Healthcare Provider Analytics and Reporting: Expanding Beyond VBC Use Cases
We will release our newest report, 2019 Healthcare Provider Analytics Market Trends Report, in the next few days. This report reviews the current market for provider analytics and evaluates offerings from 23 different vendors.
In recent years, providers invested in analytics technology to support the transition from fee-for-service (FFS) to value-based care (VBC). Vendor offerings that support the variety of pay-for-performance (P4P), pay-for-reporting (P4R), and risk-sharing programs with payers have helped them better understand the interaction of costs, quality, and utilization in the populations they serve. But the applications for analytics are broader than just VBC. Provider healthcare organizations (HCOs) are seeking to leverage these technologies more broadly to support a range of clinical, financial, and operational performance improvement goals and programs.
Provider-oriented analytics availability mirrors EHR penetration. Providers in acute and ambulatory settings have many choices for analytics across multiple use cases. Providers in post-acute settings and others with low EHR penetration have relatively fewer choices. While vendors have devised a number of ways to extend their offerings to underserved settings, not all providers take full advantage of such capabilities.
EHR vendors are often, but not always, providers’ first choice for analytics. Most EHR vendors sell analytics offerings almost exclusively to their existing EHR customers. Independent vendors – not owned by an EHR vendor or a payer – are a strong alternative to EHR companies for value-based care use cases. Claims analytics companies have deep experience with claims data sources or rely heavily on claims-related data to fuel analytics and reporting. Applications from many of these vendors emphasize cost and utilization control and feature deeply descriptive insights into risks, costs, quality, and utilization. Providers have historically been reluctant to adopt these offerings, but that is changing.
This report characterizes current analytics solutions as either “mainstream” or “advanced.” Most HCOs have experience with mainstream analytics – often cloud-hosted and reliant on relational databases that store historical data from the EHR, claims, and other sources. The resulting applications characterize and summarize performance along multiple dimensions. While this technology approach is well-established, mainstream analytics still faces challenges. Chief among these are data quality and variability. Diligence is required on the part of vendors and HCOs to ensure this data is accurate, high-quality, and up-to-date.
Data complexity challenges are only increasing because new data sources are on the horizon. The All of Us program (formerly known as the Precision Medicine Initiative) promises to unleash a torrent of novel and voluminous data types. In addition, the vast trove of unstructured data in EHRs will soon contribute to a better understanding of patient cohorts and risks. Social determinants of health (SDoH), data from smart health monitoring and fitness devices, and a variety of patient-reported and publicly-available data sets are also beginning to be used in provider analytics.
Mainstream analytics has yet to supply a variety of predictive and prescriptive insights; for that, HCOs are looking at advanced analytics.
Advanced analytics consists of interrelated technologies, the most common of which are artificial intelligence (AI)/machine learning (ML), natural language processing (NLP) and extraction, and big data technologies. These technologies and techniques are not widely deployed in healthcare, but are used to varying degrees by most of the vendors profiled in this report. The expectation is that as these technologies mature, advanced analytics will offer more and better predictive and prescriptive capabilities. Many vendors now offer optional services to help providers take better advantage of advanced analytics technologies. Increased organizational familiarity with AI technologies and algorithms should naturally increase user trust as the technologies mature and become more widespread.
Many provider organizations, with experience gained from their VBC efforts, want more benefits from analytics. Whether it is from their legacy point and departmental reporting solutions, mainstream, or advanced analytics, provider organizations see analytics and reporting as a reliable way to pursue performance improvement goals across their enterprises.
Matt Guldin · 2 years ago
Liz Gavriel · 4 years ago
John Moore · 2 months ago
Brian Edwards · 3 months ago
Brian Murphy · 2 weeks ago
HIMSS’19: Real Value in Telehealth and Virtual Care
This is the second in a series of blog posts recapping HIMSS’19; you can read all our coverage here.
My primary purpose at HIMSS’19 was gathering information and ideas for our upcoming report on the front door to care. This report will take a close look at the evolving ways patients first enter the healthcare system. Whether from retail health, telehealth, remote patient monitoring, or remote care apps, HIMSS was full of changing ideas and approaches. The conference had a utilitarian focus, looking less at generic or abstract buzzwords to get people excited, and more at what can be done right now to engage providers, payers, and ultimately patients.
My biggest takeaways:
Health systems have invested a lot into controlling referrals and leakage. While the PCP remains the central organizing hub of most healthcare, the growth of retail and remote health could lessen the PCP’s centrality in traditional referrals networks.
Unlike the Teladoc model, which employs contracted providers to provide a turnkey outsourced telehealth service, newer entrants offer operational platforms and back-end systems so HCOs can staff and run their own telehealth programs. This allows them to retain control of the patient experience. It’s an easier model for an HCO to understand and use, but whether they adopt such solutions before their competition is an open question.
