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UPMC’s McCallister and Heppenstall: The Time Is Now to Invest in HIT’s Future

February 27, 2017
by Mark Hagland
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UPMC’s Ed McCallister and Tal Heppenstall talk about UPMC’s strategic directions—and those of the industry

In the midst of the frenzied activity taking place at HIMSS17 last week in Orlando, UPMC senior vice president CIO Ed McCallister and C. Talbot “Tal” Heppenstall, Jr., executive vice president and president of UPMC Enterprises, the business and technology development arm of the vast, 25-plus-hospital health system, based in Pittsburgh, met with Healthcare Informatics Editor-in-Chief Mark Hagland on Feb. 21, to share their perspectives on the HIMSS Conference and to discuss the industry more broadly.

They also wanted to discuss the strategic partnership announced on Thursday, Feb. 16 with the Redmond, Wash.-based Microsoft Corporation to build and pilot products aimed at transforming healthcare delivery. In its story published that day, the Pittsburgh Business Times quoted Heppenstall as saying that "The partnership will result in some new companies and new products being created and concocted here in Pittsburgh over the next six months or so.” According to the Business Times’s report, “UPMC Enterprises will work with Microsoft Research, and Heppenstall said UPMC will develop and demo the products before bringing them to market.” It further quoted Heppenstall as stating that "Microsoft has technology tools they're very anxious to deploy in health care, and to the extent that they can come to UPMC and UPMC can be what we call 'customer zero' for these products, they think it's a huge benefit to them. It's Microsoft Research, so it isn't existing products you can buy off the shelf, but one's they're actually inventing.” He told the Business Times that UPMC is particularly excited to work with Microsoft on solutions involving artificial intelligence.

McCallister, who in October 2014 became CIO at UPMC, and Heppenstall, who has been with UPMC since 2003 and executive vice president since 2013, both have a long-view perspective on current developments and trends in healthcare. Below are excerpts from the interview.

When you look at what you’re seeing this year at HIMSS, in terms of discussions among colleagues, and also in terms of what you see on the exhibit floor at HIMSS17, on a scale of 1 to 10 in terms of the potential spectrum of optimism and pessimism, where are you right now?

McCallister: I’m actually extremely encouraged, because it seems more like a year of action than concept. In the past, we’ve talked about consumerism and other trends transforming healthcare. Now, I’m hearing about actions, such as the move to the cloud. In the past, we talked about, ‘to cloud or not to cloud,’ and now we’re hearing about approaches.

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Ed McCallister

Heppenstall: I agree with Ed; people are taking advantage of the opportunities there. We wouldn’t be able to do the thing we’re doing right now without the cloud; we need that level of storage and capability.

McCallister: In the past, it was a technology discussion, without the recipient of the technology at the table. Now, we’ve actually engaged physicians, instead of just deploying EMRs. We were good at digitizing healthcare, but we didn’t change clinician workflow. And I think that finally, that slide that everyone’s seen about people, process, and technology, now makes sense.

Meanwhile, at UPMC, we’re moving to a new data center—and we’re moving to a co-location data center with Involta, from Cedar Rapids. We’re going to become the anchor tenant for a new data center that’s being built 20 miles outside of Pittsburgh. So we’ll be in a hybrid cloud, which will give us the ability to expand and contract as needed, while the cost is minimal. If you build a new data center today, it’s north of $100 million; whereas, with the colocation model, we’ll probably reduce our footprint by over 50 percent, across the life of the data center. So at the end of 2017 or the beginning of 2018, we’ll reduce our server count by about 1,000, from out of close to 10,000 servers. That’s pretty impressive—it represents about 10 percent of our total count of servers. We’re moving disaster recovery and our test environment initially. And it’s a state-of-the-art, tier 3 data center.

