Artifcial Intelligence as a Growth Engine

California Management Review
2019, Vol. 61(2) 59–83
© The Regents of the
University of California 2018
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DOI: 10.1177/0008125618811931
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59
Business Models
Artifcial Intelligence
as a Growth Engine for
Health Care Startups:
EmErging BusinEss modEls
Massimo Garbuio1 and Nidthida Lin2,3
SUMMARY
The future of health care may change dramatically as entrepreneurs offer solutions
that change how we prevent, diagnose, and cure health conditions, using artificial
intelligence (AI). This article provides a timely and critical analysis of AI-driven health
care startups and identifies emerging business model archetypes that entrepreneurs
from around the world are using to bring AI solutions to the marketplace. It identifies
areas of value creation for the application of AI in health care and proposes an
approach to designing business models for AI health care startups.
KEYwoRdS: health care, artificial intelligence, disruptive technology, business
models, innovation, entrepreneurship
A n increasing number of entrepreneurs entering the health care space are harnessing old and new technologies in the solutions they take to the marketplace. One of the most active sectors is artificial intelligence (AI), which is being applied to clinical, operational, or financial solutions in health settings. In general terms, AI can be
described as computer systems that perform tasks requiring human-like intelligence. Health care executives rate AI as the most disruptive technology within
the industry,1 while consumers worldwide are open to receiving AI-enabled
health care solutions. The amount of data available for analysis by AI applications is growing exponentially, owing to smart wearables and other Internet of
Things solutions (IoT).2 In a 2010 global survey of executives in health care,
more than 90% recognized the complexity likely to be faced by their sector in
the next few years and indicated they were unprepared to deal with it.3
1University of Sydney Business School, Sydney, Australia
2University of Newcastle, Newcastle, Australia
3Macquarie University, Sydney, Australia
60 CALIFORNIA MANAGEMENT REVIEW 61(2)
There is little doubt that AI will revolutionize the way health care practitioners and executives gather information and interact with patients and their families, as well as with clinical and operational staff. There exists a wealth of
applications that are already relevant. Several services that previously required
human intervention and face-to-face interaction with patients have now moved
to centralized platforms managed by messaging bots and intelligent virtual assistants. Almost 80% of organizations participating in a 2017 survey are already
using virtual assistants to create better customer interactions.4 Other applications
are specific to clinicians, for example, providing platforms designed for cloud computations, deep learning in medical imaging, or optimized patient selections in
clinical trials.
Current research has classified health care startups on the basis of the type
of the problem addressed, for example, whether they provide telemedicine services, virtual assistance, or image recognition. However, as yet, we do not know
whether startups that solve different problems share common business models.
Nor do we know what value is created for stakeholders, ranging from clinicians,
patients, health care administrators, insurance companies, and government agencies.5 In this study, we answer this question by examining some of the most promising AI-driven health care startups and developing a framework for studying and
tracking their emerging business models.
As AI is a rapidly developing technology, a critical question regards the emergence of archetypes of business model. Questions surrounding business models are
always challenging, but even more so in health care because of context-specific
systemic and regulatory aspects and because of the presence of a fluid multi-stakeholder environment.6 A successful business model also requires a clear identification of what the value proposition is and who will be paying for such value
proposition, as the party who directly benefits from the value might not be the one
who pays for it. Indeed, AI-driven health care services not only promote service
quality to both direct and indirect service recipients but are also tasked with providing an effective cost-revenue structure for health care providers and health care
payers (i.e., insurers and governments). A deeper understanding of these aspects of
AI in health care may allow for the application of these concepts to other industries
with fewer restrictions in terms of regulation and who pays for services.7
Research Method
Technology is a driver of change in business models. Any investigation into the implications of technological change begins with a characterization of the technology’s attributes and the potential business model that can
be employed to effectively take the technology to the market. Here, we have
reviewed the deployment of AI in the health care sector using a data set drawn
from 30 health care startups around the world.
