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Artificial Intelligence & HealthTechnology Implementation in a Broken SystemRod Wallace, Ph.D. & Ruth Fisher, Ph.D.January 24, 2017

Copyright Integrated Technology Research, Inc. 2017. All rights reserved.

Rod.Wallace@ITRCorporation.comRuth@Quantaa.Com

Thank you to Lloyd Nrrenberg, Steve Omohundro, Mark Washburn, and Jm Lola, in addition to participants in the Artificial Intelligence in Business Meetup for comments & suggestions.

Artificial Intelligence Has Amazing Healthcare Applications(Stylized)

US Medical SchoolRead JournalsConsult with othersAnalyze DataDiagnose

OncologistUS Medical School

Diagnose

OncologistSloan Kettering+ IBM Watson

Read JournalsAnalyze DataDiagnose

Artificial Intelligence

Diagnose

Oncologist

(US AI model)

Artificial Intelligence

Thai Hospital

Past With AI

Sent To

Video2:18

Artificial Intelligence is a Healthcare Infant

Human-Curated Knowledge Base

Technical Overview: Question Answering

& Analytics (Wat-son)

Meta-data‘Ingestion’

Supervised Learning

Confidence-BasedConclusions

Other Apps

Opportunity: $2 Trillion

IBM Cognitive Solutions Group

Watson

Watson Health

Mobile First Security

Commerce

Analytics Cloud

IT Infrastructure

Employees: 5,000

Revenues: $17.8BB (2015)

The ‘800 pound gorilla’ for AI in healthcare is an infant

Its core algorithm is traditional

Betty’s Healthcare Experience

Expanding Circle of Influence: Cacophonous Internet Direct to Consumer Pharma Marketing Broadening Health Technology Origins

Accelerating Frequency: Addition of Computers & the Internet Data Pipelines & Big Data Artificial Intelligence

Increasingly Intricate Circle of Control: More Complex Tests, Treatments Of the Average General Practitioner’s Medicare Patients: 31% of patients with 4+ chronic conditions These patients overlap with 86 other MDs in 36 practices

Friday: late-night ER visit High fever + vomiting 2 blood cultures taken No food from mid-afternoon Improving overnight 7:30 AM Discharge

Sunday PM: Blood work returns 1 Gram Negative Rods; 1 clean Betty rushes to the ER Forgets her phone & wallet Waits 5 hours on a gurney Under a sheet, in the hall. Preliminary test results clean 2:30 AM discharge, to a taxi

AI Will Revolutionize the Health Industry– But will we become more healthy?

“American medicine is so dysfunctional, the future has to be better.” Abraham Verghese, M.D., Stanford Healthcare

Many expect AI to have a larger impact on healthcare than on anything else. -PWC AI conference

Today, well-trained professionals apply impressive healthcare technology.

Yet, life expectancy is low, and financial cost high.

How will ‘Artificial Intelligence’ impact us?

Healthcare is currently the 2nd largest AI application, outside security

-O’Reilly On Our Radar, August, 2016

Artificial IntelligenceThe science of building machines able to take actions in an environment that will achieve desired outcomes using scarce resources.

Steve Omohundro, Ph.D.

Artificial Intelligence is Automation

Automation

The Health System Includes Lifestyle Choices, Genetics, Environment, and Healthcare (Kaiser Family Foundation)

Lifestyle Choices Genetics Environment Healthcare0%

10%

20%

30%

40%

Health Impact

Causes, Risk-Factors: 10 Highest-Cost Health Conditions*Coronary Heart DiseaseSmokingHigh LDL vs HDLHypertension (not contr.)Physical InactivityObesityUncontrolled DiabetesHigh C-reactive proteinStress / Anger

Cancer

Benzene, ChemicalsExcess AlcoholAflatoxinsExcess SunlightGenetic ProblemsObesityRadiationViruses

Chronic Obstructive Pulmonary DiseaseSmoking

OsteoarthritisGenetic DispositionObesityEarlier Fractures, Injs.Kneeling / Squatting jobsContact Sports

DiabetesType 1: UnknownLow ActivityPoor DietExcess WeightPregnancy >25Family HistoryHeavy BabyHigh Blood PressureExcess Amniotic FluidMiscarriage / Stillbirth

HypertensionAfrican AmericanObesityStress / AnxietyExcess SaltFamily HistoryDiabeticSmoking

HyperlipidemiaDietExcess WeightHeavy AlcoholPhysical InactivityDiabeticUnderactive ThyroidPolycystic OvaryKidney DiseasePregnancyCertain MedicinesFamily HistorySmoking

