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What are the Top Technology Priorities for Health System Leaders in 2018? A New Survey Sheds Some Light

December 7, 2017
by Heather Landi
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A new survey provides some insight into how hospital and health system leaders are prioritizing healthcare technology investments for next year with strong indications that healthcare leaders are focused on investing in proven technology solutions that will have an immediate impact, and are proceeding cautiously with emerging technology like artificial intelligence (AI).

The survey, conducted by the Pittsburgh-based Center for Connected Medicine (CCM) in partnership with the Health Management Academy, reflects the opinions of healthcare C-suite leaders from 20 major U.S. health systems across the country. CCM is a collaborative health care executive briefing center and is operated by five partners — GE Healthcare, IBM, Lenovo Health, Nokia, and UPMC. The Alexandra, Va.-based Health Management Academy is a membership organization consisting of executives from the country’s top 100 health systems focused on sharing best practices.

CCM and the Academy conducted a quantitative survey of IT leaders, specifically CIOs, chief medical information officers (CMIOs) and chief nursing information officers (CNIOs) at about 25 leading health systems, followed by quantitative interviews with health system CIOs, CFOs and CEOs at 20 health systems about health IT trends for 2018 and how these trends fit into the overall strategy and priorities of their health systems. The corresponding survey report, “Top of Mind for Top U.S. Health Systems 2018,” focuses on five areas of health IT, namely cybersecurity, consumer-facing technology, predictive analytics, virtual care and artificial intelligence.

Healthcare Informatics spoke with Gary Bisbee Jr., Ph.D., co-founder, chairman and CEO of the Health Management Academy as well as Bryan Clutz, Ph.D., researcher director at the Academy and Melissa Stahl, research manager about the implications of the survey findings on future health system technology strategies and investments.

An overall emerging theme from the survey was that while health system leaders are excited about the prospects of emerging technologies such as AI and machine learning, yet the majority are proceeding cautiously on these technologies and continue to be focused, instead, on proven technologies and IT initiatives, such as enhancing existing electronic health record (EHR) systems, standardizing IT platforms and cybersecurity solutions.

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“One thing that was surprising to me was the priority level around AI and machine learning; it tends to be a topic that health system leaders talk about frequently. When we reached out to our informatics executives and in talking to our CMIOs, CNIOs and CIOs, many indicated that implementing AI and machine learning technology solutions in 2018 was a much lower priority,” Stahl says.

In fact, the survey found that more than half of health systems currently use AI, but 63 percent of hospital IT executives ranked the implementation of AI solutions as a “low” or “very low” priority for 2018. Executives reported that AI is in its early stages where proving its value is difficult and the technology still needs refinement, but they expect the technology to have greater impact in the future, according to the survey report. And, responding health systems expect to spend an average of 2.6 percent of their IT budget on AI in 2018.

In the report, one CIO who was interviewed said: “In think that health care is still figuring out how to get value from data in general. We have use cases, but they are not ubiquitous. Until data is ubiquitous, it makes AI hard to prioritize. Additionally, it’s great to predict something but if you don’t have a corresponding intervention it doesn’t do much. It’s interesting, but still an experiment.”

The most common areas in which health systems have, or are planning to, implement AI technologies are clinical decision support (59 percent), population health (46 percent), disease management (42 percent), readmissions (41 percent), and medical costs/health plan (38 percent).

Health system executives report that they have implemented AI in more operational areas such as revenue cycle, billing, and scheduling, but have less commonly implemented in clinical areas. Health systems are starting to utilize AI for clinical areas such as readmissions and risk scores, however, this is commonly in a pilot stage and hasn’t been fully integrated. Only 4 percent of health systems currently use AI for cancer care, and only 8 percent have plans to add AI technology for cancer care in 2018, and cited cost as a factor. Even with these challenges and slow adoption, information executives anticipate AI technology will impact the use of unstructured data at their health systems in the near future (three to five years), according to the survey.

“Overall, a major theme that kept appearing when talking to health system leaders about the challenges of implementing solutions in all of these areas was the financial pressure that these health systems are under,” Stahl says. “When they talk about implementing predictive analytics or AI initiatives or even strengthening cybersecurity, cost constraints and resource constraints, and trying to find talent, those were overarching themes in healthcare, in general. Financial pressure is really seeping into all of these trends and making it hard for health system leaders to navigate implementing a lot of these technologies, even if they see value in implementing them, but there are competing priorities and a great deal of financial pressure that makes it challenging to do so.”