Over three years, we have explored the question, “How do entrepreneurs
in health tech8 develop business opportunities and capture these opportunities
Artifcial Intelligence as a Growth Engine for Health Care Startups: Emerging Business Models 61
with innovative and technologically driven business models?” We started by
reviewing several academic and practitioner-oriented studies9 into health tech in
general and observing closely three AI-driven health care startups. The first uses
AI in the creation of a marketplace connecting providers and patients. The second
is a company operating in the digital health space. The third is developing a smart
pillow solution for the detection of sleeping disorders. We also studied the literature on AI and discussed it with those in the field, ranging from researchers in
both business and engineering to entrepreneurs. We then identified the set of
startups to study in the health care sector using Fortune and Forbes and, in particular, the list of the top 106 startups identified as innovators within the Health AI
space by CB Insights, a well-known database of startups from across the world.
We manually collected information about the funding received by each of the
startups in the dataset for which information was available in Crunchbase and
Angel.co. We matched this information with press releases, websites, newsletters,
and blogs on the development of AI, as well as health technology. We then interviewed one executive in health insurance, the CEO of a well-known AI-driven
health care company (Matteo Berlucchi, Your.MD), as well as other entrepreneurs currently utilizing some form of AI (both in health care and in other industries), and a venture capitalist who has made investments in health AI. On the
basis of these diverse sources, we developed a thorough understanding of the
sector and business models. Finally, we presented our findings to other entrepreneurs as well as executives in the field to gauge their feedback on our research.
What Is AI
As a scholarly field, AI dates back to the 1950s. However, recent advancements and innovations in information storage and processing have enabled an
explosion in the abilities and potential of intelligent systems to revolutionize
industries from agriculture and finance to health care. The fundamental principle of AI is machine learning, or the ability of a computer to improve upon its
own capabilities by continuously analyzing its interactions with the real world.
This also includes Natural Language Processing (NLP), a form of AI that analyzes the human language, helping a machine understand, interpret, and manipulate human language (e.g., text, speech). There has been dramatic growth in
the power and sophistication of machine learning and NLP in recent years due
to high-bandwidth networking and cloud computing, among other high-level
innovations.
In industries like health care, where human intelligence is both invaluable
and increasingly in high demand, the introduction of innovative, AI-powered
technologies has been lowering costs, hastening drug discovery, and improving
health outcomes. More and more, the potential of AI to revolutionize the industry
is catching the attention of key players in both health care and venture capital,
with increasing funding allocated to the sector in recent years. But in order to better advance our understanding of such phenomena, we must further break down
these disruptive new technologies into different categories.
62 CALIFORNIA MANAGEMENT REVIEW 61(2)
There are three fundamental ways through which business can or will use
AI: assisted intelligence, augmented intelligence, and autonomous intelligence.
Assisted intelligence helps improve what the business is already doing by amplifying
the value of current activities. This form of AI often involves clearly defined, rulebased, repeated tasks with common applications including data verification and
simulation to test business decisions with less risk. Medical image classification is
an example of assisted intelligence in health care services to improve accuracy
over conventional processing techniques. Augmented intelligence is an emerging
technology in AI that provides organizations with new capabilities and differs
from assisted intelligence in that it alters the nature of an activity, which as a consequence requires changes in the business model. Augmented intelligence plays a
critical role in shifting health care toward prevention, personalization, and precision. Precision medicine—a tailoring of medical treatment to target the specific
needs of an individual based on the characteristics of each patient (e.g., a person’s
genetic makeup)—will greatly benefit from augmented intelligence. Autonomous
intelligence is the advanced stage of AI that is currently being developed; this form
of AI acts on its own and chooses its action on the basis of business goals. Currently,
human-independent decision-making capabilities are not in widespread use
beyond automated stock trading and facial recognition applications. In health
care, the doctorless hospital is a future application for the autonomous intelligence system. However, this requires not only advances in AI technology but also
the ability to build in enough transparency for humans to trust the technology to
act in their best interest.10 Table 1 summarizes these applications of AI in business
and the examples of startups utilizing such technology.
