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Machine Learning Survey: Many Organizations Several Years Away from Adoption, Citing Cost

January 10, 2019
by Heather Landi, Associate Editor
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Radiologists and imaging leaders see an important role for machine learning in radiology going forward, however, most organizations are still two to three years away from adopting the technology, and a sizeable minority have no plans to adopt machine learning, according to a recent survey.

A recent study* by Reaction Data sought to examine the hype around artificial intelligence and machine learning, specifically in the area of radiology and imaging, to uncover where AI might be more useful and applicable and in what areas medical imaging professionals are looking to utilize machine learning.

Reaction Data, a market research firm, got feedback from imaging professionals, including directors of radiology, radiologists, chiefs of radiology, imaging techs, PACS administrators and managers of radiology, from 152 healthcare organizations to gauge the industry on machine learning. About 60 percent of respondents were from academic medical centers or community hospitals, while 15 percent were from integrated delivery networks and 12 percent were from imaging centers. The remaining respondents worked at critical access hospitals, specialty clinics, cancer hospitals or children’s hospitals.

Among the survey respondents, there was significant variation in the number of annual radiology studies performed—17 percent performed 100-250 thousand studies each year; 16 percent performed 1 to 2 million studies; 15 percent performed 5 to 25 thousand studies; 13 percent performed 250 to 500 thousand; 10 percent performed more than 2 million studies a year.

More than three quarters of imaging and radiology leaders (77 percent) view machine learning as being important in medical imaging, up from 65 percent in a 2017 survey. Only 11 percent view the technology as not important. However, only 59 percent say they understand machine learning, although that percentage is up from 52 percent in 2017. Twenty percent say they don’t understand the technology, and 20 percent have a partial understanding.

Looking at adoption, only 22 percent of respondents say they are currently using machine learning—either just adopted it or have been using it for some time. Eleven percent say they plan to adopt the technology in the next year.

Half of respondents (51 percent) say their organizations are one to two years away (28 percent) or even more than three years away (23 percent) from adoption. Sixteen percent say their organizations will most likely never utilize machine learning.

Reaction Data collected commentary from survey respondents as part of the survey and some respondents indicated that funding was an issue with regard to the lack of plans to adopt the technology. When asked why they don’t ever plan to utilize machine learning, one respondent, a chief of cardiology, said, “Our institution is a late adopter.” Another respondent, an imaging tech, responded: “No talk of machine learning in my facility. To be honest, I had to Google the definition a moment ago.”

Survey responses also indicated that imaging leaders want machine learning tools to be integrated into PACS (picture archiving and communication systems) software, and that cost is an issue.

“We'd like it to be integrated into PACS software so it's free, but we understand there is a cost for everything. We wouldn't want to pay more than $1 per study,” one PACS Administrator responded, according to the survey.

A radiologist who responded to the survey said, “The market has not matured yet since we are in the research phase of development and cost is unknown. I expect the initial cost to be on the high side.”

According to the survey, when asked how much they would be willing to pay for machine learning, one imaging director responded: “As little as possible...but I'm on the hospital administration side. Most radiologists are contracted and want us to buy all the toys. They take about 60 percent of the patient revenue and invest nothing into the hospital/ambulatory systems side.”

And, one director of radiology responded: “Included in PACS contract would be best... very hard to get money for this.”

The survey also indicates that, among organizations that are using machine learning in imaging, there is a shift in how organizations are applying machine learning in imaging. In the 2017 survey, the most common application for machine learning was breast imaging, cited by 36 percent of respondents, and only 12 percent cited lung imaging.

In the 2018 survey, only 22 percent of respondents said they were using machine learning for breast imaging, while there was an increase in other applications. The next most-used application cited by respondents who have adopted and use machine learning was lung imaging (22 percent), cardiovascular imaging (13 percent), chest X-rays (11 percent), bone imaging (7 percent), liver imaging (7 percent), neural imaging (5 percent) and pulmonary imaging (4 percent).

When asked what kind of scans they plan to apply machine learning to once the technology is adopted, one radiologist cited quality control for radiography, CT (computed tomography) and MR (magnetic resonance) imaging.

The survey also examines the vendors being used, among respondents who have adopted machine learning, and the survey findings indicate some differences compared to the 2017 survey results. No one vendor dominates this space, as 19 percent use GE Healthcare and about 16 percent use Hologic, which is down compared to 25 percent of respondents who cited Hologic as their vendor in last year’s survey.

Looking at other vendors being used, 14 percent use Philips, 7 percent use Arterys, 3 percent use Nvidia and Zebra Medical Vision and iCAD were both cited by 5 percent of medical imaging professionals. The percentage of imaging leaders citing Google as their machine learning vendor dropped from 13 percent in 2017 to 3 percent in this latest survey. Interestingly, the number of respondents reporting the use of homegrown machine learning solutions increased to 14 percent from 9 percent in 2017.

 

*Findings were compiled from Reaction Data’s Research Cloud. For additional information, please contact Erik Westerlind at ewesterlind@reactiondata.com.

 

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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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