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Spur Innovation with a Continuous Improvement Focus

January 30, 2017
by Carly Dunham, consultant, Freed Associates
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In health care, “innovation” is often thought of in the context of life-changing tools and technology, such as the late 1970s introduction of magnetic resonance imaging (MRI) or the more recent advent of modern telehealth.

Yet the reality in health care – steeped in tradition and entrenched cultural norms – is that “innovation” often initially comes in smaller bits and bursts before widespread, systemic adoption. Consider how often in health care new ideas or workflows figuratively start as mere embers before they spark and catch fire.

That’s why a growing number of hospitals and health systems in recent years have embraced the principles and practices of continuous improvement (CI) as a transformative way to improve or remove waste and inefficiencies from their systems and processes. CI initiatives generally consist of systematic and continuous actions that measurably improve quality and safety, and enable health care organizations to deliver the best care possible to patients and their families.

By adopting a CI culture, health care organizations commit to improving themselves and sending a distinct “you truly matter” message to their patients/customers and workforce. CI helps reduce operational expenses, as streamlined processes require less time, effort and resources. CI signals to your patients/customers that they are the ultimate judge of the quality of your services – a key consideration in today’s consumer-centric era in health care. It also says you care about having an educated, empowered and motivated workforce, as these are the most important factors to the success of a CI initiative.


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Carly Dunham

In this article, you will learn CI best practices, as well as the five most important steps to take when implementing CI efforts:

Target specific areas for improvement – Based on statistical and anecdotal input, you should have no problem identifying multiple opportunities for improvement in your organization.

Determine what processes/procedures can be modified – Up front, identify what potentially can be changed, what cannot, and proceed accordingly.

Ongoing leadership encouragement – Your organization’s leaders should proactively and visibly support CI efforts, to encourage employees to pursue CI.

Implement effective CI strategies – No matter what formal or informal CI model you use, ensure that it measurably improves quality.

Communicate improvements – It’s not enough to simply adopt improvements made possible through CI; tell your employees about it! By doing so, you’ll reward the original source of the CI idea and encourage others to provide their own CI ideas.

While all of the steps listed above are important, Step 1, “Target specific areas for improvement,” and Step 5, “Communicate improvements,” are often overlooked or shortchanged in action. To emphasize the importance of these specific action steps, see the following two client success stories below.

Targeting Productivity for Improvement

Creating a culture that embraces and applies CI begins with an organization’s leadership being very specific about the areas needed for improvement. Ineffective leaders set vague or distant goals that very few ultimately take seriously. Or these leaders set too many goals resulting in the easiest or most enjoyable goals being completed first, and the toughest ones last or not at all.

Leaders will more likely see CI-related benefits consistently happen by targeting specific areas for improvement, presenting specific goals around these areas in a clear and compelling way, and insisting upon employee efforts to achieve them.

Specificity was the mandate of a large multi-specialty clinic – a client of Freed Associates (Freed) – that wished to increase its productivity based on the volume of patients seen each day. Unsure of how or where to begin by objectively assessing themselves, the clinic’s leaders turned to Freed for help.

Freed began by initiating a time/motion study of the clinic’s most productive physician, a dermatologist, to understand his success. Freed discovered that this dermatologist had trained his team to place every clinical item in every exam room in exactly the same place. He took photos of every drawer, counter, and workspace to train his staff on where he wanted everything placed. Then, he used this same system to develop his par supply levels inside the exam and supply rooms. This way, he never wasted a second looking for anything. He could essentially navigate his exam room blindfolded and still get through a visit quickly, without rushing.

Freed used the same time/motion study process with the clinic’s other high-performing providers, without impacting clinical visits, until all of the clinic’s specialties were covered. Through the productivity insights gained, analyzed, and acted upon, Freed was able to help the clinic increase its overall productivity by 20 percent and reduce its supplies inventory, which lowered the clinic’s operating budget. Providers, staff members and even patients all noted the productivity gains and were pleased with the outcomes.

Based on the clinic’s CI-derived productivity gains, it would not be surprising if this clinic tackles other CI-related changes. By paving the way for the clinic’s employees to identify and implement improvements, the clinic’s leaders made it clear that it’s culturally appropriate – and desired – for employees to take the initiative to improve operations. That’s why well-led and properly executed CI enhancements often beget further CI improvements.

It’s also why, if you wish to undertake a significant cultural change using CI, it makes sense to first achieve multiple “smaller” CI gains. In this way, you can break past political or cultural resistance to change and reassure pertinent parties that CI can indeed make positive changes possible.

