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Special Report: Moving Along the Data Analytics Continuum, Healthcare Organizations Continue to Make Strides

July 19, 2016
by Rajiv Leventhal
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Many patient care organizations are still in “planning” mode when it comes to leveraging sophisticated data analytics tools, but the message is clear—advanced analytics will be a necessity for survival

Editor’s Note: Healthcare Informatics has compiled together a five-story Special Report section on data analytics for its July/August print issue. This story, and one that can be read here about a fast analytics team at the University of Michigan Health System, are part of that special section.

More and more, across healthcare organizations nationwide, data and analytics tools are being seen as a means to improve efficiency and quality. Yet, according to one survey from KPMG LLP, a New York City-based audit, tax and advisory firm, only a small fraction of those in the industry are using these capabilities to their fullest potential.

The March 2015 survey of nearly 300 respondents who identified themselves as being employed by providers, payers, or life sciences companies, found that only 10 percent are using advanced tools for data collection with analytics and predictive capabilities. Twenty-one percent indicated that they are still only “planning their journey.” Of the other respondents, 16 percent said they are using data in strategic decision making, while 28 percent are relying on data warehouses to track key performance indicators.

Indeed, as the provider community continues to prepare for the shift to value-based care and being at risk for various patient populations, it’s as clear as ever that sophisticated analytics tools will be a necessity going forward—even if adoption levels are still currently low. Speaking to the survey’s results, Bharat Rao, Ph.D., KPMG LLP’s national leader for healthcare and life sciences data analytics, says that many organizations are not where they need to be in leveraging this technology, and that providers need to employ new approaches to examining healthcare data to uncover patterns about cost and quality.

Dr. Rao, who has more than 60 patents tied into the realm of healthcare informatics and analytics, personally looks at analytics on a “full stage,” that moves from descriptive (what happened and why it happened) to predictive (what will happen), and then to prescriptive (what I should do about it). “I will say that we have gotten pretty good at descriptive analytics,” Rao attests. “There are tools out there that do a good job of painting a picture of the near past. It’s no longer acceptable to take 45 days to get quality measure reports back out,” he says, offering an example of improvement in that area.

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Bharat Rao, Ph.D.

However, Rao points to both the huge gap and potential for predictive analytics, and he notes that prescriptive analytics is something that doesn’t happen in healthcare today. “Readmissions tools do a reasonable job, but there is a big gap there. One thing that has changed over the last decade is the recognition by provider organizations that analytics is not a nice shiny toy, but something that has become increasingly important for them to survive,” Rao says.

Diving Into Uncharted Waters

It was a few years ago when David Seo, M.D., current University of Miami (UM) Health System associate vice president, information technology for clinical applications, and chief medical informatics officer (CMIO) of the Miller School of Medicine, and other health IT leaders at the health system began to truly understand the evolution of where healthcare was going. “Patient-centered medical homes and ACOs [accountable care organizations] were the trends under the main idea of managing risk,” Dr. Seo says. “I was getting multiple calls and visits from vendors offering analytics solutions, one after the other, and what became clear was they were not offering a true full suite of what a health system needs to manage risk. Our own EHR [electronic health record] vendor talked to us, but even what they could provide was limited.”

Seo says that UM Health System was looking for company that had a long track record of understanding data analytics and security, so it ended up choosing Lockheed Martin, a Bethesda, Md.-based global security and aerospace company with involvement in healthcare analytics. “We knew were headed towards a clinically integrated network and other things of that nature,” Seo says, noting the need to establish a data environment, implement big data analytics and predictive modeling tools, and start to stratify patient data and conduct risk assessments.

David Seo, M.D.

Seo agrees with Rao in that predictive analytics in healthcare “is still very much in its infancy no matter who you talk to.” Indeed, aside from the basics such as readmissions, true predictive analytics has not come to fruition, he notes. To this end, University of Miami Health System started out with a diabetes risk model, and clinician leaders have shown that the model can fit within providers’ workflows, Seo says. He adds that the risk model can be ordered through the organization’s order entry system, or it can have a patient ask to run that risk model themselves in test environments. “The risk model returns a score, so you understand your risk of developing diabetes over the next five years, for example. And now we are engaged with our clinical staff to [look at] things such as what is the threshold we would set to apply an intervention, for instance,” Seo says.

