Might this be a sign that AI platforms like Watson still cannot diagnose the wide range of patients’ conditions as accurately as a board-certified clinical pathologist?
Computer technology evolves so quickly, products often become obsolete before fulfilling their expected potential. Such, apparently, is the case with Watson, the genius artificial intelligence (AI) brainchild of International Business Machines Corp. (IBM) which was going to revolutionize how healthcare providers diagnose disease. In some areas of healthcare, such as analyzing MRIs and X-rays, AI has been a boon. But from a business perspective, Watson has failed to turn a profit for IBM, so it has to go.
In February, The Wall Street Journal (WSJ) reported that IBM is looking to sell its Watson Health unit because it is not profitable, despite bringing in $1 billion annually in revenue. The sale of Watson Health, the article states, would be aligned with IBM’s goal of streamlining the company and focusing its energies on cloud computing and other AI functions. Because one goal of the Watson project was to give physicians a tool to help them diagnose patients more accurately and faster, the problems that prevented Watson from achieving that goal should be of interest to pathologists and clinical laboratory managers, who daily are on the front lines of helping doctors diagnose the most challenging cases.
In a follow-up article, titled, “Potential IBM Watson Health Sale Puts Focus on Data Challenges,” the WSJ wrote, “… some experts found that it can be difficult to apply AI to treating complex medical conditions. Having access to data that represents patient populations broadly has been a challenge, experts told the Journal, and gaps in knowledge about complex diseases may not be fully captured in clinical databases.”
“I believe that we’re many years away from AI products that really positively impact clinical care for many patients,” Bob Kocher, Partner at Venrock, a venture-capital firm that invests in healthcare IT and related services, told the WSJ.
IBM Watson was promoted as a major resource to help improve medical care and support doctors in making more accurate diagnoses. However, in “IBM’s Retreat from Watson Highlights Broader AI Struggles in Health,” the WSJ reported that “IBM spent several billion dollars on acquisitions to build up Watson [Health] … a unit whose marquee product was supposed to help doctors diagnose and cure cancer … A decade later, reality has fallen short of that promise.”
IBM initially created Watson (above center) to be an AI tool capable of a wide variety of applications, starting with answering questions. In January 2011, Watson made headlines when it defeated previous Jeopardy champions Ken Jennings (left) and Brad Rutter (right) on the popular television game show.After its triumph, IBM announced it would transition Watson for use in medical applications and promoted it as a major resource to help improve medical care and support healthcare professionals in making more precise diagnoses. The company called its new division IBM Watson Health and stated that massive data sets would be the key to accomplish its healthcare mission. That same year, The Dark Report had an IBM executive do a presentation about Watson Health at the Executive War College on Laboratory and Pathology Management. (Photo copyright: CBS News.)
Watson’s Successes and Failures in Healthcare
During the years following Watson’s Jeopardy win, Watson Health made some positive advances in the fields of healthcare data analytics, performance measurements, clinical trial recruitment, and healthcare information technology (HIT).
However, Watson Health also experienced some high-profile failures as well. One such failure involved a collaboration with MD Anderson Cancer Center, established in 2013, to help the health systems’ oncologists develop new tools to benefit cancer patients. MD Anderson ended the relationship in 2018 after spending more than $60 million on the project, citing “multiple examples of unsafe and incorrect treatment recommendations,” made by the Watson supercomputer, Healthcare IT News reported.
Watson Health later readjusted the development and sales of its AI drug discovery tools and altered its marketing strategy amid reports of disappointing sales and skepticism surrounding machine learning for medical applications.
Underestimating the Challenge of AI in Healthcare
Since its inception, Watson Health has achieved substantial growth, mainly through a series of acquisitions. Those targeted acquisitions include:
Merge Healthcare, a healthcare imaging software company that was purchased for $1 billion in 2015,
Phytel, a health management software company that was purchased for an undisclosed amount in 2015,
Explorys, a healthcare analytics company that was purchased for an undisclosed amount in 2015, and
Truven Health Analytics, a provider of cloud-based healthcare data, analytics, and insights that was purchased for $2.6 billion in 2016.
“IBM’s Watson Health business came together as a result of several acquisitions,” said Paddy Padmanabhan, founder and CEO of Damo Consulting, a firm that provides digital transformation strategy and advisory services for healthcare organizations. “The decision to sell the business may also have to do with the performance of those units on top of the core Watson platform’s struggles,” he told Healthcare IT News.
