Over the course of the last 18 months artificial intelligence (AI) has matured to the point where there are several viable vendor options for nearly every use case.
AI dominated every aspect of the annual gathering of the Radiological Society of North America (RSNA18) in Chicago. Self-described ‘machine learning’ vendors with a presence on the conference floor more than doubled from 49 in 2017 to over 100 in 2018, 25 of which were first-time presenters.
I moderated a panel hosted by Life Image on practical uses cases of imaging AI and was blown away by the conversation that ensued, particularly what I learned about how the veteran radiologists feel about being “replaced.” During the question period, a senior radiologist approached the microphone to address a comment made by a more junior radiologist on the panel which he interpreted to be too pessimistic about the potential for AI. To paraphrase the elder, “Listen here sonny, you are too young to fully appreciate what you don’t know, and you don’t know how many mistakes you are truly making on a day to day basis. 1-2 percent error rate due to fatigue alone. WE NEED AI to save us from ourselves.”
Not all old school radiologists are so optimistic: “When you’re going up the ride, you get excited,” noted University of Chicago radiologist Paul Chang said during his workshop on AI. “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… It is worth the rollercoaster of hype. But I’m here to tell you that it’s going to take longer than you think.”
Last year, the major cloud vendors each had a significant footprint at RSNA, but this year the two largest, Amazon and Microsoft, were nowhere to be found. Only Google Cloud had a significant, if smaller than last year’s, presence. Donny Cheung, one of the Google Cloud team leaders, was on the panel I moderated and his message to the imaging community could be boiled down to two words: storage and compute. No dashboards or toolkits or tensorflowing, just storage and compute, a smart and refreshing strategy amidst the obvious feature creep many other vendors suffer from.
Over the course of the last 18 months artificial intelligence has matured to the point where there are several viable vendor options for nearly every use case.
While it was surprising that Amazon had no noticeable presence, it was even more surprising to find Facebook making news on the conference floor. Facebook AI Research (FAIR) has partnered with the Center for Advanced Imaging Innovation and Research (CAI2R) in the Department of Radiology at NYU School of Medicine and NYU Langone Health to release the fastMRI, an open source dataset for training and testing machine learning algorithms to reconstruct MRI images.
This offering is roughly equivalent to similar X-Ray and CT datasets released by NIH. Given that algorithms ALWAYS significantly outperform on all metrics against the data used to train them versus new data, the industry needs independent validation of AI claims so it is unlikely that Facebook moves the needle with this offering.
PACS vendors want to get in on the AI action by positioning their existing products as AI marketplaces or platforms (Philips HealthSuite Insights, PureWeb, LifeImage, GE Edison, FujiFilm REiLI, Nuance AI Marketplace, Blackford Analysis). Nuance has shown there is a viable market for these platforms, counting 40 startups and health systems among user groups for its marketplace. There is no shortage of startups taking this approach (MDW, Envoy.ai, Medimsight, Lify, Fovia). Imaging hardware vendors refused to be left out too, with many partnering with AI vendors to embed their algorithms on the “edge.”
International AI startups, particularly from Israel, China, and South Korea, stood out from the crowd in terms of their approach to product design, but only the companies from Israel have been able to break into the US market so far. One Korean company voiced frustration with the FDA, saying it couldn’t understand what was wrong with their application. I wonder if it underestimates the importance of using data from US patients to validate their algorithms?
Not everything we learned about AI at RSNA was positive. A paper presented at the conference showed that neural networks could be used to insert malignant features into mammograms giving a false positives, and then reverse the alterations without detection. Even scarier, it took about 680 images to train the algorithm that executed the adversarial attack. Cyberattacks have been increasing in healthcare over the last couple years, but mostly just for taking data hostage and demanding ransom to get it unencrypted. This type of attack would represent a frightening new paradigm in cyber-vulnerability, and it is certainly not difficult to imagine ways this could be exploited to make money. It could be used for a different sort of ransom, with every image appearing to show cancer until a ransom is paid and the adversarial attack is reversed. Another conceivable way this type of attack could be exploited would be falsifying data for clinical trials.
Matt Guldin · 2 years ago
Chilmark Team · 1 month ago
Chilmark Team · 2 months ago
Brian Edwards · 2 weeks ago
Signaling the Tipping Point of AI Value in Healthcare
Top minds across a broad spectrum of healthcare disciplines converged to discuss artificial intelligence in Boston April 23-25 at the 2018 World Medical Innovation Forum (WMIF), which drew 1700 registrants and 169 speakers. Researchers, clinicians and industry leadership paraded on stage for a collection of panels and “1:1 fireside chats” to discuss healthcare applications of AI and related technologies, their progress, roadblocks and the promise of future value. The majority of provider participants were from Harvard teaching hospitals as Partners Healthcare runs the conference.
The healthcare innovation community is always looking for applications to illustrate the potential of AI, but also to grasp a tangible that is closer to the now rather than solely as a futuristic vision. At this point, there are many diverse applications for AI and related technologies, as highlighted in the framing of the conference, which opened with a discussion of 19 “AI breakthroughs” and closed with the “disruptive dozen” (scroll to the end of the post for listing). It was encouraging that these innovative pursuits were not limited to any small facet of care or research, but instead cover a broad spectrum of healthcare challenges.
