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In Oklahoma, a Regional HMO Deploys Predictive Analytics to Drive Better Health Outcomes

August 30, 2016
by Heather Landi
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Using predictive analytics and prescriptive health insights, GlobalHealth can now predict nearly 70 percent of its hospital admissions
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Two years ago, GlobalHealth, an Oklahoma City-based health maintenance organization (HMO), began a proactive outreach program utilizing predictive and prescriptive analytics as part of its care management efforts. The goal was to identify plan members whose health was most likely to change for the worse in the next 12 months in order to intervene to reduce medical costs and improve health and wellness for plan members.

GlobalHealth is a regional HMO that covers more than 45,000 individuals in all 77 Oklahoma counties and its membership includes state and education employees, federal employees, municipal employees, Medicare Advantage members and private employers.

According to a case study released by GlobalHealth, the organization has seen notable results since implementing a predictive analytics platform from VitreosHealth as an Insights-as-a-Service (IaaS) delivery model for population risk models for predictive and prescriptive health data insights. GlobalHealth essentially combines the data insights with human outreach to better understand its members’ needs.

Since the initiative began in early 2014, GlobalHealth has experienced an 18 percent reduction in emergency room encounters and emergent hospital admissions among the target population as well as a 22 percent reduction in readmissions. GlobalHealth also has seen per-member, per-month (PMPM) medical costs for that target population reduced by 16 percent, and, more generally, spread across all members, there has been a 6 to 8 percent reduction in PMPM medical costs. In 2015, the organization realized $10 million annual savings, and GlobalHealth can now predict nearly 70 percent of its hospital admissions, according to David Thompson, GlobalHealth chief operating officer.

Back in 2013, GlobalHealth executive leaders recognized the need to utilize predictive analytics technology and prescriptive health insights to identify at-risk members and strengthen its care management program in order to provide better health outcomes for its members.

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Thompson specifically recalls a meeting three years ago with staff members and executives discussing case management rounds that proved to be the impetus for the analytics initiative. During the meeting, the discussion turned to a plan member who suffered a recent diabetic coma. “The member was a relatively healthy, young person who did have diabetes and other conditions. So we asked ourselves if there was anything we could have done to prevent this. The directors of case management said yes, we could have prevented it, if we had done this or that at this point in time, so we went through the case in reverse chronology to piece together the patterns of that case and we realized it would be helpful to have all this data in one central, manageable place. And that was the lightbulb moment when we decided we needed an analytics engine that could help predict these types of events. In the past few years we have been working on building predictive modeling into our care management techniques.”

Thompson continues, “Because we are a small plan, we had a lack of available, combined sophisticated data sets and we were challenged with identifying members for these negative health events, or what we considered to be avoidable, manageable events, such as emergent hospitalizations or readmissions. We just didn’t have great data to in order to intervene to prevent that. We’d always been good at the point of service and managing care when a member is hospitalized and transitioning them back home and identifying the community resources around them. But we were not good at predicting the diabetic comas or the members who had co-morbid, complex conditions which often manifests into a future unhealthy event.”

At the same time, Oklahoma also is a state with significant health concerns as it is consistently ranked near the bottom of all 50 states based on the healthiness of its population. Oklahoma’s adult obesity rate is 33 percent, which is the sixth highest rate in the nation, according to data from Trust for America’s Health and the Robert Wood Johnson Foundation. Nearly 22 percent of adult Oklahomans reported having a mental health issue and close to 10 percent experienced a substance abuse issue, according to Mental Health America. And, among Oklahoma’s adult population, 24 percent are smokers, compared to the national average of 19 percent, according to 2015 data from the Centers for Disease Control and Prevention.

Thompson says for organizational leaders the first step on this journey was to examine options for building an analytics program. The organization’s internal information technology team believed it could develop the necessary data infrastructure, however, it was estimated it would take two years to build. So, GlobalHealth executive leaders reviewed the options offered by external vendors.

One of GlobalHealth’s priorities was finding an analytics partner with a solution that could seamlessly integrate and evolve with the organization’s existing IT structure.

“We needed a partner who could do several things. One, have a strong data architecture and legitimate interfacing capabilities,” Thompson says. “A lot of companies say they are doing predictive modeling, but they’re not doing anything that couldn’t be done in house, as it’s still reactive and closing care gaps, but it doesn’t impact the full spectrum. From a vendor standpoint, we wanted a partner who was willing to customize and be flexible, and was on the bleeding edge.”

According to Scott Vaughn, president and CEO of GlobalHealth, while most health plans look at the members who have generated the most costs historically and focus on those members, GlobalHealth wanted a predictive analytics solution that would zero in on those members, as well as identify members who haven’t generated high costs in the last 12 months but are very likely to have serious health complications in the near future.

Once the analytics partner, VitreosHealth, was selected, GlobalHealth set about the work of analyzing its data sets and building algorithms to segment members who may be at risk of health emergencies. 