It should be noted that these acquisitions involved companies that already had a product in the market which was generating revenue. So, the proposed sale of Watson Health includes not just the original Watson AI product, but the other businesses that IBM put into its Watson Health business division.
Padmanabhan noted that there are many challenges for AI in healthcare and that “historical data is at best a limited guide to the future when diagnosing and treating complex conditions.” He pointed to the failure with MD Anderson (in the use of Watson Health as a resource or tool for diagnosing cancer) was a setback for both IBM and the use of AI in healthcare. However, Padmanabhan is optimistic regarding the future use of AI in healthcare.
“To use an oft-quoted analogy, AI’s performance in healthcare right now is more akin to that of the hedgehog than the fox. The hedgehog can solve for one problem at a time, especially when the problem follows familiar patterns discerned in narrow datasets,” he told Healthcare IT News. “The success stories in healthcare have been in specific areas such as sepsis and readmissions. Watson’s efforts to apply AI in areas such as cancer care may have underestimated the nuances of the challenge.”
Other experts agree that IBM was overly ambitious and overreached with Watson Health and ended up over-promising and under-delivering.
“IBM’s initial approach misfired due to how the solution AI was trained and developed,” Dan Olds, Principal Analyst with Gabriel Consulting Group, told EnterpriseAI. “It didn’t conform well to how doctors work in the real world and didn’t learn from its experiences with real doctors. It was primarily learning from synthetic cases, not real-life cases.”
Was Watson Already Obsolete?
Another issue with Watson was that IBM’s marketing campaign may have exceeded the product’s design capabilities. When Watson was developed, it was built with AI and information technologies (IT) that were already outdated and behind the newest generation of those technologies, noted Tech Republic.
“There were genuine AI innovation triggers at Watson Health in natural language processing and generation, knowledge extraction and management, and similarity analytics,” Jeff Cribbs, Research Vice President at Gartner Research, told Tech Republic. “The hype got ahead of the engineering, as the hype cycle says it almost always will, and some of those struggles became apparent.”
Can Artificial Intelligence Fulfill its Potential in Healthcare?
The fact that IBM is contemplating the sale of Watson Health is another illustration of how difficult it can be to navigate the healthcare industry in the US. It is probable that someday AI could make healthcare diagnostics more accurate and reduce overall costs, however, data challenges still exist and more research and exploration will be needed for AI to fulfill its potential.
“Today’s AI systems are great in beating you at chess or Jeopardy,” Kumar Srinivas, Chief Technology Officer, Health Plans, at NTT DATA Services told Forbes. “But there are major challenges when addressing practical clinical issues that need a bit of explanation as to ‘why.’ Doctors aren’t going to defer to AI-decisions or respond clinically to a list of potential cancer cases if it’s generated from a black box.”
And perhaps that is the biggest challenge of all. For doctors to entrust their patients’ lives to a supercomputer, it better be as good as the hype. But can AI in healthcare ever accomplish that feat?
“AI can work incredibly well when it’s applied to specific use cases,” gastroenterologist Nirav R. Shah, MD, Chief Medical Officer at Sharecare, told Forbes. “With regards to cancer, we’re talking about a constellation of thousands of diseases, even if the focus is on one type of cancer. What we call ‘breast cancer,’ for example, can be caused by many different underlying genetic mutations and shouldn’t really be lumped together under one heading. AI can work well when there is uniformity and large data sets around a simple correlation or association. By having many data points around a single question, neural networks can ‘learn.’ With cancer, we’re breaking several of these principles.”
So, in deciding to divest itself of Watson Health, IBM may simply be as prescient now as it was when it first embraced the concept of AI in healthcare. The tech giant may foresee that AI will likely never replace the human mind of a trained healthcare diagnostician.
If this proves true—at least for several more years—then board-certified clinical pathologists can continue to justifiably refer to themselves as “the doctor’s doctor” because of their skills in diagnosing difficult-to-diagnose patients, and because of their knowledge of which clinical laboratory tests to order and how to interpret those test results.