Across the rest of the conference were overlapping discussions of the impediments and implications of advances in AI and the many applications. These AI-centric discussions can be organized into 5 themes that broadly mirror key concerns of the healthcare industry as a whole, indicative of both the hype and potential for these technologies to fundamentally change our methods for care delivery:
Unsurprisingly, the importance of good sources of high-quality data was a core theme across all panels. In some cases, the data already exists but has not yet been analyzed. Drug manufacturers sitting on mountains of clinical trial data being a prime example. New data sources like that those coming from companies like Flatiron – recently acquired by Roche – provide a fresh resource for research. All of this data, however, exacerbates challenges of large data analysis. In order to run AI and machine learning systems, the data needs to be clean. Surveys have indicated that data scientists spend 60% of their time cleaning data. The challenge before everyone includes sourcing data (as clean as possible), cleaning and making data usable for tools.
In many cases, the limiting reagent to progress is our understanding of biology – as much as or even more so than the technology. Technology, in fact, is the means to understand the biology further. Life sciences companies like Novartis, Pfizer and others are actively using, in their words, “higher order data,” including ‘omic data sources. In doing so, they hope to reach breakthroughs faster by redefining how they consider new drug discovery.
William Lane, MD, PhD delivered one of the 19 breakthroughs that opened the conference, bloodTyper, which offers a new outlook for the long-standing method of categorizing patient blood types based on the presence or absence of 2 antibodies and 2 antigens. bloodTyper uses DNA-based categorization as opposed to serologic testing alone. Whole genome sequencing costs have declined dramatically in recent years and are expected to continue to decline making this solution viable for widespread clinical adoption. Technology enables this effort, but much research needs to be done in order to illustrate specific value and bring these new methods into practice. With continued advancement of technological capability, understanding the biology of health and/or disease will continue to be a primary obstacle.
One of the consistent themes across panel discussions was the notion that medical technology, AI in particular, is futuristic, while our care system is stone-aged. The challenge of adoption and change to the care paradigm is not a limitation of technology. Panelists repeatedly remarked about how the pairing of “Jetsons technology and a Flintstones care system” would take substantial time to evolve, because change requires evidence and trust that are not ascertained lightly.
In some cases, however, evidence-based change is already upon us. A group at MGH, represented at WMIF by Erica Shenoy, MD, PhD, is using Machine Learning to more quickly identify cases of hospital-acquired infection, identifying C Diff cases full days before the currently accepted 5-day standard process. We expect to see substantial growth in research publication volume illustrating the value of AI and Machine Learning technologies in areas like this.
On the other side of the curtain, AI is changing the way that industry operates. Drug development, in particular, is undergoing a revolution of sorts. Companies like Exscientia, with diverse data science capabilities, provide life sciences partners with a way to look across different data types including ‘omic data, research text and other sources. This allows manufacturers to potentially repurpose molecules and sub-organize disease for more precise targeting.
Not all areas of healthcare are ready environments, but the ubiquity of efforts to utilize AI technologies to accelerate processes, improve accuracy, increase access, increase bandwidth and offer precise care points to a tipping point.
Another recurring theme was the impact of AI on the workforce. As in other healthcare sub-verticals, there is a large and increasing demand for data experts. Radiologists and pathologists across the conference echoed the surprisingly optimistic resolutions of a panel dedicated to this topic both with respect to the increased demand for data science professionals in all organizations as well as the potential obviation of some roles.
The future will likely position these data scientists alongside healthcare professionals as part of multidisciplinary/cross-functional teams in care and non-care settings alike. There is also an expectation that data science literacy, at least at a high level, will become a core component of education for many healthcare professionals, not just IT specialists.
The fear of the obviation of certain roles was interestingly framed. At a very high level, the introduction of AI and related technologies will exacerbate the division between the highly educated and the less educated, as Glenn Cohen of Harvard Law School eloquently pointed out. It was also highlighted, however, that a lot of the lower skilled manual roles have already been dramatically reduced with the introduction of EMR systems. The natural targets often discussed beyond lesser skilled workforce are experts in clinical disciplines of radiology and pathology. In both contexts, AI technologies have been shown to approximate or even beat the accuracy of some clinical practice by human experts. There is also a global shortage of these types of experts. The prevailing opinion expressed was that new technologies will be used to make these professionals more efficient and effective, and it is unlikely that technology will be used to truly replace the human element.
The burden of AI technologies on regulators is substantial. They need to be able to evaluate, audit and assure quality of these new technical capabilities. Linguamatics is one example of the many companies that are making progress in crafting technology solutions for healthcare while also targeting the FDA as a customer. Providing a mechanism for auditability of otherwise “black box” AI systems is a great benefit to the regulators.
Standardization is a key to enable scalability and support industry-wide progress. Consider DICOM, for example, which offers a standard for many imaging modalities. The expectation has been set that at least some standardization must be a focus of innovators so as not to run into an even greater challenge of interoperability with more complex technology. Building gold standard training data sets was a solution highlighted that begins to address this challenge with AI.
Precision medicine was an end target for many of the applications and topics of discussion at the conference. The development of a clearer, richer phenotype and genotype (or other “’omic” and new forms of data) essentially yields the potential for a digital twin for each individual patient. To accompany this, the development of precision drugs, diagnostic capabilities and other therapies is the end promise of many of the applications of AI, machine learning and other technologies.
We will only be able to deliver on these promises if our data, technology and systems for the delivery of care are able to adapt. To get there though, we need to establish trust and confidence across the healthcare ecosystem and for patients with AI and its elevated role.
