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At the World Health Care Congress, Healthcare Leaders Share Perspectives on Analytics-Driven Initiatives

May 1, 2017
by Mark Hagland
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Healthcare leaders share what they’ve learned from leveraging machine learning and data analytics too support strategic initiatives

Can healthcare providers and plans leverage machine learning and data science to make inroads in new areas of activity? That certainly is what leaders for an integrated health system and a Blues plan are helping to make possible at their organizations. On May 1 at the World Health Care Congress, being held at the Marriott Wardman Park Hotel in Washington, D.C., Vijay Venkatesan, chief data officer at the Seattle-based Providence Health & Services, and Sherri Zink, senior vice president and chief data and engagement officer at the Chattanooga-based BlueCross BlueShield of Tennessee, shared their perspectives on some of the data-driven work they’re doing at their health system and health plan, respectively.

On Monday afternoon at the World Health Care Congress, Venkatesan and Zink shared insights from their ongoing work, in a session entitled “Analyze the Convergence of Machine Learning and Data Science to Enhance Real-Time Patient Engagement and Experience,” the two leaders offered sequential presentations on their data-driven and data-facilitated initiatives. Their session was one of the sessions that composed the Data Analytics and Technology Summit, one of 14 concurrent summits within the World Health Care Congress event.

Venkatesan, who reports directly to his health system’s CIO, told the audience Monday afternoon that “Our IS vision is to transform health through simple, reliable, and innovative approaches to technology solutions.” And in that quest, he said, “The hardest thing is finding the data. How do we find the data, and who is the oracle of the information? How do we bring together the producers and consumers of data, in an interoperable platform, to begin to have a discussion of strategy and tactics? We want to bring together the consumers of data with the producers of data, creating partnerships between data producers and consumers, and to enable consistent platforms and tool sets to enable the good use of data, and govern the issue of data.”

Speaking of the journey around the strategic leveraging of data at Providence, the third-largest not-for-profit integrated health system in the U.S. Venkatesan said that “We want to achieve the end goal of outcomes as a service. We’re really trying to help you drive to improved outcomes through data,” he said, speaking of the users of data in his organization, from frontline clinicians to clinician leaders, to administrative executives and managers. “We have a broad mandate,” he added, noting that the data analytics that he and his colleagues in the data department are developing for their organization are supporting, among other things, “population health, clinical care and personalized health, shared services, and regions and ventures,” as well as “the employed provider network, ACO, government programs, care management, payor contracting, health plan, and regional executives,” among other areas that they are supporting.

The challenge? “Data exists in various pockets in your organization; we’re a 50-hospital system with 23,000 physicians and a lot of clinics,” Venkatesan noted. “You can guarantee that there are a lot of different analytics initiatives going on. Still, even though we have all these great pockets, it’s hard to find the information across the system. The human network bottleneck problem is a real problem. It can take you 15 emails just to figure out whom to approach about data, and another 20 emails to get action. So how do we approach this human bottleneck problem that makes this conversation a bit easier?” What’s more, he noted, “It’s becoming more and more important to get to achieve speed to action. Most healthcare organizations, like ours, are under a lot of financial burden, and you have to do things in an agile and nimble way.”


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The solution has been a new mechanism, which has been named myHIway, which emerged out of a “two-part strategy,” Venkatesan said. “We built a simple platform using text mining. We built a Google-like platform called myHIway, to help people find things. We did basic topic modeling using natural language processing. It goes across all the data stores and organizes things in a way that’s easy to see. So just having a data mart isn’t enough any longer,” he stated.

“You have to create a platform where others can contribute data,” Venkatesan told his audience. “So we said, we’ll build a data lake that can host data. We created this data lake for incoming flow: raw data in any format, any size goes into it. And we started building data apps for it, and we built targeted use cases for denials management and other purposes. Our goal,” he said, was to say, yes, we’ll create an electronic data warehouse, but we’ll also invest in hosted platforms, with very targeted use cases.”

Creating myHIway has “allowed us to streamline our strategy,” Venkatesan said. “And if you’re a consumer of our services, all you have to do is to go to myHIway. And the idea is that as you build more  and more targeted, use-case apps,” demand for data support will become better rationalized over time, he added.