This is one more example of how Silicon Valley companies are lining up collaborations with in vitro diagnostics companies to gain a foothold in the clinical laboratory marketplace
For years, Apple, Google, and other Silicon Valley companies have taken progressive steps to become more engaged in healthcare. One recent example of a Silicon Valley company willing to invest in clinical laboratory testing came last year in the form of a $10 million grant Apple (NASDAQ:AAPL) made to COPAN Diagnostics of Murrieta, Calif., to increase the speed and production of the company’s COVID-19 sample collection and transport products.
The interesting aspect of this collaboration was that Apple’s primary role was to help COPAN:
streamline workflow and speed of throughput,
help with the incoming supply chain, and
help develop outgoing supply chain solutions—along with some capital investment.
From the start of the pandemic in the winter of 2020, SARS-CoV-2 sample collection kits were one of many items that were in short supply here in the United States. To help address those shortfalls, teams at Apple, COPAN, and multiple other companies across the US worked to improve the work processes, automation, and machinery COPAN uses in its manufacturing and production sites. This collaboration increased production by nearly 4,000% between April 2020 and February 2021, an Apple news release reported.
In the news release, Jeff Williams (above), Apple’s Chief Operating Officer, said, “We are proud our Advanced Manufacturing Fund is supporting companies like COPAN who are playing a critical role in the fight against COVID-19 and assisting healthcare professionals and communities across the country. This collaboration helped produce, ship, and deliver millions of sample collection kits to hospitals from coast to coast—and we believe it is this unique combination of American manufacturing and innovation that will help us emerge from this crisis and build a safer world for us all.” (Photo copyright: Apple Insider.)
Healthcare Has Long Been a Target for Big Tech
Investment in different sectors of the US healthcare system by one of the Big Tech companies is not unusual. Apple, Google, Amazon, and Microsoft have looked for ways to expand their respective footholds in the healthcare marketplace for years.
In “How the ‘Big 4’ Tech Companies Are Leading Healthcare Innovation”—published a full year before the COVID-19 pandemic began—Healthcare Weekly noted that, “At a high level, each of the ‘Big 4’ tech companies are leveraging their own core business strengths to reinvent healthcare by developing and collaborating on new tools for patients, care providers, and insurers that will position them for healthcare domination.”
In 2017, Apple announced the launch of the Advanced Manufacturing Fund, saying that the $1 billion fund was a way to give back to communities through job creation. “By doing that, we can be the ripple in the pond. Because if we can create many manufacturing jobs around, those manufacturing jobs create more jobs around them because you have a service industry that builds up around them,” Apple’s CEO Tim Cook told CNBC at that time.
In 2018, Apple boosted the fund from $1 billion to $5 billion, the Mac Observer reported.
Apple’s $10 million investment enabled COPAN Diagnostics to expand into a new facility as well as hire 250 new employees. “We are proud our Advanced Manufacturing Fund is supporting companies like COPAN who are playing a critical role in the fight against COVID-19 and assisting healthcare professionals and communities across the country,” Williams said in the news release.
COPAN and the On-Going Need for COVID-19 Test Kits
COPAN Diagnostics was founded in 1979 in Mantua, Italy, and is now a global force in the manufacture of many sample collection and transport products such as instruments, automation, swabs, pipettes, and, of course, SARS-CoV-2 sample collection and transport kits. At the time of Apple’s investment, COPAN was producing sample collection and transport products at its Murrieta, Calif., facility. But demand for these products far outweighed the supply.
In an interview, Norman Sharples, CEO of COPAN Diagnostics and head of operations for North and South America, said he was hoping to increase production in the earliest days of the pandemic when Jeff Williams, COO at Apple, contacted him regarding the Advanced Manufacturing Fund. Along with the $10 million grant, Williams offered experts in engineering and sourcing to help COPAN increase production, the San Diego Union-Tribune reported.
The result was a new manufacturing facility in Carlsbad, Calif., which increased COPAN’S production of its sample collection and transport products used in SARS-CoV-2 testing by nearly 4,000%.
“From taking the keys to the building to actually getting the California department for public inspection, which allows us to go live and sell the product, that was just over 30 days, which is an incredible campaign that Apple helped us with,” Sharples told the San Diego Union-Tribune, adding, “It wasn’t just the funding. It was [the experts from Apple] applying their know-how and expertise to tilt this up very fast.”
