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.
HIMSS18 – A Cacophony, Not Yet a Symphony
My post before HIMSS talked about how jazzed (not jaded) I was to be attending my 20th HIMSS conference. Now that HIMSS18 is in the bag – what did I learn?
On Monday I presented the results of our AI survey at the Machine Learning & AI for Healthcare preconference event at the Wynn. Although there were a few hundred seats, the event sold out weeks in advance. A number of folks I knew who planned to buy a ticket at the door were shut out. So that’s a lesson – when it comes to attending hot topics, don’t procrastinate!
Keynote speaker Lynda Chin from the University of Texas compared using of AI to having a paralegal on your team – it’s someone intelligent that could pull resources together to help you make better decisions. She summed it up simply: “Machines serving humans, not humans serving machines.”
Many other speakers came from large health systems and spoke to important use cases:
It’s become a given that these leaders and their vendors use AI and use it well. My favorite from the above examples was Srinivasan Suresh, CMIO at Children’s Hospital of Pittsburgh of UPMC. His slide highlighted that, although he had no impressive AI or ML credentials, he was still able to use these kinds of tools successfully to predict pediatric readmissions due to seizures, asthma, and pneumonia, which led to more effective interventions.
HIMSS and health IT may be more of a cacophony than a symphony, but I’m glad to be in the orchestra.
AI and the cloud were key themes this year and have become mainstream topics. For our views on Eric Schmidt’s keynote about data, analytics, and AI, see our earlier HIMSS18 recap blog.
Glad I had teammates that made it into Seema Verma’s CMS keynote the next day – her announcement about patient data access, open APIs, and Blue Button 2.0 was welcome. You may recall the previous year, given the change of administration, there was little that CMS or ONC could say about anything. Although we’re seeing some progress, it doesn’t seem substantial enough to move the needle on value-based care.
A big part of my week was meeting with NLP vendors. Chilmark Research is close to releasing our major report on this topic, and it was great to get insights from more than a dozen vendors. Some of the smaller ones are highly focused on specific use cases (Health Fidelity and Talix on risk stratification; Clinithink on matching patients to clinical trials). 3M and its partnership with Alphabet’s Verily are a powerful combination on determining the “dominoes” of costs and care. Also of note: M*Modal’s virtual provider assistant and use of ambient devices, as well as Nuance’s partnership with Epic to add more conversational AI functionality. We are seeing voice assistant success paving the way to virtual scribes – those that can “whisper” in the physician’s ear will be most valuable to ensure that decision support is not bypassed by passive systems.
As John Moore posted in his earlier HIMSS18 recap, it’s sad (well, infuriating) that we still have to address interoperability. I attended two events held by the Strategic Health Information Exchange Collaborative (SHIEC), which has been successful in providing a rallying point for 60 HIEs and 40 vendors to share knowledge and provide comments to ONC regarding TEFCA and data exchange. But it only represents a fraction of the hundreds of private and public HIEs in the country, so there is still a long road ahead. A payer committee was a welcome sign that convergence was part of their agenda.
At the opposite end of the interoperability spectrum, I attended a session by Houston Methodist on body sensors, where the distances are measured in inches and the signals are often so weak that temperature or motion (such as a kicking baby) are enough to throw them off. Sensor network fusion is the frontier – the more information you can capture from more places with more context, the better. For example, one of Methodist’s use cases was rapidly predicting a patient fall.
I also met with Somatix, a small vendor with a big idea we’ve been hearing about for years – using data from wearables to track more routine activities of daily living (some of which, like smoking, are harmful). The vendor is attempting to take this to the next level with more accurate gesture detection and predictive analytics so appropriate (and even real-time) interventions can be made using specific apps. As Brian Eastwood recently posted, we’re still waiting for wearables to provide insight. I didn’t sport a wearable at HIMSS18 (I broke two and lost another in 2017), but I’m on the lookout for a good, waterproof one.
Another key area of focus for us is the use of AI to interpret digital medical images. An impressive talk by University of Virginia and the National Institute of Health included use of speech recognition (using Carestream and Epic) to embed hyperlinks of AI-recognized areas of interest into reports for the EHR. The two-year effort showed productivity improvements of 3x over unassisted analysis and reporting.
A presentation by Entlitic claimed AI-enabled “superhuman” techniques able to detect lung cancer two years sooner than existing approaches. Their solution made it easy to compare an existing case to similar cases where timelines of data showed disease progressions. The company has 65 radiologists that label their training data, claiming only 1 in 4 that apply for the job pass their test. We’ll dive into detail about these kinds of advances in our Digital Medical Imaging Report scheduled for Q4’18.
I spent time with Ambra, a major provider of image exchange solutions (others include Nuance and lifeIMAGE). Aside from the challenge of the size of medical images, it always surprises me how difficult it is to move them around and make them available despite good standards (DICOM). It was only recently that Epic, for example, addressed image exchange, and it’s not part of many HIEs. I’m glad to see we’re moving beyond the vendor neural archiving discussion and toward a focus on the cloud and useful exchange of images in clinician workflow.
I also attended half a dozen receptions during the week. The biggest was sponsored by a large consulting firm. It was an evening of fun, but it reminded me of what was right and wrong about our industry and a conference in Las Vegas – who’s really paying that bill? My last reception was with BetterDoctor, which specializes in the quality of provider directory data. It always seemed ironic to me that the most regulated profession in the world has such a problem with accurate information (retirement, credentialing, locations, and so on).
