Rise of the Intelligent Machines in Healthcare

February 29, 2016

Kenneth A. Kleinberg, FHIMSS

Managing Director, Research & Insights

The Advisory Board Company

Conflict of Interest

Kenneth A. Kleinberg, MA

Has no real or apparent conflicts of interest to report.

Agenda

• Learning Objectives

• Overview of Intelligent Computing

• Use in Other Industries

• Uses in Health Care

• Challenges and Futures

• Summary/Wrap-Up

• Questions

Learning Objectives

• Identify what advances in intelligent computing are having the greatest effect on other industries such as transportation, retail, and financial services, and how these advances could be applied to health care.

• Compare the types of technological approaches used in intelligent computing, such as inferencing, constraint-based reasoning, neural networks, and machine learning, and the types of problems they can address in health care.

• Identify examples of the application of intelligent computing in health care and the Internet of Things (IoT) that are already deployed or are in development and the benefits they provide, such as robotic assistants, smart pumps, speech interfaces, scheduling systems, and remote diagnosis.

The Evolving Story of Intelligent Computing/AI

Intelligent computing/AI uses

algorithms, heuristics, pattern

matching, rules, machine/deep

learning, and cognitive computing to

solve problems typically performed

by humans, as well as complex

problems difficult for humans

Intelligent systems are often

inspired by biology (parallel

computation) and, through access to

large data sets, get smarter with

use

AI has been in development for

decades, but only recently

gotten good enough for people

to notice, mostly due to advances

in other industries besides

health care

The public perception of AI is often

influenced by hundreds of sci-fi

movies, fear of “bad robots,” and a

general skepticism that

“machines” will ever be able to

master human capabilities that we

hold so dear

The rise of intelligent machines

is approaching; and the world,

especially the health care

industry, is far from prepared for

what’s to come…

What How

Who

When

Why

http://www.himss.org/ValueSuite

STEPS Benefits of Intelligent Computing/AI

• Tasks get done faster and more consistently

• Enhances the abilities of human workers

• Interacting with AI can be fun!

• Clinicians have smart “assistants” they can query

• Stuff doesn’t’ fall “through the cracks”

• Larger and more complex data sets can be accessed

• Analytics can be made smarter

• Alerts and reminders can be more intelligent

• Supports more dynamic and adaptive patient engagement

• Catches problems and trends earlier

• Adapts education to the patient and context

• Reduces labor costs

• Operates continuously and with more capacity

• Becomes more effective over time

Some (Controversial) Definitions of Intelligent Computing/AI

Intelligent

Computing/AI (can learn and adapt)

Symbolic

(Logical)

Reasoning

Statistics

and

Analytics

Cognitive

Computing

(simulates human thought

processes)

Bio-inspired

Systems

• Neural networks (multilayer,

feedforward, recurrent,

convolutional)

• Genetic algorithms

• Progeny clustering

• Machine learning

• Deep learning

• Rule/Knowledge-based systems

• Induction and deduction

• Forward and backward chaining

• Fuzzy logic

• Regression

• Descriptive and inferential

• Bayesian networks

• Random forest

• Data mining

• Predictive analytics

• Computational learning

When is it Intelligent Computing?

statistician

programmer

researcher

analyst

clinician

modeler

The less

the

has to

determine

the

order of processing

order of training

data to apply

factors to focus on

steps to improve the model

the more the

system can be

described as

intelligent

IC/AI is Vastly More Powerful than Procedural Programming

Pattern Recognition

Classification

Which class does

something belong in?

Knowledge Discovery

and Data Mining

What relationships

exist?

Prediction

What will

happen?

Clustering

How many different

groups of similar

objects?

Optimization

How can it be

made better?

Scheduling/Planning

How can we

accommodate order and

constraints?

Decision Making

What should we do?

Speech/NLP/Translation

What do you say and what

did you mean?

Machine Vision/

Perception

What do you see?

Robotics

Can we effect action

in the physical world?

Typical Problem Types for Intelligent Computing

How Fast Is IC/AI Advancing: Are We There Yet?

Exponential growth:

Will AI take off thanks to

network effects and

disruptive innovations, or

will it only make modest

advances for the next

decades?

