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At Penn Medicine, Leaders Leverage Predictive Analytics to Improve Antibiotic Use

April 2, 2018
by Heather Landi
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More than half of patients in U.S. hospitals receive at least one antibiotic during their hospital stay; however, one-third of antibiotic prescriptions in hospitals involve potential prescribing problems, such as prescribing an antibiotic when it is not needed or giving an antibiotic for too long, according to the Centers for Disease Control and Prevention (CDC).

And, in hospitals, misuse of antibiotics can lead to the development of antibiotic resistance, which adversely affects morbidity, mortality, length of stay, and cost, the CDC reports.

A study, recently published in the Joint Commission Journal on Quality and Patient Safety, found that antimicrobial-resistant organisms account for more than two million infections and 23,000 deaths annually in the United States. Studies from diverse settings estimate that between 25 percent and 50 percent of antibiotic use in hospitals is sub-optimal or unnecessary. Hospital antimicrobial stewardship programs can reduce inappropriate antimicrobial use, length of stay, Clostridium difficile infection, rates of resistant infections and cost, the researchers concluded.

To combat the threat of antimicrobial resistance, The Joint Commission and the Centers for Medicare & Medicaid Services (CMS) have initiated or proposed requirements for hospitals to have antimicrobial stewardship programs, but implementation remains challenging, according to the above study, led by Shashi Kapadia, M.D., instructor of medicine, division of infectious diseases at Weill Cornell Medicine in New York City. As of 2014, only 39 percent of hospitals in the United States reported having a program that met all recommended elements of stewardship programs, and only 55 percent had any antimicrobial stewardship program infrastructure.

That study also examined top antimicrobial stewardship programs in U.S. hospitals and found that innovative programs are integrating IT systems to enable real-time interventions to optimize antimicrobial therapy and patient management.

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At Penn Medicine, based in Philadelphia, clinical leaders have developed a robust IT system for stewardship to facilitate clinical decision support and identify opportunities for intervention. Penn Medicine, the University of Pennsylvania Health System, operates six hospitals in eastern Pennsylvania. The Hospital of the University of Pennsylvania is home to one of the oldest antibiotic stewardship programs in the country, as it was formed in 1992 by associate chief medical officer Neil Fishman, M.D., according to Keith Hamilton, M.D., associate healthcare epidemiologist and director of antimicrobial stewardship at the Hospital of the University of Pennsylvania.

Working with health technology company ILUM Health Solutions, the hospital’s antimicrobial stewardship team, led by Hamilton, initiated a project to leverage health IT to improve antimicrobial prescribing, with the aim of delivering decision support within clinicians’ workflow to help ensure the right patient gets the right antibiotic at the right time.

“If we were to choose an antibiotic that is too narrow for a given patient, too narrow spectrum, then we may not be treating their infection adequately, especially those with more severe infections, like sepsis. But, if we’re giving an antibiotic that is too broad spectrum, then we may be exposing the patient to unnecessary toxicity and side effects. Ultimately, our goal is to give an antibiotic that is effective but is not more than what a patient needs,” Hamilton says.

While the hospital had an established stewardship program infrastructure, Hamilton notes that there was room for improvement in antibiotic prescribing practices. “The existing solutions to antibiotic stewardship didn’t have an efficient approach to identifying the patients who may benefit from an improvement in their antibiotic treatments. Tracking antibiotic use on a healthcare system-level is also fairly challenging to do with current solutions,” he says.

ILUM Health Solutions, a Merck subsidiary, provides enterprise-wide disease management tools and services as well as assists with antimicrobial stewardship programs. Last fall, ILUM acquired Teqqa LLC, which provides precision analytics to help physicians assess what antibiotic to prescribe to patients. The Hospital at the University of Pennsylvania has been working with Teqqa since 2014, and Hamilton and his team saw an opportunity to leverage ILUM and Teqqa’s technologies to bring relevant data and lab results right to the point of care, within the clinicians’ workflow.

When the hospital first began working with Teqqa back in 2014, the antimicrobial stewardship team focused on creating software to track antibiotic use and resistance to help clinicians determine the resistance patterns in their given settings, Hamilton says. The team then focused on moving more toward a “precision medicine-type approach” using predictive models, and that software was implemented a year ago, he notes.

Keith Hamilton, M.D.

The platform integrates real-time electronic health record (EHR), lab and pharmacy data from across disparate systems, and can pull in vital signs, drug data, microbiology and other laboratory findings. And, ILUM has developed a series of alerts identifying patients that may need a change in antibiotics, Hamilton says. “These alerts can alert both us [antibiotic stewardship teams] as well as individual clinicians where a change may be warranted and we can make those interventions,” he says.

