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At the Health IT Summit in Nashville, a Pragmatic Look at the Complexities in Leveraging Data Analytics

June 28, 2017
by Mark Hagland
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Industry leaders shared a broad range of perspectives on the current opportunities and challenges in leveraging data analytics

On Tuesday at the Health IT Summit in Nashville, sponsored by Healthcare Informatics and taking place at the Sheraton Downtown Nashville, attendees were offered a very vigorous discussion of the opportunities and challenges around the leveraging of data analytics in patient care organizations. The panel discussion, entitled “Forward-Thinking Examples of Analytics in Modern Healthcare Settings,” was moderated by Michael Hamilton, vice president, analytics, at Albany (New York) Medical Center.

Hamilton was joined on the panel by J.D. Whitlock, vice president, enterprise intelligence, at the Cincinnati-based Mercy Health; Bob Cawley, CIO at the Glens Falls, N.Y.-based Adirondack Health Institute, an independent, non-profit organization that partners with providers and patient groups to transform health in northern New York state; and William Paiva, executive director at the Stillwater, Oklahoma-based Center for Health Systems Innovation at Oklahoma State University.

“As we had agreed to do,” Hamilton said, “we wanted to discuss forward-thinking examples of analytics. A good place to start might be to discuss some of the challenges we all face in our current system of data.”

“There’s no shortage of challenges, in that regard,” Adirondack’s Cawley said. “The important thing to know is that we’re a network of providers, not a healthcare system. And that has a lot of implications for how we implement technology. We were formed in 1987, but underwent a significant transformation in 2007, when we started the Adirondack Home Health Network. It was then that we got everyone on interoperable EHRs [electronic health records], connected to a RHIO [regional health information organization], and established a regional clinical quality dashboard, and a regional all-claims database. In addition,” he said, “New York state stated a DSRIP program [Delivery System Reform Incentive Payment Program] in 2014. Adirondack joined as a participating provider system. Among the key areas of focus in the DSRIP program has been reducing avoidable high-cost utilization including ED utilization and inpatient admissions.”

What’s more, Cawley noted, “We’re also very rural, with only 700,000 people across 11,000 square miles. We have five or six hospitals; the largest is 300-350 beds, while most are small. Within the hospitals and primary care, we have pretty good adoption of primary care and adoption of the RHIO, but even within primary care, there are 15 different EHRs we’re dealing with. And nursing home, long-term care, behavioral health, if they are on an EHR, they’re not connected to the RHIO. And given that 25 percent of our providers are financially challenged,” that fact in itself is very challenging, he added. “We don’t have the resources to connect all those entities, so we’re relying on the RHIO to connect all of us. The other main source of data is the state government; since DISRIP is a Medicaid program, the primary payer for us is the state of New York, so we’re attempting to get data that way. So, trying to get all this information together is the first challenge,” he said. “If you’ve ever tried to integrate data from EHRs, you’re familiar with the amount of remediation work needed, and what’s needed to achieve the fantasy of marrying clinical and claims data and then use it.”


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“I’ll answer that question a little bit differently,” Paiva said. “We’re blessed that by the time the data comes to us, it’s cleaned up as much as possible. So we don’t deal with all the informatics issues. So once we’ve got the data, what do we actually do with it? We’ve spent the last three years figuring out what to do with the data and how to provide tools meaningful to the healthcare system. A few things we tend to focus on, as it relates to getting value out of your data: whatever tool or product you develop needs to be clinically relevant and timely. You have to provide tools that allows people to do something they couldn’t do otherwise. So it needs to be clinically relevant and to help them to do something they couldn’t do before.”

(l. to r.:) panelists Whitlock, Paiva, Cawley, Hamilton

Further, Paiva said, “We need to stop thinking about it as an either/or—either the physician or the tool. It can be both. We were able to reduce error rates for diagnosing breast cancer by 85 percent, when an algorithm we developed was used together with a pathologist diagnosis process. These tools are really designed to help clinicians. And the last thing I would say is that you need some sort of benefit to physicians beyond the clinical—either financial, to help manage a population, or to manage some kind of MACRA or CPC+ credit,” he said, referring to the Medicare Access and CHIP Reauthorization Act of 2015, and the Comprehensive Primary Care Plus program. “Here’s one story of a tool we developed that has been successful,” he continued. “We had surveyed rural physicians on their challenges. And one of the ones they mentioned was managing diabetic patients in rural areas, where they don’t have many specialists. So we focused on diabetic retinopathy, because only 10 percent of diabetics in rural areas get eye tests. We asked, could we develop a tool from the data collected in the primary care setting, to predict whether the patient has diabetic retinopathy? So we built an algorithm based on primary care visits, comorbidities, and other data. It solves a rural-physicians problem and addresses requirements under MACRA and MIPS [the Merit-based Incentive Payment System]. That’s an example of how to build tools that work and are useful.”

Albany Medical Center’s Hamilton said that, “As we continue to grow, we have affiliated hospitals operating with different EHRs, and that’s a challenge. And then, physician engagement and adoption across the enterprise, are critical. Otherwise, you’ll continue to see articles saying that data science is dead. And that’s because you didn’t plant the seeds, you just tried to reap the harvest, and tried to solve problems that didn’t exist.”

