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AI and Healthcare: Cure-All, Poison Pill, or Simply Smarter Medicine?

January 7, 2019
by Josh Gluck, adjunct professor, NYU’s Wagner School of Public Service, Industry Voice
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Artificial intelligence (AI) is moving from hypothetical to business critical in healthcare. The global healthcare AI market is expected to reach $6.16 billion by 2022. Experts have estimated that AI applications can potentially create $150 billion in annual savings for the U.S. healthcare economy by 2026, and AI can address an estimated 20 percent of unmet clinical demand. As the statistics pile up, implementing AI may seem like a cure-all to every hospital woe, from data entry to population health to imaging, and it would be easy to get overwhelmed.

Keeping all of this in mind, let’s take a step back and examine AI for what it is: a powerful technology that can play a role in improving individual and population health when implemented judiciously. In the wrong hands, it’s clear that AI tools could be misused, but with the right strategies and careful use of AI aligned with an organization’s goals, AI can be used to generate insights based on data and analytics that may have been otherwise missed. AI has the potential to improve the quality of care and reduce cost by preventing unnecessary tests and procedures, while accelerating diagnoses and improving access by better utilizing resources. In the current healthcare climate, adding value while improving patient outcomes and access is not only a stated goal but also an imperative for survival of health systems in the emerging value-based integrated care environment.

Data-Centric Architecture Makes Real World AI Possible

So how do health systems get started with AI? Many start with small projects, using infrastructure that is on hand, but quickly identify limitations and outgrow this approach.

We’ve all heard that embracing data-centric architecture will help providers create a platform on which AI will thrive. But what does this mean? Building AI models requires data, a lot of data, on a scale most health systems have not previously explored in analytics environments. In many cases, the data exists in the health system or the community, but needs to be aggregated, cleansed and organized to support AI projects.

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For timely results, health systems may need to invest in a data hub that can be used to stage data for AI models, as well as GPU-based compute environment that allows researchers to train and optimize AI systems. AI requires a departure from traditional architectures due to its large scale and computational intensity, but also requires agility and scalability as programs and use grow. Forward-looking health systems who recognize the potential of AI will invest in agile, high-performing, and cost-effective AI platforms that allow researchers to thrive. By upgrading an organization’s physical architecture and infrastructure to support AI, teams will be able to better leverage AI and accelerate the pace of innovation. But technology isn’t the only variable needed to succeed in AI.

Optimize Your Implementation: AI as a Culture Change

IT leaders know that technology cannot change in isolation; people and organizational processes also need to be brought along to support the change. Putting the appropriate AI infrastructure in place for researchers is a first step to getting started, but clinical staff and teams also need to be trained, so that they understand the models in use and are comfortable with them.

Most clinicians will initially at least express distrust with the “black box” aspects of an AI implementation. Training should cover how to use the new technology, how it works, when it does and does not replace current processes and procedures, how to discuss AI with patients, and when AI should be trusted and when it should be questioned. In addition, adding AI to treatment decision making makes IT a partner in delivering care, and clinicians will need to work more closely with IT as a result. IT should be prepared for this change to the working relationship.

Data is also an issue; the sources of the data used in AI, as well as data handling, needs to be transparent so that the clinical community is comfortable trusting the findings from AI. Beyond the technical aspects, healthcare organizations should develop their processes to support the use of AI in a tangible way. From visualization, which presents information to clinicians clearly and succinctly, to integration of AI information with workflows, all the way to automated decisions, which act on ever-evolving algorithms, analytics and AI are key to a practical and effective architecture.

A large concern from healthcare leaders around establishing an AI architecture is cost—all that data can come at a hefty price for organizations of all sizes, not to mention the costs associated with hiring the proper experts and training team members. However, AI can cut costs by automating tasks that would previously be done by clinicians or staff, freeing up their time for more crucial work. And, despite the associated costs, AI is no longer just a flashy option for healthcare organizations—it can also provide a distinct advantage in both quality of care and business performance, as AI leaders have begun to see evidence of in their organizations.

Optimize for AI: Making an Impact

We’ve understood the need for quality data and integrating that data into a secure data-centric architecture to get it ready for AI. Now, it’s time to ensure AI makes a lasting impact, both within your organization and for the patients you serve. This requires a shift in mindset from all employees, and it starts from the top.

C-suite leaders should work to create a community around AI, sharing inspiration and forming multi-disciplinary teams that elevate the AI narrative and boost economies of scale. This connection with others, from all parts of the organization, is key to reshaping the current landscape in the short-term, and lays the foundation for AI as the new normal. AI is neither the poison pill nor the ultimate cure-all, but powerful medicine in the quest for better care and lower costs.

 

Josh Gluck is an adjunct professor of health policy and management at NYU’s Wagner School of Public Service and the Vice President of Healthcare Technology Strategy at Pure Storage. Gluck has over 20 years of experience directing information technology initiatives, managing complex IT projects, leading technical and professional teams, and providing critical business strategy support. His previous roles include Deputy CIO for Weill Cornell Medicine and Director of Information Technology at New York Presbyterian Hospital.

 


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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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