structuring investments and doing deals in ai in …...top ai applications artificial intelligence...
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Structuring Investments and Doing Deals in AI in
Health Care Bobby Guy, Bruce A. Johnson, William Mahood, Jon
Pritti & William Tanenbaum
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Speakers
Bobby Guy is the Chair of the annual Healthcare Dealmakers Conference in Dallas and a frequent author and speaker on issues related to healthcare and innovation. He is a healthcare M&A attorney and shareholder with Polsinelli, and resides in the firm’s Nashville office.
He has lead several of the largest
healthcare deals in the country in the last few years, including the sales of more than 500 post-acute and behavioral facilities since 2016, and the acquisition of a number of healthcare innovation companies in healthcare services and life sciences.
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Speakers
Bill Mahood is the Practice Area Leader for Polsinelli's Mergers, Acquisitions and Divestitures practice. This M&A practice includes 110+ team members and is national in scope, with lawyers from San Francisco to Miami. Bill is a shareholder in the firm’s Kansas City office.
Bill is a general M&A lawyer, frequently acting as outside general or transactional counsel to the firm’s clients. Bill regularly represents buyers and sellers in the technology, healthcare technology and financial services fields. He is active in mid-market M&A thought leadership, through the American Bar Association’s Deal Point Study Committee and as a Board member of the Association for Corporate Growth.
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Speakers
Bruce A. Johnson is a shareholder in the health care law practice of Polsinelli PC based in Denver. Bruce brings nearly 30 years of legal and management consulting experience to his health care organization clients including hospitals and health systems, medical groups, physicians and other enterprises involved in the delivery of health care services.
Bruce works with traditional health care organizations and innovators across the nation to develop and implement arrangements to achieve client business objectives while promoting compliance and business success in today’s value-based health care payment and delivery environments.
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Mr. Pritti is a member of Houlihan Lokey’s Healthcare Group. Over the past
decade, he has worked primarily with healthcare services companies, where he
has led a range of assignments, including strategic advisory, buyside and sellside
M&A, divestitures, and financing transactions. During his career, Mr. Pritti has
completed more than 40 transactions in the healthcare sector.
Mr. Pritti’s recent notable transactions include AGS Health; GeBBs Healthcare
Solutions; Q-Centrix; MedPartners HIM; General Dynamics Information
Technology; IMA Consulting; Receivables Management Partners; Medical
Reimbursements of America; Cardon Outreach; Avadyne Health; TowerBrook /
Ascension; The Outsource Group; Intermedix; and Accretive Health
Before joining Houlihan Lokey, Mr. Pritti was a Managing Director and member of
the Valuation Committee at Cain Brothers, where he provided M&A and capital-
raising investment banking services to healthcare services companies. Prior to
Cain Brothers, he was a member of the Healthcare Investment Banking Group at
Goldman, Sachs & Co. Earlier in his career, Mr. Pritti worked in the Leveraged
Finance Group at BNP Paribas.
Mr. Pritti holds a B.B.A. from Emory University and an MBA from Columbia
Business School.
Jon Pritti
Managing Director, Head of Healthcare IT
New York
Qualifications
B.B.A. Emory University
MBA Columbia Business
School
PAST Cain Brothers
Goldman, Sachs & Co.
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Agenda
Introductions
AI Everywhere . . .
Where’s the Investment, and Why?
AI’s Applications in Healthcare
Key Deal Making Terms
Regulatory Imperatives
Takeaways and Q&A
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A Note About the HDC Quarterly Market Updates
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AI EVERYWHERE
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AI Everywhere
Understanding the Context: This presentation is about recognizing change in healthcare.
AI is part of something bigger
Watershed moment
Change always creates winners and losers.
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AI Everywhere
A Paradigm – the Acuity Principle
The implications for Tech and AI
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AI Everywhere
A Paradigm – the Acuity Principle
– In the future, health care will be provided in the lowest acuity settings appropriate for the care, because this is the most efficient and cost-effective way to provide care.