Connecting with plan members in Tennessee

At BlueCross BlueShield of Tennessee, reaching out and connecting with plan members has been a major objective of senior executives, said Sherri Zink, senior vice president and chief data and engagement officer for the plan, which covers 3.3 million members across Tennessee, including 11,000 employer groups, as well as large Medicare and Advantage and Medicaid populations.

Zink told the Data Analytics & Technology Summit attendees that the key to moving forward as a health plan in the arena of plan member engagement, is to shift one’s data analytics strategic from retrospective to predictive to prescriptive, in stages. “Who do we want to outreach to? Which clusters of members are driving the biggest medical costs?” she asked. “We often think we need to focus on the high utilizers. But oftentimes, it turns out that you need to focus on individual who may be seeking care only once in six months, or who has a sick child. Machine learning can help us to figure out what’s going on” at both ends of the spectrum, in order to identify plan members both who might be over-utilizing and who might in effect be under-utilizing.

One key area that has been developed at BCBS Tennessee, Zink noted, is the Member Scorecard. “Every year, usually twice a year, we launch the Scorecard to our members,” she said. The scorecard incorporates a number of data points, including around gaps in care, such as missed wellness visits, and the need for hemoglobin a1c or HPV tests to be scheduled, as well as including health education topics. What’s more, it’s delivered via “multi-channel delivery—everyone wants it delivered differently,” she said.

“In terms of interacting with consumers, we also pull in health assessments, activity data, lifestyle data, and interaction data from interactions with their wellness coaches and physicians,” Zink continued. “We also pull in survey comments, focus group feedback, and other forms of data, such as biometric, EMR, and medical monitor data,” she said.

Zink went into detail around some of the components of the data and information that BCBS-Tennessee shares with its plan members, in order to engage them more deeply in their health and healthcare. “The consumer journey is important,” she stressed, after explaining some of the details of BCBS-Tennessee’s consumer engagement strategy and its data facilitation. “One of the things we have to realize is that the predictive models we use can’t be ‘one-off.’ The consumer will shut you down and will no longer pay attention to you if you send them too much information that’s not meaningful to them. So we put together actionable information, and determine where we can optimize predictive models and figure out what’s going on with a particular individual. Our predictive models tee up ‘cues’ to consumers’ behavior. And they rely on an integrated predictive platform.”

Progress is being made, Zink noted. “We’ve moved into that next phase of machine learning and algorithms, in which we’re able to note the ‘cause and effect’—the relationship between actions and outcomes.” For example, she noted, “We launched seven campaigns at the same time,” reaching out to plan members using different communication strategies. Experimenting to find out what might work best, she said, “Each group we reached out to contained a control group and a trial group. And within 60 days, we found out which of the seven campaigns were most successful. And the analysis told us where this outreach was working, and where it wasn’t. And one of the things we found out was that the hour of the day when plan members were reached out to made a huge difference. We found that calling in the morning and afternoon worked better.” And leveraging data analytics facilitated deeper analysis of patterns, she noted.

“How do we sustain engagement over time? One of the things about these control and trial groups and machine learning, is that it helps us to launch better member engagement campaigns,” and to benefit by learning more as those campaigns are rolled out. “Predictive modeling has helped us to figure out how to move things successfully to members. Now, we’re zeroing in on what the most impactful population management initiatives are, and we’re improving our outreach strategies” in that context.

As they move forward in these different spheres, Zink and Venkatesan agreed, it is very important to continuously improve data analysis processes, in order to gain more and more from the important initiatives moving their healthcare organizations forward.

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Have CIOs’ Top Priorities for 2018 Become a Reality?

December 12, 2018
by Rajiv Leventhal, Managing Editor
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In comparing healthcare CIOs’ priorities at the end of 2017 to this current moment, new analysis has found that core clinical IT goals have shifted from focusing on EHR (electronic health record) integration to data analytics.

In December 2017, hospitals CIOs said they planned to mostly focus on EHR integration and mobile adoption and physician buy-in, according to a survey then-conducted by Springfield, Va.-based Spok, a clinical communications solutions company, of College of Healthcare Information Management Executives (CHIME) member CIOs.

