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The 2017 Healthcare Informatics Innovator Awards: Co-Second-Place Winning Team—Mercy-St. Louis

January 24, 2017
by David Raths
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Machine learning bolsters Mercy Health’s care pathways process

As part of a long-term effort to improve operational efficiency, the St. Louis-based Mercy Health system has spent years developing clinical pathways—a way to identify best practices for high-cost procedures such as total knee replacements and systematize them across the organization. Although the nonprofit, 45-hospital group had some success with that approach, Mercy made even greater strides once it turned to a machine learning application that uses advanced analytics to help it identify hidden patterns in its own data.

“We had a great EHR and tons of data,” says Vance Moore, president of business integration at Mercy, speaking of the organization’s electronic health record. “We had tried a couple of different data-mining solutions, and they showed promise, but they weren’t giving us what we were looking for. We had to find the truth within our data.”

The data-driven approach appears to have done that. In one example, an original care pathway developed manually at Mercy reduced the cost of total knee replacements by 7 percent. But the machine-learning approach cut an additional 5 percent off the cost of knee replacement, while improving or maintaining low rates of mortality and morbidity across all cases.

For this big-data breakthrough, the editors of Healthcare Informatics have selected Mercy as the co-second-place winning team in the 2017 Innovator Awards program. 

Mercy, which is the fifth-largest Catholic healthcare system in the United States with operations in four states, has been unified on Epic Systems’ EHR for almost 10 years and has worked to integrate that data with nonclinical data for analytics purposes. “Our clinical data set is extremely rich, so we have been doing multiple projects to try to operationalize the opportunities that come out of that,” says Todd Stewart, M.D., vice president of clinical integrated solutions. Among those efforts were the first steps to standardize care processes and the creation of care pathways, including the establishment of a governance structures to operationalize best practices. Each care pathway had a specialty council assigned to working with peers on identifying variances in care and working through common solutions where possible.


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Health IT leadership team at St. Louis-based Mercy Health system

Stewart also notes that Mercy keeps in touch with clinical leaders at other health systems working on the care pathways concept. “Our specialty council structure is modeled after work Mayo Clinic has been doing for years, and we have worked quite a bit with Intermountain Healthcare as well,” he adds.

Although the early work with care pathways was valuable, the executives noticed a few limitations holding them back. First, there were inefficiencies, because the typical pathway took up to six months to develop. Physicians found it difficult to take time away from patient care to attend quality improvement meetings. Second, the pathways were vulnerable to the biases of the clinicians involved. The best practices they identified reflected their own clinical experience, but there was no way to tell whether it was backed up by patient data. Finally, they found that at least 20 percent of Mercy clinicians failed to adopt care pathways because they were skeptical of the process, as no internal data was available to back up the best practices.

It is one thing for an administrative team to look at a best practice or set up an expert panel, and develop an optimal way to do something, Stewart says, “But anyone who has worked with a large group of physicians knows it is very difficult to motivate experienced clinicians who are driven by their own best practices and the way they were trained.” He says that they learned early on that they had to take a peer-driven approach. “If your peers are saying this is a better way to do a total knee replacement, and they are doing those procedures all day long, it is a different conversation than hearing it from an administrator who is just looking at data.”

Additionally, they had to show clinicians their own data, not industry-wide benchmark studies. “When you take that peer-to-peer process and combine it with our own data, and benchmark their results and costs against their peers internally, it is a very different discussion,” Stewart says. They can get down to the granular level of whether a scalpel tip that costs $100 more is really worth it.

In mid-2013, Mercy started realizing it had to find a better way to analyze its own data. Moore happened to be at a meeting in Silicon Valley, where he had a dinner conversation with Amy Chang, who was formerly in charge of Google Analytics. “I told her I have all this information, but I don’t know how to surface the truth out of it,” he recalls. She pointed him to a startup company called Ayasdi that was being developed by former Stanford University researchers. She told him that Ayasdi doesn’t start out with a theory and try to prove it; it starts with the unknown and provides you patterns in your data that you should investigate. Moore set up a meeting with Ayasdi executives right away.