Currently, there are little islands of the right components to make great progress and work is well underway. In oncology, for example, there is data access, a willingness and need from physicians and patients, a motivated industry (opportunity for profit or strategic position), research capacity and funding, payment and regulatory feasibility. At WMIF, Atul Gawande described these environments as “ready environments,” in that they are capable and motivated. The historical example he gave to juxtapose “ready” and “not ready” was the spread of anesthetized surgery over weeks or months, vs. antisepsis, which he estimated to take 20 years to spread.
Not all areas of healthcare are ready environments, but the ubiquity of efforts to utilize AI technologies to increase accelerate processes, improve accuracy, increase access, increase bandwidth and offer precise care points to a tipping point. Within the next year, I expect to see a thicker volume of applications and more importantly more examples of impact.
Predictions in Healthcare: Unpacking the Complications
Interest in predictive analytics in healthcare is on the rise. How long will someone be in the hospital? What are their chances of being readmitted? Which treatment will work the best?
On the surface, these seem like straightforward questions, but it turns out that prediction is not a simple topic. Questions about what data and effort goes into the prediction, how accurate it is, who gets to see it, and how actionable it is can all make a huge difference in its value.
As we start to develop our major Market Trends Report for Advanced Analytics, scheduled for release later this year, I thought I would unpack these questions a bit.
To start, it makes sense that the more data and the better data you can include in making a prediction, the more accurate it will be. However, you don’t always know what factors will matter the most, or at all.
If you over-collect data to ensure you factor in most everything, you unnecessarily raise the cost, time, and complexity, as data warehouses are not inexpensive and interoperability is still a struggle. If you under-collect, you might miss something important, and clinicians will continue to complain about the burden of documentation.
The recent focus on social determinants of health is promising, but it requires much more data collection and management, along with more sophisticated analytic models. Here, we welcome the contributions of AI and Machine Learning (ML) to help determine which variables to focus on.
Prediction is not a simple topic. Questions about what data and effort goes into the prediction, how accurate it is, who gets to see it, and how actionable it is can all make a huge difference in its value.
Accuracy has all kinds of complications. You’ve likely come across the difference between precision and recall.
With precision, you measure how right you were that something was going to happen when it did, in relation to how often you thought it was but didn’t. With recall, you measure how often you thought something was going to happen, and you were right, but you missed some that did happen. In the first situation, false alarms could be costly, leading to additional tests and much worry. In the second situation, missing an important prediction (such as a cancer diagnosis) could also be costly.
Fortune tellers make many predictions and eventually get some stuff right, but most of us don’t take them seriously; Chicken Little got into trouble when he cried out too many times. Conversely, someone who makes too many conservative predictions might have a lot of credibility but also miss too many opportunities.
Consider who wants to know or gets to see the predictions. Most clinicians, healthcare workers, and leadership teams want do the best they can, but negative predictions about their approaches or performance may not be met with welcome arms. Exposing their problems can lead to potential unwanted oversight by others, reduced funding, etc.
In other words, expect pushback about the quality of the data, the accuracy of the prediction, the motivations of the prediction team, and who is really responsible for the bad trends. Think about how predictions are supported and presented. Also consider the flip side: Positive predictions, if not accurate, may lull an organization into a false sense of security.
This leads to one of the most important questions: How actionable is the prediction, and what can be done about it? (This also brings prescriptive analytics into the discussion.) If the answer is “not much,” there may still be value in knowing it – but you have to brace for impact, as it could also lead to helplessness and negativity; consider the effect of telling a patient they have an incurable disease.
Better in most circumstances to focus on those predictions where positive action can be taken. Some actions might be obvious: You’re running out of medication x, so you’d better reorder now. Others might be more complex: What will be the effect of changing our portfolio mix?
Complexity of action can arise when dealing with interdependent systems, multiple parties, and lots of variables. (Welcome to healthcare!) Like the old game Whack-a-Mole, addressing one trend may lead to unanticipated or unwanted changes in others. Image how well you could play if you knew what was going on under the table. To properly act on predictions, you have to employ systems thinking.
As our data sources grow, our ability to analyze them using AI and ML advances. Given regulatory and market pressure to provide higher-quality care while reducing care costs, healthcare’s need to manage to value also continues to increase.
In the coming years, we’ll see a lot more use of predictive analytics in healthcare, and a lot more tools and vendors to support it. As we analyze these predictive tools, we will focus on how healthcare will employ them to deal with the complexities of data selection, model accuracy, user perception, and actionability.
18 Chilmark-Recommended Sessions for HIMSS’18
For those of you who are overwhelmed by looking through the HIMSS18 session schedule (and those of you who have been meaning to look but haven’t gotten to it yet), here’s Chilmark Research’s short list of sessions we expect to be worthwhile, as chosen by our analyst team. It is by no means an exhaustive list, but will hopefully steer our readers to a few quality sessions they may have otherwise overlooked or missed with how many options there are at any given time. We will likely have at least one team member at most of these sessions, so if something piques your interest, feel free to reach out to coordinate meeting at the session to discuss the topic further (email analysts directly or firstname.lastname@example.org for meetings).
We’d love to hear feedback on what sessions you’re excited for – feel free to leave additional suggestions in the comments.
