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.

A recent study on blockchain adoption in health care by Blackbook Market Research indicated that a large percentage of payers surveyed, and a small but growing percentage of providers, were either considering deploying or were in the process of implementing, some blockchain solution sets. Blockchain appears to have moved from the awareness and education phase to actual deployment of solutions.

 

“Blockchain can solve two of the biggest problems in health care today," says Lidia Fonseca, CIO of Quest Diagnostics, a leader in lab testing services. She is referring to the gnarly issues of interoperability and data quality. While data quality has long been an issue in health care, the interoperability challenge is a legacy of the massive digitization of patient medical records over the past eight years which have left us with proprietary electronic health record (EHR) systems that don't "talk" to one another. The result is inefficiency and waste, as stand-alone information systems slow down processes and create redundant work. 

Taking a look at the healthcare blockchain pioneers

Some technology solution providers, including IBM and Change health care, along with a number of other companies listed in the Blackbook Research study, have made initial moves in establishing blockchain capabilities and launching solutions. Federal agencies such as FDA and CDC have expressed interest in applying blockchain to find solutions for public health issues.

 

Amazon, with its long-awaited entry into health care, recently announced the launch of blockchain templates for health care, aimed at making it easier for developers to create blockchain-based projects and deploy blockchain networks via open source frameworks. In a sense, Amazon has started the democratization of the blockchain-enabled application development, something we saw before with machine learning algorithms in the wake of the big data and analytics hype a few years ago.

 

A group of large healthcare enterprises, including large payers such as Humana and United Health Group (UHG), along with Quest Diagnostics, UHG subsidiary Optum, and Multiplan Health recently came together to launch a blockchain pilot program to solve for one of the most significant data challenges in health care today – provider data management. Provider data, a fundamental enabler for all manner of healthcare transactions, is a key building block for processing claims and maintaining up-to-date provider directories. Today, most provider data is stored in siloed and independent databases. Provider data quality is estimated to be a $2.1 billion problem today, according to a report by CAHQ, a non-profit alliance focused on creating shared initiatives to streamline the business of health care. By streamlining the inefficiencies in provider data maintenance, participants can have a "single source of truth." It is estimated that up to 75 percent of provider data management costs can be eliminated using blockchain solutions.

What it will take for blockchain initiatives to succeed

While blockchain is coming of age in health care, we’re still in the early stages of the hype cycle for blockchain. The initial mania around bitcoin, the very first use case for blockchain, is now giving way to more carefully considered use cases for business with tangible benefits. Healthcare, a sector that generally lags in the adoption of technologies, is currently in a wait-and-watch mode; notwithstanding the high levels of interest among payer organizations, the Blackbook survey also points to low levels of interest among health systems, citing the undetermined cost of blockchain solutions as the major issue that stops health system executives from committing to a timeframe for deploying the technology. Our best hope is that the early pilots will bring tangible proof points and provide more confidence to the industry in the technology.

 

Health care is a team sport and so is blockchain. The more participants there are in a blockchain network, the better it is for the network and the industry. Pilot programs such as the provider data management initiative by Quest Diagnostics and others will need a much higher level of participation across the industry to reap the benefits of the network effects.

 

As with most transformative technologies, the big challenge is usually not the technology; it’s managing culture and workflow changes, driving collaboration, and an execution focus. In blockchain, there is an added dimension of a commitment and willingness to work across company boundaries, which is an entirely new paradigm for most health care enterprises.

In a unique development, a group of health care industry executives has come together to launch a peer-reviewed blockchain journal to share both the positive and the negative experiences with blockchain in health care.

 

The potential for blockchain to improve health care operating efficiencies is significant. High-value use cases include revenue cycle management, supply chain, clinical trials, and provider data management. The initial pilots will need to demonstrate the real benefits of the technology and lead to higher adoption of blockchain in the coming year.  

The big trends in healthcare today are rising consumerism, a shift to value-based care, emerging data sources, and the use of advanced technologies for improving care delivery and reducing costs. There is unprecedented innovation opportunity in the digital transformation of healthcare.

 

At a recent industry event, I moderated a panel discussion on innovations in health care with a group of technology innovators and healthcare veterans. We all agreed that technology-led innovation was accelerating rapidly, but the innovation landscape had a set of unique challenges as well. In this post, I share some of the key thoughts from the discussion.