To better understand how these types and subtypes of AI are changing the
health care technology landscape, we have classified a selection of a set of 30
startups from the list of 106 that are “transforming health care with AI,” according
to CB Insights.11 The selected startups operate across the health care value chain,
innovating everything from in-patient care, hospital management, and medical
imaging and diagnostics to patient data and risk analytics. They have received a
variety of funding, and we controlled for the year in which they received the first
round of financing. By analyzing and classifying the innovative AI applications of
these companies, we were able to extrapolate different approaches to creating,
delivering, and capturing value in the health care sector, which will provide an
example for other sectors.
Among the 30 startups, none currently offer any form of autonomous
intelligence as this class of AI is not yet widely available in the health care technology market. Instead, forms of assisted and augmented AI are disrupting the
current health-tech landscape, predominantly through the use of limited memory
machine-learning-based platforms that use a base of data stored in the computer’s
memory to inform the system’s real-time decision making. Nevertheless, we have
observed rapid advancement in autonomous intelligence technology, such as an
automated hospital pharmacy at the University of California San Francisco
Medical Center at Mission Bay.12 As the AI sector in health care continues to
mature, we predict that more sophisticated applications, such as autonomous
intelligence, will become more widespread within the overall AI sector.
63
Table 1. Three Fundamental Types of Artifcial Intelligence.
assisted Intelligence augmented Intelligence autonomous Intelligence
Defnition AI that “improves what people and
organizations are already doing” by
automation based on clearly defned,
rule-based, repetitive tasks to remove
redundancies from business operations,
improve effciency, and boost the value of
existing activity.
AI that “enables organizations and people
to do things they couldn’t otherwise do”
through sophisticated algorithms built for
natural language processing and sifting
through massive accumulations data and
records.
AI that “creates and deploys machines
that act on their own,” making decisions
based on their best interests using
machine learning algorithms that operate
independently of human instruction or
oversight.
Key Characteristics of
Startups
•• The most basic level of application of AI,
often based on pre-existing algorithms
with minimum adaptation
•• Common usage includes data verifcation
and simulation to assist business decision
making
•• A great proportion of startups currently
operating in the AI space utilize this form
of AI
•• Data required for this type of AI is often in
a raw form drawing from a single source
•• A more sophisticated level of application
of AI where the algorithm has a greater
customized component
•• Often alters the nature of the activity and
may require business model change
•• Common applications include precision
medicine and data sifting such as fnding
patterns in epidemiological data
•• Requires more sophisticated set of data
involving more than one source of data
•• The greatest level of sophistication in AI
algorithms, where decisions can be made
independent of human participation
•• Only a very limited number of startups
operate at this level
•• Autonomous system requires both
advanced AI technology and a suffcient
level of transparency in the algorithm that
builds trust in the systems
•• Input data are often drawn from a
number of sources and involve higher
level of sophistication
Exemplar Startups Aindra, a Bangalore-based AI-powered
MedTech company has developed an AI
platform utilizing medical image classifcation
technology to facilitate faster and more
accurate diagnosis of cancer.
iCarbonX provides highly individualized care
through massive data set, biotechnology, and
AI. iCarbonX forms a digital heath alliance
with heath data and personalized medicine
startups around the world to gain access to
a big data for its AI algorithm.
Mayo Clinic is working toward the doctorless
hospital. Many of its components already
exist but are waiting to be tested enough
to satisfy safety standards. For example,
surgeons are already using robots in the
operating theater to assist with surgery.
Note: AI = artificial intelligence.
64 CALIFORNIA MANAGEMENT REVIEW 61(2)
The Complexity of Value-Users in Health Care
For a solution to thrive in the marketplace, it needs to begin with a clear
definition of the value that is to be created for a particular user. The question of
value relates to the question of who is the user that the solution aims to address.
In the case of Your.MD, an AI-based application that helps patients find the
most relevant health information, whether it be to stay healthy or to better understand their symptoms, CEO Matteo Berlucchi13 highlights that he did not go
through the deliberate process of opportunity identification to come up with the
idea behind Your.MD. He was approached by a group of business executives, some
of whom were from outside the health and technology fields (including bankers)
to develop what became Your.MD. The idea had already envisioned a clear user
value aimed at addressing a critical problem.