Between shrinking reimbursements and scarce providers, behavioral and mental health care have been the first service line on the chopping block for a while now. PCPs have become the go-to provider for too many behavioral health needs, occupying increasing amounts of time and stretching their expertise thin.
Several of the telehealth and remote health platforms I saw last week had behavioral health components. There were a few well-executed apps dedicated to mental health and wellness, mainly with a CBT/DBT focus and some with solid clinical results. Helping PCPs manage this care and mitigating the effects of comorbidities on patients is an important part of addressing PCP workload and job satisfaction, as well as patient engagement. These virtual care offerings can help struggling PCPs get their patients the help they need, while still working within tight budgetary and scheduling restrictions.
Telehealth, Remote Monitoring, and Virtual Care can significantly erode established HCO business models, or complement them. The question is whether health care systems will recognize that in time.
With my background in healthcare performance analysis and improvement, I wanted to see how analytics is evolving to become more effective and efficient.
The future of analytics platforms looks less like pre-built dashboards or reports and a lot more like what Visiquate offers. Its embedded employees work directly with customer end-users to execute Agile-inspired improvement sprints supported by their analytics and reporting. Vendors are coming to grips with the challenge of operationalizing analytics for value and performance improvement. The value proposition behind both improved reporting software and process improvement is pretty well understood. Figuring out how to fit it all into an annual budget in an era of shrinking margins is the real hard part here.
A fascinating conversation about AI at the Geneia booth on Tuesday afternoon summed up the current state of AI and machine learning in the clinical world. While access to existing and new kinds of data is increasing and the ability to integrate it is getting more sophisticated, AI and ML still aren’t the clinical tools many expected them to be. Only imaging, an area where the datasets are complete and the challenges are well understood, has really begun to heavily leverage AI/ML. Everywhere else, the barriers to gathering appropriate context and rendering predictive clinical recommendations have yet to be overcome.
HIMSS’19: What to Expect, What I Hope to Find
Next week, most of the healthcare IT industry will descend on Orlando to attend HIMSS’19. This is my 12th year attending HIMSS, an event for me that is more about networking and confirming assumptions than actually learning anything new.
For years now, HIMSS and the multitude of vendors exhibiting there have feasted at the trough of federal largesse ($35B plus), via the HITECH Act passed in 2008 to foster adoption of EHRs. The HITECH Act was successful, driving EHR adoption from the low teens to over 90% today. Though some may question the value of that investment, I personally believe that over time (another 7-10 years) we will reap benefits that far exceed that initial investment.
However, now that we’ve reached that level of adoption, the market has plateaued. Sure, there were hopes of a robust EHR replacement market, but that never materialized. Then there was the hope for huge gains (profits) to be made on the shift from volume to value through the sale of PHM solution suites. That didn’t pan out too well either as the fate of the ACA was left in the lurch with a change in administrations. Also, quite frankly, PHM is a complex sell, requiring significant change management that few healthcare organizations were ready to commit to and few vendors had the services to support.
The provider health IT market is going through a significant transition and it’s not going to be pretty. Clearly, the party is over and one has to wonder: Why does HIMSS continue to exist? Why are all these vendors here? Are we on the Titanic, seemingly blind to the economic icebergs that surround us?
But I digress.
What is important is that the EHR has become the central nervous system to provider organizations. Secondly, this market will continue to consolidate rapidly with few independent EHRs surviving the shakeout. Those left standing will attempt a number of different strategies to drive continued growth in a plateauing market.
It remains to be seen how successful these strategies will be but rest assured, even if successful, no EHR vendor is completely safe from a future acquisition.
This sets the stage for what to expect at HIMSS’19:
And what I hope to find at HIMSS’19:
May your trip to HIMSS’19 be a success, however you define it. And if you see us in the halls, do not hesitate to stop and say hello – maybe we’ll have a few quick on-the-fly notes to share.
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.
2019 Predictions: M&A, Big Tech, and the Fate of ACOs
The Meaningful Use gravy train finally came to an end in 2018. As the strongest EHR vendors struggle to define new revenue streams, weaker ones faded from view through acquisitions or leveraged buy-out. Meanwhile, funding for ‘digital health’ start-ups continued to increase, though it likely hit the high water mark in 2018. And lest we forget, Amazon, Apple and Google continue their forays into the healthcare sector as the market is simply too big to ignore.
So what’s in store for 2019?
We brought together our analysts’ brain trust and came up with the following baker’s dozen of 2019 predictions. Over the near decade of making these annual predictions, our batting average has consistently been well above .500, so don’t ever say we didn’t give you an advanced warning on the following:
Revenue cycle management M&A activity will continue to pick up with the most notable acquisition by Optum as it doubles down on its Optum 360 managed revenue cycle business and acquires Conifer Health Solutions from Tenet.