Bisbee adds, “The CEOs and CFOs want to see technologies that are ready to go and ready to implement. They’re thinking about resource allocation, and when it comes to wearables or when it comes to machine learning or AI or any of these specific technologies, they understand that these technologies are foundational to what they are interested in, but they want to see what is the technology that is going to make us more efficient, or improve care quality or lower cost. The technology executives are much closer to, ‘Okay, how can we integrate AI into our information systems?’ There is a little bit of a different level in how these various groups—the CEOs and CFOs and the information technology executives—look at technology.”

Looking at the use of predictive analytics in healthcare, more than half of survey respondents are using or plan to begin using genomic testing and data analytics as part of providing personalized medicine to patients. Those efforts are focused on oncology, anesthesia and pharmacogenetics.

“Across the health systems that we talked to, executives indicated an excitement around predictive analytics and seem ready to implement predictive analytics across the health systems as they are looking to use it to improve quality and safety, to reduce readmissions, to improve clinical decision support and to improve efficiencies,” Stahl says. “Many health systems are early on in their maturity, have done some pilots, are seeing some results there and are planning to scale it across their health system. We’re expecting to see a lot of growth in predictive analytics and acceptance of this within the next two years.”

Health system leaders cited resource allocation as a major challenge to implementing predictive analytics as well as organizational culture, standardizing the clinical practice and leveraging unstructured data.

Cybersecurity Investments

The survey found that 92 percent of health systems plan to increase spending on technology to boost cybersecurity in 2018, while less than half (42 percent) plan to increase IT leadership dedicated to cybersecurity and 67 percent are adding cybersecurity staff.

Stahl says the majority of health systems have leadership around cybersecurity, such as a chief information security officer (CISO), and are now focused on investing in technology areas to strengthen current capabilities in the area of data security, such as vulnerability scanning and detection.

Health system executives also plan to invest in technologies to help improve overall strategy and response: 54 percent plan to invest in identifying threats (asset management, governance, risk assessment); 50 percent plan to invest in protection (access control, awareness and training); 50 percent plan to invest in detection (continuous monitoring, detection processes); 21 percent plan to invest in recovery (business continuity/disaster recovery planning) and 17 percent plan to invest in response (cybersecurity incident response and analysis).

Other areas executives reported planning to focus cybersecurity resources include security services, outside monitoring services, and retaining consultants for cybersecurity assessment and attack/breach response.

Clutz with Health Management Academy notes that there is a critical cybersecurity talent shortage in healthcare, which may factor into health systems’ plans to invest more in technology and other resources rather than internal staffing. “Trying to find the talent in this space is really difficult as health systems evaluate what they can outsource versus what they try to accomplish internally,” he says.

While health systems are highly focused on improving cybersecurity, many challenges remain for healthcare leaders. Approximately one-third of executives reported lack of talent (38 percent) and immature IT solutions on the IT Security Maturity Model (33 percent) as top challenges. Health systems also commonly listed competing priorities, medical devices, costs relating to cybersecurity operations and anticipating emerging threats as challenges to improving cybersecurity.

What could be welcome news to many cybersecurity experts, the survey found that only 17 percent of health systems reported that they have opened bitcoin wallets to be prepared to pay for a ransomware attack.

Consumer-Facing Technology Investments

While health system leaders recognize the potential of patient-generated data, mobile apps and wearables lagged patient portals and home monitoring equipment as sources expected to generate valuable patient-generated data in 2018. Less than a quarter of respondents expect wearables (17 percent) or mobile health apps (21 percent) to be sources of valuable patient-generated data in 2018. However, executives said they are planning for patient-generated data to make up a larger portion of a patient’s health record in the future.

Over half (54 percent) of responding health systems integrate patient-generated data into the EHR, with 46 percent integrating structured, useful data and 8 percent integrating unstructured data.

“In our discussion, healthcare leaders definitely indicated interest in utilizing data from mobile apps and wearables but it’s just not on the horizon for 2018; they haven’t really developed the infrastructure or ability to utilize that data in the short term.” Stahl says. “The benefit of patient portals and home monitoring is that it’s much more structured and standardized data and they can be ensured of the quality of the data, whereas with mobile health apps and wearables, executives indicated they were hesitant around the validity of the data and the unstructured nature and that presented challenges with them trying to integrate that data into their EHRs to use it in a way that provides value.”


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

December 6, 2018
by Heather Landi, Associate Editor
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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.

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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.”

 


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At RSNA 2018, An Intense Focus on Artificial Intelligence

November 29, 2018
by Mark Hagland, Editor-in-Chief
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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.”

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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.

 

 


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Amazon Launches Machine Learning Initiative to Mine Data from EHRs

November 29, 2018
by Rajiv Leventhal, Managing Editor
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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.

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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.”


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