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Spotlight

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNINGHOW THE POWER OF AI/ML CAN:

• BUILDDATAANDANALYTICSCOMPETENCY

• PREDICTSEPSISANDOTHERHEALTHRISKS

• DETECTWHATIMAGINGCANMISS

• Q&AWITHOURSPONSORINTEL

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SPOTLIGHT | ARTIFICIAL INTELLIGENCE AND MACHINE LEARNINGHIMSS20

You’re dealing with people’s lives, so you have to make sure that you're using the best data and applying the right algorithms. Then you can trust that you’re getting something meaningful and valuable that’s going to provide better care, greater efficiency and/or reduced costs.

Ian HoffbergManager and Thought Advisory HIMSS

• Healthcare IT News covered how Mayo Clinic has launched an ini-tiative that will apply AI to de-identified data from across Mayo and other health systems and comb scientific literature to gain insights into new medical advances;2 how Summa Health is leveraging a natural language processing-driven analytics solution to help iden-tify emergency department patients with incidental lung nodules for follow-up; and how Sutter Health is utilizing an AI solution that checks patients’ symptoms based on severity and medical history — and then identifies potential causes and next steps.3

• An article in Built In, an online publication that covers tech markets in the United States, cited 32 use cases for AI in healthcare. The report pointed to uses such as detecting cancer earlier, diagnosing deadly blood diseases faster, finding better candidates for devel-opmental drugs, streamlining the patient experience, automating healthcare’s repetitive processes, and more.4

All these use cases come as no surprise to Hoffberg, who contends that AI and ML are positioned to “have a great impact on all areas of healthcare — clinical, administrative, and operational.” Hoffberg, however, predicted that AI and ML solutions “could really take hold in diagnostic areas that require imaging pattern recognition such as ra-diology and pathology, while the most significant short-term advances are going to be made on the administrative and operational side.”

The potential associated with artificial intelligence and machine learning in healthcare is piquing the interest

— and sparking the imagination — of healthcare industry professionals.

“It's very futuristic, kind of sci fi-ish in a sense, to think about where the technology could take us, and how it can improve clinical care outcomes and reduce the cost and waste that we currently have in the healthcare industry,” said Ian Hoffberg, manager, thought advisory, at HIMSS.

This fascination with AI and ML is being driven by the plethora of coverage that the technologies have recently received in academic journals, healthcare publications, and business media:

• The Lancet Oncology featured an article that explains how a team of researchers in Sweden discovered an AI platform capable of accurately diagnosing prostate cancer in tissue samples, offering the potential to speed diagnostics and reduce costs. The findings suggest that AI systems can be trained to detect and grade cancer with an accuracy rate equal to that of international prostate pathology experts.1

Envisioning a more intelligent future in healthcare

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SPOTLIGHT | ARTIFICIAL INTELLIGENCE AND MACHINE LEARNINGHIMSS20

The challenge is to implement AI and ML solutions on a more wide-spread basis. To do so, healthcare organizations need to overcome various obstacles such as ensuring these applications are using clean, reliable data; eliminating unintentional bias of underlying data; under-standing how algorithms are performing; and assuring data security. By addressing these challenges, healthcare organizations can develop the trust needed to successfully leverage AI and ML solutions.

“With AI and ML, you must have room to fail, but there’s only a certain level of failure that’s acceptable in healthcare,” Hoffberg noted. “You’re dealing with people’s lives, so you have to make sure that you're using the best data and applying the right algorithms. Then you can trust that you’re getting something meaningful and valuable that’s going to provide better care, greater efficiency and/or reduced costs.”

Learning opportunities at HIMSS20Attendees at HIMSS20 will discover plenty of opportunities to gather the intelligence needed to bring AI and ML visions to fruition. The HIMSS AI/ML Circle, a HIMSS20 special interest group, will provide participants with insights and guidance on the most relevant educa-tion and exhibits, along with networking opportunities before, during, and after the conference.

The Machine Learning & AI for Healthcare Forum (from 8 a.m.–4:30 p.m. on Monday, March 9, at the Rosen Centre, Executive Ballroom) will focus on building data and analytics competency as well as on real-world AI and ML best practices. Innovation Live (Hall E, Booth 8200) will make it possible for attendees to visit 20 AI-focused companies in one dedicated space on the HIMSS20 exhibit floor.

In addition, a variety of healthcare professionals will offer their AI and ML implementation perspectives during educational sessions.

