From the Lens of a CIO: Moving Forward on Value-Based Care Efforts Without a Roadmap (Part 2) | Healthcare Informatics Magazine | Health IT | Information Technology Skip to content Skip to navigation

From the Lens of a CIO: Moving Forward on Value-Based Care Efforts Without a Roadmap (Part 2)

July 22, 2016
by Heather Landi
| Reprints
CIOs of leading health systems share the challenges, successes and lessons learned in building clinically integrated networks as part of their organizations’ value-based care efforts.
Click To View Gallery

In the second part of a two-part article about the challenges CIOs face in building clinically integrated networks, several health IT leaders share their perspectives on the critical role data analytics plays in the shift to value-based care and the need for collaborative leadership moving forward.

In part one of the story, published last week, George Conklin, CIO at the Irving, Texas-based Christus Health, a 60-hospital integrated healthcare delivery system and Mary Alice Annecharico, senior VP and CIO at Henry Ford Health System, a five-hospital health system based in Detroit as well as Tonya Edwards, M.D., physician executive at Impact Advisors, provided a look at building clinically integrated networks from the lens of a CIO and the challenges they face.

Healthcare Informatics Assistant Editor Heather Landi interviewed Conklin, Annecharico and Dr. Edwards following the Scottsdale Institute’s Spring CIO Summit in Arizona, in which 14 CIOs from leading healthcare organizations convened to discuss the most important health IT-related challenges facing CIOs. The Summit was hosted by the Scottsdale Institute, a Minn.-based not-for-profit membership organization of health systems advanced in IT, and sponsored by Impact Advisors, a Naperville, Ill.-based healthcare IT consultancy and moderated by Ralph Wakerly of Minneapolis-based consultancy C-Suite Resources. Insights from the discussions at the spring CIO Summit are outlined in the report, “Creating Clinically Integrated Networks: Challenges, Successes, Lessons Learned.”

Driven by the accelerating trend toward alternative payment models that reward quality of care rather than volume of services rendered, many of the organizations represented at the Scottsdale Institute CIO Summit have been preparing for value-based care with the development of clinically integrated networks for some time, while others are just getting started. Last year’s passage of the Medicare Access and CHIP Reauthorization Act (MACRA), which rapidly accelerates the transition to value-based payments, has especially spurred health systems to optimize and expand their clinically integrated networks, which presents CIOs with a number of IT challenges.

Conklin, Annecharico and Edwards discuss many of those challenges and lessons learned, and excerpts of the second part of those discussions are below. The interviews have been edited for length.

Webinar

Experience New Records for Speed & Scale: High Performance Genomics & Imaging

Through real use cases and live demo, Frank Lee, PhD, Global Industry Leader for Healthcare & Life Sciences, will illustrate the architecture and solution for high performance data and AI...

One of the key findings of the report was data analytics is the key to the kingdom, what does that mean?

Annecharico: We have struggled for so many years in the industry trying to cobble together inputs from all these different systems that we use and without regard for how do we sanctify that data, how do we master the data, so there is one true source of data—and that may be your financial data, your quality data, your clinical data, or could very well be your provider data—and then pulling it together and normalizing it, so that when we talk about a length of stay, or we talk about an event of care, we’re all talking about the same definition of that data. So, once organizations get to that point, we begin realizing that operations have to be bound by quickly turning over constant data. It has to be readily available and not staid, not two months old, in order to make good clinical decisions, good business and good strategic decisions, and in order to help us understand where our populations are and where we need to grow the business, or where we need to shrink the business. That’s absolutely vital to our organizations. It also help us with the measures that [the Centers for Medicare & Medicaid Services] and other regulatory bodies are looking for in terms of our quality outcomes and our cost performance. It will help us to keep the doors open, but it will also help us to conform across our systems, to a single standard, and an availability of data that now is really driving the business.

Mary Alice Annecharico

And, the CIO, as well as other executive leaders within an organization, is no longer going to be successful based on their personalities or the experience that they bring to an organization, they will be judged solely, like an organization’s health will be judged, by the availability and the agility that they can utilize data to help drive decisions and help with the business strategy of the organization.