Wednesday, March 7, 1:00pm-2:00pm; Venetian, Palazzo D
CME Credits: AAHAM 1.00; ABPM 1.00; ACPE 1.00; CAHIMS 1.00; CME 1.00; CNE 1.00; CPHIMS 1.00; PDU 1.00
Why Attend: The application of analytics to time purchasing of medical supplies – in this case medication – is something we typically don’t cover in our own research, but is a growing consideration in the move to VBC. This session will be of interest to healthcare system CFOs and COOs that are looking to find new ways to streamline business operations and identify opportunities to reduce their unit costs of care delivery.
Wednesday, March 7, 4:00-5:00pm; Venetian, Murano 3304
CME Credits: ABPM 1.00; ACHE 1.00; CAHIMS 1.00; CME 1.00; CNE 1.00; CPHIMS 1.00
Why attend: AI will have some of its near-term and largest impacts in radiology (lines at RSNA last year were around the block). This session will show how AI and analytics are bringing together clinicians, technologists, and data scientists to go beyond what any could do alone.
Wednesday, March 7, 11:30 to 12:30 a.m., Venetian, Murano 3304
CME Credits: ABPM 1.00; ACHE 1.00; CAHIMS 1.00; CME 1.00; CNE 1.00; CPHIMS 1.00
Why attend: Individualized care plans are increasingly becoming a requirement for a number of state and federal value-based reform programs. This session will provide an overview of that as well as looking at how these various programs are looking at creating a longitudinal care plan across multiple settings of care.
Thursday, March 8, 11:30 to 12:30 a.m., Venetian, Murano 3301
CME Credits: ACHE 1.00; ACPE 1.00; CAHIMS 1.00; CME 1.00; CNE 1.00; CPHIMS 1.00
Why attend: The shift to home-based care is going to be an important part of value-based care especially since CMS’ decision to reimburse providers for remote patient monitoring as of January 1st. This session presents two Davies award winners and how they used home-based IT including remote patient-monitoring to improve the quality of care while reducing costs.
Wednesday, March 7, 8:30 to 9:30 a.m.; Venetian, Palazzo G
CME Credits: CAHIMS 1.00; CME 1.00; CNE 1.00; CPHIMS 1.00
Why attend: Healthcare organizations cannot identify actionable strategies and tactics for improving patient engagement without hearing from patients firsthand about their experiences within the system – both good and bad. In this session, two patients will discuss their personal experiences and also share tips to help HCOs raise patients’ voices and even compensate them for sharing their time and expertise.
Thursday, March 8, 8:30 to 9:30 a.m., Sands Show Room
CME Credits: ABPM 1.00; ACHE 1.00; CAHIMS 1.00; CME 1.00; CNE 1.00; CPHIMS 1.00; PDU 1.00
Speaker: Iris Berman ,Vice President, Telehealth Services, Northwell Health
Why attend: Effective telehealth implementation requires significant strategic planning; otherwise, solutions are deployed on an ad hoc basis, different business units adopt different strategies and tactics, and scaling a telehealth program becomes increasingly difficult. This session covers the ins and outs of planning for a telehealth program — and then scaling it across a network of hospitals.
Tuesday, March 7, 4:00pm – 5:00pm; Venetian, Murano 3301
CME Credits: ABPM 1.00; ACHE 1.00; CAHIMS 1.00; CME 1.00; CNE 1.00; CPHIMS 1.00
Why attend: IHIE is probably the foremost exchange organization in the U.S. and understands how efforts like CommonWell and Carequality can help move the needle on exchange.
Thursday, March 8, 4:00pm – 5:00pm; Venetian, Murano 3301
CME Credits: ABPM 1.00; ACHE 1.00; CAHIMS 1.00; CME 1.00; CNE 1.00; CPHIMS 1.00
Speaker: David Hay, Product Strategist, Orion Health
Why attend: We expect this will provide a good overview of how FHIR will simplify the mechanics of data exchange and reduce the learning curve for developers.
Tuesday, March 6, 4:00-5:00pm; Venetian, Lando 4204
CME Credits: ABPM 1.00; ACHE 1.00; CAHIMS 1.00; CME 1.00; CNE 1.00; CPHIMS 1.00
Why attend: Caravan Health has an unique model for establishing ACOs, as discussed in last March’s Vendors Enabling the ACO Report. Learning from their experiences working with smaller, rural providers to establish cross-state MSSP ACOs will highlight some of the unique problems facing more loosely organized ACOs (opposed to IDN-based ACOs). Furthermore, MIPS is generally pretty confusing to a lot of people in the industry, and this session should help clarify some of that confusion to give ideas on how to adjust strategy for success.
Wednesday, March 7, 4:30pm – 5:30pm; Sands Hall G, Booth 11955ET
Why attend: What happens post-discharge has broad implications for how well providers can do in risk sharing contracts. Hopefully, this will provide some ideas on how these under-technologied providers can participate more fully.
Tuesday March 6, 1:00-2:00pm; Venetian, Galileo 901
CME Credits: AAHAM 1.00; ABPM 1.00; ACHE 1.00; AHIMA 1.00; CAHIMS 1.00; CME 1.00; CNE 1.00; CPHIMS 1.00; PDU 1.00
Why attend: Closing care gaps has been on HCO leaders’ minds for a few years now, but there are still plenty of headaches when it comes to activating physicians to use tools at the point of care. Hear from Boston-based ACO Steward Health what they are doing to solve this problem – the part of the description that hooked us? The use of these data to “ensure appropriate reimbursement to fund [VBC] programs.”