Ayasdi, which Healthcare Informatics profiled in 2016 as one of its “Up and Coming” companies, has created clinical variation management tools that leverage both machine learning and what it calls “topological data analysis” (TDA) to extract insights from millions of data points. TDA brings together machine learning with statistical and geometric algorithms to create compressed representations and visual networks that allow organizations to more easily explore critical patterns in their data.

In a 2016 interview with Healthcare Informatics, Ayasdi CEO Gurjeet Singh noted that health systems want to wring out all the variation in their systems, so that they can determine which type of surgery is best for patients with specific co-morbidities. “A hospital system like Mercy believes that a system for discovering and operationalizing care paths could save them $50 million to $100 million over the course of three years,” he said.

In its initial work with Ayasdi, Mercy picked three care pathways it had already established to see if the machine learning could improve on what it had already done, or if it could at least validate the work Mercy had done. “Maybe it doesn’t work at all, and we can minimize our investment,” Moore remembers thinking. 

“It turns out that even with a procedure we had just completed, it was able to show an improvement of 5 percent,” Moore says. “And in the ones we had not done yet, it showed a savings of 15 percent. All of a sudden, that trial gave us the hope that we could extend the use of Ayasdi to the next level.”

As an example of the type of hidden insight Ayasdi helped discover, one group of surgeons’ patients consistently had a shorter length of hospital stay and shorter time to ambulation than other total knee surgery replacements across Mercy. These doctors prescribed a medication that is not widely used at an earlier postsurgical time than their peers. The medication reduced patients’ pain so they could get out of bed and walk around sooner, improving their outcomes and reducing costs.

“One of the attractive things about Ayasdi was its ability to rapidly explore very large, complex data sets to find significant relationships and then generate hypotheses for you,” Stewart says.

As Mercy enthusiastically embraced the objectivity that the machine learning approach brought, one of the challenges has been getting comfortable sharing so much data with Ayasdi. “We have tended to say, ‘Tell me the data you want and we will send it over.’ Their approach is the opposite,” Moore explains. “They say, ‘Send us all the data and we will find patterns within it.’ That has been an internal struggle and one for Ayasdi as well. But we are going to be involved in more third-party relationships, so we are going to have to get more savvy about extraction, conditioning and transmission of data in both directions,” he says.

Another thing the work with Ayasdi helped Mercy realize was that some of its data is incomplete and potentially inaccurate. “They would ask if we knew a certain field was only being filled in 60 percent of the time,” Moore says. “We didn’t know that.” That led them to investigate what was happening in the care setting. In some cases, they realized they could stop collecting that data because it was wasting people’s time. In other cases, they had to mandate collecting it across the board because they were missing critical information.

Stewart says the Ayasdi tool is valuable for monitoring adherence to the pathway as well as non-adherence. “We could be extremely rigid in saying that everyone has to use a pathway and be 100 percent compliant or their pay goes down,” he says, “but there has to be a balance with that because it is almost like a biological system. You have to allow for some mutations. We have learned that sometimes a group doing something different than the care pathway could have better outcomes or lower cost. That can cause you to re-examine your pathways.”

To date, Mercy has been able to work through approximately 35 care pathways, and plans to address as many as 80. “The 35 we have done make up a pretty substantial part of the care we provide,” Moore reports, “so we have to decide whether to prioritize refining the ones we have already built or continue building out the ones we haven’t done yet. Eventually we will do both.”

In the last year, the care pathways support team of six nurses and nurse practitioners has moved from Moore’s department to the quality department. “I am an operations guy, not a clinical guy, and initially we were approaching this almost as an engineering exercise: build a process that highlights variation and let the clinicians work it out,” he says. “Now that the process has been worked out, it has become more of a clinical and quality activity.”

What has been most beneficial about the new approach, Moore says, is that it is bringing objective information to the surface. That has led to a much more collaborative culture, he added. “Everybody is now focused not only on reducing variation, but also reducing cost and improving quality. We are expanding our use of knowledge and tools to positively impact care.”

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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 ( 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 (  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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