Conklin: The consensus is that having more and better data is going to help us to ensure that we’re better able to deliver on our mission to provide high quality healthcare to all comers to our organization. But also will allow us to better evaluate markets and be sure that we put our community-based entities in exactly the right location. So, analytics help us make better decisions from a business perspective, and helps us to make better decisions from a clinical perspective. And that’s an obvious one, so when you appear at one of our free-standing ERs and we collect all your data and find out you have an allergy, and then you show up a clinic or acute care hospital, it ensures that we know about that information up front and are able to build that into the treatment plan that we create for you.

What is the sense of the progress that organizations are making in the area of data analytics?

Edwards: Progress around data analytics is all over the board. Some folks are just starting, and some healthcare organizations, particularly those that already have in place insurance arms, are much further along related to analytics. There are really significant challenges in healthcare around analytics for several reasons. First, we don’t share information well in healthcare, and trying to integrate information is difficult. The governance and data normalization practices are extremely challenging with healthcare. We have a lot of heterogeneous data with a lot of data that we may ultimately have to try to analyze, using natural language processing, because it’s not structured data. From the financial side, it’s fairly easy to analyze data. On the clinical side, it is not easy at all. It’s coming in many different shapes and forms. Providers may document actually the same thing in five or six different ways, so there’s standardization of processes that needs to happen in order to document in the same way so we can pull information in the same way. And then you may have challenges with laboratory data, for instance, where hemoglobin A1c that is performed by LabCorp looks different than A1c that’s performed by Quest or others, and being able to compare apples to apples. I’d say the challenges in analytics are around integrating non-standard data and data governance and normalization of that data so that then you can even begin to attempt to analyze the data.

Tonya Edwards, M.D.

The second challenge is that traditionally in healthcare we don’t have the types of resources that we need in order to be able to analyze that data well. For instance, it’s a new idea to have data scientists within healthcare organizations. We’ve very much been about analyzing retrospective data and reporting, essentially, and we’re just now beginning to move into being able to look forward into predictive and much less prescriptive analytics.

Another challenge identified in the report was demonstrating return on investment. Why have organizations found this challenging?

Edwards: The biggest challenge there is that CIOs and healthcare leadership teams have a tremendous number of demands, everybody wants capital dollars, operating dollars, and we’re at a time of shrinking margins for most healthcare organizations or systems. Those scarce resources are going to get allocated in areas where you can prove a return on investment. So many CIOs have, with the implementation of electronic health records (EHRs), for instance, where it was a situation where they anticipated significant ROI that may not have been realized, and there was not as much value out of the EHRs as was anticipated. It’s more difficult to prove, to bring value, and you really have to get in front of healthcare leadership and do some small projects that really do improve efficiency or save dollars or improve patient care in a way that, ultimately and indirectly, saves dollars or increases revenue in order to prove the value and be able to move forward into larger projects. It’s just a basic need at this point where we have to use our scarce resources in the best way possible.

Can you give some examples of how organizations are focusing on low-hanging fruit to demonstrate ROI?

Conklin: From a low hanging fruit perspective, how do we create and establish a long-term relationship with you as a person, something that’s “sticky,” which makes you want to come back and get service from us. So, part of that has been, historically, our service mentality and the models of care that we have built and developed that are very focused on the person, the needs of the individual, beyond just the medical care that’s given. What are you all about, what are your needs? We build a care plan around all those needs, and so create a relationship with you that is very “sticky.” That’s easy to do and inexpensive to do. We’re working now to actually build that out and we currently have a contract with a company that’s providing phone consultation services so patients can call them up and speak to a doctor and get some basic input, or a prescription. We’re in the process of building out that functionality and capability for ourselves. It’s relatively inexpensive to do that, and that’s low hanging fruit, and makes people want to come back for service again.

With this work of building clinically integrated networks, how is the leadership role for CIOs changing?

Annecharico: It will require more collaborative leadership. And it’s not all about the CIO. The CIO will be at the table to think strategically about the end point. But if we are really looking at clinically integrated networks, it’s a series of CIOs who need to be at the table and need to lead, inform and guide senior leadership in what does the data do and what does the data mean to help them be all in the same place.

