Can AI Help to Save the Practice of Radiology for the Future? | Mark Hagland | Healthcare Blogs Skip to content Skip to navigation

Can AI Help to Save the Practice of Radiology for the Future?

December 5, 2017
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The discussions around artificial intelligence at last week’s RSNA Conference spoke to the numerous needs for the technology, as radiology lurches forward into the new healthcare

In what was perhaps one of the most memorable openings in literature in English, Charles Dickens began his immortal A Tale of Two Cities with this: “It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, it was the season of light, it was the season of darkness, it was the spring of hope, it was the winter of despair, we had everything before us, we had nothing before us, we were all going direct o heaven, we were all going direct the other way—in short, the period was so far like the present period, that some of its noisiest authorities insisted on its being received, for good or for evil, in the superlative degree of comparison only.” And yes, that was one long, run-on sentence….!

And yes, participating in RSNA 2017, this year’s edition of the annual RSNA Conference (sponsored by the Oak Brook, Ill.-based Radiological Society of North America), did bring to mind Dickens’ astonishing opening to his great 1859 novel.

And though I saw no one at RSNA 2017 who reminded me at all of Sydney Carton, Lucie Manette, Charles Darnay, or Madame Defarge, I did actually think a bit about France in 1775 (on the eve of the French Revolution). Here’s the thing: the practice of radiology, as we’ve all known it, is moving into uncharted territory now, as the financial, operational, and medical practice model on which it’s been based, is shifting under the feet of today’s radiologists. With both Medicare and private-insurer payment under accelerating threat (let’s face it, diagnostic imaging procedures are an easy target for reimbursement deficit-hawk types), and with the demands for speed of turnaround for interpretive reports also accelerating, there are literally not enough hours in the day for practicing radiologists to make up growing income shortfalls from ongoing reductions in payment from all sources.

And really—let’s be real—none of us are going to be seeing radiologists standing on street corners attempting to sell $1 homeless-resident newspapers anytime soon. In 2016, the median compensation for non-interventional radiologists in the U.S. was $503,255, according to the American Medical Group Association (AMGA), as reported by RSNA; that was up from $490,399 in 2015.

Indeed, RSNA’s Richard Dargan, in his story, quoted Howard Forman, M.D., a professor of radiology, public health, economics and management at Yale University, as stating that “The take-home message here is that we are faster and better at reading studies, and we’ve improved the way we deliver images and the way we process reports and communicate results. There’s no reason to think that this will change anytime soon,” Dr. Forman said.

But, be that as it may, radiologists do feel themselves under threat, as fee-for-service medicine gradually begins to collapse, and radiologists feel more and more need to prove their value in the new, value-based, healthcare. That challenge was the subject of so many conversations at RSNA this year; and every single radiologist I spoke to agreed that radiologists need to begin to seriously leverage artificial intelligence/machine learning/deep learning technologies and strategies in order to demonstrate their value in the emerging healthcare.

Multiple uses envisioned for AI in radiology

In that context, on Friday, Dec. 1, Mia DeFino published an interesting article in Diagnostic Imaging, entitled “Learning from Deep Learning in Radiology,” which highlighted the AI/deep learning emphasis at this year’s RSNA. As DeFino pointed out, “There are many opportunities to use AI and deep learning in medical imaging: image quality control, imaging triage, efficient image creation, computer-aided detection, computer aided-classification, and automatic report drafting.” And she reported on a presentation by Lucio Prevedello, M.D., of The Ohio State University, who told his audience that “[D]eep learning can help in other ways aside from helping label images, such as improving process efficiency when dealing with many high priority cases. For example,” DeFino noted of Dr. Prevedello, “[H]is lab has been able to filter incoming images based on priority using deep learning. The algorithm looks at the images to identify brain hemorrhage or stroke, if the computer detects one of the flagged factors, the patient will move up on the priority list to have their images analyzed first. If the algorithm does not detect any critical factors, the patient’s case falls towards the bottom of the priority list.”

That is just one of numerous potential ways in which radiologists might leverage AI capabilities. Citing another initiative, this one involving Curtis Langlotz, M.D., Ph.D. of Stanford University, DeFino wrote, “For example, at Stanford, Langlotz described a deep learning algorithm that can improve MRI image quality and suggested a future where the MRI machine can notify the technologist that the images are too fuzzy to be read accurately. Through this type of approach, it is possible to improve MRI image quality and have the patient spend less time in the machine.”

