Artificial Intelligence and

Healthcare at the CrossroadsGuy Barnard, MA, MBAChief Executive Officer, Co-Founder, Synchronous Health, Inc

September 2017

“The artificial intelligence will see you now”

Executive Summary

Models and approaches to Health are not solving the problem.

Status quo is not a viable strategy.

Technology has repeatedly failed to deliver the promised benefits, and so any

emerging trend can and should be met with skepticism.

Advances in technology, expansion of consumerism, infusion of capital, and

entries of new players, have led to much noise and confusion.

What’s different now is that stakes are higher and timelines so compressed, that

understanding and preparation are essential.

In fact, some will argue our sustained existence may depend on it, but regardless,

the essence of success may be simpler than one might think, but the pitfalls

unfortunately easier.

Consider these battle learnings from a field that is still emergent.

Why the explosion in AI?

Source: VentureScanner database of 826 companies in Artificial Intelligence Category

Analogy: The Cambrian Explosion,

500 million years agoDramatic burst of transformational changes

Ninety percent of the

animal phyla that exist

today appeared in this

short window of time

Deep LearningStatisticalbased AI

Rules based AI

GraphicsProcessing Units (GPU)

Von NuemannArchitecture

Moore’s Law

Natural Sight andLanguage

Bots

NLP Killer App

EdgeComputing

Cloud

Compute

VirtualizedExperience

Empathy

Self-Service

Multiple crossroads

leading to the

AI Cambrian

Explosion

After 240 minutes of

training.

This is where the magic

happens. It realizes that

digging a tunnel through

the wall is the most

effective technique.

After 120 minutes of

training

It plays like an expert!

After 10 minutes of

training

The bot tries to hit the ball

back but is too clumsy to

score much.

Example: Deep LearningGoogle’s DeepMind

This bot plays Atari breakout.

It is given nothing more than

the pixels on the screen and

the goal to optimize the score.

Video plays in slideshow mode

Source: Nature Vol 518, 26 Febuary 2015; http://tinyurl.com/atariai accessed August 30, 2017

MILEPOST

Example: Moore’s Law . . . .

1965, “Electronics” Magazine4 data points 120 years of data points

2016 Kurzeil / Jervetson Update

GPU

Specialized electromagnetic

Hollerith

TabulCator

von Neumanarchitecture

IBM

Blue Gene

NVIDIA

GTX

$199

SPEEDOMETERSource: Electronics (1965); Steve Jervetson Moore's Law over 120 Years (December 2015), DFJ Venture Capital

. . . . Extended by Gaming

Applications in Healthcare ?Expert Augmentation, Clinical Efficacy and Efficiency,

Consumer Engagement, Enabling processes and more

Source: Association of American Medical Colleges , The Complexities of Physician Supply and Demand 2016 Final Report, IHS analysis;Accenture Artificial Intelligence: Healthcare’s New Nervous System

700,000

750,000

800,000

850,000

900,000

950,000

2017 2019 2021 2023 2025

Supply Demand Shortfall

US Clinician Demand vs. Supply Forecast, 2017 –

2025

15-20% unmet demandaddressableby AI

Myths & Detours

Always need experts to operate at top of

license

Creativity and human-to-human work flows

cannot be digitized

AI in healthcare is about robotic surgery and

amazing diagnoses/decisions

AI at scale is too expensive and will

generate huge cloud invoices

Privacy is the same concern it was last

year

AI brings less bias and more objectivity

The biggest safety risk is a bad decision

that leads to a poor outcome

Myth FactEvidence overwhelming that relying on data

and algorithms* usually leads to better

decisions than even expert humans

Moving from the Art of Medicine to the

Science of Lifestyle Health (I)

Your members/employees/patients are

increasingly turning to virtualized

experiences (ATM, self-checkout, etc.)

Creative abilities are expanding rapidly

Always need experts to operate at top of

license

Creativity and human-to-human work flows

cannot be digitized

AI in healthcare is about robotic surgery

and amazing diagnosis/decision support

Back-office processes may be some of the

biggest early wins; (re)think recruiting,

revenue cycle management, underwriting,

fraud, etc.

Consumer engagement, prevention and

post-acute are ripe for disruption

* Where data and algorithms are available

Myth Fact

AI at scale is too expensive and will

generate huge cloud invoices

The amount of compute power available to

startup in a garage is unprecedented; open-

source, as-a-service, or on-device

Moving from the Art of Medicine to the

Science of Lifestyle Health (II)

Privacy is the same concern it was last

year

Breadth of data combined with the compute

power to re-associate make security an

exponentially bigger challenge

AI brings less bias and more objectivity Biases are often codified in training

datasets; the AI models start as just that --

models of existing work flows

The biggest safety risk is a bad decision

that leads to a poor outcome or even

death

No facts yet on this one, but plenty of

concerns about potentially way more;

planning and research advised

Questions to Consider

Do you have an AI strategy? Who is responsible for machine learning in your

organization? For back-office business processes too?