For the six health system assembly that we are involved in here in the state of Michigan, we have an advantage and it was more coincidental than anything, as everyone will be in the Epic environments. The data sharing capabilities are already enhanced because we have the means of being able to do that in the Epic environment, but there will be the need for us to aggregate our claims and contract data in a way that will be really helpful. I think the CIO is a key part of it, but I also believe that all of the data-driven outcomes are not always within IT, and here they are not. I will tell you that we have an operations group that does data analytics as well as the population health analytic work; we provide the infrastructure and the end user tool that our leaders and clinicians use to interpret the data, but it is not run or managed by IT. I think more and more, we are realizing that there is a business strategy that is starting to help us shift as we become more and more data-driven healthcare organizations.

Conklin: I’m a little bit different from other CIOs. I come from a clinician perspective, as I’m a psychologist. The future is going to be about the CIOs, people sitting in my seat, who are going to have to understand and speak the language of the people running the organization as well as delivering the clinical care. They can’t be technicians, they can’t go to a meeting and talk in techno-babble, that’s a big turn off and people will not think of you as a partner. Usually when you bring up technical stuff, it’s brought up as an obstruction, such as why you can’t do something. What the CIOs need to be doing is not saying, “yes, but,” but rather, “yes, and.” The CIOs need to be talking to people about “This is what I can give you” as well as “I will give you want you want and even much more.”

And, absolutely yes, there is a need to be more collaborative. If CIOs approach a job primarily as a technician, we’ll get relegated into a support service very quickly, and we’ll lose any strategic standing within the organization. They have to be focused around the value that they can return, they have to be seen as an essential member of the leadership team and they have to contribute beyond what their particular area of expertise or interest is.

Edwards: Collaborative leadership has been a key for organizations that have been forming clinically integrated networks for some time. What we find is some of the historic leadership structures, hierarchical structures, don’t work as well because we have many different areas of expertise that are really needed. We find that it’s not just business operations that are leading these practices, we have to have IT leaders and clinical leaders, especially chief nursing officers, chief medical officers, CMIOs or CNIOs, who are informing some of these relationships as well as other operational leaders. There will be new relationships with chief analytics officers, chief quality officers and others. And they will need to work collaboratively because all these areas of expertise are important as we manage data across the continuum. We have to understand how are we going to use the data, what are the most important processes to apply the data to in order to make strategic changes that are going to improve efficiency, improve patient care and improve patient access. You can’t do that without these key leaders working together to understand what’s going to be the best use of resources. There is just a tremendous number of matrix relationships that become very important, as the senior leadership team works very closely together, across silos, rather than within silos.


The Health IT Summits gather 250+ healthcare leaders in cities across the U.S. to present important new insights, collaborate on ideas, and to have a little fun - Find a Summit Near You!


/article/analytics/lens-cio-moving-forward-value-based-care-efforts-without-roadmap-part-2
/article/analytics/how-one-community-hospital-leveraging-ai-bolster-its-care-pathways-process

How One Community Hospital is Leveraging AI to Bolster Its Care Pathways Process

December 6, 2018
by Heather Landi, Associate Editor
| Reprints
Click To View Gallery

Managing clinical variation continues to be a significant challenge facing most hospitals and health systems today as unwarranted clinical variation often results in higher costs without improvements to patient experience or outcomes.

Like many other hospitals and health systems, Flagler Hospital, a 335-bed community hospital in St. Augustine, Florida, had a board-level mandate to address its unwarranted clinical variation with the goal of improving outcomes and lowering costs, says Michael Sanders, M.D., Flagler Hospital’s chief medical information officer (CMIO).

“Every hospital has been struggling with this for decades, managing clinical variation,” he says, noting that traditional methods of addressing clinical variation management have been inefficient, as developing care pathways, which involves identifying best practices for high-cost procedures, often takes up to six months or even years to develop and implement. “By the time you finish, it’s out of date,” Sanders says. “There wasn’t a good way of doing this, other than picking your spots periodically, doing analysis and trying to make sense of the data.”

What’s more, available analytics software is incapable of correlating all the variables within the clinical, billing, analytics and electronic health record (EHR) databases, he notes.