Even as COVID-19 vaccines roll out, demand for SARS-CoV-2 tests—along with the necessary specimen collection and transport supplies—will likely continue. As the economy reopens, workers return to offices, and students return to in-person schools, precautionary screening for COVID-19 will remain necessary. “I think demand is going to flatten a little bit, but in any case, the baseline is going to be high because of surveillance,” Sharples said. “The back-to-work programs will drive more surveillance.”
Pandemic Increases Big Tech’s Dominance in Healthcare
Where many businesses and entire industries struggled with the pandemic, Big Tech apparently did not. In late October 2020, CBS News reported, “America’s largest technology companies are thriving despite the economy’s woes, according to earnings posted by Google-parent Alphabet, Amazon, Apple, Facebook, and Twitter on Thursday.”
Along with growing profits, Big Tech companies also consolidated their dominance. “As the pandemic made us even more dependent on digital technology, it has made the systemic importance and enormous power of the tech giants even more apparent,” according to an article in SciencesPo, titled, “Is the COVID-19 Pandemic a Victory for Big Tech?”
Might Big Tech Investments Target Clinical Laboratory Testing?
There’s no reason to believe that the big technology companies will slow their investment in healthcare anytime soon, and that investment may benefit clinical laboratories. In fact, in “11 Recent Big Tech Partnerships in Healthcare,” Becker’s Hospital Review listed several technology companies that will likely affect pathology laboratories.
Big Tech investment in genetic testing, artificial intelligence, telehealth, and other technologies may alter how clinical laboratories operate and revolutionize the healthcare industry.
By training a computer to analyze blood samples, and then automating the expert assessment process, the AI processed months’ worth of blood samples in a single day
New technologies and techniques for acquiring and transporting biological samples for clinical laboratory testing receive much attention. But what of the quality of the samples themselves? Blood products are expensive, as hospital medical laboratories that manage blood banks know all too well. Thus, any improvement to how labs store blood products and confidently determine their viability for transfusion is useful.
One such improvement is coming out of Canada. Researchers at the University of Alberta (U of A) in collaboration with scientists and academic institutions in five countries are looking into ways artificial intelligence (AI) and deep learning can be used to efficiently and quickly analyze red blood cells (RBCs). The results of the study may alter the way donated blood is evaluated and selected for transfusion to patients, according to an article in Folio, a U of A publication, titled, “AI Could Lead to Faster, Better Analysis of Donated Blood, Study Shows.”
Improving Blood Diagnostics through Precision Medicine and Deep Learning
“This project is an excellent example of how we are using our world-class expertise in precision health to contribute to the interdisciplinary work required to make fundamental changes in blood diagnostics,” said Jason Acker, PhD, a senior scientist at Canadian Blood Services’ Centre for Innovation, Professor of Laboratory Medicine and Pathology at the University of Alberta, and one of the lead authors of the study, in the Folio article.
The research took more than three years to complete and involved 19 experts from 12 academic institutions and blood collection facilities located in Canada, Germany, Switzerland, the United Kingdom, and the US.
“Our study shows that artificial intelligence gives us better information about the red blood cell morphology, which is the study of how these cells are shaped, much faster than human experts,” said Jason Acker, PhD (above), Senior Research Scientist, Canadian Blood Services, and Professor of Laboratory Medicine and Pathology at the University of Alberta, in an article published on the Canadian Blood Services website. “We anticipate this technology will improve diagnostics for clinicians as well as quality assurance for blood operators such as Canadian Blood Services in the coming years,” he added. Clinical laboratories in the US may also benefit from this new blood viability process. (Photo copyright: University of Alberta.)
To perform the study, the scientists first collected and manually categorized 52,000 red blood cell images. Those images were then used to train an algorithm that mimics the way a human mind works. The computer system was next tasked with analyzing the shape of RBCs for quality purposes.
Removing Human Bias from RBC Classification
“I was happy to collaborate with a group of people with diverse backgrounds and expertise,” said Tracey Turner, a senior research assistant in Acker’s laboratory and one of the authors of the study, in a Canadian Blood Services (CBS) article. “Annotating and reviewing over 52,000 images took a long time, however, it allowed me to see firsthand how much bias there is in manual classification of cell shape by humans and the benefit machine classification could bring.”
According to the CBS article, a red blood cell lasts about 115 days in the human body and the shape of the RBC reveals its age. Newer, healthier RBCs are shaped like discs with smooth edges. As they age, those edges become jagged and the cell eventually transforms into a sphere and loses the ability to perform its duty of transporting oxygen throughout the body.