The Implications for Tech and AI? Core to efficiency and accuracy
The Implications for Low Acuity Settings v High Acuity Settings?
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AI Everywhere
The simple definition of AI: “Machine Learning”. Broader definition later.
Did you know …
Today, Not Tomorrow
AI is ubiquitous in the first world, right now
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AI Everywhere July 13, 2019
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AI Everywhere
The Foundation of AI is Data
Collecting Data
Mining Data
Deriving Conclusions
Delivering Solutions (Proliferation)
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AI Everywhere
What’s the Biggest Change Happening in Healthcare Right Now?
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AI Everywhere
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AI Everywhere
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AI Everywhere
For the future of healthcare, this is about:
– Interoperability of systems
– Ownership of the data
– Consumer adaptation
– Industry adaptation
– Resources (i.e., money) and integration
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AI Everywhere
The Warning -
Harvard Biz Rev:
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AI Everywhere
The Good News
– You’re already using lots of AI – you just didn’t know it
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AI Everywhere
The Result for M&A Activity in Health Care?
– AI is integral to many deals outside the AI space, without even being recognized
– AI is already becoming integral to many healthcare companies
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WHAT AREAS ARE ATTRACTING INVESTMENT AND WHY?
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Leading Healthcare Technology / Tech-Enabled Franchise
*Selected transactions were executed by Houlihan Lokey professionals while at other firms
90+% TRANSACTIONS CLOSED IN
OR ABOVE PITCH RANGE
SINCE JAN. 1, 2016
26 CLOSED HCIT / TECH-
ENABLED SERVICES
TRANSACTIONS SINCE
JAN. 1, 2015
~30% TRANSACTIONS SOLD TO A
STRATEGIC SINCE JAN. 1,
2016
~$230 million AVERAGE TRANSACTION
SIZE SINCE JAN. 1 2016
~70% TRANSACTIONS SOLD TO A
FINANCIAL SPONSOR
SINCE JAN. 1, 2016
2017
2016
2015
2018
2019
Sellside Advisor
Project
Cougar
Provider of Patient
Financial Engagement
Solutions
Prep
Exp. Close: Q4 2019
Sellside Advisor
Project
Prime
SaaS Solutions for the
Post-Acute Care
Market
Signed SPA
Exp. Close: July 2019
We are one of the most experienced Healthcare Technology / Tech-Enabled Services teams on Wall Street with more than 60 closed transactions
2017
2018
Sellside Advisor
Project
Aviator
Revenue Integrity
Solutions
Prep
Exp. Close: Q4 2019
Sellside Advisor
Project
Aegis
Release of Information
and Revenue Integrity
Services
In Market
Exp. Close: Q4 2019
Sellside Advisor
Project Atlas Healthcare Tech-Enabled
Financial Integrity
Solutions Company
a PE Fund
Sellside Advisor
Project
Ozark
Coding and CDI
Services
In Market
Exp. Close: Q4 2019
2016
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has been acquired by
Financial Advisor
has been acquired by
Sellside Advisor
has been acquired by
Sellside Advisor
has received a significant
investment from
Sellside Advisor*
a portfolio company of
has been acquired by
Sellside Advisor
a portfolio company of
has been acquired by
a MEDNAX company
Sellside Advisor
has divested its Commercial Health
Insurance Payer Products Business
to
a portfolio company of
Sellside Advisor
has completed a control transaction
with
Financial Advisor
the healthcare software assets of
have been acquired by
Sellside Advisor
OneContentTM, a content
management software asset of
has been acquired by
a portfolio company of
Financial Advisor
has completed a financing
consisting of
$24,000,000
Series D
Convertible Preferred Stock
Placement Agent
a portfolio company of
has been acquired by
Sellside Advisor
has been acquired by
Sellside Advisor
a division of
has been acquired by
Sellside Advisor
has completed a financing
consisting of
$24,000,000
Series D
Convertible Preferred Stock
Placement Agent
a portfolio company of
has been acquired by
Sellside Advisor
has been acquired by
Sellside Advisor
has been acquired by
Sellside Advisor
The Connect business unit of
Has been acquired by
Sellside Advisor
a portfolio company of
has been acquired by
Sellside Advisor
has been acquired by
Sellside Advisor
in partnership with
has acquired
a portfolio company of
Buyside Advisor
has partnered with and made an
equity investment in
Buyside Advisor
has received a significant
investment from
Sellside Advisor
has been acquired by
Sellside Advisor
Has completed a Tender Offer.