Another limitation is that care pathways are vulnerable to the biases of the clinicians involved, Sanders says. “In medicine, what we typically do is we’ll have an idea of what we want to study, design a protocol, and then run the trial and collect the data that we think is important and then we try to disprove or prove our hypothesis,” he says.

Webinar

Experience New Records for Speed & Scale: High Performance Genomics & Imaging

Through real use cases and live demo, Frank Lee, PhD, Global Industry Leader for Healthcare & Life Sciences, will illustrate the architecture and solution for high performance data and AI...

Sanders says he was intrigued by advances in machine learning tools and artificial intelligence (AI) platforms capable of applying advanced analytics to identify hidden patterns in data.

Working with Palo Alto, Calif.-based machine intelligence software company Ayasdi, Flagler Hospital initiated a pilot project to use Ayasdi’s clinical variation management application to develop care pathways for both acute and non-acute conditions and then measure adherence to those pathways.

Michael Sanders, M.D.

Flagler targeted their treatment protocols for pneumonia as an initial care process model. “We kicked around the idea of doing sepsis first, because it’s a huge problem throughout the country. We decided to use pneumonia first to get our feet wet and figure out how to use the tool correctly,” he says.

The AI tools from Ayasdi revealed new, improved care pathways for pneumonia after analyzing thousands of patient records from the hospital and identifying the commonalities between those with the best outcomes. The application uses unsupervised machine learning and supervised prediction to optimally align the sequence and timing of care with the goal of optimizing for patient outcomes, cost, readmissions, mortality rate, provider adherence, and other variables.

The hospital quickly implemented the new pneumonia pathway by changing the order set in its Allscripts EHR system. As a result, for the pneumonia care path, Flagler Hospital saved $1,350 per patient and reduced the length of stay (LOS) for these patients by two days, on average. What’s more, the hospital reduced readmission by 7 times—the readmission rate dropped from 2.9 percent to 0.4 percent, hospital officials report. The initial work saved nearly $850,000 in unnecessary costs—the costs were trimmed by eliminating labs, X-rays and other processes that did not add value or resulted in a reduction in the lengths of stay or readmissions.

“Those results are pretty amazing,” Sanders says. “It’s taking our data and showing us what we need to pursue. That’s powerful.”

With the success of the pneumonia care pathway, Flagler Hospital leaders also deployed a new sepsis pathway. The hospital has expanded its plans for using Ayasdi to develop new care pathways, from the original plan of tackling 12 conditions over three years, to now tackling one condition per month. Future plans are to tackle heart failure, total hip replacement, chronic obstructive pulmonary disease (COPD), coronary artery bypass grafting (CABG), hysterectomy and diabetes, among other conditions. Flagler Hospital expects to save at least $20 million from this program in the next three years, according to officials.

Finding the “Goldilocks” group

Strong collaboration between IT and physician teams has been a critical factor in deploying the AI tool and to continue to successfully implement new care pathways, Sanders notes.

The effort to create the first pathway began with the IT staff writing structured query language (SQL) code to extract the necessary data from the hospital’s Allscripts EHR, enterprise data warehouse, surgical, financial and corporate performance systems. This data was brought into the clinical variation management application using the FHIR (Fast Healthcare Interoperability Resources) standard.

“That was a major effort, but some of us had been data scientists before we were physicians, and so we parameterized all these calls. The first pneumonia care path was completed in about nine weeks. We’ve turned around and did a second care path, for sepsis, which is much harder, and we’ve done that in two weeks. We’ve finished sepsis and have moved on to total hip and total knee replacements. We have about 18 or 19 care paths that we’re going to be doing over the next 18 months,” he says.

After being fed data of past pneumonia treatments, the software automatically created cohorts of patients who had similar outcomes accompanied by the treatments they received at particular times and in what sequence. The program also calculated the direct variable costs, average lengths of stay, readmission and mortality rates for each of those cohorts, along with the statistical significance of its conclusions. Each group had different comorbidities, such as diabetes, COPD and heart failure, which was factored into the application's calculations. At the push of a button, the application created a care path based on the treatment given to the patients in each cohort.

The findings were then reviewed with the physician IT group, or what Sanders calls the PIT crew, to select what they refer to as the “Goldilocks” cohort. “This is a group of patients that had the combination of low cost, short length of stay, low readmissions and almost zero mortality rate. We then can publish the care path and then monitor adherence to that care path across our physicians,” Sanders says.