Blood donations are processed, packed, and stored for later use. Once outside the body, the RBCs begin to change their shape and deteriorate. RBCs can only be stored for a maximum of 42 days before they lose the ability to function properly when transfused into a patient.
Scientists routinely examine the shape of RBCs to assess the quality of the cell units for transfusion to patients and, in some cases, diagnose and assess individuals with certain disorders and diseases. Typically, microscope examinations of red blood cells are performed by experts in medical laboratories to determine the quality of the stored blood. The RBCs are classified by shape and then assigned a morphology index score. This can be a complex, time-consuming, and laborious process.
“One of the amazing things about machine learning is that it allows us to see relationships we wouldn’t otherwise be able to see,” Acker said. “We categorize the cells into the buckets we’ve identified, but when we categorize, we take away information.”
Human analysis, apparently, is subjective and different professionals can arrive at different results after examining the same blood samples.
“Machines are naive of bias, and AI reveals some characteristics we wouldn’t have identified and is able to place red blood cells on a more nuanced spectrum of change in shape,” Acker explained.
The researchers discovered that the AI could accurately analyze and categorize the quality of the red blood cells. This ability to perform RBC morphology assessment could have critical implications for transfusion medicine.
“The computer actually did a better job than we could, and it was able to pick up subtle differences in a way that we can’t as humans,” Acker said.
“It’s not surprising that the red cells don’t just go from one shape to another. This computer showed that there’s actually a gradual progression of shape in samples from blood products, and it’s able to better classify these changes,” he added. “It radically changes the speed at which we can make these assessments of blood product quality.”
More Precision Matching Blood Donors to Recipients
According to the World Health Organization (WHO), approximately 118.5 million blood donations are collected globally each year. There is a considerable contrast in the level of access to blood products between high- and low-income nations, which makes accurate assessment of stored blood even more critical. About 40% of all blood donations are collected in high-income countries that home to only about 16% of the world’s population.
More studies and clinical trials will be necessary to determine if U of A’s approach to using AI to assess the quality of RBCs can safely transfer to clinical use. But these early results promise much in future precision medicine treatments.
“What this research is leading us to is the fact that we have the ability to be much more precise in how we match blood donors and recipients based on specific characteristics of blood cells,” Acker stated. “Through this study we have developed machine learning tools that are going to help inform how this change in clinical practice evolves.”
The AI tools being developed at the U of A could ultimately benefit patients as well as blood collection centers, and at hospitals where clinical laboratories typically manage the blood banking services, by making the process of matching transfusion recipients to donors more precise and ultimately safer.
The AI protein-structure-prediction system may ‘revolutionize life sciences by enabling researchers to better understand disease,’ researchers say
Genomics leaders watched with enthusiasm as artificial intelligence (AI) accelerated discoveries that led to new clinical laboratory diagnostic tests and advanced the evolution of personalized medicine. Now Google’s London-based DeepMind has taken that a quantum step further by demonstrating its AI can predict the shape of proteins to within the width of one atom and model three-dimensional (3D) structures of proteins that scientist have been trying to map accurately for 50 years.
Pathologists and clinical laboratory professionals know that it is estimated that there are around 30,000 human genes. But the human proteome has a much larger number of unique proteins. The total number is still uncertain because scientists continue to identify new human proteins. For this reason, more knowledge of the human protein is expected to trigger an expanding number of new assays that can be used by medical laboratories for diagnostic, therapeutic, and patient-monitoring purposes.
DeepMind’s AI tool is called AlphaFold and the protein-structure-prediction system will enable scientists to quickly move from knowing a protein’s DNA sequence to determining its 3D shape without time-consuming experimentation. It “is expected to accelerate research into a host of illnesses, including COVID-19,” BBC News reported.
This protein-folding breakthrough not only answers one of biology’s biggest mysteries, but also has the potential to revolutionize life sciences by enabling researchers to better understand disease processes and design personalized therapies that target specific proteins.
In November, DeepMind’s AlphaFold won the 14th Community Wide Experiment on Critical Assessment of Techniques for Protein Structure Prediction (CASP14), a biennial competition in which entrants receive amino acid sequences for about 100 proteins whose 3D structures are unknown. By comparing the computational predictions with the lab results, each CASP14 competitor received a global distance test (GDT) score. Scores above 90 out of 100 are considered equal to experimental methods. AlphaFold produced models for about two-thirds of the CASP14 target proteins with GDT scores above 90, a CASP14 press release states.
