GAFAMS, STARTUPS & INNOVATION IN HEALTHCARE by PHARMAGEEK

Two-thirds of the attendees polled at a recent innovation summit by The Economist agreed on one thing: health care is the sector that will benefit most from artificial intelligence (AI) technologies.

In health care, which is in the midst of an industry transformation and a digitalization of key aspects of patient engagement and care management, the role of data, analytics and AI are central to the organizational mission. However, it is easy to get caught up in one aspect or another when extolling (or decrying) the role of AI, while ignoring the near-term potential as well as the limitations of the technology.

How can AI play a role in health care today? In the words of S. Somasegar, a venture capitalist, there are three ways in which AI can impact a business today. Upwards, meaning that AI can take on intelligent capabilities that enable a higher level of interaction with humans; downwards, implying an ability to reduce costs; and outwards, which is to take AI to the edges of our computing infrastructure.

Voice recognition and natural language processing (NLP) technologies help us move up in health care by enabling remote-monitoring and home health care through a “natural” interface with humans. In the health care enterprise, NLP technologies can “read” complex medical literature and provide doctors and clinicians with intelligent choices for diagnosis and treatment options.

With the emergence of cheap computing and storage infrastructure, AI technologies help manage vast arrays of servers and networking equipment, detecting and remediating the most common problems without human intervention. “Purpose-built” hardware with inbuilt AI capabilities are becoming the norm in high-volume and time-sensitive operations that require running machine-learning algorithms on large data sets and doing it at low costs.

The notion of edge computing, a paradigm that takes analytics and AI to the edges of a computing infrastructure, has lately become important in the context of the Internet of Things (IoT) and smart devices. In health care, the proliferation of intelligent devices, in and out of hospital settings, has created many new opportunities. Tom Bianculli, Chief Technology Officer of Zebra Technologies, a firm that provides mobile devices, scanners and RFID-enabled tags used in hospital environments, talks about “digital diaries” that can log every minute and every second of a device’s operation in the context of patient care. Using a network of tags and near-field communication equipment, Bianculli is now able to track a mobile device in a caregiver’s hands as she makes her way through a hospital floor, recording and analyzing everything from her precise location to her pace of walking to the direction in which she is headed with the device. Extending it to outpatient or even home health care, the deployment of intelligent devices that can analyze data at the “end point” and sending it back to a back-end system can save lives by reducing the time involved in alerting caregivers to medical emergencies.

To some, all of this may sound futuristic. However, it doesn’t have to be complex use cases and high risk situations involving patient lives that determine whether AI is suitable for a health care institution. The vast majority of AI use cases involve “low-hanging fruit” that automates aspects of operations that are routine and repetitive in nature. AI can release humans from mundane tasks and enable them to work on more exciting and value-added tasks. In some industries with an acute shortage of skilled human resources such as health care, this may even be a necessity for long-term sustainability. 

The use of AI technologies comes with responsibilities as well. In the wake of recent disturbing news about a driverless car causing a fatal accident and the alleged misuse of Facebook profile data to influence the last presidential elections, there was a somber tone to the discussion at The Economist event. The gathering of AI technologists and industry leaders using AI to advance their business goals paused to reflect on how AI can be force for good and bad. Among the concerns: AI technologies by themselves may not reveal any inherent biases, but may unleash all manner of biases that reflect the biases of the humans who design the systems. There is a growing sense that AI should be used not just for the right predictions, but also to make predictions for the right reasons. While AI is coming on par with humans in aspects such as reading radiology images, the same neural network algorithms have potential for discriminatory profiling based on facial recognition and other decisions that have implications for society. The usefulness of AI models also depends on the data sets: as an example, selective representation of demographic profiles in a data set can give rise to biased conclusions on populations represented by that dataset.

The underpinning of success with AI lies in the underlying data. Fortune 500 companies are spending up to 50 percent or more of their IT budgets on information integration today, and no sector is more acutely aware of this than health care, with its complex environment of proprietary electronic health record (EHR) systems and emerging data sources. Unlike in other sectors such as consumer finance and retailing which are long used to multi-channel engagement with customers based on an omni-data capability that can aggregate and integrate data from a wide variety of sources, health care remains more siloed today than any other sector. The implications for AI adoption are clear: it will be slower than in other sectors.