Wednesday, March 7, 11:30am – 12:30pm; Sands Hall G, Booth 11955ET
Why attend: Getting payers and providers to see eye-to-eye on information sharing is not easy. DaVinci is trying to reduce the need for participants to reinvent wheels.
Wednesday, March 7, 2:30am – 3:30pm; Venetian, Lando 4204
CME Credits: CAHIMS 1.00; CPHIMS 1.00
Why attend: Convergence among healthcare stakeholders has been taking many forms, as covered in our inaugural Convergence conference (session recordings) in October 2017. This session looks at some of the organizations taking lead on implementing these new types of business models, sharing their lessons learned to help others that are entering this revamp cycle.
Monday, March 5, 9:55-10:20am; The Wynn Las Vegas, Lafleur
Why attend: As AI gains in healthcare, the hype grows as well. This session will discuss survey results of Learning Health System initiatives organizations and their vendors regarding their opinions of where AI will have its greatest effect, how these solutions are being sourced, and to what degree they would allow AI systems to influence their own treatment.
Tuesday, March 6, 3:00-3:45 p.m., HIMSS Spot (Level 2, Lobby C)
Speakers: Brian Eastwood, Engagement Analyst, Chilmark Research; 19 other Social Media Ambassadors
Why attend: HIMSS Social Media Ambassadors are selected based on their ability to influence industry discourse, identify emerging technologies, amplify awareness of health IT’s importance, and honor those leading the effort to shift the industry’s IT priorities. Hear from Brian and the 19 other HIMSS18 Social Media Ambassadors about how social media informs and broadens their industry expertise.
Wednesday, March 7, 11:30am-12:30pm; Venetian, Palazzo G
CME Credits: ACHE 1.00; CAHIMS 1.00; CME 1.00; CNE 1.00; CPHIMS 1.00
Why Attend: This session will discuss how EHR vendor developer programs have evolved to include App Stores, how formal they are, what types of data they allow access to, and how they handle oversight, security, and revenue sharing. It will be of interest to a growing number of EHR clients, partners, and even competitors desiring access to the “keys to the kingdom” to offer new and innovative functionality.
Tuesday, March 6, 8:30-9:30am; Venetian, Palazzo K
CME Credits: ACHE 1.00
Speaker: Seema Verma, Administrator, Centers for Medicare and Medicaid Services
Why attend: Learn how the new administration is using IT to manage CMS offerings and services. Hear first hand from the organization’s top executive what to expect from the nation’s largest payer in the next couple years.
Tuesday, March 6, 9:30-10:30am; Sands Hall G Booth 11955ET
Why attend: If you’re curious about blockchain in healthcare, this is the panel for you. Expect a good overview of applications and uses in healthcare today, what’s actually possible, and where this technology could be applied near term for most impact. Definitely bring your tough questions as this session is loaded with experts.
What We’ve Been Commenting On
Lately, there have been quite a few big developments in healthcare, including Allscripts acquiring Practice Fusion, Apple’s PHR, and the mysterious Amazon-JP Morgan Chase-Berkshire Hathaway healthcare company. Not all of these developments have enough detail yet for Chilmark to analyze the impact on the future of the health IT market in-depth, but we are commenting elsewhere on the wider possibilities for the healthcare industry.
Blockbuster digital health funding to spill to 2018
Brian Eastwood in HealthcareDive
“’We think next year is when we’ll begin to see [predictive analytics] go beyond simply accounting for and noting social determinants of health and barriers to care and start to use that information to inform care plan decisions,’ Eastwood said. Vendors able to adequately take this on will emerge as key players in the care management and population health markets as the year progresses, he added.”
Health IT eyes M&A as market grows up
Ken Kleinberg in HealthcareDive
“The EHR market is saturated [and] consolidation is very clear…The movement to analytics and population care, that’s where the action is now,” Kleinberg said. “There’s a tremendous amount of innovation still possible.”
Apple debuts medical records on iPhone
Brian Eastwood in HealthcareDive
“Apple is widely accepted as understanding the user experience,” Eastwood said. “If all of the sudden, a substantial chunk of the population has the capability to tap into a patient portal in a way they haven’t before, then it could be a gamechanger.
Why AI tools are critical to enabling a Learning Health System
Ken Kleinberg in HealthcareIT News
“The Learning Health Systems continually improve by collecting data and processing it to inform better decision making. As the amount and complexity of big data continues to increase, organizations are challenged to fully take advantage of it,” said Kleinberg. “AI systems are particularly suited to analyze huge data sets to discover meaningful and actionable insights, and even to carry out actions.”
Apple steps into Epic System’s arena with medical records iPhone app
Brian Eastwood in The Capital Times
“(Health record companies) will still be building their core products,” said Eastwood. “They’ll still be maintaining the records…[Regarding rumors of Apple or Amazon creating EHRs], right now, it’s still a little bit in the realm of fantasy.”
How Amazon, JPM and Berkshire could disrupt healthcare (or not)Health IT eyes M&A as market grows up
John Moore in HealthcareDive
“‘I’m not holding my breath for big changes,’ Moore said. Instead, he expects incremental change are more likely over the next three to five years.”
Will Amazon’s push into health care impact Epic Systems’ future?
Ken Kleinberg in The Capital Times
“Software to power the applications for health care providers come predominantly from a few large players like Epic and Cerner,” Kleinberg wrote. “It’s a great question to ask to what degree they can take their provider and software application expertise and apply it to the needs of payers.”
