structuring investments and doing deals in ai in …...top ai applications artificial intelligence...

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


Sellside Advisor


Sellside Advisor

has received a significant

investment from

Sellside Advisor*

a portfolio company of


Sellside Advisor



a MEDNAX company

Sellside Advisor

has divested its Commercial Health

Insurance Payer Products Business

to


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



Financial Advisor

has completed a financing

consisting of

$24,000,000

Series D

Convertible Preferred Stock

Placement Agent



Sellside Advisor


Sellside Advisor

a division of


Sellside Advisor

has completed a financing

consisting of

$24,000,000

Series D

Convertible Preferred Stock

Placement Agent



Sellside Advisor


Sellside Advisor


Sellside Advisor

The Connect business unit of

Has been acquired by

Sellside Advisor



Sellside Advisor


Sellside Advisor

in partnership with

has acquired


Buyside Advisor

has partnered with and made an

equity investment in

Buyside Advisor

has received a significant

investment from

Sellside Advisor


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

0

10

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


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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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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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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Two software

makers have paid out

more than $200M:

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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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https://sloanreview.mit.edu/article/the-problem-with-ai-pilots/

https://sloanreview.mit.edu/article/the-problem-with-ai-pilots/


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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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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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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Polsinelli PC, Polsinelli LLP in California | polsinelli.com

Polsinelli PC provides this material for informational purposes only. The material provided herein is general and is not intended to be legal advice. Nothing herein should be relied upon or used without consulting a lawyer to consider your specific circumstances, possible changes to applicable laws, rules and regulations and other legal issues. Receipt of this material does not establish an attorney-client relationship.

Polsinelli is very proud of the results we obtain for our clients, but you should know that past results do not guarantee future results; that every case is different and must be judged on its own merits; and that the choice of a lawyer is an important decision and should not be based solely upon advertisements.

© 2019 Polsinelli® is a registered trademark of Polsinelli PC. In California, Polsinelli LLP.

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