The survey from one year ago found that across hospitals, 40 percent of CIO respondents said deploying an enterprise analytics platform is a top priority in 2018. Seventy-one percent of respondents cited integrating with the EHR is a top priority, and 62 percent said physician adoption and buy-in for securing messaging was a top priority in the next 18 months. What’s more, 38 percent said optimizing EHR integration with other hospital systems with a key focus for 2018.

Spok researchers were curious whether their predictions became reality, so they analyzed several industry reports and asked a handful of CIOs to recap their experiences from 2018. The most up-to-date responses revealed that compared to last year when just 40 percent of CIOs said they were deploying an enterprise analytics platform in 2018, harnessing data analytics looks to be a huge priority in 2019: 100 percent of the CIOs reported this as top of mind.

Further comparisons on 2018 predictions to realities included:

  • 62 percent of CIOs predicted 2018 as the year of EHR integration; 75 percent reported they are now integrating patient monitoring data
  • 79 percent said they were selecting and deploying technology primarily for secure messaging; now, 90 percent of hospitals have adopted mobile technology and report that it’s helping improve patient safety and outcomes
  • 54 percent said the top secure messaging challenge was adoption/buy in; now, 51 percent said they now involve clinicians in mobile policy and adoption

What’s more, regarding future predictions, 87 percent of CIOs said they expect to increase spending on cybersecurity in 2019, and in three years from now, 60 percent of respondents expect data to be stored in a hybrid/private cloud.

CIOs also expressed concern regarding big tech companies such as Apple, Amazon and Google disrupting the healthcare market; 70 percent said they were somewhat concerned.

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How One Community Hospital is Leveraging AI to Bolster Its Care Pathways Process

December 6, 2018
by Heather Landi, Associate Editor
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Managing clinical variation continues to be a significant challenge facing most hospitals and health systems today as unwarranted clinical variation often results in higher costs without improvements to patient experience or outcomes.

Like many other hospitals and health systems, Flagler Hospital, a 335-bed community hospital in St. Augustine, Florida, had a board-level mandate to address its unwarranted clinical variation with the goal of improving outcomes and lowering costs, says Michael Sanders, M.D., Flagler Hospital’s chief medical information officer (CMIO).

“Every hospital has been struggling with this for decades, managing clinical variation,” he says, noting that traditional methods of addressing clinical variation management have been inefficient, as developing care pathways, which involves identifying best practices for high-cost procedures, often takes up to six months or even years to develop and implement. “By the time you finish, it’s out of date,” Sanders says. “There wasn’t a good way of doing this, other than picking your spots periodically, doing analysis and trying to make sense of the data.”

What’s more, available analytics software is incapable of correlating all the variables within the clinical, billing, analytics and electronic health record (EHR) databases, he notes.

Another limitation is that care pathways are vulnerable to the biases of the clinicians involved, Sanders says. “In medicine, what we typically do is we’ll have an idea of what we want to study, design a protocol, and then run the trial and collect the data that we think is important and then we try to disprove or prove our hypothesis,” he says.


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Sanders says he was intrigued by advances in machine learning tools and artificial intelligence (AI) platforms capable of applying advanced analytics to identify hidden patterns in data.

Working with Palo Alto, Calif.-based machine intelligence software company Ayasdi, Flagler Hospital initiated a pilot project to use Ayasdi’s clinical variation management application to develop care pathways for both acute and non-acute conditions and then measure adherence to those pathways.

Michael Sanders, M.D.

Flagler targeted their treatment protocols for pneumonia as an initial care process model. “We kicked around the idea of doing sepsis first, because it’s a huge problem throughout the country. We decided to use pneumonia first to get our feet wet and figure out how to use the tool correctly,” he says.

The AI tools from Ayasdi revealed new, improved care pathways for pneumonia after analyzing thousands of patient records from the hospital and identifying the commonalities between those with the best outcomes. The application uses unsupervised machine learning and supervised prediction to optimally align the sequence and timing of care with the goal of optimizing for patient outcomes, cost, readmissions, mortality rate, provider adherence, and other variables.