FDA Guidance on Clinical Decision Support: Peering Inside the Black Box of Algorithmic Intelligence
Last week, the FDA finally released its long-awaited Draft Guidance on Clinical Decision Support. Following the release, STAT News mentioned experts were disappointed because the agency gave no insight into how it views artificial intelligence. Indeed, a “Command+F” search for “Artificial Intelligence” returns zero results. However, it is unnecessary for the agency to use the term “AI” to provide guidance on how it will consider associated technologies and use cases. The FDA does use the word “algorithm” in its guidance, and although algorithms can vary in sophistication, much of today’s AI technology is based on algorithmic intelligence. The suggestion that the FDA did not address the topic becasue it failed to explicitly mention AI within the document shows the challenges for those unfamiliar with understanding this complex subject.
Nearly all AI will remain under FDA oversight. However…It would be useful for the agency to offer meaningful reference to machine learning or deep learning among the examples of potential use cases.
In fact, the FDA has been reviewing technology with AI components (e.g., rule-based systems, machine learning) for more than a decade. RADLogics received FDA approval for their machine learning application in 2012, widely considered the first AI for clinical use approved by the agency. HealthMyne received FDA clearance for its imaging informatics platform in early 2016. In 2017 at least half a dozen companies received FDA clearance for machine learning applications, including Arterys, the first company to receive approval for a deep learning application, and Butterfly Network, which had 13 different applications approved along with its “ultrasound on a chip” device in late October. Others to receive clearance in 2017 include Quantitative Insights, Zebra Medical Vision, EnsoData and iCAD.
The first indirect reference to products using AI comes in the first paragraph of Section III, in which the agency begins addressing specific examples of companies that will not be exempted from review. Note that the first bolded sentence below is inclusive of nearly every application.
“Under section 520(o)(1)(E), software functions that are intended to acquire, process, or analyze a medical image, a signal from an in vitro diagnostic device, or a pattern or signal from a signal acquisition system remain devices and therefore continue to be subject to FDA oversight. Products that acquire an image or physiological signal, process or analyze this information, or both, have been regulated for many years as devices. Technologies that analyze those physiological signals and that are intended to provide diagnostic, prognostic and predictive functionalities are devices. These include, but are not limited to, in vitro diagnostic tests, technologies that measure and assess electrical activity in the body (e.g., electrocardiograph (ECG) machines and electroencephalograph (EEG) machines), and medical imaging technologies. Additional examples include algorithms that process physiologic data to generate new data points (such as ST-segment measurements from ECG signals), analyze information within the original data (such as feature identification in image analysis), or analyze and interpret genomic data (such as genetic variations to determine a patient’s risk for a particular disease).”
The word “algorithm” is used four times in the document and in each instance the use provides significant insight into the agency’s thinking. The word is first used in the second highlighted sentence above, which provides general examples of algorithms which will continue to be reviewed as medical devices. The guidance goes on in a later section to provide the following more specific examples of algorithms that continue to require premarket approval:
“Software intended for health care professionals that uses an algorithm undisclosed to the user to analyze patient information (including noninvasive blood pressure (NIBP) monitoring systems) to determine which anti-hypertensive drug class is likely to be most effective in lowering the patient’s blood pressure.
“Software that analyzes a patient’s laboratory results using a proprietary algorithm to recommend a specific radiation treatment, for which the basis of the recommendation unavailable for the HCP to review.”
The agency continues to describe the underlying features that must be present for an algorithmically-driven CDS recommendation to be exempted from review, specifically a company must clearly state and make available:
The first three would seem to be reasonable enough for developers of AI products to provide users, but the fourth is basically impossible. The “black box” nature of most AI systems built using machine learning methods means even leading AI experts cannot unpack an algorithm and fully understand the rationale for a given recommendation, even with full transparency and access to the training data (which is no trivial matter in and of itself).
This is especially clear when taking into consideration additional guidance provided elsewhere in the document regarding software functions that will require oversight:
A practitioner would be unable to independently evaluate the basis of a recommendation if the recommendation were based on non-public information or information whose meaning could not be expected to be independently understood by the intended health care professional user.
Frankly, the agency provided great insight and clarity if you are reading the document to be inclusive of all known AI technologies today. The conclusion is clear that nearly all AI will remain under FDA oversight. However, there are terms that could be used in the final guidance that aren’t buzzwords, such as machine learning, supervised learning and unsupervised learning, among others. It would be useful for the agency to offer meaningful reference to machine learning and/or deep learning among the examples of potential use cases that remain under oversight as medical devices.
In Chilmark’s annual predictions for 2018, we forecast that two dozen companies will receive FDA clearance for products using AI, machine learning, deep learning and computer vision, which would mark a 400-percent increase from 2017. It would be helpful if the agency would create a dedicated channel for engaging companies developing AI products and perhaps even provide guidance on how they evaluate training data sets.
Back to the Crystal Ball: Our 2018 Healthcare IT Market Predictions
Our favorite post of the year is this one. As analysts, we come together with our propeller hats on to collectively look ahead at the key trends in the year to come in the healthcare sector. While there are any number of predictions one might make for this dynamic market, we will stick to what we know best: Healthcare IT and the broader issues that influence this sector.
Following is our annual Baker’s Dozen. As always, love getting your feedback in the comment section. Let the dialog begin.
Merger & acquisition activity continues; Humana or Cigna acquired.