The market landscape for healthcare innovation

In the past few years, we have seen several significant changes in the market for technology-led innovation in health care. Here are some important trends.

 

• A shift towards the virtualization of health care. Many routine health care services are now available on your smartphone – a perfect example would be urgent care visits or routine consultations. According to one report, there are over 300,000 health apps in the Apple and Android stores. However, only a small number have reached critical mass, indicating that consumer preferences for virtual care are changing slowly.
• Huge amounts of venture capital pouring into digital health: $11.5 billion in 2017, according to one report. The first quarter of 2018 has seen continued investments in digital health, serving as a validation of the promise of digital health innovation and the opportunities in the digital transformation in health care. At the same time, many of these startups are struggling, exits are not keeping pace with expectations, and a few that have raised very large amounts of money, such as Outcome Health, have gotten into trouble for trying to find short-cuts to growth and profitability.
• Innovations from big technology firms are also struggling to gain traction. Recent troubles at IBM’s Watson Health business which has reportedly laid off significant numbers of employees in the face of market and organizational challenges indicate a deeper problem for the business model itself.
• New data sources such as genomics, wearables and social determinants are driving a whole new way of managing patient populations. Unstructured data, such as clinical notes, is now the new goldmine that people are digging into, with the help of emerging technologies such as AI. Other emerging technologies, like blockchain, are still in early stages but with great potential. However, data interoperability, especially with the big electronic health record (HER) systems like Epic and Cerner, remains a challenge.
• We are in the early stages of breakthroughs such as gene-editing with CRISPR (powered by massive data analytics capabilities) that are likely to transform healthcare, along with an explosion in smart sensors and wearables. Other technologies, such as augmented reality (AR) and virtual reality (VR) are in very early stages but show enormous potential in transforming the way healthcare is delivered in the future.

Health care’s innovation focus and the players

A recent survey by Modern Healthcare indicates that health care consumerism is the no. 1 area for innovations, followed by clinical practice, or care delivery, and payment reform or alternate payment models. Most respondents in the survey felt that innovation was accelerating.

 

Data from Rock Health, a venture capital (VC) firm, lines up with the survey responses. While disease diagnosis and treatment remain significant focus areas, consumer empowerment is emerging as a strong funding category, confirming the rise of consumerism in health care. As healthcare shifts progressively away to virtual care delivery models, interest in telemedicine, remote monitoring, and alternate care delivery models continue to drive innovation.

 

It’s not just VC firms that are funding and driving technology-led innovation in health care. We are seeing health systems getting into the innovation game themselves by setting up funds.

 

Examples include Partners Healthcare, UPMC, Intermountain and Mayo Clinic, to name a few. While these funds are relatively small, the opportunity for promising startups with innovative solutions to accelerate the path to product validation and market acceptance improves with the support of the sponsoring health system.

 

We are also seeing some non-traditional partnerships emerging. The big announcement earlier this year by Amazon and Berkshire Hathaway, and the more recent announcement by a consortium of healthcare companies to invest in blockchain technology are examples.

 

Despite health care's reputation as a slow follower of technology, the innovation ecosystem is buzzing. In my book, the Big Unlock, I refer to four categories of technology solution providers: The Custodians such as the EHR vendors, who have the data and the workflow; the Enablers, which are big companies like Google, Microsoft, and Salesforce who have invested in health cloud infrastructures that can be rented for building digital health experiences; the Arbitrageurs, which include global consulting and technology services firms who rely on information and labor arbitrage for developing and delivering technology solutions; and finally the Innovators, which include the hundreds of startups and VC-funded companies who are developing entirely new ways to deliver health care. Every one of these categories is innovating in their own way.

 

At the heart of the innovation ecosystem is a final category of innovators, namely the healthcare enterprises. Leading health systems are innovating with health care delivery models and pricing/contracting models and are using technology to enable their digital transformation.

Into the great wide open

Despite all the activity and the fierce competition, there is good news for innovators; the market is wide open, and there is no single dominant entity in the digital health innovation landscape. Each of the categories of technology providers I refer to have their unique strengths and many would like to become that one dominant solution provider of choice.