Finally, having the data and the AI capability doesn’t ensure improved quality or reduced costs in health care. You need intervention models in place to do something with the data and have care plans for doing the preventive intervention, which can be challenging if the data is incomplete (as often the case with EHR data) or outdated (as with health insurance claims data). In an era of high-volume and high-velocity real-time data, these limitations will restrain the adoption of AI technologies.

As computing costs drop and AI technologies mature, health care and other industries will have to invest and catch up or get left behind in the great digital transformation under way. As someone said to me, there is a penalty for inaction. That penalty may be too big a cost to pay for most enterprises today. 

National Health IT week (NHIT Week). Healthcare IT has gone through dramatic changes in the past decade. Driven by a need to rein in runaway healthcare cost increases, the federal govt passed the Affordable Care Act (ACA) and its companion HITECH Act that used the Meaningful Use program of financial incentives to promote the implementation of electronic health record (EHR) systems. During this time, we have gone from what was largely a paper-based healthcare ecosystem to almost total penetration of digitized medical records. Health systems are now at the cusp of the next wave of IT-led transformation defined by these questions:

• What do we do with all the EHR data?
• What do we do about the explosion of new data?
• How is the market changing on us?

The coming years will see the transformation of healthcare delivery, driven by three important trends as they pertain to the role of information technology:

Data, analytics and artificial intelligence (AI)

The first AI-powered diagnosis of images was approved by the FDA this week. The arrival of data-driven, algorithmic decision-making using advanced computing infrastructure augments and enhances the capability of human physicians in the delivery of care. As the volume and variety of data have exploded in the past few years, so have the opportunities to derive additional meaning to predict and manage disease conditions. The digital transformation of healthcare is predicated on harnessing the power of data and analytics, and billions of dollars in venture capital money are pursuing that goal. However, AI has a “black box” problem which will impact adoption rates, as will ongoing concerns about data privacy, security, and ransomware.

What to look for: Increased adoption and transparency with AI models, increased use of personal genomic data, and the use of advanced analytics to solve public health issues such as the opioid crisis.

Changing healthcare markets

The healthcare consumer has long suffered the rising costs of healthcare. However, we may be approaching an inversion point driven by the burning questions of affordability and accountability in healthcare. Employers are taking matters into their hands and underwriting their employee healthcare costs, as we are beginning to see from the examples of GM and Walmart. By plying employees with wellness screenings and preventive care models, and by contracting directly with healthcare providers, employers hope to bring healthcare expenses under control while keeping their employees healthy.  Other factors at play; high deductible health plans have sharply reduced healthcare consumption among the older population while the younger generation is eschewing traditional paternalistic healthcare provider relationships for convenience and virtual care models. Digital health upstarts are coming up with innovative new models, built largely on the premise of virtual, AI-enabled, superior experiences enabled on smartphones and powered by remote data collection. Healthcare providers, for their part, find themselves sandwiched between giant pharma companies and health insurance companies, struggling to consolidate to gain and maintain negotiating power.

What to look for: M&A, industry consolidation, and the emergence of non-traditional players, all looking to use technology to “own” the healthcare consumer of the future. Case in point: the new Amazon-Berkshire Hathaway-JP Morgan healthcare venture led by Dr. Atul Gawande.

The digital health platform of the future

The past decade has been an incredible windfall for a handful of dominant EHR vendors who hit the lottery when the ACA and HITECH Acts came into being. However, the rapid, large-scale implementation of EHR systems with poor user experience design created an epidemic of physician burnout even as hospitals digitized clinical workflows and patient medical records. The physician community has pushed back, even as the Office of the National Coordinator for Health IT (ONC) has waged war on the lack of data interoperability between proprietary vendor systems. While EHR maintenance and optimization continue to consume a significant portion of enterprise IT budgets, a new breed of challengers, including large tech firms with deep pockets and vast experience in building consumer-focused platforms, is looking to dominate the digital health landscape of the future. EHR vendors are rising to the challenge, enhancing their platforms and embracing the growing adoption of emerging industry standards such as Fast Health Interoperability Resources (FHIR).

What to look for: A breakout digital health platform by a big tech firm such as Apple or Amazon that will integrate EHR data and create superior experiences with last mile solutions while lowering healthcare costs through predictive and preventive care models.

At a recent healthcare conference I attended in Silicon Valley, I met an entrepreneur who introduced himself briefly thus: “I’m the CEO of [his company]. We’ve raised $50 million in VC money.” That was it. No introduction to his product, no mention of how many employees, what clients they serve, or anything else about his business.