The hospital quickly implemented the new pneumonia pathway by changing the order set in its Allscripts EHR system. As a result, for the pneumonia care path, Flagler Hospital saved $1,350 per patient and reduced the length of stay (LOS) for these patients by two days, on average. What’s more, the hospital reduced readmission by 7 times—the readmission rate dropped from 2.9 percent to 0.4 percent, hospital officials report. The initial work saved nearly $850,000 in unnecessary costs—the costs were trimmed by eliminating labs, X-rays and other processes that did not add value or resulted in a reduction in the lengths of stay or readmissions.

“Those results are pretty amazing,” Sanders says. “It’s taking our data and showing us what we need to pursue. That’s powerful.”

With the success of the pneumonia care pathway, Flagler Hospital leaders also deployed a new sepsis pathway. The hospital has expanded its plans for using Ayasdi to develop new care pathways, from the original plan of tackling 12 conditions over three years, to now tackling one condition per month. Future plans are to tackle heart failure, total hip replacement, chronic obstructive pulmonary disease (COPD), coronary artery bypass grafting (CABG), hysterectomy and diabetes, among other conditions. Flagler Hospital expects to save at least $20 million from this program in the next three years, according to officials.

Finding the “Goldilocks” group

Strong collaboration between IT and physician teams has been a critical factor in deploying the AI tool and to continue to successfully implement new care pathways, Sanders notes.

The effort to create the first pathway began with the IT staff writing structured query language (SQL) code to extract the necessary data from the hospital’s Allscripts EHR, enterprise data warehouse, surgical, financial and corporate performance systems. This data was brought into the clinical variation management application using the FHIR (Fast Healthcare Interoperability Resources) standard.

“That was a major effort, but some of us had been data scientists before we were physicians, and so we parameterized all these calls. The first pneumonia care path was completed in about nine weeks. We’ve turned around and did a second care path, for sepsis, which is much harder, and we’ve done that in two weeks. We’ve finished sepsis and have moved on to total hip and total knee replacements. We have about 18 or 19 care paths that we’re going to be doing over the next 18 months,” he says.

After being fed data of past pneumonia treatments, the software automatically created cohorts of patients who had similar outcomes accompanied by the treatments they received at particular times and in what sequence. The program also calculated the direct variable costs, average lengths of stay, readmission and mortality rates for each of those cohorts, along with the statistical significance of its conclusions. Each group had different comorbidities, such as diabetes, COPD and heart failure, which was factored into the application's calculations. At the push of a button, the application created a care path based on the treatment given to the patients in each cohort.

The findings were then reviewed with the physician IT group, or what Sanders calls the PIT crew, to select what they refer to as the “Goldilocks” cohort. “This is a group of patients that had the combination of low cost, short length of stay, low readmissions and almost zero mortality rate. We then can publish the care path and then monitor adherence to that care path across our physicians,” Sanders says.

The AI application uncovered relationships and patterns that physicians either would not have identified or would have taken much longer to identify, Sanders says. For instance, the analysis revealed that for patients with pneumonia and COPD, beginning nebulizer treatments early in their hospital stays improved outcomes tremendously, hospital leaders report.

The optimal events, sequence, and timing of care were presented to the physician team using an intuitive interface that allowed them to understand exactly why each step, and the timing of the action, was recommended. Upon approval, the team operationalized the new care path by revising the emergency-department and inpatient order sets in the hospital EHR.

Sanders says having the data generated by the AI software is critical to getting physicians on board with the project. “When we deployed the tool for the pneumonia care pathway, our physicians were saying, ‘Oh no, not another tool’,” Sanders says. “I brought in a PIT Crew (physician IT crew) and we went through our data with them. I had physicians in the group going through the analysis and they saw that the data was real. We went into the EMR to make sure the data was in fact valid, and after they realized that, then they began to look at the outcomes, the length of stay, the drop in readmissions and how the costs dropped, and they were on board right away.”

The majority of Flagler physicians are adhering to the new care path, according to reports generated by the AI software's adherence application. The care paths effectively sourced the best practices from the hospital’s best doctors using the hospital’s own patient groups, and that is key, Sanders notes.