 

While it does not seem likely that we will see a dominant digital health innovator in the near term, the window of opportunity for innovators is narrowing. As the high value “white spaces” get filled up and the risks of failure increase, VCs are committing larger and larger amounts of funding to more mature companies in the hope of a successful exit. New entrants in the innovation landscape will either need to find new white spaces or build “better mousetraps” to challenge well-capitalized incumbents on their turf. At the same time, as the pace of exits picks up, VC firms will look for new investment opportunities for their liquidation gains. For now, it’s best for digital health innovators to operate with an abundance mindset. It’s an “all you can eat” world out there. 

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.  

According to The Alzheimer’s Association, there are over 5 million Americans with Ad. It is the sixth leading cause of death. More than 15 million caregivers provided an estimated 18.1 billion hours of unpaid care at a value of approximately $221.3B. The impact of this disease is also well-illustrated in a recent  PBS documentary.  While it might seem incongruous on the surface to discuss digital technology and a population with significant cognitive challenges, I will illustrate how it can be beneficial at different stages of the disease’s course.

 

Cognitive Assessment Tools.  Most tools for assessing cognitive abilities have been of the traditional written form, as offered by the Alzheimer’s Association.  The ability of digital tools to detect early diagnosis of Ad is important in medical and social planning for the patient and family. Some have taken traditional diagnostic tools and transformed them into a digital platform. Such is the case with Quest Diagnostics’ CogniSense.  A more transformational approach is one seen with a utilization of the Anoto Pen which can measure the writing instrument’s position up to 80 times per second. An exciting study by the Lahey Medical Center and MIT’s Computational Science and Artificial Intelligence Laboratory looked at using the Anoto Pen versus traditional cognitive assessment tools for Ad and other diseases. This method has already shown advantages over traditional tools, described in an MIT News piece: “… while healthy adults spend more time on the dCDT [digital clock drawing test via Anoto] thinking (with the pen off the paper) than “inking,” memory-impaired subjects spend even more time than that thinking rather than inking. Parkinson’s subjects, meanwhile, took longer to draw clocks that tended to be smaller, suggesting that they are working harder, but producing less — an insight not detectable with previous analysis systems…”  A digital platform called Neurotrack claims it has the ability to detect Ad at its earliest stages by assessing recognition memory, a function specific to the brain’s hippocampal region which is affected early in the course of Ad. Digital assessment tools like these can also save clinician time and offer a better objective patient assessment.

 

Cognitive Improvement tools. A handful of small studies have shown that ‘brain exercise’ in the form of cognitive augmentation games decreases the risk in normal individuals of getting Ad. One would naturally ask if this carries over to those already diagnosed AD. Some earlier studies suggested this was the case. An older review of multiple small studies showed that while they suggest that brain exercises slowed progression of cognitive decay they did not affect mood or the ability to care for oneself.  It is worthy of noting that patients with larger baseline ‘cognitive reserve’ do better to a point then characteristically have a rapidly progressive course. In a previous post, I discussed the merits of music as an ideal digital health tool. Music should be considered as a potentially much appreciated and useful tool.  Relative to Ad specifically, I would reference the incredibly informative and moving award-winning film Alive Inside, documenting the response of patients with severe Ad to music relevant to their personal past. An intriguing interactive game/tool is Tovertafel, a Dutch technology which projects via suspended box visuals onto a table.  There are various exercises and games on the platform which are both enjoyable and mentally stimulating. Less sophisticated yet popular games are offered by the Alzheimer’s Association.

 

Tools for monitoring daily activities. Technologies have been developed to aid patients with mild to moderate disease and their caregivers to make daily activities easier and safer. SmartSole makes an innersole with a GPS locator with an associated smartphone app and call service for alerts. Silver Mother by Sen.se is a customizable digital tech platform (front door position, room temperature, and water and food containers) connecting caregivers with love ones’ activities of daily living.  For patients with early dementia or for caretakers to connect with loved ones at a distance, grandCAREis a very comprehensive platform and service.

 

While one might associate digital tools with those of us who are “connected,” their utility in the realm of Ad can be profound.  I would submit that the potential for digital tech to prolong independence and/or improve lives of caregivers in the home or at a distance must be the subject of clinical studies.  Public health policy might very well change as a result of such outcome studies.

In my book, The Big Unlock, I describe the four major categories of technology providers as Custodians, Enablers, Arbitrageurs, and Innovators. Each of these categories of providers has staked a claim to reimagining the digital future of healthcare.