The AI application uncovered relationships and patterns that physicians either would not have identified or would have taken much longer to identify, Sanders says. For instance, the analysis revealed that for patients with pneumonia and COPD, beginning nebulizer treatments early in their hospital stays improved outcomes tremendously, hospital leaders report.

The optimal events, sequence, and timing of care were presented to the physician team using an intuitive interface that allowed them to understand exactly why each step, and the timing of the action, was recommended. Upon approval, the team operationalized the new care path by revising the emergency-department and inpatient order sets in the hospital EHR.

Sanders says having the data generated by the AI software is critical to getting physicians on board with the project. “When we deployed the tool for the pneumonia care pathway, our physicians were saying, ‘Oh no, not another tool’,” Sanders says. “I brought in a PIT Crew (physician IT crew) and we went through our data with them. I had physicians in the group going through the analysis and they saw that the data was real. We went into the EMR to make sure the data was in fact valid, and after they realized that, then they began to look at the outcomes, the length of stay, the drop in readmissions and how the costs dropped, and they were on board right away.”

The majority of Flagler physicians are adhering to the new care path, according to reports generated by the AI software's adherence application. The care paths effectively sourced the best practices from the hospital’s best doctors using the hospital’s own patient groups, and that is key, Sanders notes.

“When we had conversations with physicians about the data, some would say, ‘My patient is sicker than yours,’ or ‘I have a different patient population.’ However, we can drill down to the physician’s patients and show the physician where things are. It’s not based on an ivory tower analysis, it’s based on our own data. And, yes, our patients, and our community, are unique—a little older than most, and we have a lot of Europeans here visiting. We have some challenges, but this tool is taking our data and showing us what we need to pursue. That’s pretty powerful.”

He adds, “It’s been amazing to see physicians rally around this. We just never had the tool before that could do this.”

While Flagler Hospital is a small community hospital with fewer resources than academic medical centers or larger health systems—for example, the hospital doesn’t have a dedicated data scientist but rather uses its in-house informatics staff for this project—the hospital is progressive in its use of advanced analytics, according to Sanders.

“We’ve been able to do a lot of querying ourselves, and we have some sepsis predictive models that we’ve created and put into place. We do a lot of real-time monitoring for sepsis and central line-associated bloodstream infections,” he says. “Central line-associated bloodstream infections are a bane for all hospitals. In the past year and a half, since we’ve put in our predictive model, we’ve had zero bloodstream infections, and that’s just unheard of.”

Sanders and his team plan to continue to use the AI tool to analyze new data and adjust the care paths according to new discoveries. As the algorithms find more effective and efficient ways to deliver care that result in better outcomes, Flagler will continue to improve its care paths and measure the adherence of its providers.

There continues to be growing interest, and also some hype, around AI tools, but Sanders notes that AI and machine learning are simply another tool. “Historically, what we’ve done is that we had an idea of what we wanted to do, conducted a clinical trial and then proved or disproved the hypothesis, based on the data that we collected. We have a tool with AI which can basically show us relationships that we didn’t know even existed and answer questions that we didn’t know to ask. I think it’s going to open up a tremendous pathway in medicine for us to both reduce cost, improve care and really take better care of our patients,” he says, adding, “When you can say that to physicians, they are on board. They respond to the data.”

 


More From Healthcare Informatics

/article/analytics/rsna-2018-intense-focus-artificial-intelligence

At RSNA 2018, An Intense Focus on Artificial Intelligence

November 29, 2018
by Mark Hagland, Editor-in-Chief
| Reprints
Artificial intelligence solutions—and discussions—were everywhere at RSNA 2018 this week

Artificial intelligence solutions—and certainly, the promotion of such solutions—were everywhere this year at the RSNA Conference, held this week at Chicago’s vast McCormick Place, where nearly 49,000 attendees attended clinical education sessions, viewed nearly 700 vendor exhibits. And AI and machine learning promotions, and discussions were everywhere.