“When we had conversations with physicians about the data, some would say, ‘My patient is sicker than yours,’ or ‘I have a different patient population.’ However, we can drill down to the physician’s patients and show the physician where things are. It’s not based on an ivory tower analysis, it’s based on our own data. And, yes, our patients, and our community, are unique—a little older than most, and we have a lot of Europeans here visiting. We have some challenges, but this tool is taking our data and showing us what we need to pursue. That’s pretty powerful.”

He adds, “It’s been amazing to see physicians rally around this. We just never had the tool before that could do this.”

While Flagler Hospital is a small community hospital with fewer resources than academic medical centers or larger health systems—for example, the hospital doesn’t have a dedicated data scientist but rather uses its in-house informatics staff for this project—the hospital is progressive in its use of advanced analytics, according to Sanders.

“We’ve been able to do a lot of querying ourselves, and we have some sepsis predictive models that we’ve created and put into place. We do a lot of real-time monitoring for sepsis and central line-associated bloodstream infections,” he says. “Central line-associated bloodstream infections are a bane for all hospitals. In the past year and a half, since we’ve put in our predictive model, we’ve had zero bloodstream infections, and that’s just unheard of.”

Sanders and his team plan to continue to use the AI tool to analyze new data and adjust the care paths according to new discoveries. As the algorithms find more effective and efficient ways to deliver care that result in better outcomes, Flagler will continue to improve its care paths and measure the adherence of its providers.

There continues to be growing interest, and also some hype, around AI tools, but Sanders notes that AI and machine learning are simply another tool. “Historically, what we’ve done is that we had an idea of what we wanted to do, conducted a clinical trial and then proved or disproved the hypothesis, based on the data that we collected. We have a tool with AI which can basically show us relationships that we didn’t know even existed and answer questions that we didn’t know to ask. I think it’s going to open up a tremendous pathway in medicine for us to both reduce cost, improve care and really take better care of our patients,” he says, adding, “When you can say that to physicians, they are on board. They respond to the data.”


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At RSNA 2018, An Intense Focus on Artificial Intelligence

November 29, 2018
by Mark Hagland, Editor-in-Chief
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Artificial intelligence solutions—and discussions—were everywhere at RSNA 2018 this week

Artificial intelligence solutions—and certainly, the promotion of such solutions—were everywhere this year at the RSNA Conference, held this week at Chicago’s vast McCormick Place, where nearly 49,000 attendees attended clinical education sessions, viewed nearly 700 vendor exhibits. And AI and machine learning promotions, and discussions were everywhere.

Scanning the exhibit floor on Monday, Glenn Galloway, CIO of the Center for Diagnostic Imaging, an ambulatory imaging center in the Minneapolis suburb of St. Louis Park, Minn., noted that “There’s a lot of focus on AI this year. We’re still trying to figure out exactly what it is; I think a lot of people are doing the same, with AI.” In terms of whether what’s being pitched is authentic solutions, vaporware, or something in between, Galloway said, “I think it’s all that. I think there will be some solutions that live and survive. There are some interesting concepts of how to deliver it. We’ve been talking to a few folks. But the successful solutions are going to be very focused; not just AI for a lung, but for a lung and some very specific diagnoses, for example.” And what will be most useful? According to Galloway, “Two things: AI for the workflow and the quality. And there’ll be some interesting things for what it will do for the quality and the workflow.”

“Certainly, this is another year where machine learning is absolutely dominating the conversation,” said James Whitfill, M.D., CMO at Innovation Care Partners in Scottsdale, Ariz., on Monday. “In radiology, we continue to be aware of how the hype of machine learning is giving way to the reality; that it’s not a wholesale replacement of physicians. There have already been tremendous advances in, for example, interpreting chest x-rays; some of the work that Stanford’s done. They’ve got algorithms that can diagnose 15 different pathological findings. So there is true material advancement taking place.”