Communicating CI Successes

Communication was crucial to the CI improvements that Freed recently achieved on behalf of an HMO’s IT department – and to the department’s long-term adoption of a CI-centric culture. The process improvements achieved by a single team in the IT department – subsequently communicated to the rest of the department – proved so compelling that it changed how the entire IT department now conducts all of its business.

This HMO’s IT business intelligence (BI) team had been experiencing several challenges related to business team reporting requests. The BI team had seen huge growth in demand for services due to the Affordable Care Act. The BI team had not added resources to support its efforts, and its limited resources were bogged down with business team requests, due to new regulations and requirements. Despite the BI team’s name, due to staff attrition, there were no true business analysts on the team.

Instead, all business unit requests for reports went straight to the BI team’s developers. They created and delivered what they thought was requested, yet often found that their outputs were not what was desired. As a result, the BI team’s work needed to be redone. The team’s morale declined. Freed was brought in to help.

Through stakeholder interviews, Freed learned that the BI team developers were not asking the right questions upon receiving business team requests, and therefore, did not understand the full requirements and underlying business needs. Similarly, the business teams did not understand who was responsible for different parts of the process, especially for user acceptance testing (UAT). Lastly, there were no clear guidelines for estimating the time and resources required to deliver the reports.

Freed was able to add value quickly for the client by tackling these challenges incrementally. First, Freed addressed the incomplete requirements by creating a tool to help the business leaders better define their data needs and help the developers with their requirements-gathering and design documentation. Once this step was implemented, Freed updated overall process expectations, so that all parties would understand their service level agreements, and educated the business teams on UAT. Lastly, Freed created a tool to estimate the time and resources needed for a BI team project, based on the perceived complexity of various project stages. The BI team communicated the usefulness of this tool to all of the IT department’s employees, who soon adopted it for their own use.

Adopting CI Best Practices

Through your gradual adoption and implementation of CI, you will likely gain a set of best practices to replicate for CI initiatives in the future. Some of the most common CI best practices include:

Start small – As noted earlier, create small CI “wins” to pursue larger CI initiatives later. Starting small will gives you the chance to iron out any wrinkles in your CI processes.

  • Emphasize mistakes – Not only expect mistakes, encourage them! While mistakes may be anathema in health care, the reality with any CI initiative is that trial and error is often a must to achieve a greater long-term good. Learn from your mistakes, improve on them, and emerge with something better.
  • Encourage widespread participation – Employees are a wellspring of ideas for CI as long as you take their ideas seriously, respond to them appropriately and act on them as needed. If you receive a great idea, that’s terrific! On the other hand, if you get an idea that is not feasible, let the source know why you will not move forward with the idea.
  • Document “lessons learned” – Create a means for parties involved with CI to document their lessons learned throughout the initiative. Don’t wait until the project is over to document this information, as participant memories are often unreliable.
  • Schedule a formal “lessons learned” session – Immediately after a CI initiative, convene all pertinent parties to have them share all lessons learned for future reference. This session will be easier if participants bring their documented lessons learned.
  • Communicate frequently and broadly -- Besides discussing and determining CI-related improvements internally, share them externally with colleagues from other departments or units as well as with relevant business partners and vendors.
  • Identify and incorporate improvements – Based on the lessons learned, use this information to improve future CI efforts. The goal is to enable all involved with the success of your organization to learn new ways to help it improve.

Do not be afraid to introduce CI successes from other industries. For example, the baseline benefit of air traffic control systems – to allow users to track the location and status of airborne planes – was successfully translated to a health care system to allow clinicians, staff, and even family members to track patients and patient flow in real time.

Finally, be sure to consider the human element and impact of CI. Specifically, strive to ensure that any CI-related changes also benefit the people doing the work. In addition, consider providing incentives or rewards to employees who present successful CI ideas. You want to encourage others to share their CI inspirations!


Creating a CI culture which helps improve your patient/customer processes and empowers and supports your employees and encourages them to be better is the ultimate win-win for any health care organization. Couple these people-centric benefits with the fact that CI leads to distinct business advantages, including higher-quality service outputs and less re-work, and you have the ingredients for a more productive and successful organization. CI can truly be a difference-maker.


Carly Dunham joined  Freed Associates in 2015. Prior to her position with Freed Associates, Carly was a consultant with Optum, a division of UnitedHealth Group. In her career, Carly has focused on development and operations management of consumer-facing tools, and educating members about their benefits and associated health care costs. She also has experience shaping corporate culture with an emphasis on mindfulness.

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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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