Mercy Health’s Whitlock said, “To Bob’s point about interoperability challenges, integrating ambulatory EHRs, is extraordinarily difficult and takes a lot of time. The good news is, we’re down to the last few practices on this. The bad news is, we’re struggling with those final integration steps. So the ambulatory EHR integration struggle is real. And then to William’s point about making sure you’re developing tools that can actually be used, I agree-don’t go spending a year doing something that’s not going to be used.”

Instead, Whitlock said, “Make sure you have your clinical business leadership ready for what you’re actually building. Meanwhile, I think that one thing we’re doing a nice job with is that we have an enterprise performance dashboard with 34 indicators, tied to clinical, operational and strategic factors or elements, and we publish it monthly. And our leadership from the top is into it, and in fact, it came from them. The downside is that my enterprise data warehouse team spends half of its time producing this, and it’s rather manual. The good news is that it’s really well-accepted throughout our organization, because it’s sponsored and supported by our regional leadership.”

Moving Towards Predictive Analytics

Inevitably, forward-thinking forms of analytics tend to be built out of a drive towards predictive analytics, the panelists agreed. “When you think about forward-thinking analytics, a couple of buzzwords or buzz terms come up—machine learning, and predictive analytics in particular,” Hamilton said. “William, what are your thoughts on that?”

“We’ve moved quite a bit into artificial intelligence and machine learning,” Paiva responded, “because I was getting frustrated with descriptive analytics projects. What was happening,” he said, “was that all the projects involving descriptive analytics ended up with the same basic conclusion—that there’s variance in healthcare—whether in drugs, readmissions, or whatever. In our case, we decided to try to develop predictive tools to put into the hands of physicians and other clinicians and administrators, in order to help them better manage the health of populations. So far,” he said, we’ve focused on two populations—patients with CHF, or congestive heart failure—and those with chronic kidney disease, or CKD. We’re trying to predict which CHF patients will decompensate, and which CKD patients will end up in unplanned dialysis. And there’s actually zero information in the literature on this; so we started looking into artificial intelligence and machine learning.”

Paiva went on to speak of the broader picture, noting that “Investments in artificial intelligence have increased from $100 million per quarter, three years ago, to about $1 billion per quarter invested now. In the last three years, the healthcare industry has been the largest recipient of those monies. Within healthcare,” he added, “there are two categories that have received the most funding—clinical risk analytics and diagnostic imaging analytics.” Even so, he said, “There’s been a fundamental lack of education in terms of healthcare people understanding analytics and analytics people understanding healthcare; that lack of understanding runs both ways. So we launched an 18-month program within our medical school, in which the students learn about topics such as design thinking and minimal viable products, and other subjects, and they learn about those topics before they begin their clinical rotations. We’ve also launched a healthcare informatics program within medical school.” Both of those innovations, he said, relate to the fact that “There remains a gap between the [IT] people developing products and innovations, and healthcare people not even knowing what they are. Look at the org charts of all our hospitals,” he said. “How many chief digital officers are sitting on the management team? How many chief analytics officers are even reporting to a senior executive? Healthcare organizations have to catch up to where we’re going.”

Responding to Paiva, Whitlock said, “In terms of predicting chronic kidney disease, of all the things to predict, that’s a pretty good one. And one interesting thing—what was most interesting about this to me was that, for the patients with all the right labs, they did a nice job of predicting the progression of kidney disease, but that was only 10 percent of the potential people affected by it.”

“That’s why you need to move into artificial intelligence and machine learning, to support better analytics,” Paiva responded. “The reality is that if you’re doing predictive modeling, it’s a challenge. And the reason we went after CHF and CKD is that that’s where the money is. So I would say that, in addition to all my other criteria, it’s always good to follow the money.”

“And we just talking about the fact that predictive analytics aren’t always the best option,” Hamilton said. Sometimes, bundled analytics are good.”

“That’s right,” Whitlock responded. “If you want to do some advanced analytics around hospital length of stay and cost, and want to take post-inpatient-stay and look at where you’re losing money or making money on your bundled payments, and line it all up, since the data is coming from different places, that can be done.” Further, he said, “A key factor turns out to be around discharges to SNFs”—skilled nursing facilities. “You’d be astounded at the variation among our facilities based on culture—some are at 10 percent in terms of discharges to SNFs, while some are 15 percent, some are at 30 percent.”

“We took a somewhat different approach to that,” Cawley said. “When you’re looking in particular at ED [emergency department] use, so much of what’s driving avoidable ED use isn’t even medical, it’s socioeconomic, it’s behavioral health. So we’re trying to put together work groups that are multidisciplinary, representative work groups, to try to figure out what’s going on. And one of the hospitals did an ED study, found their high utilizers, and found that they were doing all clinical assessments, but the utilization of the high utilizers had nothing to do with the clinical or medical factors.”

“I agree,” Hamilton responded. “A lot of what we spend a lot of our time on is getting the right data, and then getting it into the right format to do predictive analytics on it, and we don’t have unlimited budget or time, and we have to focus on the biggest return on investment, and focus on the easy wins.”







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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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