Lifestyle ChoiceGeneticsEnvironmentCo-MorbidityOther Factor

Other 3 ConditionsTrauma DisordersMental DisordersBack Problems

* Account for >50% of total healthcare cost

Healthcare, Lifestyle Choices, Genetics, and Living Environment are Deeply Inter-Related

Our Health System Aligns Information, Resources,& Actions to Maintains Our Health at Reasonable Cost

More Healthy

Patient Segment

Health Advice;

Ability to Pay,Other

Environment

Healthy; Infrequent User

Material Healthcare User

Major Treatment

Ongoing Poor Health

SocialInformation

Advertis-ing

EducationMedia

Dead

Lifestyle choices (food, exercise, etc.)Medical Treatment

Program Adherence

Health Advice;

Ability to Pay,Other

Environment

Health Advice;

Ability to Pay,Other

Environment

Health Advice;

Ability to Pay,Other

Environment

Information Health Advice OtherEnvironmentAction Lifestyle Choice Healthcare

Healthcare Resources

Doctors

Nurses

Pharmaceuticals

Machine Use

Tests

Computers

…

…

• The Highly-Complex, Inefficient US Health System

• AI: A uniquely powerful– yet delicate– technology

• How We Create the Best Outcomes

Agenda

OECD Median US05

101520253035404550

• US Healthcare is already tech-intensive

MRI Use per ‘000 People (2013)

CT Scanners per MM population (2012)

Technology-RichWell-trained Professionals

From a Systems Engineering Perspective, US Health System Has Been Increasingly ‘Out of Control’ Since 1980

100% MoreSpending ($s)

3 Year Shorter Life

Expanding Inefficiency

Outcomes Consistent with Other Countries

The ‘Out of Control Cost’ Is Related More to Rising # of Diagnoses & Treatments, than by Rising Cost per Patient

Higher Cost / Patient

# Diagnosed & Treated

Change in Healthcare Spending ‘96-10 (part of the ‘out of control era’)(20 Highest-Expense Conditions)

Unhealthy American Lifestyle

Population Weight Diversification1960’s-80’s (% of population): 1/3: 0 lbs 1/3: 13 lbs 1/3: 25 lbs

The Case of Betty

Expanding Circle of Influence: Cacophonous Internet Direct to Consumer Pharma Marketing Broadening Health Technology Origins

Accelerating Frequency: Addition of Computers & the Internet Data Pipelines & Big Data Artificial Intelligence

Increasingly Intricate Circle of Control: More Complex Tests, Treatments Of the Average General Practitioner’s Medicare Patients: 31% of patients with 4+ chronic conditions These patients overlap with 86 other MDs in 36 practices

Arthritic Diabetic COPD Hyperlipidemia Depressed

8 prescriptions

4 Doctor Appointments / Month3 Medical GroupsDoes her hair for each one

Lives in a Food Desert, but isa master at cooking cheesecake

Confused by health advice Which medication, when, refills How to eat, exercise When to re-visit doctor

Medicare + Gap Insurance

31% of Medicare patients have 4+ chronic conditions (86 / 36)

The Case of Technology Impacting Data

Rapid researchBusiness PatternsEducated Patients

1976: Computer1982: Public Internet

2000: Big Data 2015: AI

Complex AnalysisHigher DimensionalityData Volume

Automated DecisionsDecreasing TransparencyUnique Logic

System Complexity & Data Overload are an Enemy

Over the years, access to useful data has continued to increase, but the ability to apply analytical insights to strategy has declined. As the volume and complexity of data grows at exponential rates, companies wrestle with how to turn the data into useful insights that can guide.MIT Sloan Management Review

Since 1980, Need for Health System Controls Has Increased

Increasing # of Patients: Unhealthy Lifestyle Growing Environmental Challenges ‘A Test for Everything’ ‘A Treatment for Everything’ More Insurance

Expanding Circle of Influence: Social Networks Consumer Pharma Marketing Health Tech

Accelerating Frequency: Computers Big Data Artificial Intelligence

Increasingly Intricate Circle of Control: More Complex Tests, Treatments Complex Patients Complex Businesses

Need for Control

In Market Systems, You Expect Economic Tools to Control

Regulation Constrain What’s AllowedTo increase benefits, and/or ensure benefits to all

Artificial Demand: Physician-Induced Demand

Professional Organizations, Standards Share Best Practices related to coordination, complexity, externali-

ties

Firm

‘Principals’ (Managers / Owners) Devise Organization System Make Decisions, Communicate‘Agents’ (Employees) Execute