Meanwhile, Dr. Whitfill said, “At the same time, people are realizing that coming up with the algorithm is one piece, but that there are surprising complications. So you develop an algorithm on Siemens equipment, but when you to Fuji, the algorithm fails—it no longer reliably identifies pathology, because it turns out you have to train the algorithm not just on examples form just one manufacturer, but form lots of manufacturers. We continue to find that these algorithms are not as consistent as identifying yourself on Facebook, for example. It’s turning out that radiology is way more complex. We take images on lots of different machines. So huge strides are being made,” he said. “But it’s very clear that human and machine learning together will create the breakthroughs. We talk about physician burnout, and even physicians leaving. I think that machine learning offers a good chance of removing a lot of the drudgery in healthcare. If we can automate some processes, then it will free up our time for quality judgment, and also to spend time talking to patients, not just staring at the screen.”


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Looking at the hype cycle around AI

Of course, inevitably, there was talk around the talk of the hype cycle involving artificial intelligence. One of those engaging in that discussion was Paul Chang, M.D.., a practicing radiologist and medical director of enterprise imaging at the University of Chicago. Dr. Chang gave a presentation on Tuesday about AI. According a report by Michael Walter in Radiology Business, Dr. Chang said, “AI is not new or spooky. It’s been around for decades. So why the hype?” He described computer-aided detection (CAD) as a form of artificial intelligence, one that radiologists have been making use of for years.

Meanwhile, with regard to the new form of AI, and the inevitable hype cycle around emerging technologies, Dr. Chang said during his presentation that “When you’re going up the ride, you get excited. But then right at the top, before you are about to go down, you have that moment of clarity—‘What am I getting myself into?’—and that’s where we are now. We are upon that crest of magical hype and we are about to get the trench of disillusionment.” Still, he told his audience, “It is worth the rollercoaster of hype. But I’m here to tell you that it’s going to take longer than you think.”

So, which artificial intelligence-based solutions will end up going the distance? On a certain level, the answer to that question is simple, said Joe Marion, a principal in the Waukesha, Wis.-based Healthcare Integration Strategies LLC, and one of the imaging informatics industry’s most respected observers. “I think it’s going to be the value of the product,” said Marion, who has participated in 42 RSNA conferences; “and also the extent to which the vendors will make their products flexible in terms of being interfaced with others, so there’s this integration aspect, folding into vendor A, vendor B, vendor C, etc. So for a third party, the more they reach out and create relationships, the more successful they’ll be. A lot of it will come down to clinical value, though. Watson has had problems in that people have said, it’s great, but where’s the clinical value? So the ones that succeed will be the ones that find the most clinical value.”

Still, Marion noted, even the concept of AI, as applied to imaging informatics, remains an area with some areas lacking in clarity. “The reality, he said, “is that I think it means different things to different people. The difference between last year and this year is that some things are coming to fruition; it’s more real. And so some vendors are offering viable solutions. The message I’m hearing from vendors this year is, I have this platform, and if a third party wants to develop an application or I develop an application, or even an academic institution develops a solution, I can run it on my platform. They’re trying to become as vendor-agnostic as possible.”

Marion expressed surprise at the seemingly all-encompassing focus on artificial intelligence this year, given the steady march towards value-based healthcare-driven mandates. “Outside of one vendor, I’m not really seeing a whole lot of emphasis this year on value-based care; that’s disappointing,” Marion said. “I don’t know whether people don’t get it or not about value-based care, but the vendors are clearly more focused on AI right now.”

Might next year prove to be different? Yes, absolutely, especially given the coming mandates coming out of the Protecting Access to Medicare Act (PAMA), which will require referring providers to consult appropriate use criteria (AUC) prior to ordering advanced diagnostic imaging services—CT, MR, nuclear medicine and PET—for Medicare patients. The federal Centers for Medicare and Medicaid Services (CMS) will progress with a phased rollout of the CDS mandate, as the American College of Radiology (ACR) explains on its website, with voluntary reporting of the use of AUC taking place until December 2019, and mandatory reporting beginning in January 2020.

But for now, this certainly was the year of the artificial intelligence focus at the RSNA Conference. Only time will tell how that focus plays out in the imaging and imaging informatics vendor space within the coming 12 months, before RSNA 2019 kicks off one year from now, at the conference’s perennial location, McCormick Place.



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