Major mergers and acquisitions that mark the end of 2017 (CVS-Aetna, Dignity Health-CHI and rumored Ascension-Providence) will spill over into 2018. Both Humana and Cigna will be in play, and one of them will be acquired or merged in 2018.
Retail health clinics grow rapidly, accounting for 5 percent of primary care encounters.
Hot on the health heels of CVS’ acquisition of Aetna, growth in retail health reignites, albeit off a low overall footprint. By end of 2018, retail health clinic locations will exceed 3,000 and account for ~5% of all primary care encounters; up from 1,800 and ~2%, respectively, in 2015.
Apple buys a telehealth vendor.
In a bid to one-up Samsung’s partnership with American Well, and in a bid to establish itself as the first tech giant to disrupt healthcare delivery, Apple will acquire a DTC telehealth vendor in 2018.
Sixty percent of ACOs struggle to break even.
Despite investments in population health management (PHM) solutions, payers still struggle with legacy back-end systems that hinder timely delivery of actionable claims data to provider organizations. The best intentions for value-based care will flounder, and 60% of ACOs will struggle to break even. ACO formation will continue to grow, albeit more slowly, to mid-single digits in 2018 to just under 1,100 nationwide (up from 923 as of March 2017).
Every major EHR vendor delivers some level of FHIR support, but write access has to wait until 2019.
While some of the major EHR vendors have announced support for write access sometime this year and will definitely deliver this support to their most sophisticated customers, broad-based use of write APIs will happen after 2018. HCOs will be wary about willy-nilly changes to the patient record until they see how the pioneers fare.
Cloud deployment chips away at on-premises and vendor-hosted analytics.
True cloud-based deployments from name brand vendors such as AWS and Azure are in the minority today. But their price-performance advantages are undeniable to HIT vendors. Cerner will begin to incent its HealtheIntent customers to cloud host on AWS. Even Epic will dip its toes in the public cloud sometime in 2018, probably with some combination of Healthy Planet, Caboodle, and/or Kit.
True condition management remains outside providers’ orbit.
Providers will continue to lag behind payers and self-insured employers in adopting condition management solutions. There are two key reasons why: In particular, CMS reluctance to reimburse virtual Diabetes Prevention Programs, and in general, the less than 5% uptake for the CMS chronic care management billing code. In doing so, providers risk further isolation from value-based efforts to improve outcomes while controlling costs.
Mobile-first becomes dominant platform for over 75% of care management solutions.
Mobile accessibility is critical for dynamic care management, especially across the ambulatory sector. More than 75% of provider-focused care management vendors will have an integrated, proprietary mobile application for patients and caregivers by end of 2018. These mobile-enabled solutions will also facilitate collection of patient-reported outcome measures, with 50% of solutions offering this capability in 2018.
Solutions continue to document SDoH but don’t yet account for them.
A wide range of engagement, PHM, EHR, and care management solutions will make progress on documenting social determinants of health, but no more than 15% of solutions in 2018 will be able to automatically alter care plan interventions based on SDoH in 2018.
ONC defines enforcement rules for “data blocking,” but potential fines do little to change business dynamics that inhibit data liquidity.
The hard iron core of this issue is uncertainty about its real impact. No one know what percentage of patients or encounters are impacted when available data is rendered unavailable – intentionally or unintentionally. Data blocking definitely happen,s but most HCOs will rightly wonder about feds willingness to go after the blockers. The Office of the National Coordinator might actually make some rules, but there will be zero enforcement in 2018.
PHM solution market see modest growth of 5-7%.
Providers will pull back on aggressive plans to broadly adopt and deploy PHM solution suites, leading to lackluster growth in the PHM market of 5%to 7% in 2018. Instead, the focus will be on more narrow, specific, business-driven use cases, such as standing up an ACO. In response, provider-centric vendors will pivot to the payer market, which has a ready appetite for PHM solutions, especially those with robust clinical data management capabilities.
In-workflow care gap reminders replace reports and dashboards as the primary way to help clinicians meet quality and utilization goals.
This is a case where the threat of alert fatigue is preferable to the reality of report fatigue. Gaps are important, and most clinicians want to address them, but not at the cost of voluminous dashboards or reports. A single care gap that is obvious to the clinician opening a chart is worth a thousand reports or dashboards. By the end of 2018, reports and dashboards will no longer be delivered to front-line clinicians except upon request.
At least two dozen companies gain FDA-approval of products using Machine Learning in clinical decision support, up from half a dozen in 2017.
Arterys, Quantitative Insights, Butterfly Network, Zebra Medical Vision, EnsoData, and iCAD all received FDA approval for their AI-based solutions in 2017. This is just the start of AI’s future impact in radiology. Pioneer approvals in 2017 — such as Quantitative Insights’ QuantX Advanced breast CADx software and Arterys’s medical imaging platform — will be joined by many more in 2018 as vendors look to leverage the powerful abilities of AI/ML to reduce labor costs and improve outcomes dependent on digital image analysis.
What are your healthcare market predictions for 2018?
Keeping Score: Reviewing Our 2017 Predictions
In keeping with a Chilmark Research tradition, once again we step into our “way-back machine” to review our 2017 predictions for the healthcare sector – of course with a health IT flavor.
Our score is far from perfect, but we did quite well with our 2017 predictions: 7 Hits, 2 Misses and 4 Mixed. If this were a batting average, we would be instantly recruited into the majors:
Later this week we’ll publish our 2018 Predictions. Stay tuned.