I wondered if this is the new digital health startup landscape, where raising millions of venture capital or VC money is the goal, not necessarily a means to a goal.  

Let the facts speak for themselves. Digital health funding exploded in the first half of 2018, at $6.8 billionuntil the end of Q3, according to Rock Health, one of several firms that tracks these numbers. More investors, bigger average ticket sizes, more companies funded; there is no better time to be a digital health entrepreneur. Money seems to be there for the asking. Big money, early on. Series A looks like the old Series B. (The last time I saw anything like this was when people were buying second and third homes a decade ago with easy credit.)

What do the latest digital health funding numbers tell us?

Let us parse the latest numbers a bit. The most significant funding category in the report has been “on-demand healthcare services,” which includes any form of telemedicine and at-home healthcare services. This makes sense. Healthcare is shifting away from the clinic and hospital environments to home-based and virtual healthcare. Indeed, this shift is the whole premise of digital health transformation at this time (never mind that the reimbursement model for virtual care delivery is yet to evolve). Other dominant funding categories included consumer health information, fitness and wellness, and disease monitoring. All these categories of healthcare services are primarily delivered through mobile apps or with the help of remote sensors and devices from which data is harvested to personalize the service.

The slew of digital health startups, continuing to raise eye-popping amounts of venture capital money, flies in the face of what the naysayers have predicted for the past couple of years – that most of these startups are “zombies” headed for certain death in the next 18 to 24 months. Evidently, that has not happened. Is there something we’re missing?

The Rock Health mid-year funding report provides a couple of clues to solve the mystery. Firstly, the number of exits, especially in the form of IPOs, has significantly trailed behind the fund-raising. While more companies are progressing to later funding rounds and getting there faster than before, there does not seem to be a definite end in sight. Maybe the dam is about to break, and we will be flooded with IPOs next year, but it certainly does not look like it now. Secondly, the exits that are happening are those where one digital health company is acquiring another. This could also explain why the conference I attended felt as if it was a group of digital health startups pitching to other digital health startups. Hardly anyone in attendance seemed to be from the VC or healthcare enterprise community unless they were there in the role of speaker or panelist.

So, the first thing we learn from the latest funding report is that digital health startups are not scaling fast enough to merit IPOs or big exits. This is the rock they are pushing up against. I have discussed this in a previous column here.

The “kill zones” for digital health innovation

In my book, The Big Unlock, I classified the technology vendor landscape as Custodians (electronic health record or EHR firms), Enablers (big tech platform companies like Google and Microsoft), Innovators (startups) and Arbitrageurs (consulting firms). There is a degree of inter-dependency among all these categories of vendors when it comes to transforming healthcare through technology. However, there are significant asymmetries in the interdependencies.

As an example, innovative startups cannot go on their own if they are looking to sell their solutions to large enterprises. For the most part, digital health startups are focusing on “last mile” solutions. These solutions must sit on another platform such as a Google or Microsoft Health Cloud, or more likely an EHR, such as a Cerner or Epic. While many of these new digital health solutions are innovative and user-friendly, there are many reasons they do not reach scale quickly. In this piece, I discuss the several “kill zones,” or situations a startup can find itself in and become vulnerable to external forces.

A common kill zone is the pilot phase deployment for a digital health solution in a large healthcare enterprise. “Death by pilot,” “pilotitis” and “pilot purgatory” are some of the colorful terms used to describe this painful situation. The gaps in handoffs between innovation groups and operations in the health system is another. Innovation groups by and large are set up as separate, stand-alone units which, while facilitating innovation, often fail in the process of transiting the solution to a broader adoption through the enterprise IT function. The no man’s land between the Chief Innovation Officer and a CIO is a potential kill zone for startups.

Even if digital health startups successfully cross the Rubicon into CIO-land, they face the ultimate kill zone: a dominant EHR vendor who has a similar solution on their product roadmap. Many health systems will choose to go with the EHR vendor’s solution by default even if the choice is to go with an inferior solution.

“Kill zones,” where innovators can be put out of business simply by being in the wrong place at the wrong time, are common threats to startups across the spectrum. Digital health startups need a “moat” – an unsurpassable advantage from a superior product or service to keep the big tech firms at bay. The investors pouring money into these startups are presumably betting on some of the startups achieving just that. For the vast majority of digital health startups that do not have it, they can only hope to continue to raise money and stay afloat till they become cash flow positive or score a successful exit. The other options are not pretty.