AI Winters: AI has already

gone through a few phases of

hype and troughs of

disillusionment (1974-80, and

1987-93)

Surpass human

intelligence: Some

predict we’ll see the

“singularity” of machine

intelligence in the next

few decades

Unpredictable Timing :

Some advances seem to

never arrive (speech

recognition), while others

come upon us unexpectedly

(GPS driving directions)

60s 70s 80s 90s 2000 2010 2020 2030 50s 2040

AI and intelligent computing

advances are starting to

accelerate

2050

IC/AI Being Used Successfully in Other Industries “Under the Covers”

Transportation

Autopilots, self-driving cars,

space vehicles, complex

scheduling

Example: American Airlines Sabre System

Retail and Manufacturing

Shopping assistants, product

launches, logistics, robotic factories

Example: Amazon Machine Learning Service

Financial Services

Auto-trading, check cashing, fraud

detection, market prediction

Example: Securities Observation, News Analysis,

and Regulation System (SONAR)

Emergency Response

Biohazard response,

environmental changes,

police/military presence

Example: DigitalGlobe’s Tomnod

Service and Support

Booking assistants and tech

support

Examples: USAA’s Military Veterans Advisor

Gaming and Simulation

Video games, entertainment,

simulations, education/training

Example: Computer Go

Security, Crime

Prevention, Military

Identification, case

analysis, logistics

Example: Avigilon

Commonalities

• Complex challenges with lots of data

• Speed and consistency are important

• Resistance from existing workers

• A gradual adoption over years (or longer)

• Eventually it’s no longer considered AI

Motion Requires Real-Time Control and Adaptation to Changing Conditions

Source: Health Care IT Advisor research and analysis.

IC/AI in Transportation

Autopilots

Large airline jets are increasingly automated, can land in zero-zero

conditions (control of power, direction, braking). Pilots cannot start

the approach without having these systems operational. Example: Honeywell Flight Control Systems

Self-Driving Cars

Trials in California have had a few accidents. There are challenges in

determining level of threat/impact on vehicle (a paper bag versus a

rock), driving in reduced visibility conditions, and human drivers. Example: Google and Uber

Spacecraft and Rovers

Time lags are so great they require some self-sufficiency to carry out

essential commands. Human handlers are not always sure how the

system will react to commands. Example: Rosetta and Philae spacecrafts

Complex Scheduling

Algorithms allow for automated scheduling that maximizes

efficiency and/or resource time. Used for trains, public

transportation, logistics, and military operations. Example: Stottler Henke’s Aurora system used by Boeing

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Dealing with a Ton of Information and Decisions

Self-Driving Cars: The Long Road (Pun Intended)

How Far We’ve Gotten

”

“That's artificial intelligence.

How do I get around obstacles

and detours and noticing all

those things. That's quite a

task and that shows us how

far we've gotten through

simulating intelligence.”

Steve Wozniak

Our Goal

”

“We don't claim that the cars are

going to be perfect…Our goal is

to beat human drivers..”

Sergey Brin, Google Co-founder

Source: www.kurzweilai.net/fully-self-driving-cars-expected-by-2030-says-forecast; spectrum.ieee.org/automaton/robotics/artificial-intelligence/how-google-self-driving-car-works; www.techrepublic.com/article/wozniak-talks-self-driving-cars-apple-watch-and-how-ai-will-benefit-humanity/; www.inc.com/associated-press/google-self-driving-cars-accidents.html; Health Care IT Advisor research and analysis.

No Industry Knows Consumers and Leverages That Knowledge Better

Source: Health Care IT Advisor research and analysis.

IC/AI in Retail and Manufacturing

• What did you buy before?

• What did those like you buy?

• What do you buy seasonally

or after certain events?

• How much will you spend?

• How can we place the right items in

the right stores, warehouses, and

distribution centers, at the right

time?

• How can we get items to the

consumer more quickly?

• How can we build products

constantly – 24/7?

• How can we leverage Intelligent

design and 3-D printing?

• How can we predict maintenance

requirements?

• Which markets will be most

successful?

• What do particular segments want?

Logistics

Shopping Assistants/Agents Product Launches

Automated Factories

Example: Netflix, Operator from Uber

Example: Spiegel’s use of

NeuralWare,Inc,

Example: Quintiq

Example: Ford’s Global Study and

Process Allocation System (GSPAS)

SA

WY

ER

Milliseconds Determine the Winner and Identifying Risk Reduces Liability

Source: http://www.slideshare.net/0xdata/paypal-fraud-detection-with-deep-learning-in-h2o-presentationh2oworld2014; http://www.npr.org/2015/10/20/445337189/would-you-let-a-robot-manage-your-retirement-savings; Health Care IT Advisor research and analysis.

IC/AI in Financial Services

Auto-Trading

Split-second decisions

based on immense data

that involve billions of

dollars. These problems

are too complex to leave

to humans.

Example: TickCOM’s iSTRAT

Check Cashing

Image recognition of

checks facilitates

deposits with high

accuracy. More difficult

cases are bounced back

to humans.

