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Top Ten Tech Trends 2018: A Social Determinants of Health Technology Market is Slowly Emerging

September 4, 2018
by Heather Landi, Associate Editor
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Editor’s Note: Throughout the next week, in our annual Top Ten Tech Trends package, we will share with you, our readers, stories on how we gauge the U.S. healthcare system’s forward evolution into the future.

"Social determinants of health” may be the latest popular buzz term in healthcare, yet there are leading hospitals, medical groups, and health systems, as well as accountable care organizations (ACOs) and health insurers, moving forward with efforts to identify the upstream factors that influence patients’ health. In fact, according to a recent survey by Change Healthcare and the HealthCare Executive Group (HCEG), more than 80 percent of payers are integrating social determinants of health into their member programs.

“There are a million examples every day about the importance of social determinants of health,” says Robert Fields, M.D., senior vice president and chief medical officer for population health at the New York City-based Mount Sinai Health System. “If you’re seeing a patient with diabetes, you can write a thousand prescriptions for insulin, but if they don’t have stable housing or electricity in their house, or if they have transportation issues, it’s unlikely they will be able to fill the insulin prescription, store it appropriately, or administer it appropriately.”

Healthcare leaders engaged in these efforts have found that technology is foundational to this work in the collection of social determinants data as well as for data exchange across the care continuum, workflow integration and analytics to risk stratify the highest-need individuals. From a technology solutions perspective, this remains a nascent field, many industry leaders say, and many organizations are taking a homegrown approach using their electronic health record (EHR) systems and bolt-on applications to collect and use social determinants data in various pilot projects.

“The vendors are trying to do more, but they have been slow,” Fields says. “For most organizations, they are having to piecemeal it; some of it is homegrown, but then there is actually a booming industry of vendors that fall into different categories.” At Mount Sinai, Fields is involved in efforts to address social determinants for the 400,000 covered lives in the health system’s ACO, Mount Sinai Health Partners. Previously, he had led similar efforts at Asheville, North Carolina-based Mission Health Partners, a Medicare ACO affiliated with the Mission Health healthcare system.

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According to Fields, the technology solutions that are most beneficial for social determinants of health work include predictive analytics, network registries of community-based organizations, and referral platforms as part of care management solutions.

According to a report from Patchwise Labs, one particular area where vendors are playing an integral role is the technical work to enable bi-directional integrations between platforms and native EHR systems, which is critical to make otherwise siloed clinical data available to community organizations. These platforms also help to incorporate non-clinical components of patient care plans and social needs into a centralized clinical record and enable healthcare leaders to begin leveraging non-clinical data and metadata for advanced analytics work.

Healthcare organizations that are moving forward in this area are leveraging technology platforms such as NowPow, which connects both sides of the referral process—providers and community organizations—to more efficiently connect patients to social services, as well as Aunt Bertha, a social services search tool that can be used by healthcare providers and social workers. The Aunt Bertha platform is now integrated into Epic’s App Orchard via FHIR (Fast Healthcare Interoperability Resources). Another vendor in this space is San Antonio, Texas-based TavHealth, which offers a cloud-based collaboration platform to connect healthcare providers, payers and community organizations.

There are also care coordination solutions, such as Eccovia Solutions, that help to bridge the gap between primary care and community services by sharing patient information. Eccovia, based in Salt Lake City, focuses on “whole person care” by incorporating social determinants data and is used by state Medicaid agencies, ACOs and Medicaid Waiver programs. Some hospitals and health plans also leverage Pieces Technologies’ case management platform that aggregates patients’ clinical and social history.

Looking at the social determinants of health technology solutions market, Bradley Hunter, research director at Orem, Utah-based KLAS Research, says population health vendors are working to add social determinants functionalities as well. “Most vendors have providers who are talking about including social determinants of health in their data set, and that goes from the EHR vendors, the Epics, Cerners and athenahealths of the world, to the best-of-breed vendors, such as Arcadia and Health Catalyst. There’s definitely a lot of interest in it, but I don’t hear a lot about it in practice. As far as organizations actually bringing in all that data right now, I think it’s very sparse at this point.”