Of course, Langlotz said in his presentation, “There is a hype cycle for emerging technologies—we are at the peak for inflated expectations about deep learning and machine learning, the trough of disillusionment is two to five years away. Some have predicted that radiologists will be replaced by robots, but because of the nature of a radiologists’ work, it is unlikely that a computer will be able to fully develop the complex analytic and reasoning skills required to completely replace human radiologists,” as it will still require human judgment to discern the meaning behind automated processes and make real clinical decisions. Still, Langlotz had said in his presentation, “Up to 10 percent of patient deaths are related to some type of diagnostic error and 4 percent of radiology interpretations contain clinically significant errors,” with deep learning potentially significantly improving error rates and patient safety incidents.

Looking—gingerly—towards the future

“I think we’re in the eye of the storm now in terms of the hype cycle,” Rasu Shrestha, M.D., the chief innovation officer at the UPMC health system in Pittsburgh, told me, on the exhibit floor last week. “You could see the storm brewing; and we in the imaging industry had a lot to do with that, because it’s a technology whose time has come,” at a moment when the volume of data and information involved in radiological practice is exploding, and the need for speed is accelerating as never before. And, despite the level of hype right now, Dr. Shrestha underscored his view that radiologists will perforce need to leverage AI tools and strategies simply to stay productively and effective in practice.

Jonathan Messinger, M.D., a practicing neuroradiologist at the six-hospital Baptist Health South Florida integrated health system, put it even more bluntly. “We need to find a way to prove our value in the arena of care; otherwise, we’re going to get passed by,” Dr. Messinger told me, adding that, “You’ve seen everyone starting to freak out over the idea of artificial intelligence potentially replacing human beings, but that’s not going to happen at all. It will create augmented intelligence. And in fact, the radiologists who don’t use AI will be left out,” as the push towards value accelerates.

Meanwhile, James Whitfill, M.D., the chief medical officer at Innovation Care Partners in Phoenix (formerly Scottsdale Health Partners), a physician-led clinical integration network, told me that “Machine learning could provide us with a once-in-a-generation type of breakthrough that could dramatically increase our efficiency in new ways, as physicians, including as radiologists, but also across all the specialties. The reality,” he added, “is that physician offices and hospitals lack the resources to change a lot of what we do; but if machine learning ended up living up to its hype, it might truly provide a breakthrough in improved efficiency and effectiveness that we haven’t seen happen yet.”

Of course, none of this is going to happen automatically—or even with exceptional speed. “Clearly, machine learning is everywhere this year”—but, “Not much of it is for real yet,” imaging and imaging informatics consultant Joe Marion told me on the exhibit floor last week. “Everybody wants to jump on the bandwagon.” Still he added, development is accelerating apace. In that context, he said, “[T]here are numerous options that vendors could take here. They could develop their own applications, do third-party applications, or work with academic researchers to develop solutions. So there's a mix of approaches.”

In any case, all those I spoke with at RSNA are agreed on one thing: artificial intelligence/machine learning/deep learning is a phenomenon whose time has come, in the radiological world.

 

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AI in Imaging: Where’s the Bang for the Buck?

January 23, 2019
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Over the past year much has been written about the capability of Artificial Intelligence (AI), and what it will mean for imaging services.  At last year’s RSNA, AI was the featured topic and received the lion’s share of publicity. 

The glamorous aspect of AI and Machine Learning has been how AI can assist the radiologist with diagnosis of imaging studies.  A key area of focus has been in chest imaging (https://www.auntminnieeurope.com/index.aspx?sec=sup&sub=aic&pag=dis&ItemID=616828) where there has been some success in triaging abnormal chest images.  The upside of such applications is improved diagnostic efficiency, particularly as healthcare moves toward value-based care.  The downside is that such algorithms require substantial amounts of data to validate, and they will need to go through the FDA approval process, which will take time before they can be fully implemented.

Ultimately, AI imaging applications will pay off.  But, what about the other potentially less-glamorous aspect of applying AI/Machine Learning to the diagnostic process?  By that, I am referring to its use in terms of workflow orchestration.  Aside from interpreting imaging content, AI/Machine Learning applied to workflow orchestration can provide valuable information and assistance in preparing a case for interpretation. 

Take for example Siemens Healthineer’s AI-Rad Companion application (https://www.healthcare.siemens.com/infrastructure-it/artificial-intelligence/ai-rad-companion).  The application provides automated identification, localization, labeling and measurements for anatomies and abnormalities.  Such a capability can improve the radiologist’s efficiency without necessarily employing an algorithm to assess the image.