What’s more, Hamilton says, the technology platform provides a user-friendly, interactive platform that allows the antimicrobial stewardship team to track antibiotic use in a hospital, on an ongoing basis, to better identify overall areas where improvement may be needed. And, the platform allows the user to focus in on patient-level data to better identify educational interventions or changes in guidelines that may be warranted, he says.

“The platform allows us to create a real-time, continually updated table of susceptible and resistance data for our institution, which, in turn, informs the guidelines that we set up for antibiotic use on a health-system level,” he says, adding, “But where even that falls short is that a guideline indicates what is appropriate for at least a majority of the patient population, but every patient is different. There are some patients who may not fit into guidelines, and we know based on prior research which risk factors may identify a patient as not being one that actually fits into those guidelines and may need an antibiotic that is broader spectrum in order to effectively treat their infection.”

To this end, ILUM has developed a predictive model that clinicians can use in real-time to better identify what antibiotics are going to work for a given patient. “Those predictions also can alert our stewardship team before we even get susceptibility data for a given patient’s infection so we can get them the antibiotic they need much sooner,” he says.

The predictive model is based on risk factors associated with antibiotic resistance, and integrates patient data from the EHR. Risk factors include prior antibiotic exposure, infections with other resistant microorganisms, age and prolonged hospitalizations in the intensive care unit (ICU). Clinicians can then use the software to actually predict the chances of susceptibility of a particular antibiotic given to a patient based on those risk factors. The software automatically pulls those risks factors from the health record, and comes up with predictions on what percentage likelihood a given antibiotic would be effective.

One significant way prescriptions are managed across the health system is through prior approval, meaning prescribers need authorization to prescribe certain antibiotics. To improve this process, Hamilton and his team worked with ILUM to implement a platform, available via smartphone and through a web version, enabling clinicians to request approval for antibiotics in a more efficient way.

As a result of implementing the health IT tools, clinicians can easily access the information they need to make an antibiotic decision. “The platform pulls all the biology data, recent antibiotic exposure that the patient has had and it’s very easy to have that data in front of you in the palm of your hand,” Hamilton says. “Clinicians use it as an informational tool as they are making decisions. They can also run a predictive model on their patient, just by looking up their patient and pressing go. They can access our institutional treatment guidelines through the software program as well, and the clinicians use it to communicate with our stewardship team both for asking permission to use antibiotics as well as asking general questions of the stewardship team.”

The platform also provides an interactive, dynamic, Web-based antibiogram to replace the program’s traditional static antibiogram (antibiograms are overall profiles that reveal the antimicrobial susceptibility testing results when a specific microorganism is subjected to a battery of antimicrobial drugs). Since implementing the new interactive antibiogram, the website has had about 3,000 hits per month, compared to only 30 hits per month with the previous table antibiogram, Hamilton says. “That’s definitely a tribute to how well the software was designed and how much the clinicians find the software useful. Right now, they are going outside their workflow, because the software is not in our EHR, so even despite that, they are still seeking out the information,” he says.

For the antimicrobial stewardship teams, the software provides an assessment of the hospitals’ current antimicrobial usage, the effectiveness of these programs, and helps providers re-focus their program as needed. “It basically forms a command center where we can get alerts and then reach out to the prescribers to make suggestions on how to optimize those antibiotics. It also allows us to more easily respond to requests for approval and questions and then also allows the team to more easily track antibiotic use on a health system-level and on a unit level,” he says.

He continues, “That has allowed us to start to provide feedback to individual patient care units on their antibiotic use and develop action plans on those units, and to further optimize their antibiotic prescribing practices. It also has allowed each provider to become an antibiotic steward themselves, and it empowers unit-based staff to make sure that their patients are receiving the right antibiotics.”

Improving the efficiency of antimicrobial stewardship programs and leveraging IT to enable real-time interventions can have a notable impact on patient care. “Our primary goal is to improve patient outcomes; that’s really the driving force of antibiotic stewardship. But, as an added benefit, antimicrobial stewardship programs have consistently shown to decrease cost, both cost of antibiotics as well as hospital costs,” Hamilton says, adding, ‘If you are effectively treating patients for the infections that they have, their length of stay in the hospital is going to be shorter as well, so that leads to dramatic decreases in healthcare costs related to antimicrobial stewardship as well as decreases in adverse events, such as antibiotic resistant infections and C. difficle infections as well.”

 

 


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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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