Bradley Hunter

Currently, few healthcare organizations are investing in social determinants of health technology, according to a recent Patchwise Labs report. Market adoption of commercial tools for screening and referrals is currently under 4 percent, representing an estimated investment of $88 to $92 million.

However, as the shift to value-based care requires health systems to address the factors impacting health outcomes, this technology market is expected to grow quickly, with adoption of social innovation technology for healthcare poised to triple over the next five years, according to the report.

By 2023, 12 to 15 percent of health systems and managed care organizations (MCOs) will have invested in these tools, the report states. Adoption is expected to triple in five years’ time, driven by a growing business case for standardization around data capture, communication, and analytics, as well as key policy and market trends specific to social determinants of health, says Naveen Rao, founder and managing partner of Patchwise Labs.

Providers Making Early Progress with SDoH Screening Tools

Many leading health systems and hospitals are pushing forward with efforts with a focus on either building or investing in tools for social needs screening and referrals. Screening tools are often the first step to help identify social needs such as food insecurity, housing, transportation, education, exposure to crime, literacy, socioeconomic conditions, social support and access to medical services.

One of the many challenges, however, is that there are no standardized tools for collecting social determinants data and each organization has its own unique approach.

The Boston-based Partners Healthcare is one organization on the forefront of these efforts. As a participant in the Mass Health (Medicaid) ACO, Partners Healthcare is required to screen Medicaid ACO patients for social determinants factors and has integrated that process into its primary care practices. Once patients complete the questionnaires, the data is uploaded into the patient’s medical record and positive screens are flagged for the physician, who can then put in a referral to the appropriate community resource specialist or community health worker, Rose Kakoza, M.D., assistant medical director for Medicaid for the Center of Population Health at Partners Healthcare, explains.

Rose Kakoza, M.D.

“As far as IT, there was a lot of work to build this platform in Epic, and, for patients that screen positive, we’re able to link the positive screens with codes in Epic for that encounter. We partnered with Epic to figure out how to map the screening results to the appropriate ICD-10 codes,” she says. “This IT platform is allowing us to set up the infrastructure that we need to better capture what the needs are and the complexity of the needs and then better resource our practices to best meet those needs on the ground.”

At the University of Arkansas Medical Sciences (UAMS) Medical Center in Little Rock, clinicians in primary care offices and clinics are prompted by their EHR to ask patients questions about their personal life regarding their housing situation, eating habits and social isolation. Stephen Mette, M.D., the medical center’s chief clinical officer, says clinical and IT leaders began an effort about two years ago to embed these questions in the EHR. “We are now rolling this out to all providers, including the specialists,” Mette says.

The Danville, Pa.-based Geisinger Health System has taken an innovative approach with an IT- and data analytics-driven Fresh Food Farmacy initiative to address food insecurity and to improve patients’ diabetes management. The program leverages the health system’s EHR functionalities and data analytics dashboards to track patients’ progress. Project leaders have seen significant improvements in clinical outcomes for patients enrolled in the Food Farmacy program, to date.

Mount Sinai Health Partners has partnered with Lumeris, a St. Louis-based health plan and managed services vendor, to use its analytics platform to identify patients’ social needs and then risk stratify patients.

“Lumeris leverages publicly available data, such as census data, and also purchases social determinants data, like credit agency data, and then combines that with claims data, and the platform takes tens of thousands of factors and runs it through artificial intelligence (AI) and machine learning to come up with predictive modeling” for patients at risk of hospital admission, Fields says.

Many healthcare leaders say better data and more robust local partnerships will enable scalability and accountability in social determinants of health programs. Moving forward, providers also will need robust technology solutions that focus on workflow integration, bi-directional data exchange and analytics, as well as tools that can help digitally close the loop on community resource referrals.


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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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