Other workflow applications can assess the study and mine relevant information from the EHR to present to the radiologist, again with the goal of improving their efficiency and efficacy.  Still other applications match radiologist reading assignments with available studies to improve reading efficiency.  In another twist, one company has demonstrated a capability to further analyze cases, using AI to assign the next appropriate case to a radiologist without the need for a worklist. 

As healthcare providers consolidate, there is a growing need for improvement in resource utilization across facilities.  Smart worklists that can present cases to individual radiologists across facilities can improve the overall efficiency and efficacy of interpretation.  Rule sets that address radiologist availability, reading sub-specialties, location, etc. can help “level-load” reading resources. 

My point is that while AI applications that manipulate images may hold great promise for the future of diagnosis, areas such as workflow orchestration might offer more immediate results in an environment of changing healthcare.  Providers should take a close look at these applications to assess whether they can achieve a more immediate impact on imaging operations.

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National Library of Medicine Creating Scientific Director Position

January 23, 2019
by David Raths, Contributing Editor
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New position will oversee Lister Hill National Center for Biomedical Communications and National Center for Biotechnology Information

As part of a reorganization of its intramural research activities, the U.S. National Library of Medicine (NLM) has launched a search for a scientific director. The scientific director will oversee a group of 150 scientific personnel, developing new approaches to data science, biomedical informatics, and computational biology.

In a blog post on the library’s website, director Patti Brennan, R.N., Ph.D., called the move a big step in revving up its intramural research operation.

One of the 27 Institutes and Centers of the National Institutes of Health (NIH), NLM creates and hosts major digital resources, tools, and services for biomedical and health literature, data, and standards, sending 115 terabytes of data to five million users and receiving 15 terabytes of data from 3,000 users every weekday.

NLM’s strategic plan for 2017-2027 positions it to become a platform for biomedical discovery and data-powered health. NLM anticipates continued expansion of its intramural research program to keep pace with growing demand for innovative data science and informatics approaches that can be applied to biomedical research and health and growing interest in data science across the NIH.

A Blue Ribbon Panel recently reviewed NLM’s intramural research programs and recommended, among other things, unifying the programs under a single scientific director. That shift also aligns the library with NIH’s other institutes and centers, most of which are guided by one scientific director.

NLM’s  intramural research program includes activities housed in both the Lister Hill National Center for Biomedical Communications (LHC) and the National Center for Biotechnology Information (NCBI). The researchers in these two centers develop and apply computational approaches to a broad range of problems in biomedicine, molecular biology, and health, but LHC focuses on medical and clinical data, while NCBI focuses on biological and genomic data.

But the Blue Ribbon Panel noted that the boundaries between clinical and biological data are dissolving, and the analytical and computational strategies for each are increasingly shared. “As a result, the current research environment calls for a more holistic view of biomedical data, one best served by shared approaches and ongoing collaborations while preserving the two centers’ unique identities, wrote Brennan, who came to NIH in 2016 from the University of Wisconsin-Madison, where she was the Lillian L. Moehlman Bascom Professor at the School of Nursing and College of Engineering.

She added that having a single scientific director should lead to a sharper focus on research priorities, fewer barriers to collaboration, the cross-fertilization of ideas and the optimization of scarce resources.

The new scientific director will be asked to craft a long-range plan that identifies research areas where the NLM can best leverage its unique position and resources. We’ll also look for ways to allocate more resources to fundamental research while streamlining operational support. “Down the road, we’ll expand our research agenda to include high-risk, high-reward endeavors, the kinds of things that raise profound questions and have the potential to yield tremendous impact,” she wrote.

Besides the scientific director, the NLM is also recruiting three investigators to complement its strengths in machine learning and natural language processing.

 

 

 

 

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Survey: Digital, AI Top Priorities in 2019, but EHRs Will Dominate IT Spend

January 22, 2019
by Heather Landi, Associate Editor
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Digital, advanced analytics, and artificial intelligence (AI) are top spending priorities for healthcare executives in 2019, but electronic health record (EHR) systems will dominate technology spending budgets, according to a recent technology-focused healthcare survey.

Damo Consulting, a Chicago-based healthcare growth and digital transformation advisory firm, surveyed technology and service provider executives and healthcare enterprise executives about how the demand environment for healthcare IT is changing and will impact the industry in the coming year. Damo Consulting’s third annual Healthcare IT Demand Survey also analyzes the challenges for healthcare organizations and the perceived impact of macro-level changes.