Example: Cyphermint

Market Prediction and

Portfolio Management

Robo-advisers can

identify best investment

options and determine

how portfolios should

change. Example: Blooom

Fraud Detection

Involves many different

forms of modeling and

inputs from many sources.

Applies to credit cards,

mortgages, insurance risk,

etc.

Example: PayPal’s Fraud Detection

Case in Brief: Fraud Prevention at PayPal

• Transaction Level: Employs machine learning and statistical models to flag fraudulent behavior up-front; uses more sophisticated

algorithms once transaction is completed

• Account Level: Monitors account-level activity to identify abusive behavior; abusive patterns include frequent payments and

suspicious profile changes

• Network Level: Monitors account-to-account interaction; frequent transfer of money from several accounts to one central account

Source: Health Care IT Advisor research and analysis.

1) http://www.forbes.com/sites/neilhowe/2015/05/14/artificial-intelligence-paves-the-way-for-ambient-intelligence/

2) http://www.usatoday.com/story/tech/2015/07/06/health-care-technologies/29160173/

Who Will Be Changing Our World?

Amazon

Apple

Baidu

Facebook

Google and Deep Mind

IBM

Microsoft

OpenAI (Musk and others)

SRI International

Toyota

BlueBrain Project

DARPA

HumanBrain Project

Big Data Research and

Development Initiative

Corporate Research Centers Government Agencies

$17B Amount invested in artificial

intelligence since 20091

Carnegie Mellon

Georgia Tech

MIT

Stanford University

UC-Berkeley

Universities

$2B Amount invested in digital

start-ups in 20142

2,000+ Number of start-ups that

have key words “digital

health” or are new health

care technologeies2

High Aspirations

”

Larry Page, Google “Oh, we’re really making an AI”

“Over a quarter of all attention and resources" at

the Microsoft Research main lab now focused on

AI-related activities” Eric Horbitz, Microsoft

Intelligent Information

Gathering and Sensing (IoT)

1

What do we know about

the patient and his

changing environment to

aid in his health?

Six Related Categories of Application Development and Use

2 Intelligent Interaction

and Service

3

How can we communicate

with our systems in a more

natural manor?

What’s wrong with the

patient and what type of

evolving treatment plan

would be most effective?

Intelligent Diagnosis

and Care Plans

Intelligent

Medical Devices

4

How can we automate and

adjust medical devices to

be more real-time,

accurate, and responsive?

5 Robotics

6

What roles can robots take

on to assist with the

mundane, dangerous, or

complex jobs of humans?

Advanced

BI/Analytics

What can we learn from our

data, and how can we

predict futures states and act

on that knowledge?

Applications of IC/AI in Health Care

Audience Response Polling Question 1

Where will IC/AI in HC will have the greatest impact?

a. Internet of Things

b. Decision Support/Diagnosis

c. Robotics and Smart Devices

d. Analytics

Enabling Situational Awareness and Action with IoT

Frameworks + AI-based Tools + Progressive Providers

“Ambiant” agent and

machine intelligence-based

platform provides alerting

and workflow management

processes

Provides systems Integration

and services, partner

ecosystem development, and

the “Intelligent Health System

Framework”

Opened North America’s

“first fully digital” medical

facility in Toronto, October

2015

Evaluates innovation in

real-world settings

Hospital Example:

“Code Blue”

• How is it triggered (connected

medical devices?)

• Who is it sent to (who is on the

care team?)

• Who is nearest with the right

skills (and able to respond?)

• When will they arrive?

• Who needs to bring what

devices (crash cart) or medical

supplies (and where are these

items?)

• Who else needs to be notified

and what are the ripple

effects?

Source: CGI; ThoughtWire; Mackenzie Innovation Institute; Humber River Hospital

Answers Questions, and Explains “Reasoning”

MD Anderson Patient Concierge Using Cognitive Computing

Source: MD Anderson, Houston, TX; Health Care IT Advisor research and analysis.

Can You Understand Me Now?

Speech and NLP Advances for Health Care

Source: Nuance; Health Care IT Advisor research and analysis.