According to MIT Technology Review, DeepMind’s discovery is significant. That’s because its speed at predicting the structure of proteins is unprecedented and it matched the accuracy of several techniques used in clinical laboratories, including:
Unlike the laboratory techniques, which, MIT noted, are “expensive and slow” and “can take hundreds of thousands of dollars and years of trial and error for each protein,” AlphaFold can predict a protein’s shape in a few days.
“AlphaFold is a once in a generation advance, predicting protein structures with incredible speed and precision,” Arthur D. Levinson, PhD, Founder and CEO of Calico Life Sciences, said in a DeepMind blogpost. “This leap forward demonstrates how computational methods are poised to transform research in biology and hold much promise for accelerating the drug discovery process.”
Science reported that AlphaFold, which scored a median of 87—25 points above the next best predictions—did so well that CASP14 organizers worried DeepMind may have been somehow cheated. To validate the results, they asked AlphaFold to complete a “special challenge”—modeling a membrane protein from an ancient species of microbes called archaea, which they had been unable to model satisfactorily using X-ray crystallography. AlphaFold returned a detailed image of a three-part protein with two long helical arms in the middle. “It’s almost perfect,” Andrei Lupas, PhD, Director at the Max Planck Institute for Developmental Biology, told Science. “They could not possibly have cheated on this. I don’t know how they do it.” (Graphic copyright: DeepMind/Nature.)
“Even tiny rearrangements of these vital molecules can have catastrophic effects on our health, so one of the most efficient ways to understand disease and find new treatments is to study the proteins involved,” Moult said in the CASP14 press release. “There are tens of thousands of human proteins and many billions in other species, including bacteria and viruses, but working out the shape of just one requires expensive equipment and can take years.”
Science reported that the 3D structures of only 170,000 proteins have been solved, leaving roughly 200 million proteins that have yet to be modeled. Therefore, AlphaFold will help researchers in the fields of genomics, microbiomics, proteomics, and other omics understand the structure of protein complexes.
“Being able to investigate the shape of proteins quickly and accurately has the potential to revolutionize life sciences,” Andriy Kryshtafovych, PhD, Project Scientist at University of California, Davis, Genome Center, said in the press release. “Now that the problem has been largely solved for single proteins, the way is open for development of new methods for determining the shape of protein complexes—collections of proteins that work together to form much of the machinery of life, and for other applications.”
Clinical laboratories play a major role in the study of human biology. This breakthrough in genomics research and new insights into proteomics may provide opportunities for medical labs to develop new diagnostic tools and assays that better identify proteins of interest for diagnostic and therapeutic purposes.
Patients in health systems that use the Cerner EHR can now track and share specific health metrics with their healthcare providers
In what may be first steps toward becoming a full-service digital healthcare platform, Health information technology (HIT) developer Cerner (NASDAQ:CERN) is partnering with Amazon (NASDAQ:AMZN) to bring cloud-based health tracking services to its EHR customers. People who use Amazon’s Halo service—which includes a wristband device and smartphone app to monitor specific health metrics—can now import that data directly into Cerner electronic health record (EHR) systems for sharing with healthcare providers.
This may turn out to be a pioneering effort by one of the nation’s major providers of EHR systems to pull in useful health data from a variety of non-traditional sources and incorporate them into a patient’s electronic health record. Cerner has a major market share of EHR systems (exceeded only by Epic) and has a laboratory information system (LIS) that is used by many clinical laboratories.
For this fact alone, strategic planners at medical laboratories and anatomic pathology groups should follow this development. That is particularly true of those labs operated by hospitals and health systems that decide to add this new feature to their existing Cerner EHR. If data is flowing into the EHR from patients’ Amazon Halo service, for example, it is not a big leap to imagine that clinical lab test data from the patients’ EHRs might later flow back to the Halo service where it would be instantly accessible to those patients.
This collaboration, according to a Cerner press release, “allows consumers to easily connect vital health and well-being information with their broader healthcare teams. … Historically this type of data has been siloed or difficult to obtain. Wearable technology, such as the Amazon Halo, can help achieve greater interoperability across healthcare when integrated directly into a patient’s electronic health record (EHR).”