Despite the hype and media attention around alternative primary care clinics (e.g. Oak Street Health, Chen Med, One Medical), the actual number of physical locations serving patients will remain paltry at less than ten percent of the number of retail health clinic locations.
Walgreens will likely make the first move to acquire Humana in 2019, but Walmart will outbid Walgreens to win Humana over.
The number of FDA approvals for algorithms in 2018 was impressive and shows no signs of abating. Additionally, 2020 will see a further tripling of regulatory approvals for AI.
Consumers’ use of telehealth will continue to see rapid growth and rising competition leading to significant consolidation among the plethora of vendors. By year-end, a major non-healthcare-specific consumer brand will join the mix, and the market will be down to five direct-to-consumer (DTC) nationwide brands.
By the end of 2019, every major healthcare analytics vendor will provide a cloud-hosted offering with optional data science and report development services.
Cloud offerings have become far more robust, concurrent with HCOs’ struggles to recruit IT talent and control costs. Amazon’s AWS and Microsoft’s Azure will be clear winners while Google’s own cloud infrastructure services will remain a distant third in 2019.
Laws and regulations to-date have not compelled providers to freely share data with patients. ONC’s information blocking rule, which will be released before the end of 2018, will make it easier to transfer data to other organizations but will do little to open the data floodgates for patients, clinicians, and developers.
Despite loud protests, the vast majority of provider-led MSSP ACOs will take on downside-risk as CMS shows flexibility in waivers. However, hospital-led ACOs, who continue to struggle with standing up a profitable MSSP ACO, will exit the program in 2019.
Continued changes in post-acute care reimbursement, especially from CMS, combined with the migration to home-based services, puts further economic strain on these facilities. Nearly twenty percent of post-acute care facilities will shutter or merge in 2019.
The warning signs are there over the last couple of months that the stock market has become skittish. This will extend well into 2019 (if not lead to a mild recession). It will hardly be an ideal time to do an IPO, and those planned by Change Healthcare, Health Catalyst and others will wait another year.
Elon Musk will have a nervous breakdown leading him to reinvent the healthcare system from his bed during his two-week recovery at Cedars-Sinai.
Revisiting Our 2018 Predictions
As is our custom here, we like to look back on our predictions for the closing year and see just how well we did. Some years we do amazingly well, others we over-reach and miss on quite a few. For 2018, we got seven of our 13 predictions spot-on, two were mixed results and four predictions failed to materialize. If we were a batter in the MLB we would have gotten the MVP award with a .538 batting average. But we are not and have to accept that some years our prediction average may hover just above the midpoint as it did this year.
Stay tuned, 2019 predictions will be released in about one week and it is our hope that they will inspire both rumination and conversation.
(Note: the bigger and plain text are the original predictions we made in 2017, while the italic text is our review of 2018).
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.
MISS – neither happened. However, Cigna did pick-up PBM service Express Scripts and rumors continue to swirl about a possible Humana-Walmart deal or more recently, even a Walgreens-Humana deal.
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.
MISS – Modest growth in 2018 for retail health clinics with an estimate of around ~2,100 by year’s end. Telehealth, which is seeing rapid growth and on-site clinics may be partially to blame.
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.
MISS – Apple continues to work on the periphery of care with a focus on driving adoption of its Health Records service in the near-term with a long-term goal of patient-directed and curated longitudinal health records.
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).
HIT – MSSP performance data showed only 34% earned shared savings in 2017 (up from 31% in 2016) and by year’s end it is estimated there will be ~1,025 ACOs in operation.
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.
HIT – FHIR-based read APIs are available from all of the major EHR vendors. Write APIs are still hard to find. To be fair, HCOs as a group are not loudly demanding write APIs.
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.
HIT – adoption of cloud computing platforms is accelerating quickly across the healthcare landscape for virtually all applications. Cloud-hosted analytics is seeing particularly robust growth.
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’s 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.
HIT – Awareness of the CCM billing code (CPT code 99490) remains moderate among providers and adoption is still estimated at a paltry less than 15%.
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.
MIXED – While the majority of provider-focused care management vendors do have an integrated mobile application (proprietary or partnership), collecting PROMs is still a functionality that remains limited through an integrated mobile solution.
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.
HIT – despite all the hoopla in the market about the need to address SDoH in care delivery, little has been done to date to directly affect dynamic care plans.
The hard, iron core of this issue is uncertainty about its real impact. No one knows what percentage of patients or encounters are impacted when available data is rendered unavailable – intentionally or unintentionally. Data blocking definitely happens but most HCOs will rightly wonder about the federal government’s 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.