Arnie Milstein, MD, professor of medicine at Stanford University and director of its Clinical Excellence Research Center, for example, will offer his insights on what organizations must do to realize the full potential of AI and ML during Brass Tacks and Brainware: How to Really Succeed with AI (from 11:30 a.m.–noon on Wednesday, March 11, at the Orange County Convention Center, Room W300).

“To reach the full potential of AI within a healthcare organization requires full support from the top leadership of the organization down, including those in IT,” Milstein said. “Leadership must understand and embrace the value of AI to ensure that it becomes integral to the DNA of the entire company rather than simply an IT strategy.

More specifically, Millstein recommended that organizations develop and nurture a multiyear strategy assessing data assets and usability and emphasizing data integration, technical storage/computing chal-lenges, and workflow integration. He added that AI and ML success also requires investment in the technical and data infrastructure — for both the organization’s own data and business systems as well as linkages to critical partner and/or vendor solutions. 

Jane Griffiths, RN, the chief nursing information officer at the Dubai Health Authority, will add to the discussion by describing how her organization is using AI and ML to “mine” big data, identify trends, and improve patient outcomes during Crystal Ball Gazing or Using Artificial Intelligence (from noon–1 p.m. on Tuesday March 10, at the Orange County Convention Center, Room W303A). More spe-cifically, she will explore how Dubai is using AI and ML to accurately predict which patients are at risk of sepsis, enabling for rapid ordering of appropriate diagnostic tests and implementation of treatment. AI and ML are also predicting 20 hours in advance which patients are at risk of deteriorating and the likelihood of patients “not attending” appointments.

“Deteriorating patients are internationally the biggest risk and biggest problem to identify in hospitals,” Griffiths said. “Being able to use algorithms to identify patients early before they actually display signs and symptoms of deterioration can assist clinical staff to monitor patients, intervene early, and implement treatment to save lives.”

Philip Held, research director at Rush University Medical Center, will relate how his organization is planning to use machine learning to predict patients‘ probable symptom improvement over the course of intensive one-to-three-week PTSD treatment programs during Using Machine Learning to Predict PTSD Treatment Outcomes (from 10:30–11:30 a.m. on Tuesday, March 10, at the Orange County Convention Center, Room W209C). For this program to succeed, it is important to understand which types of patients are apt to benefit from the intensive format.

“By using these machine learning algorithms, we can identify the people who are likely to respond and those who are not likely to respond,” Held said.

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SPOTLIGHT | ARTIFICIAL INTELLIGENCE AND MACHINE LEARNINGHIMSS20

Machine Learning makes it possible to leverage incoming data to quickly assess patients. “When we get treatment records from prior treatments they have engaged in, oftentimes those are hundreds and hundreds of pages that, really, no clinician can go through and understand,” he said. “For clinicians to comprehend that in a timely manner is almost impossible. ML makes it possible to make sense of this data and to determine which patients will or will not benefit from the treatment program. In fact, the ML model predicts with 85% likelihood which person is not going to respond to treatment.”

Mark Michalski, MD, former executive director of the Center for Clinical Data Science at Massachusetts General and Brigham and Women’s Hospitals, will address how machine learning models change the course of patient care during The Lifecycle of a Machine Learning Algorithm, (1-2 p.m. on Wednesday, March 11, at the Orange County Convention Center, Room W230A). Michalski will specifically ad-dress how the organization is leveraging ML and AI to detect missed aortic aneurysms in imaging data.

“Oftentimes emergency clinicians are looking for something acute, something really traumatic that they have to deal with immediately. But there’s all this other information that’s in the CT scans that they don’t normally see,” Michalski said. For example, emergency clinicians often do not catch abdominal aortic aneurisms, slight dilations of the largest artery of the body. Unfortunately, when these aneurisms go undetected and get too big, they could rupture.

“Frequently these ruptures are fatal. Catching them before that happens is really critical,” Michalski noted. “So, we decided to work with some of my colleagues in the radiology department to create a system that just checks every CT scan that comes through the emer-gency department or anywhere else, for abnormal aortic aneurism. So, if someone comes into the ER and gets treated for injuries received in a car accident, the ML system might detect the abnormality and feed that information into the EMR, flagging it as something that should be followed up on in a few months.”

“Whether you’re just beginning to learn about AI, or you’re more advanced, HIMSS20 attendees will be able to roll up their sleeves and get some great education,” Hoffberg concluded. “A lot of practical knowledge will be provided, making it possible for healthcare professionals to bring their AI and ML visions to life.”