Fairness Opinion
A newly formed investment vehicle
owned by
has invested $200 million in
in exchange for convertible
preferred stock and warrants
Financial Opinion
The Applications for Artificial Intelligence in Healthcare are Vast
Source: Bain
Remote Prevention and Care Virtual agents can serve patients
outside of their doctor’s office 24-7
Drug Discovery and Development Increase R&D productivity by
leveraging past screening results
Marketing and Sales Identify providers and patients who
are likely to be receptive to a
company’s products
Diagnostic Support Machine vision to assist in medical
imaging and other clinical tests
Treatment Pathways and Support AI tools to create individualized
treatment plans and reduce errors
Operations NLP to automate the writing and
review of medical records
Support Functions Automate standard tasks and
processes through voice recognition
and NLP
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Providers Biopharma
Payers Medtech
Investment and Application Across the Healthcare Continuum
AI is Poised to Significantly Contribute to Healthcare in the Near-Term
Source: Accenture, CBInsights
Top AI Applications
Artificial intelligence is a self-running engine for growth that presents a myriad of applications for healthcare. The number of start-ups
and volume of capital deployed in artificial intelligence companies across the healthcare spectrum is rapidly increasing
Robot-Assisted
Surgery $40B
Virtual Nursing
Assistant $22B
Administrative
Workflow $18B
Fraud Detection
Systems $17B
Dosage Error
Reduction $16B
Connected
Machines $14B
Trial Participant
Identifier $13B
Preliminary
Diagnosis $5B
Automated Image
Diagnosis $3B
Cybersecurity $2B
(Estimated Near-Term Value)
AI Healthcare Funding is at a Historic High
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20
30
40
50
60
70
80
$0
$100
$200
$300
$400
$500
$600
$700
1Q 2Q 3Q 4Q 1Q 2Q 3Q 4Q 1Q 2Q 3Q 4Q 1Q 2Q 3Q 4Q 1Q 2Q 3Q 4Q 1Q 2Q
2013 2017 2016 2015 2014 2018
Equity Deals
Equity Funding
Deep Learning & Predictive Analytics NLP
Machine Vision
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($ in millions)
In April 2018, Roche completed its $1.9 billion acquisition of Flatiron Health. This acquisition combines the efforts of two companies
committed to improving the lives of cancer patients by making optimal use of healthcare data and analytics
Deal Rationale
Announcement Date Feb-18
Previous Stake Held 13%
Stake Acquired 87%
Transaction Size $1,900
Implied Enterprise Value $2,174
Curates and develops real-world evidence for cancer research,
enabling researchers and providers to learn from the experience of
every oncology patient encounter
Partners with more than 265 community cancer clinics, six major
academic research centers, and 14 therapeutic oncology companies
The front-end users of Flatiron Health are oncologists and the data
customers are pharma companies
Continues to operate as a separate legal entity post-acquisition and
has retained its current business model
Founded in 2012 and headquartered in New York, NY
Case Study: Sale of Flatiron Health to Roche
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Roche Background
Swiss healthcare company founded in 1896 and headquartered in
Basel, Switzerland
Over 94,000 employees and roughly $59.6 billion revenue in 2018
Operating through two main divisions:
The Pharmaceuticals Division develops medicine for various
disease areas, focusing on oncology, immunology, infectious
disease, ophthalmology, and neuroscience
The Diagnostics Division serves customers spanning the entire
healthcare spectrum, focusing on four diagnostic areas: Molecular,
Professional, Tissue, and Diabetes Care
Leading biotech developer with approximately 20 research and
diagnostic development sites worldwide
Largest investor in cancer treatment R&D globally
Flatiron Health Background
Transaction Overview
has been acquired by
Roche held a nearly 13% stake prior to the acquisition
Allows both companies to accelerate progress toward data-driven
personalized healthcare in cancer
Oncology therapeutics contributes approximately 60% of Roche’s
total revenue
Flatiron will provide Roche much needed real-world evidence in
oncology with its ability to pull relevant insight from unstructured data
Patents for three key Roche drugs expire by 2020, driving the need
for Roche to produce new products in the short term
The acquisition will position Roche to embrace an evolving clinical
trial validation landscape via regulatory-worthy data