Market

Price Signals Resource ValueContracts Facilitate Cooperation

Economic Control Mechanisms Failing

Increasingly Directive Regulation Electronic Medical Record Mandates Value-Based Medicine Directives

Professional Organizations Breaking Down

Management ControlBreakdowns

Increasing Quality Data– NOT Decision Quality What’s Going ON?? Data Overload Organization Control Failing

Competition Failures

Price is MeaninglessQuality Dimension ComplexInefficient Tech DevelopmentResource Mis-Allocation: (Technology, MDs, Induced-Demand)

• Complex Systems Fail w/o Effective Controls

• US Health System Became Increasingly complex

• Increasing Complexity Weakened Economic Controls

• US Health System systemically failed: Current market mechanisms unable to effectively control system

US Health System Economic Control Failure

• The Highly-Complex, Inefficient US Health System

• AI: A uniquely powerful– yet delicate– technology The Powerful, yet Delicate, Technology A Health Tech to which We Must Pay Attention With Unique Strengths & Weaknesses

• How We Create the Best Outcomes

Agenda

Every AI Application implicitly or explicitly assumes a certain world model. E.g., Automated Anesthesiologist

Many AI applications leverage systems with: Model Built of Sub-Models Layered Parameterized

Challenge: unrealistic, inconsistent models

AI Depends on an Appropriate World Model, Applied Well

Applied Mathematical Models in Human Physiology,

Technique for probabilistic relationships, & lower-dimension problems with uncertainty

Easy to ‘Lie with numbers’ with these models:

AI is Generally Reliant on Clean Statistics & Logic Tools

Israel

Malaysia

Actual Spend

Projected Spend

Diagnosis:•Liver Disease Diagnosis•Personalized Lung Cancer Survival and Treatment•Psychiatric Illness Diagnosis•Online Self-Diagnostic Tool

Treatment Selection•Patient-specific drug recommendations•Ovarian Cancer Treatment

Clippy the Microsoft Assistant

Sample Applications: Belief (Bayes’) Network

‘What Does a Leaf Look Like?’• Concept is Complex• Different Perspectives• Exceptions• Complicating Factors

Limited human:• Data Entry• Model Building• Data Processing

Develop equations similar to human ‘concepts’

Easy to bias; challenging to systematically de-bug

Commonly Leveraging Neural Net Tools: Conceptual

               

               

               

               

               

               

               

Input Group

Convolutional, Grouped Layers Learn Features:

Additional ‘hidden layers’ link features Output

FeaturesConvolutional Net Layer 1

Convolutional Net Layer 3Features

Grouped

Net Layer 2

All to All

Net Layer 4All to All

Sample Model: Convolutional Neural Networks (CNNs)

Grouped Input

Note: neural nets w/ manylayers are ‘deep learning’

Image Recognition:•Cancer Detection (ID’ed new

approach)• Diagnosing malaria, TB, intestinal parasites

Natural Language: • Digital Assistants

General Pattern Recognition:• Hospital Re-admission

Forecast• ID Embryonic Cell State

Sample Neural Network Applications

Click to Play

* Illustrates ability for computers to think creatively & conceptually

Sample Model:Deep-Q Learning (Reinforcement Learning)

Reinforcement Learning using Neural Nets Makes the world into a video game.

State ActionReward

World orSimulation

Algorithm

HermanHiddema at the English language Wikipedia

• Artificial Intelligence is a powerful, yet delicate, technology

- Dependent on an appropriate world model, applied well

- Generally reliant on clean probability, statistics, and logic

- Commonly leveraging conceptually-capable, neural net tools – ‘self-programming’, yet challenging to systematically debug

Artificial Intelligence In Review

• The Highly-Complex, Inefficient US Health System

• AI: A uniquely powerful– yet delicate– technology The Powerful, yet Delicate, Technology A Health Tech to which We Must Pay Attention With Unique Strengths & Weaknesses

• How We Create the Best Outcomes

• Conclusions

Agenda

AI is Built for PersuasionWe treat

computers

as if

they are

human (anthropomorphize)

But will share our

most private

thoughts.

Built for Persuasion!

Select Development Areas

Top Healthcare Companies Investing Heavily in AI:

• GE Healthcare• Nuance• Mayo Clinic• Siemens Healthcare• McKesson• Massachusetts General Hospital

Leading Companies are Investing Heavily in AI

And a Growing Army of Startups is Chasing ($9.1BB)(all industries)

Progress in AI is REAL and FAST!