Increased

processing

power, cloud,

virtualization

Increased data and

cases from millions of

conversations and

encounters (with

specific regions,

accents, specialties,

users)

Algorithms that look at

words, snippets, and

sentences, and

increasingly, paragraphs,

documents, the EHR

(medical ontologies), and

additional context

Advances in probabilistic

(e.g., Max-Entropy Markov

Models) and logical (rules)

reasoning approaches and

the ability to use them in

combination (and in

multiple passes)

Advances and

investments in

consumer

digital

assistants (Siri,

Cortana, Google

Now, Alexa, M)

Human machine interaction – still a long way to go

Medical NLP and fact extraction– between “crawl”

and “walk” – already providing useful functionality

Medical speech recognition for specialties – at the

“run” phase - on par with human accuracy –- 97-99%

Menu-driven commands – really good – almost

flawless Better built-in

mobile device

microphones,

signal processing

Current State of Achievement

IBM Watson Health Launched in 2015 – Cognitive Computing

Company in Brief: IBM Watson Health (Part of IBM Watson Group)

Technical Approach

• Uses hundreds of computational techniques, including machine learning; conducts

NLP queries on structured and unstructured data; generates hypotheses, scores

evidence, and returns answers

• Uses IBM DeepQA software, Apache UIMA Architecture, clusters of Linux servers, and Hadoop

Key Factors for Success

• Focuses on breadth and depth scale, combination of approaches, and parallel processing

• Supports partner development with APIs, offers cloud capabilities

Feb 2011: Nuance,

Columbia University,

University of Maryland

Oct 2012: Cleveland

Clinic, Case Western

Reserve University

Feb: Memorial Sloan

Kettering, WellPoint,

Maine Center for

Cancer Medicine

Oct: MD Anderson’s

Moon Shot Program

Jun 2014: GenieMD

Mar: Modernizing

Medicine

Apr: IBM Watson Health

established; Apple,

Johnson & Johnson,

Medtronic; acquires

Explorys, Phytel

Jul: CVS

Aug: Acquires Merge

Healthcare

Sep: Boston Children's

Hospital, Columbia

University Medical Center,

ICON plc, Sage

Bionetworks, Teva

Pharmaceuticals

2011 – 2012 2013 – 2014 H1 2015 H2 2015

Source: IBM

Intelligent Medical Devices: Reducing Workloads

Case in Brief: Anesthesiology Automation—

Johnson & Johnson Sedasys

• FDA approval in 2013 for “narrow use” with expert available

(uses propofol)

• In use at four U.S. hospitals for colonoscopies and

endoscopies

• Business case: Anesthesiologist requires four years of

medical school and a median salary of $277K per year

• Now being tested for heart and brain surgery

Case in Brief: Artificial Pancreas and Smart

Infusion Pumps—Medtronic MiniMed Connect

• SMARTGUARD mimics some functions of a healthy pancreas;

predicts low glucose levels in advance and stops pump

• Insulin pump and continuous glucose monitoring can talk

directly to smartphone

• Partnered with Samsung

Source: Medtronic; Johnson & Johnson

Robotics: To Serve (and More)

Forecasted Impact from Robotics

$67B Spending on

robots in 2020 22% Reduction in U.S. labor

costs in by 2025

Hospital-Based

Robots

University of California

San Francisco at Mission

Bay uses 25 TUG Robots

by Aethon. They travel

481 miles per day in

1,300 trips, equating to a

time savings of 315

hours.

Similarly, Yujin Robots

can deliver drugs, linens,

and meals, and also cart

away medical waste,

soiled sheets, trash.

Robotic

Assistants

Developed in Japan,

the latest generation of

the Robobear medical

assistant can lift

patients into and out of

beds, help position

humans into sitting and

standing positions, and

lift patients from

wheelchairs.

Telepresence

Partner’s HealthCare

uses Vecna’s VGo

robots to provide

remote care to

children in their

homes. The robot

can do “rounds” on

the patient every

day, taking pictures

and gathering data

to track progress.

Aethon TUG Vecna VGo

Pets

Huggable is a

collaboration between

Boston Children’s

Hospital and MIT. The

social robot prototype

recently started a 90-

person study to

determine whether it has

therapeutic value for

children enduring long

hospital stays.

Another example is

Paro, the roboseal,

developed by the

Japanese firm AIST.

Home Assistants

GiraffPLUS, from the

European Union,

combines a network of

sensors that collects

physiological and

environmental data with

a telepresence robot for

social interaction. The

data is fed wirelessly to

doctors and utilizes

Skype to conduct remote

doctor consultations. It’s

geared toward older

patients who live alone.

Huggable GiraffPLUS RIBA Robobear

Source: http://www.cnbc.com/2015/07/06/robot-use-on-the-rise-through-2025.html

New Data and New Tools Bring Better, Stronger, and Faster Predictions

Source: http://www.ayasdi.com/; http://www.ayasdi.com/wp-content/uploads/2015/02/Healthcare_Mount_Sinai_Solution_Brief.pdf; Health Care IT Advisor interviews and analysis.