($ in millions)
Recent Funding of AI-Enabled Healthcare Companies
Source: Pitchbook
($ in millions)
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Amount
Date Company Description Raised Round
Jun-19 Frontier Medicines Platform intended to further accelerate the path to drug discovery $67 Series A
Jun-19 Lark Technologies Chronic disease prevention and management platform 46 Series C
May-19 Tempus Health care data analytics platform designed to improve patient outcomes 200 Series F
May-19 Cardinal Analytx Healthcare analytical services intended to predict healthcare spending 22 Series B
May-19 Noom Mobile health coaching software designed to provide intelligent nutrition and exercise coaching 58 Series E
Apr-19 BostonGene Biomedical software designed for advanced patient analysis and personalized therapy 50 Series A
Apr-19 PathAI AI-powered research platform intended to improve the accuracy of cancer diagnosis and treatment 60 Series B
Apr-19 Viome Wellness as a service intended to help people track their microbiome health 25 Series B
Apr-19 GNS Healthcare Big data analytics platform intended to develop an analytics tool for precision medicine and population health 23 Series D
Apr-19 Verana Health Data insights platform intended to optimize drug and device development 80 Series D
Apr-19 Enlitic Deep-learning software designed to improve diagnostic healthcare 15 Series B
Mar-19 HealthJoy Assistance platform intended to simplify the healthcare experience and reduce complexity 13 Series B
Feb-19 Kensci Risk prediction platform intended to identify clinical, financial and operational risk to save costs 22 Series B
Jan-19 Exscientia AI driven drug discovery technologies designed to address new drugs in areas of complex disease 26 Series B
Jan-19 Sophia Genetics Clinical genomics analysis platform designed to perform diagnostic testing 77 Series E
Jan-19 Innovaccer Data activation platform designed to help healthcare organizations activate their data silos 35 Series B
Jan-19 insitro Test case for the readiness of machine learning to improve drug R&D 100 Series A
Jan-19 Teckro Clinical trials processing platform designed to rethink every element of clinical research 25 Series C
Dec-18 K Health Software designed to provide artificial intelligence based personal health assistance 25 Series B
Dec-18 Whiterabbit Medical software intended to aid in early detection of cancer 21 Series A
Oct-18 Gauss Surgical AI-enabled platform and medical device intended to provide real time monitoring of surgical blood loss 20 Series C
Sep-18 IDx AI system for the autonomous detection of diabetic retinopathy 33 Series A
Sep-18 Clarify Health Solutions Real-time analytics and care guidance platform incorporating predictive analytics and machine learning 57 Series B
Jul-18 Olive (Robotic Process Automation) AI and robotic process automation software designed to improve efficiency while reducing administrative errors 33 Series D
Jul-18 Accolade Personalized health and benefits platform 50 Series E
Jul-18 Potrero Medical Predictive health monitoring system designed to improve patient care 27 Series C
May-18 OM1 Predictive analytics created to provide stakeholders with an analytics platform 21 Series B
Feb-18 Paige.ai Pathology guidance engine designed to generate an unprecedented breadth and depth of digital slides 25 Series A
AI’S APPLICATIONS IN HEALTHCARE
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AI in Health Care
AI is an “umbrella” concept that is made up of numerous subfields such as machine learning, which focuses on the development of programs that can teach themselves to learn, understand, reason, plan, and act (i.e., become more “intelligent”) when exposed to new data in the right quantities.^
Select current applications in the health care space:
– Voice-to-text transcription
– Fraud detection
– Cybersecurity
– Natural-language processing in research and clinical decision support
– EHR data and research
– Medical imaging and diagnostics
– Other
^ Source: PLI “Emerging Technologies in HealthCare” webinar, March 29, 2019, Jitendra Barmecha, MD, MPH, SFHM, FACP , CIO, SBH Health System, Bronx, NY. 29
Vendor AI Businesses
Overview: AI and machine learning used to support organizations currently engaged in the health care delivery system
Business Model: Vendor sells AI product/service that will augment/support health care delivery system operations other than clinical services care to enhance efficiency, avoid loss etc.