The report indicates technology vendors will continue to struggle with long sales cycles as they aggressively market digital and AI. For the second year in a row, the rise of non-traditional players such as Amazon and Google will have a strong impact on the competitive environment among technology vendors while EHR vendors grow in dominance.

Among the key findings from the survey, IT budgets are expected to grow by 20 percent or more, with healthcare executives indicating they are more upbeat about IT spend growth than vendors. All the healthcare executives who participated in the survey said digital transformation initiatives are gaining momentum in their enterprises.

However, the majority (75 percent) agree that rapid change in the healthcare IT landscape makes technology decisions harder and only 58 percent believe there are plenty of viable and ready-to-deploy solutions available today in emerging technologies such as AI and digital health solutions. Seventy-one percent agree that federal government policies have provided a boost to healthcare IT spend this past year.

Top IT priorities for healthcare enterprise executives in 2019 are digital, advanced analytics and AI. Of the survey respondents, 79 percent said accelerating digital health initiatives was a top priority and 58 percent cited investing in advanced analytics and AI capabilities as top priorities. However, modernizing IT infrastructure (25 percent) and optimizing EHRs (21 percent) are also significant priorities.

Technology vendors also see AI, advanced analytics and digital transformation as top areas of focus for next year, as those areas were cited by 75 percent and 70 percent of technology and service provider executives, respectively. Thirty-three percent of those respondents cited EHR optimization and 25 percent cited cybersecurity and ransomware. Thirteen percent cited M&A integration as a top area of focus in 2019.

However, EHR systems will dominate technology spending budgets, even as the focus turns to digital analytics, the survey found. Technology and service provider executives who participated in the survey identified EHR system optimization and cybersecurity as significant drivers of technology spend in 2019. Sixty percent of respondents said enterprise digital transformation and advanced analytics and AI would drive technology spend this year, but 38 percent also cited EHR optimization and cybersecurity/ransomware. One executive survey respondent said, “For best of breed solutions, (the challenge is) attracting enough mindshare and budget vs. EHR spends.”

When asked what digital transformation means, close to half of healthcare executives cited reimaging patient and caregiver experiences, while one quarter cited analytics and AI and 17 percent cited automation. As one executive said, “The biggest challenge for healthcare in 2019 will be navigating tightening margins and limited incentives to invest in care design.”

Healthcare executives are divided on whether digital is primarily an IT-led initiative, and are also divided on whether technology-led innovation is dependent on the startup ecosystem.

The CIO remains the most important buyer for technology vendors, however IT budgets are now sitting with multiple stakeholders, the survey found, as respondents also cited the CFO, the CTO, the CMIO and the chief digital officer.

“Digital and AI are emerging as critical areas for technology spend among healthcare enterprises in 2019. However, healthcare executives are realistic around their technology needs vs. their need to improve care delivery. They find the currently available digital health solutions in the market are not very mature,” Paddy Padmanabhan, CEO of Damo Consulting, said in a statement. “However, they are also more upbeat about the overall IT spend growth than their technology vendors.”

Looking at the technology market, healthcare executives perceive a lack of maturity in technology solution choices for digital initiatives, as well as a lack of internal capabilities for managing digital transformation. In the survey report, one executive said, “HIT architecture needs to substantially change from large monolithic code sets to an API-driven environment with multiple competing apps.”

A majority of healthcare enterprise executives view data silos and lack of interoperability as the biggest challenges to digital transformation. And, 63 percent believe the fee-for-service reimbursement model will remain the dominant payment model for the foreseeable future.

In addition, cybersecurity issues will continue to be a challenge for the healthcare sector in 2019, but not the biggest driver of technology spending or the top area of focus for health systems in the coming year, according to the survey.

Healthcare executives continue to be confused by the buzz around AI and digital and struggle to make sense of the changing landscape of who is playing what role and the blurred lines of capabilities and competition, according to the survey report. When asked who their primary choice is when looking for potential partners to help with digital transformation, 46 percent of healthcare executives cited their own internal IT and innovation teams, 17 percent cited their EHR vendor and 8 percent cited boutique consulting firms. A quarter of respondents cited “other.”

For technology vendors, the biggest challenge is long cycles, along with product/service differentiation and brand visibility.

The rise of non-traditional players, such as Amazon, Apple, and Google, will have a strong impact on the competitive healthcare technology environment, the survey responses indicated. At the same time, deeply entrenched EHR vendors such as Epic and Cerner will grow in dominance.

 

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