References

1 Peter Strom, MSc, et al., “Artificial intelligence for diagnosis and grading of prostate can-cer in biopsies: a population-based diagnostic study,” The Lancet Oncology, January 8, 2020, https://www.thelancet.com/journals/lanonc/article/PIIS1470-2045(19)30738-7/fulltext.2 Mike Miliard, “Mayo clinic launches first platform initiative,” Healthcare IT News, January 15, 2020, https://www.healthcareitnews.com/news/mayo-clinic-launches-first-platform-initiative. 3 Bill Siwicki, “Forward-looking providers made strides with AI in 2019,” Healthcare IT News, December 11, 2019, https://www.healthcareitnews.com/news/forward-looking-providers-mak-ing-strides-ai-2019.4 Sam Daley, "Surgical robots, new medicines and better care: 32 examples of AI in healthcare,” Built In, September 23, 2019, https://builtin.com/artificial-intelligence/artificial-intelligence-healthcare.

ML makes it possible to make sense of this data and to determine which patients will or will not benefit from the treatment program. In fact, the ML model predicts with 85% likelihood which person is not going to respond to treatment.”

Philip Held Research Director Rush University Medical Center

www.himss.org | ©2020Sponsored by

In an interview, Chris Gough, Worldwide General Manager for the Health and Life Sciences team at Intel Corporation, discusses the transformational power of AI in healthcare and its value in the center of patient care.

and the medical community, Intel is creating the technology to enable better patient experiences of care as we have seen with Montefiore and GE Healthcare.

Another key challenge lies in the data silos that exist in most health-care organizations. We often work with organizations that have multiple EMR systems, different picture archiving and communication systems, or PACS, for different departments, and multiple data warehouses (with “omics” data growing in importance). Before you can take full advantage of capabilities like ML, your data needs to be consolidated and cleaned and/or normalized in accordance with a robust data management strategy.

What should healthcare organizations do to take advantage of this innovation?Intel has multiple software and market-ready technology solutions that will help IT management with insights into the optimal combination hardware and software.

I recommend following these six fundamental steps:

1. Focus on the business problems you’re trying to solve.2. Set goals and guidelines now and for the future.3. Understand the impact to your culture, operations, and workflow.4. Identify and cultivate the skills you will need.5. Consider your technology requirements. 6. Implement your data solution.

The only way we can meet the Quadruple Aim is through more comprehensive and effective use of healthcare data.

What should readers do next?At HIMSS20, from March 9-13 in Orlando, Intel is offering panel discussions on solving healthcare’s biggest challenges, highlighting AI and ML applications. Register via session links below.

• March 11, 7:15 am - 8:15am EST: 5 Ways AI is sharpening value-based care

• March 12, 7:15 am - 8:15am EST: 4 Data Journeys: Edge to Cloud AI

• March 12, 11:15 am - 12:15pm EST: The future of care at the edge and insights from early adopters

QA&Igniting clinical opportunities through artificial intelligence

Chris GoughWorldwide General Manager for the Health and Life Sciences team at Intel Corporation

Why will AI and machine learning be transformational for healthcare?AI helps make sense of data. Healthcare accounts for 30% of all data generated globally. One in 3 adults have multiple chronic conditions, and we’re now facing a global shortage of 15 million caregivers by the year 2030. AI has the potential to reduce the manual workload of clinicians, freeing them to spend more time on patient care.

Machine learning, or ML, capabilities empower organizations to become more proactive and predictive, determining which patients are at high risk of an adverse event and which interventions can help prevent those events from taking place. This enables healthcare organizations to apply scarce resources where they are needed most.

Where has AI has been successfully deployed?Here are a couple examples where Intel has collaborated with the industry to deploy AI capabilities:

1. Montefiore Medical Center developed a solution that enables the quick deployment of a multitude of ML models. One of these models determines which patients are at high risk of requiring intubation and makes the associated risk score available to clinicians at the point of care in the electronic medical record. With this information, clinicians proactively intervene and prevent a portion of these patients from being admitted to the intensive care unit. Montefiore runs its various models on a variety of Intel hardware and optimized software, enabling the flexibility needed to accommodate a variety of use cases.

2. The second example comes from our collaboration with GE Healthcare’s innovative work in deep learning-based X-ray image analysis for pneumothorax (collapsed lung). Utilizing a technology platform based on Intel® AI technologies, GE Healthcare has optimized algorithms for the Critical Care Suite available on Op-tima XR240amx mobile X-ray systems. With this capability, X-ray images are captured, an indication of pneumothorax is detected within seconds, and radiologists are quickly notified.

What are some of the barriers that are limiting AI adoption?Patients and medical professionals alike are cautious about the use of AI. Trust is a big issue for both and drives some of the skepticism that inhibits adoption. Through ongoing collaboration with our partners