Buyers: Organizations involved in health care delivery or payment, such as insurance companies, employers, hospitals, physicians and other providers
Examples:
– Revenue cycle management/administrative activities for health care providers
– Voice-to-text transcription,
– Fraud detection for health care providers and payors
– Population/episode of care management
– Cybersecurity applications
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Provider AI Businesses
Overview: AI and machine learning used by organizations currently engaged in the health care delivery system to enhance clinical care and performance
Business Model: AI application helps to increase provider organization revenues, decrease costs, improve financial and other performance (i.e., quality) in connection with delivery of health care services
Buyers: Providers delivering, consumers receiving, payors of health care services
Examples:
– Natural-language processing in research and clinical decision support
– Access and use of clinical information contained in electronic health records
– Use of AI technology in connection with medical imaging and diagnostics
– Diagnostic machines/clinical diagnostic tools incorporating AI
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Innovator AI Businesses
Overview: AI and machine learning used by organizations not currently engaged in payment or delivery of health care delivery in connection with the prevention, diagnosis, or treatment of some aspect of human health
Business Model: Sell product or service involving AI to consumers; receive revenues and/or other value (i.e., data) from use that can be commercialized
Buyers: Consumers, select payers to augment/supplement traditional health care delivery system and services
Examples:
– Clinical diagnostic tools
– Wearables
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The Opportunities to Improve Care
Cityblock Health -- Google sub that trawls patients’ data to provide better care for low-income city dwellers, many of whom covered by Medicaid and have Medicaid data.
Alphabet is training AI to identify cancerous tissues and retinal damage.
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The Opportunities to Improve Care
Smartphones and “wearables” -- future AIs will provide automated medical diagnosis from a description of your symptoms, spot behavioural traits that suggest depression or risk of cardiac disease.
Data aggregation – will make it easier to find people with similar diseases and track responses to various treatments. Fragmented data prevents this. – DADA Foundation and rare diseases.
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The Opportunities to Improve Care
Sea Hero
Quest:
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New Drug
Development:
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KEY DEAL MAKING TERMS AND CONDITIONS
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Key Deal Making Terms and Conditions
Major Issues To Consider – A Roadmap – Data – “C’mon guys, it’s all ball bearings these
days”*
– What Is the Value Proposition
– Designing the Due Diligence Investigation
– All Roads Lead to Representations and Warranties and Sharing of Risk
– Recognizing AI components in non-AI Deals
*Source: Irwin Fletcher a/k/a Fletch F. Fletch
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Key Deal Making Terms and Conditions - Critical Due Diligence Issues
For an AI Company: – Intellectual Property
– People
– Legislative/Regulatory Risk; Data Security/Privacy • Dynamic legal environment
• 25% of all data hacks in US are in health care
– Liability Risk – Unreliability equals liability, and the liability scheme is still under development
– Do you understand how it works? (AI can reach right conclusions through wrong process, which results in errors when expanded)
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Key Deal Making Terms and Conditions - Critical Due Diligence Issues
Two software
makers have paid out
more than $200M:
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Key Deal Making Terms and Conditions - Critical Due Diligence Issues
Interoperability examples: – Think about EMR/HER (see the autocorrect? we just experienced AI . . .) – $100 million award in 2013 for hospital against software
company for defective patient accounting system – Harvard Business Review:
Even once your systems have been built, there is the issue of integrating AI systems into your organization. Unless you are employing some AI capabilities that are embedded within existing packaged application systems that your company already uses (e.g., Salesforce Einstein features within your CRM system) the fit with your business processes and IT architecture will require significant planning and time for adaptation. The transition from pilots and prototypes to production systems for AI can be difficult and time-consuming. (December 2018)
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Key Deal Making Terms and Conditions - Critical Due Diligence Issues
For a Health Care Company Target that has embedded AI:
– Ownership of the data and the AI tools
– Ownership of the intellectual property
– Data privacy and cybersecurity
– Ability to integrate
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Key Deal Making Terms and Conditions
Indemnity in Acquisition Scenario – Start-Up v Established Company
Indemnity in Contract Scenario – Wherewithal of Vendor to Indemnify in an Uncertain World
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Key Deal Making Terms and Conditions
Healthcare is a Highly Regulated Field
Survey: Name One Area of Health Care That Is Not Regulated . . .
