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At the Cleveland HIT Summit, Looking at the Opportunities in Using Sociodemographic Data

March 28, 2017
by Mark Hagland
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Michigan Medicine’s Justin Pestrue looks at the potential of demographic data to improve pop health efforts

There is a very significant opportunity to leverage sociodemographic and socioeconomic data in new ways in order to more effectively care for patients in the context of accountable care organization (ACO) and population health work; and much of that opportunity has yet to be fully plumbed. That was the core message of the presentation “Effectively Using Sociodemographic Data in Healthcare Analytics,” presented by Justin Pestrue, administrative director of quality analytics at Michigan Medicine (the new name for the University of Michigan Health System), Ann Arbor, Mich., on Friday, March 24, during day two of the Health IT Summit in Cleveland, sponsored by Healthcare Informatics, and  held at the Hilton Cleveland Downtown, in Cleveland, Ohio.

Pestrue began by noting something that numerous industry leaders have pointed out and are aware of: only a small proportion of patients’ health status can directly be impacted by care delivery in patient care organizations (some say that proportion accounts for perhaps 20 percent of the overall impacts on the health of individuals, with personal lifestyle and behavioral choices, personal environmental influences, and other influences being far stronger overall).

“One of the biggest opportunities we have in healthcare analytics,” Pestrue told his audience, “is how we use sociodemographic and socioeconomic data to deliver great care for patients. This is important data to consider.” One obstacle? The fact that it remains challenging to gather, analyze, and use sociodemographic and socioeconomic data. Pestrue showed his audience a slide of a photo showing a man bending down searching for something in the grass under a streetlamp. The parable he told was this one: “So there was this man who was walking down the street one night, and he saw a man searching for something in the grass under a street lamp. The first man said, “Are you looking for something?” The second man said, “Yes, my car keys.” The first man said, “Do you think they’re very nearby here?” The second man said, “No, not at all, I lost them far from here.” “Well, why are you looking for them here?” “Well, because this is where the light is.” The point of the parable, Pestrue told his audience, is that so often, leaders of patient care organizations work with data that is relatively easy to access and work with, rather than data that might be very important but is harder to access and work with.

“So we have a real tendency to look at the data that’s easier to get—and that’s often the elements that are in our EHRs [electronic health records] and in our billing systems. But sociodemographic and socioeconomic data can do a really good job of helping us to better understand our patients,” he noted, with sociodemographic data include gender, race, ethnicity, age, and place of residence, and socioeconomic data including education level, income, etc., and both types of data being corralled together under the rubric “social determinants of health.”


Justin Pestrue

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“Among the conversational themes around these elements,” Pestrue told his audience, “are equity and justice; issues around policies and payments; and the elements around quality of care and population health management.” Speaking of the federal Centers for Medicare and Medicaid Services, he noted that “CMS is trying to identify how we appropriately risk-stratify populations. There’s a lot of work going into how demographics play into that,” he noted, adding that, “When I worked for a faith-based healthcare organization, they made it a part of their mission to ask, are we serving our population as equitably as possible, and fulfilling our mission?”

Meanwhile, at Michigan Medicine, Pestrue told his audience, “We’re looking much more closely at our quality outcomes and population health initiatives.” And, in that context, he noted, “The National Quality Forum has an ongoing Disparities Project. And one of their great recommendations is that while you want to account for risk, there are some challenges in adjusting for demographics. In addition, a lot of APMs [alternative payment models] have specific written requirements about using socioeconomic data as well.” Importantly, he said, “We were all introduced to these ideas when a lot of the public health data came out suggesting that very little of the longevity of people is determined by their engagement in the healthcare system. Healthcare plays about a 10-to-20-percent influence—the remaining factors are behaviors, social impacts, and genetics. So, if the healthcare system is only accounting for a small proportion, how do we account for all the other elements? That is one of the biggest opportunities we have—not only impacting our population health and public health challenges, but how are these factors being thought of in an ED visit or when a patient is on a chronic disease registry?”

At Michigan Medicine, Pestrue said, he and his colleagues have been drilling down in particular on looking at the underlying conditions that may help to explain variations in patient utilization. And, he said, drilling down into their data, he and his colleagues found that “There are racial groups using emergency services at more than twice the rate of other groups. So it’s clear that sociodemographics has an impact on how we use healthcare.” In that context, he and his colleagues have gone to work and drilled down into the data to try to uncover patterns. They took their covered-life population of 200,000, and divided that population into groups, depending on how many chronic disease registries those patients are on—zero to five. Then, they layered on top of that categorization work further segmentation using the LACE tool—an acronym that stands for “length of stay, acuity, comorbidity, and emergency department visits”—and looked at LACE-related data from the previous five years. They then risk-stratified which patients might be readmitted.