Scanning the exhibit floor on Monday, Glenn Galloway, CIO of the Center for Diagnostic Imaging, an ambulatory imaging center in the Minneapolis suburb of St. Louis Park, Minn., noted that “There’s a lot of focus on AI this year. We’re still trying to figure out exactly what it is; I think a lot of people are doing the same, with AI.” In terms of whether what’s being pitched is authentic solutions, vaporware, or something in between, Galloway said, “I think it’s all that. I think there will be some solutions that live and survive. There are some interesting concepts of how to deliver it. We’ve been talking to a few folks. But the successful solutions are going to be very focused; not just AI for a lung, but for a lung and some very specific diagnoses, for example.” And what will be most useful? According to Galloway, “Two things: AI for the workflow and the quality. And there’ll be some interesting things for what it will do for the quality and the workflow.”

“Certainly, this is another year where machine learning is absolutely dominating the conversation,” said James Whitfill, M.D., CMO at Innovation Care Partners in Scottsdale, Ariz., on Monday. “In radiology, we continue to be aware of how the hype of machine learning is giving way to the reality; that it’s not a wholesale replacement of physicians. There have already been tremendous advances in, for example, interpreting chest x-rays; some of the work that Stanford’s done. They’ve got algorithms that can diagnose 15 different pathological findings. So there is true material advancement taking place.”

Meanwhile, Dr. Whitfill said, “At the same time, people are realizing that coming up with the algorithm is one piece, but that there are surprising complications. So you develop an algorithm on Siemens equipment, but when you to Fuji, the algorithm fails—it no longer reliably identifies pathology, because it turns out you have to train the algorithm not just on examples form just one manufacturer, but form lots of manufacturers. We continue to find that these algorithms are not as consistent as identifying yourself on Facebook, for example. It’s turning out that radiology is way more complex. We take images on lots of different machines. So huge strides are being made,” he said. “But it’s very clear that human and machine learning together will create the breakthroughs. We talk about physician burnout, and even physicians leaving. I think that machine learning offers a good chance of removing a lot of the drudgery in healthcare. If we can automate some processes, then it will free up our time for quality judgment, and also to spend time talking to patients, not just staring at the screen.”

Webinar

Experience New Records for Speed & Scale: High Performance Genomics & Imaging

Through real use cases and live demo, Frank Lee, PhD, Global Industry Leader for Healthcare & Life Sciences, will illustrate the architecture and solution for high performance data and AI...

Looking at the hype cycle around AI

Of course, inevitably, there was talk around the talk of the hype cycle involving artificial intelligence. One of those engaging in that discussion was Paul Chang, M.D.., a practicing radiologist and medical director of enterprise imaging at the University of Chicago. Dr. Chang gave a presentation on Tuesday about AI. According a report by Michael Walter in Radiology Business, Dr. Chang said, “AI is not new or spooky. It’s been around for decades. So why the hype?” He described computer-aided detection (CAD) as a form of artificial intelligence, one that radiologists have been making use of for years.

Meanwhile, with regard to the new form of AI, and the inevitable hype cycle around emerging technologies, Dr. Chang said during his presentation that “When you’re going up the ride, you get excited. But then right at the top, before you are about to go down, you have that moment of clarity—‘What am I getting myself into?’—and that’s where we are now. We are upon that crest of magical hype and we are about to get the trench of disillusionment.” Still, he told his audience, “It is worth the rollercoaster of hype. But I’m here to tell you that it’s going to take longer than you think.”

So, which artificial intelligence-based solutions will end up going the distance? On a certain level, the answer to that question is simple, said Joe Marion, a principal in the Waukesha, Wis.-based Healthcare Integration Strategies LLC, and one of the imaging informatics industry’s most respected observers. “I think it’s going to be the value of the product,” said Marion, who has participated in 42 RSNA conferences; “and also the extent to which the vendors will make their products flexible in terms of being interfaced with others, so there’s this integration aspect, folding into vendor A, vendor B, vendor C, etc. So for a third party, the more they reach out and create relationships, the more successful they’ll be. A lot of it will come down to clinical value, though. Watson has had problems in that people have said, it’s great, but where’s the clinical value? So the ones that succeed will be the ones that find the most clinical value.”