The second thing we have going on is a biometrics initiative. With a company called Certify. We’re 60-percent deployed, and we’ll be moving into the physician offices next. And those are just two examples. We’re not a data center company or a bio-device company. Instead, we invest in companies that take us to the next level. The third thing is the machine learning initiative that we’re embarking on. In terms of UPMC core, and the great things we do—we’ve not been shy to invest in technologies to invest in being a market leader. And the partnership with Microsoft highlights where we’re trying to drive unnecessary costs out of the current model, and invest where the future will be. If we don’t do it ourselves, we’ll have to adapt to somebody else’s change.

Heppenstall: We announced it Thursday—a new strategic partnership between UPMC and Microsoft Research, to bear on a series of problems. The first problem we’re focusing on is physician empowerment. Physicians went to medical school to take care of patients, and two of every three hours spent now by physicians is in typing on computers. We think we can make a significant dent in that problem, and solve not just a physician empowerment problem, but also improve patient care. So we’re really excited about this initiative. That’s the first of several problem areas we’re going to be tackling. Microsoft Research works the same way that UPMC Enterprises does, which is to scope out a problem and then find a solution and then commercialize it.


Tal Heppenstall

McCallister: Yes, with UPMC Enterprises, we’re able to cycle it through real-world problems and solutions.

Heppenstall: One of the areas on which Microsoft Research is extremely focused on is speech recognition and artificial intelligence. You need artificial intelligence to do speech recognition, and the more data you feed into a speech recognition model, the better it gets. So that’s a huge opportunity.

What are two, three, or four of the most urgent things you’re really focused on now as CIO?

McCallister: At the outer shell layer, it would be cybersecurity—securing the data and putting the patient at the center of all that we do, with the patient in the center, whether moving to the hybrid cloud, or anything. So, starting with security. That’s the proverbial thing of what keeps you up at night. So we’re trying to do what we can to make sure that cybersecurity is a top priority. Second, it’s putting the patient, whether a member of our health plan or a patient in a UPMC facility, or a member of a senior community whose needs we’re addressing through Medicare Advantage plans—you don’t do technology to people, but to give people the opportunity to do what they need to do. MU did a fantastic job of digitizing an industry that was very paper-intense. But we didn’t necessarily focus on their workflow, so clinician workflow is exactly what we’re focusing on now, which ties into what we’re trying to do with Microsoft.

Again, it ties around the data. We’re still on the journey around big data. One thing that we have at UPMC is an abundance of data, being an integrated delivery system. We touch the payer world, the pharmacy world, the lab world, the patient care world. So tying everything into that actionable layer of data, being able to liberate that data in a very secure way, so that we can enhance the lives of patients. It ultimately all comes full circle. It’s not a matter of whether this industry will transform, it’s transforming as we speak.


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AMIA Charts Course to Learning Health System

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Initiative seeks to create virtuous cycle where clinical practice is not distinct from research

In September 2015, at AcademyHealth’s Concordium 2015 meeting in Washington, D.C., I saw a great presentation by Peter Embi, M.D., who was then an associate professor and vice chair of biomedical informatics as well as associate dean for research informatics and the chief research information officer at the Wexner Medical Center at Ohio State University. 

That day Dr. Embi outlined some of the limitations of the traditional approach to evidence-based medicine —  that it is a research/practice paradigm where the information flow is unidirectional, and clinical practice and research are distinct activities, with the research design as an afterthought. “We want to leverage information at the point of care and in engagements with patients so we can systematically learn. That is what the learning health system is all about,” Embi said.

But in the current model, he noted, there is little consideration of research during planning of health systems. That limits the ability to invest in and leverage clinical resources to advance research. Also, there are no financial incentives for non-researchers to engage in research. Research as an afterthought also leads to regulatory problems and wasted investments.

Embi argued for moving from “evidence-based medicine” to an “evidence-generating medicine” approach, which he defined as the systematic incorporation of research and quality improvement into the organization. Rather than findings flowing only from research done looking back at historical data, this approach creates a virtuous cycle where clinical practice is not distinct from research.