Risk-based contracting for health IT solutions accelerates. MISS
While the rate of participation by providers in value-based payment models increased modestly, and more states adopted value-based payment models, the rate of risk-based contracting for health IT saw little growth in 2017. This is due to several factors: The Trump administration’s adoption of more voluntary approaches to future participation in value-based payment models, including bundles; challenges defining appropriate risk-sharing/pricing models, and legacy PMPM or PMPY licensing models.
HCOs demand clear ROI on their health IT spend. MIXED
Healthcare organizations (HCOs), while becoming more cognizant of the need for health IT to generate a true return on investment (ROI), have not significantly changed their purchasing decisions to reflect this change. This may simply be a function of an EHR hangover, where purchasing decisions are driven by breadth and depth of existing relationships with current vendor(s).
Progressive HCOs admit – their patient portals suck. MISS
As it turns out, progressive HCOs have been impressed with the portals they’ve built with solutions from their EHR vendors such as Allscripts, Cerner and Epic. But challenges remain: Increasing portal adoption among patients with slow Internet connections and/or a technology learning curve, along with a rising tide of consumer-centric engagement solutions, centered on holistic lifestyle and condition management, which have garnered interest from payers and employers interested in cutting costs while also improving outcomes.
Despite the hype, healthcare Internet of Things (IoT) stays on periphery. HIT
Connected devices remain visible and useful within healthcare facilities, especially to monitor ICU and post-surgical patients, but IoT security concerns have tempered enthusiasm for more widespread deployment. Outside the hospital, device use remains limited to remote patient monitoring (RPM) pilots. For example, the National Institutes of Health will use Fitbit devices in the All of Us precision medicine study, while Stanford Medicine is partnering with Apple for an atrial fibrillation trial.
Artificial intelligence (AI) and machine learning will remain outside the clinic. HIT
This year’s hype with AI and machine learning stretched definitions to the breaking point — even a basic statistical technique is now marketed as machine learning. AI-based diagnostic approaches are still struggling to be useful, even as an aid to clinicians. That being said, academic medical centers increasingly use NLP to mine unstructured text, crowds lined up around the block at RSNA to learn more about image recognition, and progressive HCOs regularly use these advanced analytics techniques to examine areas such as sepsis, length of stay, and readmissions risk.
Consumers find AI avatars as valuable as they are personal. MIXED
Virtual assistants have made some inroads for managing chronic conditions such as cancer and type 2 diabetes, though tools like Cortana, Google Now, and Siri still struggle with mental health and remain better positioned to deliver educational content and other prepackaged information. We see interest in virtual assistants that analyze patient input and recommend interventions. This brings value to the engagement experience, with much faster responses than emails or phone calls to physician offices. As solutions’ data sets grow, so will more personalized interventions and user experiences.
21st Century Cures Act interoperability provisions a dead letter. HIT
This prediction stands. Senate HELP Committee chair Lamar Alexander recently chided Jon White of the ONC for the time it was taking to to finalize rules about what constitutes “information blocking.” Rules may eventually be released but there will be wiggle room for all. In addition the environment for enforcement does not appear to favor patients.
EHR vendors get serious about API programs. HIT
All of the major EHR vendors either initiated or upgraded their API programs. FHIR-based APIs are the centerpieces of these efforts. Integrating the Healthcare Enterprise (IHE) is also busy FHIR-enabling its profiles. Developers today have way more opportunity to access FHIR APIs than they did last year. The one blind spot in these programs is write access to EHR data. Independent software vendors (ISVs) want write APIs, but most large systems and their EHR vendors have demurred.
Precision medicine fails to grow substantially outside of oncology. HIT
Despite an increasing number of vendors and continued investment, the precision medicine space remains challenging. While genetic-specific treatments and drugs are slowly making inroads for certain disease, the broader theme of social determinants of health (SDoH) is gaining more attention and will more dramatically influence population-wide health improvement.
Blockchain moves from hype to traction. MIXED
This has moved from hype to a serious topic of discussion – particularly for security and patient-controlled access to medical records. Traction, in terms of market solutions or installed and scaled applications, remains elusive. The largest healthcare systems experiment with blockchain, but a clear focus on use cases remains illusory.
HCOs continue to expand regionally via M&A. HIT
Through the end of September, 87 hospital and health system transactions occurred, and it is expected that the total for 2017 will slightly exceed last year’s total of 102 completed deals. The biggest moves of 2017 stand to be the Dignity Health-CHI merger, which would create the nation’s 10th largest hospital system – a monster that may quickly be eclipsed by the rumored Ascension-Providence St. Joseph merger. M&A is clearly moving beyond regional plays. Color-by-numbers barriers to M&A are dropping, with the recently announced CVS-Aetna and UnitedHealth-DaVita deals as prime examples.
Best-of-Breed PHM and analytics vendors continue to stay one-step ahead. MIXED
From a market share perspective, the EHR vendors and Optum racked up more wins in the last year than the independents. That said, the independents still have products and offerings that can go far deeper than the majors. While they stay ahead on the product front, they fell behind in the market. (The same can be said for care management vendors.)
HIMSS’17 will be far calmer and less frenetic. HIT
We were pleased that HIMSS17 focused less on hype and more on value, with demonstrations of operational use cases for network management, bedside patient engagement, data sharing, and clinical decision support. We also heard many vendors discussing services as an add-on to technology deployments, particularly for at-risk pricing and other value-based care initiatives.