MIXED – Last December we said, “The hard iron core of this issue is uncertainty about its real impact.” Still true. Supposedly, rulemaking on information blocking is complete but held up in the OMB. The current administration does not believe in regulation. So “data blocking” may be defined but there was and will be no enforcement or fines this year.
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.
HIT – PHM remains a challenging market from both payment (at-risk value-based care still represents less than 5% of payments nationwide) and value (lack of clear metrics for return on investment) perspectives. All PHM vendors are now pursuing opportunities in the payer market, including EHR vendors.
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.
MISS – Reports and dashboards are alive and well across the industry and remain the primary way to inform front-line clinicians about care gaps.
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.
HIT – With about a month left in 2018 the count of FDA approved algorithms year to date is approaching 30 and could potentially hit three dozen by year end. This is a significant ramp up in the regulatory pipeline, but more is needed in the way of clear guidance on how they plan to review continuously learning systems and best practices for leveraging real-world evidence in algorithm training and validation.
What do you think of 2018 for health IT?
Unlocking Healthcare’s Big Data with NLP-powered Ambient and Augmented Intelligence
It wouldn’t be a radical statement to say NLP bridges the human-computer divide more than many technologies. ROI has been elusive, leaving prospective adopters reluctant to embrace it despite the numerous opportunities for NLP-driven solutions. NLP technologies have reached an inflection point with the emergence of advanced deep machine learning methods that are on-par with humans for an ever-increasing list of core natural language skills, such as speech recognition and responding to questions. In our newest report, Natural Language Processing: Enabling the Potential of a Digital Healthcare Era, we profile 12 vendors, all with a track record in text mining and speech recognition, including 3M, Artificial Intelligence in Medicine (Inspirata), Clinithink, Digital Reasoning Systems, Health Catalyst, Health Fidelity, IBM Watson Health, Linguamatics, M*Modal, Nuance, Optum and SyTrue. Each has a reputation for delivering solutions that serve a particular set of use cases or customer groups, distinctions we capture using heat maps for each company.
NLP is particularly well suited to address two huge problems in healthcare – easing the clinical documentation burden for clinicians and unlocking insights from unstructured data in EHRs. Documentation consumes an ever-increasing portion of clinician’s time. Recent research has shown physicians spend as much as half of their work day (6 hours of a 12 hour shift) in the EMR. Another recent study showed clinicians spend two hours on clinical documentation for each hour spent face-to-face with patients. Unsurprisingly it is often cited as a key factor contributing to physician burnout. Ambient Intelligence refers to passive digital environments that are sensitive to the presence of people, aware context-aware, and adaptive to the needs/routines of each end user. The familiar virtual personal assistants (VPAs), such as Amazon’s Alexa and Google’s Assistant, are familiar examples.
Speech recognition technology is approaching 99-percent accuracy, a milestone that some argue means that voice will become the primary way we interface with technology. I am skeptical of this prediction, at least when it comes to the broader utility of voice-based interfaces for consumers. The visual display, with its links and rich media, is an indispensable element of the modern digital experience.
Smart speakers, the input device for speech recognition, are the hottest technology trend of the moment, with an adoption curve that exceeds even the smartphone (see graphic below from Activate). We expect the smart speaker to rapidly become a fixture in both the home and office setting, following a similar path to maturity as the smartphone, offering applications for consumers and enterprises.
Interest and adoption in healthcare is already apparent. In September Nuance announced a smart speaker virtual assistant that uses conversational cloud-based AI (Microsoft Azure) to engage physicians during clinical documentation. In late November a post on the Google Research Blog described internal research and a pilot at Stanford investigating the potential to use a similar smart speaker interface and Automatic Speech Recognition (ASR) technology to create a virtual scribe.
Startups are taking on this problem too. Saykara, led by former executives at Nuance and Amazon, is developing a virtual assistant similar to Google’s. The company claims to have far more advanced speech recognition technology than its heavyweight competitors. Other are developing ambient scribes to passively document patient encounters, including Suki.ai , Robin Healthcare, and Notable Health.
EHR vendors are also making investments in ambient intelligence. Epic has partnered with Nuance and M*Modal to embed their ambient scribe technology directly into clinical workflows. Allscripts and athenahealth have partnered with startup NoteSwift. eClinicalWorks has launched a virtual assistant called Eva. Eva operates is initially intended to respond to queries for things like recent lab data or past clinical note content.
Barriers remain on the road to ubiquitous adoption of NLP technology by healthcare enterprises. NLP provides HCOs a low-risk opportunity to experiment with advanced machine learning and deep learning technologies, but its not the type of technology that can be implemented optimally by just any analyst in the IT department, but instead requires specialized expertise that is in short supply. While free text and mouse clicks will dominate the clinical documentation landscape in the near-term, healthcare enterprises will soon expect their users to talk their applications.
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.