Still, Marion noted, even the concept of AI, as applied to imaging informatics, remains an area with some areas lacking in clarity. “The reality, he said, “is that I think it means different things to different people. The difference between last year and this year is that some things are coming to fruition; it’s more real. And so some vendors are offering viable solutions. The message I’m hearing from vendors this year is, I have this platform, and if a third party wants to develop an application or I develop an application, or even an academic institution develops a solution, I can run it on my platform. They’re trying to become as vendor-agnostic as possible.”

Marion expressed surprise at the seemingly all-encompassing focus on artificial intelligence this year, given the steady march towards value-based healthcare-driven mandates. “Outside of one vendor, I’m not really seeing a whole lot of emphasis this year on value-based care; that’s disappointing,” Marion said. “I don’t know whether people don’t get it or not about value-based care, but the vendors are clearly more focused on AI right now.”

Might next year prove to be different? Yes, absolutely, especially given the coming mandates coming out of the Protecting Access to Medicare Act (PAMA), which will require referring providers to consult appropriate use criteria (AUC) prior to ordering advanced diagnostic imaging services—CT, MR, nuclear medicine and PET—for Medicare patients. The federal Centers for Medicare and Medicaid Services (CMS) will progress with a phased rollout of the CDS mandate, as the American College of Radiology (ACR) explains on its website, with voluntary reporting of the use of AUC taking place until December 2019, and mandatory reporting beginning in January 2020.

But for now, this certainly was the year of the artificial intelligence focus at the RSNA Conference. Only time will tell how that focus plays out in the imaging and imaging informatics vendor space within the coming 12 months, before RSNA 2019 kicks off one year from now, at the conference’s perennial location, McCormick Place.

 

 


Related Insights For: Analytics

/article/analytics/amazon-launches-machine-learning-initiative-mine-data-ehrs

Amazon Launches Machine Learning Initiative to Mine Data from EHRs

November 29, 2018
by Rajiv Leventhal, Managing Editor
| Reprints
Officials believe that the new software will go a long way in helping to analyze patient records

Continuing its aggressive push into healthcare, Amazon announced this week that it’s launching a machine learning service that will aim to mine data from electronic health records (EHRs).

According to company officials in a blog post earlier this week, the software, Amazon Comprehend Medical, is “a HIPAA-eligible machine learning service that allows developers to process unstructured medical text and identify information such as patient diagnosis, treatments, dosages, symptoms and signs, and more.”

They continued, “Comprehend Medical helps healthcare providers, insurers, researchers, and clinical trial investigators as well as healthcare IT, biotech, and pharmaceutical companies to improve clinical decision support, streamline revenue cycle and clinical trials management, and better address data privacy and protected health information (PHI) requirements.”

As Amazon officials noted, a core issue in healthcare and health IT today is that a large amount of critical data is stored as unstructured medical text, such as medical notes, prescriptions, audio interview transcripts, and pathology and radiology reports. “This means that being able to identify this information can be a manual and time-consuming process, which either requires data entry by high skilled medical experts, or teams of developers writing custom code and rules to try and extract the information automatically,” Amazon officials outlined. “In both cases this undifferentiated heavy lifting takes material resources away from efforts to improve patient outcomes through technology,” they added.

But what Amazon Comprehend Medical will aim to specifically do is allow developers “to identify the key common types of medical information automatically, with high accuracy, and without the need for large numbers of custom rules. Comprehend Medical can identify medical conditions, anatomic terms, medications, details of medical tests, treatments and procedures. Ultimately, this richness of information may be able to one day help consumers with managing their own health, including medication management, proactively scheduling care visits, or empowering them to make informed decisions about their health and eligibility,” according to officials.

Webinar

Experience New Records for Speed & Scale: High Performance Genomics & Imaging

Through real use cases and live demo, Frank Lee, PhD, Global Industry Leader for Healthcare & Life Sciences, will illustrate the architecture and solution for high performance data and AI...

Indeed, Amazon executives are quite bullish on the machine learning capabilities of the new software. Taha Kass-Hout, a senior leader with Amazon’s healthcare and AI efforts told the Wall Street Journal that “During testing, the software performed on par or better than other published efforts, and can extract data on patients’ diseases, prescriptions, lab orders and procedures,” according to a report this week.