Seo further emphasizes the importance of the health system’s work around different validations, which he says is a necessity before a risk model of this scope goes into production. He explains two key areas around validations. First, the validation of a phenotype or a diagnosis using EHR data needs to be validated for the system. “If I am going to say you do or do not have diabetes for example, that needs to be valid, and you need to understand what the positive predicted value of that phenotype is,” he says.

Second, he says, the diabetes prediction needs to be valid for a specific population. “I like to say that population health will be local, so the diabetes model that we pull from the literature has been validated form a highly specialized population that perhaps is of different racial or ethnic origins from our south Florida population. So what we’re doing is validating the phenotype in our population, and also understanding what the performance of that model is in our population. These are two important steps before going live with this prediction model,” Seo says.

Other healthcare organizations are making their own advancements in the analytics space. Rao points to industry leaders such as Mayo Clinic and Cleveland Clinic, where prescriptive analytics, such as precision medicine, is happening in pockets. “Leading organizations are starting to get ahead of the curve, and are recognizing that healthcare is changing into an at-risk model, so you as an organization will be responsible for care outside your four walls,” Rao says. “You are now responsible for the entire cost of the patient, so you have to track what happens to them,” he adds. As such, the organizations that are at the cutting edge are recognizing that even though today the portion of [value-based] payments is only 5 to 10 percent, it will be 60 percent by 2020, according to some folks, he says. “Providers are gearing up to get the data infrastructure, care coordination tools, analytics tools, and contracting tools to deal with that transition. That’s starting to happen,” Rao says.

Moving forward, Seo stresses that while vendors are now rolling out the tools to make disease management easier, health systems need to re-engineer their operations since it’s not just about looking at the doctor-patient relationship anymore, but rather healthcare leaders have to think about it now in terms of one-to-many simultaneous relationships. “Healthcare organizations have to readjust their care delivery patterns to fit this population health idea,” he says. “It can be hard, and it’s not the way we traditionally practice medicine. Also, some of your population will be managed this way while others won’t be, while finally keeping in mind that you have pressures of new payment models,” Seo says, speaking to all of the challenges health IT leaders now face.

Seo additionally notes two core challenges that can become present in this area of analytics and disease management: provider behavior and patient engagement. Regarding the former, simply turning on alerts in a system, or sending alerts at the point of care, will lead to failure if that’s the objective someone is looking for, he says. “We have engaged with subject matter experts who are M.D.s, as we have been developing our work around diabetes, and they have been involved in a number of our activities and interactions. They have been fully participatory, they have bought in and [been supportive], and if you don’t get that, you won’t get true change in physician behavior,” Seo says. “Doctors can be very good in finding a way around something they don’t agree with, so that’s what you’ll get. Or you will get compliance without commitment to the process. Provider behavior starts with engagement early in the process from all levels.”

Regarding patient engagement, Seo says that in newer accountable care models, there’s accountability for all parties involved—including patients and their families. “You want to give patients a method to interact with their own information. Our mindset has been to give them as much data as we can in a safe and appropriate manner so in their discussions with physicians, they understand what’s going on and can participate in their own healthcare.”

Rao says that from an analytics perspective, the top challenge above all is, that when looking at this transformation of care, there is a large portion of the healthcare provider population which is doing well in the current fee-for-service system, and there isn’t an incentive enough for them to change. “So there is a mindset that says we need to change, and that needs to come,” Rao says. When the feds made the announcement about 60 percent of care being tied to quality, I expected there to have been seismic shockwaves through the community, but people haven’t reacted like that yet. It’s almost as if they think it might not happen or after the election things will change,” he speculates.

Rao further notes the amount of unstructured data that is locked away. “How to you make it accessible for analytics? We collect data and notes every day on patients, and that needs to become actionable,” Rao says. He adds that the good news is that there are tools here to help that are about to become more sophisticated. “People say healthcare data is messy, but nothing was messier than the Internet. Google, Yahoo, Bing and Microsoft have done the greatest job in making that unstructured data useful, and now [the Internet] is the single greatest resource in history of mankind,” Rao says. “It’s about taking that free text and crunching it in a way to find the patterns that make sense. That’s a technology revolution waiting to happen.”


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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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