 

First, the Custodians: These are the big electronic health record (EHR) vendors like Epic and Cerner who have the data and the workflow. As systems of record, they enjoy the long-term strategic commitment of the health systems they serve and are the first port-of-call whenever a health system decides to enable new functionality for enhancing the patient and caregiver experience. 

Systems of record have certain limitations and are arguably weak in several areas, such as advanced analytics, which is critical for a digitally reimagined healthcare experience. Along came the Enablers to address this problem. Big technology firms such as Google, GE Healthcare, Microsoft, Salesforce and IBM’s Watson Health business have built technology stacks that integrate multiple emerging and traditional data sources, including EHR systems, and have incorporated some proprietary data sources as well, such as images in the case of GE Healthcare.

 

These big technology stacks include inbuilt advanced analytics capabilities that can deliver insights to power digital health experiences. Google’s Deep Mind, for instance, recently analyzed eye scans from over 125,000 patients to build an algorithm that could detect diabetic retinopathy, the number one cause of blindness in some parts of the world, with over 90 percent accuracy. The company claimed the accuracy of the analysis was on par with board-certified ophthalmologists.

 

The Arbitrageurs are mostly technology agnostic consulting firms such as Accenture and Deloitte, as well as India-heritage firms such as Wipro and Infosys, that rely on information and labor-arbitrage models to build digital experiences from scratch using the preferred technology tools that exist in health systems.

The Last Mile problem

All three categories of technology providers described above have stopped short of building ready-to-deploy digital health experiences, which leads us to the Last Mile problem in healthcare. Despite the powerful computing and data analytics infrastructure that big technology firms have invested in, there is a shortage of viable, proven digital health experiences for health systems and their key stakeholders in healthcare delivery i.e. patients and caregivers. The challenge – and the opportunity – has fallen to the fourth category of technology providers, namely the Innovators.

By definition, the Innovators are typically startups that have come up with a whole new way of addressing an existing problem with technology-enabled healthcare experiences, or for filling a gap in the current healthcare experience with technology. Digital health startups raised an estimated 11.5 billion in 2017, and money continues to pour into the sector, despite the slow pace of exits. Despite the promise, a report by IQVIA indicates that while over 318,000 health apps and 340 consumer wearable devices are now available worldwide, 85 percent of the apps had fewer than 5000 installs. The few apps that did reach critical mass demonstrated strong clinical evidence, robust integration with the established workflow integration, and high user ratings – prerequisites for any digital health solution looking to break into the health systems marketplace.

Addressing the bottleneck

The need for last mile applications is enormous, and yet the innovation ecosystem has not built and implemented viable applications fast enough and at scale to meet the demand. Common challenges include:

• Extended cycles to hit prime time usage: most health systems follow a traditional approach that takes promising new solutions through the phases of a free pilot, paid pilot, and enterprise adoption. The process could take years, and many solutions remain in "pilot purgatory" for an extended period, often failing to break through to enterprise adoption. Health systems need a newer, more agile model, to assess and deploy promising solutions more quickly and efficiently
• Too many standalone solutions: the digital health landscape is littered with thousands of point solutions that stand in isolation, with no established connectivity to systems of record which is the price of entry for any new solution. Health systems are loath to sign up dozens of point solutions and take on the burden of integrating and managing these solutions. They prefer to default to the many solutions that EHR vendors have built or are actively building (or claim to be building) that effectively make stand-alone solutions redundant, despite the superior experience architectures that startups are known for. A potential approach for startups is to align with one of the big Enabler companies who, through established relationships with health systems, can create a pathway to adoption and growth.
• An absence of scale: No single platform addresses all the needs of a digital health enterprise today, unlike the mature enterprise resource planning (ERP) systems of the manufacturing and financial sectors. There is a significant opportunity for Enables companies to build ready-to-deploy innovation ecosystems through partnerships with digital health startups. However, Enabler platforms too have increased and are at risk of becoming too fragmented to present a real alternative to health systems looking for scale and velocity in the digital transformation journeys.

The digital transformation of healthcare is in its early stages, and the gold rush is underway. Wanted: creative approaches to solving the Last Mile problem and unifying the fragmented ecosystem of point solutions and stand-alone technology enablement platforms.