“Our team did a little study looking at how many readmissions the LACE score predicts or explains,” Pestrue said. “And we ran a model and compared it to strictly demographic experiences of patients.” And, pointing to a slide with data on it, he told his audience, “You can see that the R squared is 0.057; but looking at a model that strictly involves sociodemographic factors, it’s 0.047. What that means is that 80 percent of what we get from LACE, we can get from a pure sociodemographic model. So that points to how important these factors are in the outcomes of our patients.”

In any case, Pestrue told his audience, “We created a risk-adjusted readmission rate adjusted by the LACE score itself. And we factored in the clinical conditions and then broke out the rates of readmission. Not surprisingly, males are readmitted at higher rates. In the LACE-Plus model, if you’re a male, you get more points, they recognize that that risk is higher. Different payer groups account for differences, and also gender. Being male. If you’re a single male who’s on Medicare, your likelihood is going to be tremendously higher.”

Drilling down further into the data, Pestrue told his audience that he and his colleagues went deep on location-related demographic data, scanning the service area around Ann Arbor by zip code. What they discovered was interesting, because zip code alone turned out to be a rather crude indicator of health status. Instead, drilling down to the sub-zip code level, they found a variety of “hot spots” of neighborhoods of people with challenging sociodemographic and socioeconomic characteristics. Zeroing in on a map showing different sizes and colors of circles and explaining what the colors and sizes meant to the audience, Pestrue explained the map as a map of readmissions in their service area by zip code, with circles showing areas within zip codes of notably higher or lower rates of readmissions.

“One of the things I wanted to illustrate is that, often when we look at zip codes, we’re missing the market,” Pestrue said. “The biggest white circle in the middle of the map—a zip code on the western edge of Ann Arbor—is in the zip code I live in.” As it turned out, the “most insightful circle,” was a mobile home park on the edge of a mostly-affluent area. What does that circle mean? It means that the residents of that trailer park are living with less-favorable sociodemographic and socioeconomic characteristics than are their neighbors in that zip code overall. “We have a tremendous amount of readmissions coming from that one mobile home park. We haven’t done anything about that yet, but we’ll be taking a closer look at what’s happening in that area. And I didn’t pick that zip code to analyze for that reason; it just happened. But I think if we do this across most of our collective catchment areas, we’ll see more dots like this. In the public housing sphere, they’re doing a lot of research into depreciation areas and deprivation indexes. They’re taking census data and figuring out where we’re under-resourced, and where residents have a lot of challenges in paying for transportation or medications, and this is a project in Utah where they’re studying that. A lot of studies are finding that the tighter the neighborhood studied, the sharper the data will be. So we’re starting to look for deprivation indexes in our area as well.”

Pestrue told his audience that much work remains to be done in all these areas, but that some of the initial work that he and his colleagues have done at Michigan Medicine points to the tremendous potential inherent in drilling down into sociodemographic and socioeconomic data, and cross-hatching such social determinants of health-related data with clinical and claims data available to the leaders of patient care organizations. And, in that context, Pestrue cautioned that “There’s a risk-adjustment trap. When we as an organization or industry risk-adjust out for these demographics, we have to be very careful to not then do analysis on those risk-adjusted models around the factors put into the models in the first place. Let’s say there’s been a historic disparity around a particular racial group,” he said. “If we say, that group has historically had negative outcomes, and we take a risk-adjusted model and stratify it by race, it washes out the actual factors involved. I’ve seen that before.”

So, what is possible around demographic data? Pestrue recommended to his audience that they “understand the importance of SDS and SES on your organization’s results.” In that, he said, “Healthcare analytics teams should have core competencies in SDS and SES factors and geo-analytics. There are tremendous insights we can gain from all of that data.”

To do so effectively, he said, “We need to actively work to capture accurate actors and supporting data. We need to be able to geocode patient addresses, and capture from multiple channels. And how much are we willing and able to ask the tough questions?” he asked. It’s important, he said, to engage models that utilize this information, while understanding the risks. And he said, while many remain concerned about the idea of purchasing data—after all, there are entities who purchase credit scores from credit agencies and use that data in ways that can harm consumers—in the context of trying to improve the management of services to patients and consumers at risk, it might well be worth purchasing some forms of data in order to enrich the ability to better organize the management of services to one’s community.

In the end, Pestrue said, there is a tremendous opportunity to leverage sociodemographic and socioeconomic data to enrich the data analytics work that can support robust population health management and care management initiatives in patient care organizations health system-wide. And, he said, we are at the beginning of a very long journey around learning how to optimally use that data to improve population health management and service optimization 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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