To rework my “I’m Jazzed” comment from the top with a music metaphor, HIMSS is more like a blaring of thousands of different instruments with each of the “sections” competing to be louder than the other – and the sounds of Vegas don’t help. There are many great musicians and an increasing number of duets (e.g., partnerships, ACOs), but we’re still playing off too many different pages. Adding to the problem is the conductor (the government) changing every few years.
It may be more of a cacophony than a symphony, but I’m glad to be in the orchestra. I hope you are, too.
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 email@example.com 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.
Top 7 Things to Look for at HIMSS17
The final countdown has begun. In a few short days I and the rest of the Chilmark Research team will make our annual pilgrimage to the big health IT confab, HIMSS17, to rub shoulders with some 45,000 of our closest friends.
I have a love-hate relationship with this event. I love the opportunity to meet with many leading advocates, innovators, developers, and users of IT who are all truly trying to improve the delivery of care – to improve the patient experience. This is my/our tribe. These individuals and even organizations are who we as analysts seek out, looking to have an in-depth conversation. These conversations are enlightening, help us further refine our research agenda, and provide us the opportunity to accurately report on exactly what is working and, frankly, what may still be more vaporware than software.
HIMSS is not all sweet-smelling roses. What frustrates me the most is the hype. Now I know that vendors pay a pretty penny to exhibit their wares at HIMSS and, having been on the other side of the fence, I know intimately the challenges of trying to differentiate yourself from all the others that surround you. But what I truly hate is when the latest fad or buzzword enters the hype-cycle and every single vendor claims to have that solution, to address that buzzword du jour.
Years ago I remember walking by booth after booth of vendors claiming they had a Health Information Exchange (HIE) solution. When I saw such proudly displayed in the Dell booth, I knew it was all BS and decided to separate the wheat from the chaff with our first report on the HIE market. That report was a huge success and really put wind in the sales of what was then a very small company. In recent years it has been population health management (PHM), a misnomer if there ever was one. Until the last 12-18 months, not a single vendor has been capable of fully supporting an organization’s PHM strategy. There are simply too many moving parts to PHM. Simplistic care gap analysis with robo-emails and calls is not PHM. The sad thing is, these buzzwords get so over-used, so misconstrued, and so abused that they become meaningless – I’m coming close to detesting the term PHM.
Enough of a preamble. The following is what I will be on the lookout for at HIMSS – and you should, too.
1. Artificial intelligence (AI) is big, everywhere, but are we truly seeing traction? This term, along with machine learning and cognitive computing, will be on prominent display at HIMSS17. I would not be surprised if this is the top buzzword at HIMSS this year, but what I really want to know is how AI is actually being used. What are the use cases? Where will it scale first (e.g., consumer, clinician, back-office, radiology…)? What impact will it have on existing processes and workflows? How will it impact staffing levels?
2. Precision medicine: Is it real or still theoretical? There’s been a lot of hype on precision medicine the last couple of years but little action. Will be interested to learn how much traction some of the early innovators are getting (NantHealth) and what new entrants (2bPrecise) are planning. Tremendous amount of opportunity here that overlaps some of the current work being done in AI – how will these two come together in the future to improve and optimize the delivery of care for the individual?
3. How will care management and patient engagement merge – who is thinking beyond silos here? A core tenet to effective care management is patient activation and self-care, yet care management and patient engagement solutions remain by and large completely divorced from one another. Who among vendors, providers, and consultants is truly thinking beyond silos and looking to effectively meld care management and patient engagement?
4. Who is using IoT and PGHD at scale? Despite all the talk of health Internet of things (IoT) and patient generated health data (PGHD), our research to date has yet to find an organization that is using IoT and PGHD at scale for remote care monitoring. Sure, there are plenty of pilots, but as one healthcare executive said to me: Its one thing to do such for 100 patients, quite another for 10,000 – we’ve yet to figure out how to scale the workflow across the enterprise and community.
5. Outcome measures, anyone? The focus of quality initiatives (and measures) for years has been on process measures. We are getting to a level of maturity in the industry’s use of IT that we need to be thinking beyond process measures to outcome measures. In a recent briefing, a leading HIT vendor proudly showed how its clients excel at meeting process measures. When asked how these same clients do on outcome measures – well, that was a question the company had no answer for. It’s time we start figuring this out. I want to know who is doing this on a routine basis and for what use cases.
6. How are vendors looking to support PHM and VBC? Core to our research in 2017 is gaining a better understanding of provider-payer convergence. Our thesis is that providers have a core competency in population health management (PHM), whereas payers have competency in value-based care (VBC). As these two entities increasingly collaborate to improve access and care delivery within a community, how will vendors provide a platform to support such requirements?
7. What’s next? a.k.a., reading the Trump administration tea leaves and what may unfold across the healthcare sector over the next few years. The new administration has made a lot of pronouncements pertaining to healthcare, from pharmaceutical pricing to repealing the Affordable Care Act. We still do not know how all this will play out, and that lack of clarity is likely to impact budgets. I want to know: How big is that impact and how might it impact IT spend going forward? While I can already imagine every vendor telling me that it has a great pipeline, a great backlog, etc., I remain unconvinced. I’ll be digging deep on this one.