Officials noted that since there will be no servers to provision or manage, developers will only need to provide unstructured medical text to Comprehend Medical. The service will “read” the text and then identify and return the medical information contained within it, they explained.

Importantly, as pointed out in a CNBC report on the Amazon initiative, Christina Farr noted that “It looks unlikely at this point that Amazon will compete directly with medical records companies like Allscripts and Cerner, as there are plenty of money-making opportunities to work with those vendors and provide services like population health and clinical trial support.”

Rather, Farr continued, “Amazon is most directly taking on UnitedHealth Group's Optum, which is already in the space, as well as technology rivals Apple and Alphabet.” To this point, according to the WSJ report, “The market for storing and analyzing health information is worth more than $7 billion a year, according to research firm Grand View Research, a business in which International Business Machines Corp.’s Watson Health and UnitedHealth Group Inc.’s Optum already compete.

Amazon Pushes Further into Healthcare

It’s been quite the year of convergence for Amazon and the healthcare industry. In January, Amazon, Berkshire Hathaway, and JPMorgan Chase & Co announced they were teaming up on an initiative to improve satisfaction and reduce costs for their companies’ employees. Although not many details are known about this collaboration, the organizations named Atul Gawande, M.D., as CEO of the initiative, back in June.

Meanwhile, in August, Amazon said it would be part of another endeavor related to healthcare—to remove interoperability barriers and to make progress on adoption of health data standards. For this, Amazon will be teaming up with Microsoft, Google, IBM, and others to jointly commit to support healthcare interoperability by advancing healthcare standards such as HL7 (Health Level Seven International), FHIR (Fast Healthcare Interoperability Resources), and the Argonaut Project.

Also this summer, Amazon acquired PillPack, a Boston-based online pharmacy startup.

Liam Bouchier, principal with the Illinois-based consulting firm Impact Advisors, says that this latest initiative is simply another example of Amazon “doing what it does best”—working to analyze large data sets as a means to gain meaningful insights into the consumer through a variety of different ways. “This concept does make some in the healthcare industry uncomfortable, which is understandable given the amount of regulations, including HIPAA, that are a challenge to manage daily,” he says, importantly adding that EHRs were not the norm even 10 years ago. “Now, the [EHR] market is saturated with almost every health system and healthcare provider partaking,” he points out.

To this point, as explained in the WSJ article, “Amazon Web Services won’t see the data processed by its algorithms, which will be encrypted and unlocked by customers who have the key, Dr. Kass-Hout said. Its service is designed to conform with privacy rules laid out in the federal Health Insurance Portability and Accountability Act,” he said, per that report.

As Bouchier notes, in healthcare there are companies that provide this type of NLP (natural language processing) service to varying degrees of success, many of whom use Amazon Web Services as their infrastructure. “Is this really that big a leap for Amazon?” he asks. “The analytics market is full of vendors that provide a variety of analytics tools using both claims and clinical data primarily. The difference is these vendors started with healthcare in mind, yet few can scale and provide their services in a cost-effective manner to the customer and to the health system.”

But a key difference, he asserts, is that “they almost most assuredly don’t have the depth of knowledge and know-how to execute successfully in the same manner along with the depth of data and knowledge Amazon possess on the world population today.”

In the same announcement this week, Amazon also said that it was working closely with Seattle’s Fred Hutchinson Cancer Research Center in an effort to identify patients for clinical trials who may benefit from specific cancer therapies. Fred Hutch was able to evaluate millions of clinical notes to extract and index medical conditions, medications, and choice of cancer therapeutic options, reducing the time to process each document from hours, to seconds, according to officials.

“Curing cancer is, inherently, an issue of time,” Matthew Trunnell, CIO, Fred Hutchinson Cancer Research Center, said in a statement. “For cancer patients and the researchers dedicated to curing them, time is the limiting resource. The process of developing clinical trials and connecting them with the right patients requires research teams to sift through and label mountains of unstructured medical record data. Amazon Comprehend Medical will reduce this time burden from hours per record to seconds. This is a vital step toward getting researchers rapid access to the information they need when they need it so they can find actionable insights to advance lifesaving therapies for patients.”


See more on Analytics

betebet sohbet hattı betebet bahis siteleringsbahis