Using the predictive analytics solution provided by VitreosHealth, GlobalHealth has been able to reduce patient populations’ health complications by anticipating their actions. Thompson says GlobalHealth uses a novel approach to risk stratification to identify opportunities. The analytics platform leverages data sources including electronic health records, claims, health risk assessments, socioeconomic and wellbeing data for predictive risk and prescriptive care management.

“We’re able to look at specific characteristics of plan members, such as where they live, whether they are compliant with their referrals and compliant with medications, state-wide education levels, behavioral and mental health diagnosis, or characteristics that will likely result in a worse health state if we don’t intervene in order to help manage those plan members from a clinical standpoint,” Thompson says.

Traditionally, organizations use historical utilization to understand population risk and cost differences with respect to variations in care, non-compliance to evidence-based care guidelines and opportunities for care management, according to the GlobalHealth case study. In this model, the population is typically stratified as critical, high utilizers (of benefits), moderate risk and healthy.

GlobalHealth’s model uses a multi-dimensional risk stratification approach using predictive risks, such as disease-specific risk, composite risk and utilization risk, combined with outcomes like hospitalizations and ER visits to understand a member’s state of health at any point in time. In this model, members are stratified as either critical, high utilizer, hidden risk (high risk relative to care being received) and healthy and unknown.

According to GlobalHealth, those classified as critical account for 50 percent of the total population spend and have high-cost intervention in progress. High utilizers includes those with non-clinical risk, such as socioeconomic and accessibility, and non-compliant members with avoidable ER visits. Hidden risk members have diseases that are not well-managed with high-cost intervention on the horizon and are headed toward the “critical” stratification if unmanaged. And, for members in the healthy and unknown classification, it is assumed there is some hidden unknown such as new and young members with short medical histories.

Using this approach, GlobalHealth reviews the changes in the state of health risk from year to year to identify drivers of changes in costs and risks, or, essentially, to identify “mover populations” or members moving from the “hidden risk” status to “critical” or from “healthy and unknown” to “high utilizers.” By identifying these “mover populations,” GlobalHealth identified the cohorts of population to target for care management programs, and designed the right care management programs supported with the optimal resources, Thompson says.

GlobalHealth, working with VitreosHealth, ran the data through regression analysis tests and clinical teams vetted the diagnosis codes. Those tests enabled GlobalHealth to fine-tune the algorithms to eliminate any false positives and better interpret the data.

The health plan launched its proactive outreach program in early 2014 as a pilot project. In 2014, the outreach program targeted and engaged about 3,000 plan members, and that number has risen to about 7,000 plan members today, according to Thompson. Within the next six months, the organization plans to increase this number to 10,000 members, which will represent approximately 20 percent of GlobalHealth's total patient population.

While the data analytics technology is one piece of the program, Thompson says another critical piece is proactive outreach by nurse and clinician outreach managers who contact plan members over the phone to address any care gaps or to connect members with the community resources they may need. “We help the member navigate the healthcare system,” Thompson says.

Initially, GlobalHealth used its existing case management staff for the program but wasn’t getting the kind of traction it needed, so the organization created a separate unit dedicated to member outreach. GlobalHealth’s vendor partner created monthly dashboards for each care management program to better understand the performance drivers and also created an app that prioritizes the plan members to call and incorporates the members’ detailed clinical and socioeconomic information, Thompson says.

From a technology standpoint, one key to the data analysis and outreach program’s success has been ongoing performance tracking using the analytics dashboards which enables fine-tuning of the care management plans, Thompson says.

“Some key lessons that we’ve learned through this process of implementing predictive analytics is that your data is never going to be perfect. If you wait for it to be perfect, you’re going to miss out on an opportunity to intervene and make a difference,” Thompson says. “Many times we are identifying members at risk for a health event, using this model, a sophisticated regression analysis, and it looks at types of members, their zip code, demographic and diagnostic categories, and based on that, we know they have a specific progression through time. If you intervene sooner rather than later, it’s going to have a bigger impact.”

Thompson continues, “Another big lesson we learned is that if you don’t dedicate resources, you will only get so much value out of it. During the pilot years, we were splitting up resource. For the nurses, half their workload was managing referrals and the other half of their workload was doing proactive calls, and then we quickly realized that to create efficient and effective programs, you have to dedicate the resources to it.”

For organizations wanting to build similar care management programs that utilize predictive analytics, Thompson says organizations need to have the agility to evolve, he says. As an example, in the course of this work, GlobalHealth executive leaders combined departments to work together instead of in silos and reorganized the corporate structure by hiring and creating new positions.

Moving forward, the organization’s executive leaders plans to continue to evolve the data analysis and outreach program by bringing in more data sources, including clinic, laboratory and imaging center medical records, to further refine the analysis, Thompson says. This year, the organization has a plan in place to reduce healthcare costs by 10 percent, representing $25 million in savings.

 

 

 


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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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