BI Analytics

Company in Brief: Ayasdi

• Headquartered in Menlo Park, CA

• Grown out of the mathematics

department of Stanford University and

initially funded by DARPA

• Uses Topological Data Analysis and

machine learning to capture the “shape

of the data”

• Applying multiple algorithms eliminates

approach biases and reveals previously

obscured patterns

–In Mt. Sinai’s diabetes study the data

exhibited clusters, loops, flares, and

line patterns revealing several distinct

subgroups that were previously

unidentified

• Collaborators: Mercy, UCSF, Merck,

Michael J. Fox Foundation, FDA

Ayasdi Topological Map for Mt. Sinai Diabetes Study

Ayasdi aims to make complex data

useful for healthcare providers and

payers through Machine Intelligence,

which represents the next generation of

healthcare data analytics.” Gurjeet Singh, CEO and Co-Founder. Ayasdi

Audience Response Polling Question 2

Will your job be eliminated by IC/AI in 10 years?

a. Highly unlikely

b. Possibly

c. Highly likely

d. All ready happened

Major Challenges to IC/AI in Health Care

Complexity: Medical issues don’t

appear in isolation and coordination

of care is difficult.

Business Challenges Legal and Ethical Challenges

Threat to human jobs: Strong fear

associated with technology displacing

human workers.

Cost: The high costs for

developing, testing, certifying, and

implementing can be a barrier.

Workflow: How do AI solutions fit

into existing workflows? How much

effort is required to use it? Does it

interfere or annoy unnecessarily?

Competing Priorities: EHRs,

portals, Meaningful Use, Payment

Report, ACOs.

Regulation: Health IT regulations

are hotly debated at the national

level. Finding the right balance of

public health protection and

fostering innovation is key.

Legal: Juries still award large sums

when health care is not applied

properly or expected outcomes are

not achieved.

Liability: How do we deal with

computer failings? It raises the issue

of data de-identification, privacy,

security, and espionage.

Human Touch: How will we interact

with AI? How strongly will we require

the human touch and human

compassion in health care?

Audience Response Polling Question 3

What is the largest barrier to IC/AI in healthcare?

a. Technical

b. Clinical complexity

c. Costs, skills

d. Regulation, legal, ethical

Can All These Brilliant Minds Be Wrong?

Source: Health Care IT Advisor research and analysis.

You Know You’re Onto Something When…

The development of full artificial intelligence could spell the end of

the human race.” Stephen Hawking, Theoretical Physicist; author of

A Brief History of Time

With artificial intelligence we are summoning the demon.”

Elon Musk, Founder of Tesla Motors,

SpaceX, and PayPal

I agree with Elon Musk…and don't understand why some people

are not concerned.” Bill Gates, Founder of Microsoft

and the Bill & Melinda Gates Foundation

AI Doomsday?

IC/AI Scenario Planning: Where Will We Be in 20 Years?

AI Fizzles

No Major

Breakthroughs

Every Company

Loves You

Promises, Promises

Battle of the Giant

Intelligences

Niche Advantages

“Do they have your best

interests in mind?

Which AI-run governments,

corporations, and systems will

dominate?

How many more times must we

open our pocketbooks ?

Intelligent curiosity or

secret weapon?

AI Super

Intelligence

Singularity and

Consciousness

AI Limited

Niche Companies and Research

Al Ubiquitous

All Major Corporations

Audience Response Polling Question 4

Do you welcome AI or do your fear it?

a. Fear it will be our downfall

b. Concerned – highly cautious

c. Excited by it – welcome it

d. Love it – humanity’s savior

Graphic

Steps to Intelligent Computing/AI Success

Combine the experience, knowledge, and human

touch of clinicians with the power of intelligent

computing to achieve more than either alone

Use Intelligent Computing to provide higher levels of

patient engagement and education, such as adaptive,

personalized response and gaming

Use intelligent computing to tackle the complexity and

expanse of new data sources to push the boundaries

of precision medicine and population health

Summary/Key Takeaways

Satisfaction

Treatment/Clinical

Electronic

Information/Data

Prevention and Patient

Education

Savings

Focus on the advantages of intelligent computing –

these systems should be viewed as assistants, not

threats

Use IC to reduce labor costs, increase consistency,

discover new clinical knowledge, and offer scalable

return on investment for value- and risk-based care

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Final Thought 32

Our technology, our machines, is part of our humanity. We created them to extend ourselves, and that is what is unique about human beings.”

Ray Kurzweil

Questions

Kenneth A. Kleinberg, FHIMSS

Managing Director, Research & Insights

The Advisory Board Company

2445 M St NW, Washington, DC 20037

202-266-6318

KleinbeK@advisory.com

Twitter: @kkleinberg1

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