Are you systematically tracking the performance of the decisions by people and

algorithms? What data should you start collecting?

Which key decisions or operations, if any, would you consider turning over

entirely to AI? Which will be the hardest to turn over? Why?

Which work flows require an empathetic understanding of the human condition?

Should any of these be shifted in time or space?

What are the safety considerations? Is the current governance process

sufficient? Are you monetizing data or adopting “private by design”?

What are the skills/job types you will need in 2018? in five years?

What is your edge computing strategy? Is augmented reality in your facility or

on your roadmap? Where will vision/speech have the most impact? What are

the opportunities from new sensors / inputs ?

STRATEGY

DATA

PROCESSES

PEOPLE

SAFETY

TECHNOLOGY

Source: Synchronous Health AI strategy

GPS MAP

Field Learnings in Population Health (I)Context

Screen

Match

Engage

Measure!

FROM

20,000 business rules

$1B platform investment

Annual IT investment /

operating cost $125M

600 IT staff

Wellness portal

+ 20 apps

11 call centers

Conditions and risks

TO

Machine learning + human-

enabled AI

Bots not docs not apps

Human compassion

+ AI technology

Lifestyles, habits, behaviors

Serverless

Empathy screening of

specialists, bot interviews

Digital anthropologists,

Chief Human and Digital

Talent Officer

Aim: Reduced Costs. Improved Performance. Improved Health. Better Experience. Source: Barnard experience

Intent: Track feelings

Mood: sadness

GPS: 36.1246085,-86.8487669, 48°

Time: 2016-09-

16T19:20:30.45+01:00

People nearby: Kai, Ailani

Media: It’s My Life, Bon Jovi, 65db

Ambient light: Outside Movement:

Walking

Weather:Raining

Steps: 354 steps walked today

Location context: Home

Day of week context: Normally at work

Clip: 92352352352352.mp415

Evidence-based design,on proven methodologies

Designed for behaviors of consumers,not for a healthcare system

Accessibleand convenient

Advanced technology thatgets out of the way:

human experts that operate at top of license, with empathy,

powered by AI

Prove-as-you-go metrics

Life context specificity: Leverage tiny data, not big data,

to deliver highly targeted interventionsin the moment someone needs it,

at scale

Simple on-ramps, withaligned benefit/incentive design

Lifestyles

Behaviors

Habits

1 2 3

4

5

6

7

Field Learnings in Population Health (II)Seven Tenets that Seem to Matter

Karla, I am feeling sad.

Intent: Track feelings

Mood: sadness

GPS: 36.1246085,-86.8487669, 48°

Time: 2016-09-

16T19:20:30.45+01:00

People nearby: Kai,Ailani

Media: It’s My Life, Bon Jovi, 65db

Ambient light: Outside Movement:

Walking

Weather:Raining

Steps: 354 steps walked today

Location context: Home

Day of week context: Normally at work

Clip: 92352352352352.mp4

Okay, I’ve tracked your feelings of sadness

for your specialist.

Example (I): Human-Enabled AI in Support of

Depression

Humanslabel data

Machines learn fromlabeled data

competitive advantage from data +

networked humans

AI

Example (II): Human Compassion Bridges Time,

Space, and “Uncanny Valley”

Questions to Consider (Recap)

Do you have an AI strategy? Who is responsible for machine learning in your

organization? For back-office business processes too?

Are you systematically tracking the performance of the decisions by people and

algorithms? What data should you start collecting?

Which key decisions or operations, if any, would you consider turning over

entirely to AI? Which will be the hardest to turn over? Why?

Which work flows require an empathetic understanding of the human condition?

Should any of these be shifted in time or space?

What are the safety considerations? Is the current governance process

sufficient? Are you monetizing data or adopting “private by design”?

What are the skills/job types you will need in 2018? in five years?

What is your edge computing strategy? Is augmented reality in your facility or

on your roadmap? Where will vision/speech have the most impact? What are

the opportunities from new sensors / inputs ?

STRATEGY

DATA

PROCESSES

PEOPLE

SAFETY

TECHNOLOGY

Source: Synchronous Health AI strategy

GPS MAP

Where Opportunity

and Preparation Meet

AI at the Crossroads

“Once this

Pandora’s box

is opened, it will

be hard to

close”

Source: Open letter to the united nations convention on certain conventional weapons, August 2017

“Let's say you create a self-improving AI to pick strawberries and it gets

better and better at picking strawberries and picks more and more and it

is self-improving, so all it really wants to do is pick strawberries. So

then it would have all the world be strawberry fields. Strawberry fields

forever.”

“Our biggest existential threat”

“The development of full artificial

intelligence could spell the end of

the human race”

“We need to rethink the way

we have built society on top

of the web”

“I don't understand why some

people are not concerned.”

“The future is scary and very bad for

people”

“It’s more important right now to build

consensus in the industry and academia

around what are the things that would have a

chilling effect.”

So keeping AI beneficial is

probably important.

But can we afford not to be

using it now?

Where Opportunity

and Preparation Meet

AI at the Crossroads