Cerner’s integration of the Amazon Halo Band and smartphone app (above) into its electronic health record (EHR) system allows users to share collected healthcare metrics with doctors in health systems that use the Cerner EHR. How long will it be before clinical laboratories that use Cerner’s laboratory information systems (LIS) will be able to incorporate similar metrics into their LIS as well? (Photo copyright: Amazon.)
Using Artificial Intelligence to Empower Healthcare Consumers
The Halo wristband, along with its accompanying smartphone app, “combines a suite of AI-powered health features that provide actionable insights into overall wellness … [and] uses multiple advanced sensors to provide the highly accurate information necessary to power Halo,” an Amazon press release states.
Data collected by Amazon Halo that are now importable into Cerner EHRs, according to the press release, include:
Activity: Informed by American Heart Association physical activity guidelines and the latest medical research, Amazon Halo awards points based on the intensity and duration of movement, not just the number of steps taken.
Sleep: Amazon Halo uses motion, heart rate, and temperature to measure time asleep and time awake; time spent in the various phases of sleep including deep, light, and REM; and skin temperature while sleeping.
Body: Amazon Halo lets customers measure their body fat percentage from the comfort and privacy of their own home, making this important information easily accessible.
Tone: This feature uses machine learning to analyze energy and positivity in a customer’s voice so they can better understand how they may sound to others, helping improve their communication and relationships.
Labs: Amazon Halo Labs are science-backed challenges, experiments, and workouts that allow customers to discover what works best for them specifically, so they can build healthier habits.
Leveraging Patient Generated Health Data
In the Cerner press release, David Bradshaw, Senior Vice President of Consumer and Employer Solutions at Cerner, said, “The healthcare industry is undergoing a digital revolution, where physicians are increasingly looking to leverage patient-generated health data to help keep them healthier and out of the doctor’s office.
“Our work with Amazon Halo,” he continued, “highlights the importance of using artificial intelligence and other leading-edge technologies to accelerate healthcare innovation and improve health outcomes. Cerner is focused on continuing to lead a wave of breakthrough innovation, and this integration with Amazon Halo is a step toward this goal.”
The first healthcare provider to offer the Amazon Halo service to its Cerner EHR users is Sharp HealthCare of San Diego. Some Sharp Health Plan members will participate in wellness programs and eventually have the option to link their Sharp and Halo data directly into the healthcare system’s Cerner EHR.
Sharp HealthCare includes 2,600 physicians, four acute care facilities, and three specialty hospitals.
“Technology is revolutionizing the way we care for patients and how consumers care for themselves, and at Sharp we strive to embrace innovative ways to leverage leading technology to engage consumers in managing their health,” said Michael Reagin, SVP and Chief Information and Innovation Officer at Sharp HealthCare, in the Cerner press release.
“With more relevant information at their fingertips, our populations will be empowered to make more informed decisions about the health and well-being of themselves and the communities they serve,” he added. “We are pleased to work with Cerner and Amazon Halo to offer our members, patients, and clinicians an opportunity to have a more connected health record.”
Cerner Expanding to Include Population Health and Precision Medicine
Cerner may be evolving toward a cloud-based platform that pulls in data from hospital and doctors’ office EHRs—as well as data gather by wearable devices—and uses that information for population health and precision medicine analysis to guide healthcare providers.
Last year, Cerner announced a collaboration with the Amazon Web Services (AWS) cloud platform, reportedly in an effort to pivot beyond its traditional health records business.
“Moving forward, I think Cerner will look more like a health platform company and less like an EHR company,” Dan Devers, SVP, Cloud Strategy, and Chief IP Officer at Cerner, told Fierce Healthcare. “As you play out the trend in healthcare, I see Cerner very much operating at the health network level—so beyond the enterprise of a single health system. Given the power of the cloud and the work we’re doing, I see Cerner having much more relevance into broader networks and providing nationwide capabilities.”
Cerner is aiming to provide consumers with more power regarding their own healthcare by equipping them with easy, fast, and efficient methods to access their personal information and provide healthcare professionals with useful data about individual patients.
Given the value and importance of clinical laboratory data, innovative lab managers should strive to be aware of collaborations like the one between Cerner and Amazon Halo. Remaining alert for opportunities to participate in these types of arrangements could provide labs with added revenue streams and inventive ways to offer customers value-added services.