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DEVELOPING LEGAL ISSUES
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Developing Legal Issues
Who Owns The Data? – Apple’s Jan 2018 proposal to let patients access
their own data
– State law
– An example from the Army
The GDPR, and the Workarounds – Why Legislation isn’t Working
Ownership, Privacy, and Cybersecurity
Digital therapeutics (“Digiceuticals”) and the FDA (the FDA is regulating “apps” now . . . )
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AI Health Care Business Model Key Questions
Customers and Uses
Revenue Model and Sources Data Sources and Uses
Business Model
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Legal/Regulatory Considerations
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Key Regulatory Imperatives: Vendor Model
Vendor - AI used to support health care delivery system and organizations (other than direct patient care)
Examples: – Voice-to-text software to support revenue cycle functions of hospital
– Population health analytic software
Legal/Regulatory Imperatives: • Intellectual property
• Technology licensing
• HIPAA/GDPR and other privacy/security
• Cybersecurity
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Key Regulatory Imperatives: Provider Model
Provider – AI used by health care organizations in connection with the delivery of patient care
Examples: – Remote monitoring focused on avoidance of readmissions – Clinical diagnostic/predictive tools using AI (e.g., diabetic retina exams) – Use of AI in technologies/clinical service delivery activities
Legal/Regulatory Imperatives:
– HIPAA/GDPR and other privacy/security (e.g., CCPA and other state laws) – FDA – Licensure – Enrollment/reimbursement – Health care regulatory – Product liability – Data ownership/commercialization – Cyber Insurance
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Key Regulatory Imperatives: Innovator Model
Innovator – AI used in consumer applications outside of traditional health care delivery system
Examples: – Apps focused on health and wellness (e.g., monitor heart rate, weight loss etc.) using
consumer technologies such as Apple Watch or others – Identify health related conditions (e.g., obesity)
Legal/Regulatory Imperatives:
– FDA – FTC – GDPR/CCPA and other privacy/security – Consumer protection – Payment – Product liability – Data ownership/commercialization
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KEY TAKEAWAYS
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Key Takeaways
We live in a Cylon world and don’t even know it
There is huge opportunity to improve outcomes
AI will be one of the drivers helping pushing acuity to the lowest appropriate setting for the care – and it will disrupt as it does so
Change will create winners and losers
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Key Takeaways
A Principle for Winning: Follow consumer adoption of technology closely, and align with it in healthcare
Early adoption does not guarantee winning, but late adoption in an arms race often identifies losers
Look for global solutions and interoperability
Live within a paradigm that drives all your organizational tech decisions – very few tech decisions are one-off’s
If you can’t beat them, join them – find the right partners early
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Key Takeaways
In the M&A World:
– Specific diligence to AI is critical
– Many non-AI health care deals have AI components that may be central to the business model – recognize and diligence them
– IP is becoming a bigger component of all provider and health care service deals
– Integration is a bear. . .
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Q & A
QUESTIONS?
CONCLUDING THOUGHTS?
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