uber and Facebook, etc. And I’m going to throw out to you the question, is this something we should embrace, or be concerned about? Is it inevitable that someone will figure it out? I remember everyone was concerned when Google and HealthVault got involved. And then Apple was going to get involved in healthcare. Maybe it would be something that would be a flash in the pan, or maybe something that we should help to bring into the business?”

“Google is one of the greatest machine-learning organizations out there,” Kwiatek noted. “I think that technology related to providers is ultimately going to happen, but there will be a period of transition of many years; I don’t think that Apple is going to be providing healthcare anytime soon.

“I truly believe in the retail-ization of healthcare,” Nguyen testified. “You look at CVS and Walmart, they have large investments in HC. Ultimately, HC will be put in the hands of individuals. There are startups doing amazing things. There’s a company called Dermpath, and you put a patch on your skin and send it off to a lab and they tell you if it’s malignant or not. That requires data and analytics. And if you can diagnose yourself—that will require a lot of machine learning and analytics. But that requires education of consumers. Right now, all that knowledge is housed in the minds of physicians and clinicians, but the data will be freed to create more predictive and descriptive information that will help facilitate clinical team workflow, and will be translated down to the consumer level. Consumers will eventually be able to take care of themselves. And you can’t do CPR or resuscitation at home, and that’s when you go into the ED. But much care will happen at home.”

“And what will things look like in 10 or 20 years?” asked SAS’s Pitts. “We’ve gone about it the wrong way in healthcare, compared to how other industries have done it. In the Blues plans, we’ve tried to do things creating databases, to see who’s a four-star or five-star provider, who’s costly and not? But the problem is, we don’t use them [those quality rankings systems]. And as a consumer, my challenge is getting an appointment. So I think we need to use some of the same types of data strategies that Amazon and others have used outside healthcare. For example: when do I get my haircut at GreatClips? The app tells me, here are the three GreatClips places near me, here’s the wait. And you know what? I didn’t check to see quality ratings or price ratings, I went to a particular place because of the convenience. Now imagine if consumers had that information about providers? My challenge has always been, if I know the highest-quality provider, as a health plan, how do I get the patient to go to that provider? You need to provide analytics to consumers that speak to convenience; you embed that technology into a process in a way that consumers actually find it valuable.”

An audience member stated, “The one thing we’re missing here is the patient experience. While they’re waiting in the waiting room,  in the doctor’s office, there’s no reason they can’t participate in their own care. And getting access to that data could take place while patients are waiting.”

“Do you have somebody who wakes up in the morning every day in your organization, who’s thinking about those problems?” Blackwelder asked. “And you don’t have such a person, do you want one? Or don’t you want one?”

Pitts offered, “Jeff Hammerbacher, founder of Cloudera, said, the best innovations involve getting consumers to click on apps. We can apply some of those same learnings to healthcare.”

“We’re looking at how we integrate our marketing and clinical teams,” Nguyen noted. “Marketing has traditionally looked at promoting services. Now we’re looking at integrating the data they’re using, to segment and to introduce messaging. And we’re looking to see how many clicks patients need to go through, to see the messaging we’re trying to send. All of those end up as data points that we can use to better define messaging, for marketing or for clinical engagement across the population.”

And, in response to an audience question, “What are we competing over” as patient care organizations? Nguyen replied that “Competition really depends on the seat you’re in. If you’re the CFO, you’re competing for the revenues. If you’re the CMO, you’re competing on quality. For me, as the CEO of population health, we’re looking to be successful in managing the costs of care while trying to achieve the Triple Aim. In northern Virginia, we have probably the three top zip codes, income-wise, in the country. And 70 percent of our population is covered by commercial, employer-based health insurance. And we’ve got 70 percent of our market. And we’re so big that we command a higher payment from payers. So some of the health plans are trying to create narrow networks to steer patients away from us. And while these are rich people, we thought some would pay out of pocket to stay with us, and even with this particular payer with 35,000 lives in a commercial ACO, we started to see some fall-off. So competition around effectiveness—delivering care at an efficient, effective cost, is beginning to heat up. There are people who will pay for convenience, but that’s not everyone.”





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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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