With mobile phones becoming ubiquitous and providing you with computing
power on your hands and an easy connection to centralized computing power,
there must be a way to get people the health information they need for free when
they need it . . . So mobile phones plus health equals something useful for a lot of
people. The original idea was just to give information to people.
The original idea did not include a business model that outlined how taking
information to the masses would work. Only after the original value creation idea
was formed did the typical methodological approach of market analysis begin.
One insight was realizing that the health care industry is far behind other industries when it comes to digitization and transfer of control to the end user. In many
cases, using examples from other industries to solve a problem is applied consciously or unconsciously by entrepreneurs in discovering new approaches to
business model innovation as well as strategies that can then be adapted to their
specific industry.14 It is at this point that Berlucchi started to think about solving
the problem in a very patient-centric way. Note that health-related searches are
among the most common searches in Google but also those associated with highly
paid advertisements. However, search engines do not provide the most accurate
information and are not a curated marketplace of health care information. It is
important to make the distinction that the provision of information is only one
step toward an AI-enriched use of information in health care. Startups like Jvion
provide “cognitive clinical success machines” in which the company’s software
predicts and prevents patient-level diseases as well as financial losses for health
care providers. Jvion’s predictive solution looks at the patient population and predicts the risk of an illness or condition before symptoms occur.
Another important consideration at the very start of building a new venture is identifying who are the users. In many instances, there are a number of
individuals who benefit in different ways from an AI-driven solution. It is thus
critical to understand who the primary and secondary users are as well as who
will pay for the solution. The question of who pays is particularly important in
health care, where insurance providers or national health systems ultimately bear
some of the costs.15 This has already happened in relation to medical devices that
Artifcial Intelligence as a Growth Engine for Health Care Startups: Emerging Business Models 65
are enabled by IoT technologies. One example is a health care provider that detects
potential issues in a prosthetic joint using data sensors to summarize the force
distribution and pressure patterns. This helps deliver value to the patient by
promptly alerting them to see a medical professional, as well as value to the provider, since unnecessary costs due to remedial treatment or prolonged recovery
are avoided. When several stakeholders concurrently benefit from the solution,
monetization becomes a very interesting question—are all parties involved liable
to pay or should one side be subsidized, as often happens in platform business
models such as gaming systems?16
Another case in point is the following scenario derived from a real situation. Say you have founded a health tech startup that collaborates with a university-based nano-tech research center to develop a noninvasive solution via a
smart pillow to help diagnose and monitor sleeping conditions. This solution very
closely resembles Beddit (which was acquired by Apple in 2017) or any of the
other companies (such as Withings, Aura, or Resmed S+ sleep sensor) that have
attempted this path of diagnosis and monitoring of sleeping conditions. You have
identified broad target markets, including end users for at-home use, sleep clinics,
airlines for monitoring of sleeping conditions on planes, hospitals, aged care facilities, and so on. Employees, especially shift workers, and health insurers would
also benefit. Evidence suggests that shift work increases the possibility of mistakes, which is of critical importance in certain high-risk occupations requiring a
high level of precision (e.g., complex machinery, surgeons, and nurses). You have
also received interest from a robotics company, which seeks to use the smart pillow device for data acquisition, as this is useful to anyone who sees value in measuring sleep and providing insights into how to improve it. You can see enormous
value in your innovation to sleepers (end users), families, medical professionals,
employers, and even insurers as poor sleep is a precursor to heart disease and
dementia.
Your task here is to decide who you are really creating value for and what that
value is. Fundamentally, you need to create hypotheses to test these alternative
futures. These then allow you to tweak your technology accordingly, depending
on the specific value/user combination you are exploring.17 The data is a crucial
aspect in this regard. The value is not limited to the user who has a sleeping disorder but is available to other individuals. Hence, potential users for the smart
pillow may be:
• patients with potential sleeping disorders, to monitor sleep and develop strategies for sleep management;
• aged care facilities, to improve quality of care and better resource management;
• sleep clinics and hospitals, to monitor sleeping disorders;
• employers, to help in decreasing mistakes due to poor sleep and shift workers’ productivity and safety; and
• insurance companies, to better profile their customers and, when legally possible, adjust the premiums.