Flash forward to 2019 and Dr. Embi is now president & CEO of Regenstrief Institute Inc., vice president for learning health systems at IU Health, and chairman of the Board of Directors of the American Medical Informatics Association (AMIA). And he is still advocating for a shift to evidence-generating medicine. He and AMIA colleagues recently published a paper in JAMIA offering more than a dozen recommendations for public policy to facilitate the generation of evidence across physician offices and hospitals now that the adoption of EHRs is widespread.

The paper cites several examples of current high-visibility research initiatives that depend on the EGM approach: the All of Us Research Program and Cancer Moonshot initiative, the Health Care Systems Research Collaboratory, and the development of a national system of real-world evidence generation system as pursued by such groups as the US Food & Drug Administration (FDA), Patient-Centered Outcomes Research Institute (PCORI), National Institutes of Health (NIH), and other federal agencies.

The paper makes several recommendations for policy changes, including that the Trump administration should faithfully implement 2018 Revisions to the Common Rule as well as establish the 21st Century Cures-mandated Research Policy Board. The administration must implement this provision to better calibrate and harmonize our sprawling and incoherent federal research regulations.

Another recommendation is that the HHS Office of Civil Rights (OCR) should refine the definition of a HIPAA Designated Record Set (DRS) and ONC should explore ways to allow patients to have a full digital export of their structured and unstructured data within a Covered Entity’s DRS in order to share their data for research. In addtion, regulators should work with stakeholders to develop granular data specifications, including metadata, and standards to support research for use in the federal health IT certification program.

The AMIA authors also suggest that CMS leverage its Quality Payment Program to reward clinical practice Improvement Activities that involve research components. This would encourage office-based physicians to invest time and resources needed to realize EGM, they say.

Based on the paper’s findings, AMIA is launching a new initiative focused on advancing informatics-enabled improvements for the U.S. healthcare system. The organization says that a multidisciplinary group of AMIA members will develop a national informatics strategy, policy recommendations, and research agenda to improve:

• how evidence is generated through clinical practice;

• how that evidence is delivered back into the care continuum; and

• how our national workforce and organizational structures are best positioned to facilitate informatics-driven transformation in care delivery, clinical research, and population health.

A report detailing this strategy will be unveiled at a December 2019 conference in Washington, D.C.

 

 

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Definitive Healthcare Acquires HIMSS Analytics’ Data Services

January 16, 2019
by Rajiv Leventhal, Managing Editor
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Definitive Healthcare, a data analytics and business intelligence company, has acquired the data services business and assets of HIMSS Analytics, the organizations announced today.

The purchase includes the Logic, Predict, Analyze and custom research products from HIMSS Analytics, which is commonly known as the data and research arm of the Healthcare Information and Management Systems Society.

According to Definitive officials, the acquisition builds on the company’s “articulated growth strategy to deliver the most reliable and consistent view of healthcare data and analytics available in the market.”

Definitive Healthcare will immediately begin integrating the datasets and platform functionality into a single source of truth, their executives attest. The new offering will aim to include improved coverage of IT purchasing intelligence with access to years of proposals and executed contracts, enabling transparency and efficiency in the development of commercial strategies.

Broadly, Definitive Healthcare is a provider of data and intelligence on hospitals, physicians, and other healthcare providers. Its product suite its product suite provides comprehensive data on 8,800 hospitals, 150,000 physician groups, 1 million physicians, 10,000 ambulatory surgery centers, 14,000 imaging centers, 86,000 long-term care facilities, and 1,400 ACOs and HIEs, according to officials.

Together, Definitive Healthcare and HIMSS Analytics have more than 20 years of experience in data collection through exclusive methodologies.

“HIMSS Analytics has developed an extraordinarily powerful dataset including technology install data and purchasing contracts among other leading intelligence that, when combined with Definitive Healthcare’s proprietary healthcare provider data, will create a truly best-in-class solution for our client base,” Jason Krantz, founder and CEO of Definitive Healthcare, said in a statement.

Related Insights For: Analytics

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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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