Mitigating NN Weakness

Reinforcement Learning, *

Adversarial Nets

Human Collaboration, Persuasion

‘Retro’: NN + Traditional AI

Select Development Areas

And Many, Many More!

• Artificial Intelligence is a health technology to which we MUST pay attention

- AI is built for persuasion: It’s like our best friend– but more so

- Industry leaders and startups are investing billions

- Progress is real– and FAST!

Artificial Intelligence In Review

• A powerful, yet delicate, technology

• A health technology to which we MUST pay attention

• A technology with unique strengths and weaknesses– unlike human intelligence, traditional computer tools, or anything else

Artificial Intelligence In Review

• The Highly-Complex, Inefficient US Health System

• AI: A uniquely powerful– yet delicate– technology

• How to Create the Best Outcomes Recognize The System & Its Complexity Lifestyle Choice Healthcare Apply the Right Lens to Optimize the System

Agenda

Health Advice;

Ability to Pay,Other

Environment

Healthy; Infrequent User

Material Healthcare User

Major Treatment

Ongoing Poor Health

SocialInformation

Advertis-ing

EducationMedia

Dead

Lifestyle choices (food, exercise, etc.)Medical Treatment

Program Adherence

More Healthy

Health Advice;

Ability to Pay,Other

Environment

Health Advice;

Ability to Pay,Other

Environment

Health Advice;

Ability to Pay,Other

Environment

Case Study: Patient Program Adherence as a System

But I’m not sick! I’m out of money!

That doc is a crock!

Something better online.

Incentive to monitor is low

Race, income, & insurance status explain 65% of hospital readmission rates

Disappointing Results to Date

Worse than manual alternatives

No material benefits

Worse than not tracking

What’s Being Promised by AI Isn’t Working Out…

Illustrative Study:Subjects asked for opinion.

2 weeks went by

‘Plant’ argued against opinionPowerfully

Subject opinions were flipped

Subjects believed new opinion had always been theirs -- strongly!

Goethals, G.R., & Reckman, R.F., The perception of consistency in attitudes, Journal of Experimental Social Psychology, 1973, 9, 491-501.

BUT, Persuasion AI is PromisingOur computers know:Everything about us How we talk Who we talk to What we do When we do it Our Mood Body Situation

All persuasion techniques How to question us How to listen to us How to talk to us

Can be skilled at applying

We anthropomorphize themWe share secrets with them

Technical Inqwire Bibliography

And that Persuasion Can be TailoredWe have the potential to benefit from personalized AI recommendation, e.g.,: Calorie Count

Supplement Use

Mercury Limits

Exercise

Medical Checkups

Basis: Genetics / Epi-genetics Mood Schedule,…

Business Drive May Limit Persuasion Benefits

On-Desire Advertising: Advertising cue: stomach grumble

Tone: Just broke up w/ boyfriend

Promotion: Free drone delivery

AI may replicate current,unhealthy weighting of advertising focus:

Food products highly-marketed to youth: Added-sugar drinks Traditional (US) snacks Candy / Frozen Dessert Fast Food (‘QSR’) Low-sugar Cereal*

<0.5% of advertising to youth on fruits & vegetables

“Food industry executes voluntary initiative to advertise healthier options to kids.”

Adult food advertising has broadly similar pattern.* Laws limit advertising of high-sugar cereals to youth

Biased input biases AI output– and we have a history of biased health information, e.g.:Sugar industry paid 3 Harvard professors $15K each to shift blame from sugar to fat.

The 65-year war against saturated fats appears based on bad science.

PSAs, Mammograms, & Statins may all have negative benefit, (as currently prescribed)

No strong evidence for ‘Breakfast’ as most important meal

The FDA (& other information sources) eliminate journalist independence on key issues.

Information Problems May Limit Persuasion Benefits

* Sources: NY Times, Wall Street Journal, Director of Scripps, Scientific American

2000 2015 Illustrative 20300

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

9,886 11,699 11,699

12,871 16,742 20,928

16,873 19,251

24,064 10,318

13,918

17,397 18,514

14,095

10,571 11,115

10,957 8,218 19,836

18,983 14,238

31,036 32,970

39,564

Accelerating Labor Displacement May Negativey Impact

Accelerating Automation

Net employment not impacted–this time.

Source: Bureau of Labor Statistics, Authors’ Analysis

Other

Transportation

Business (Ex-sales)

Manufacturing, Construction,Utilities

Medical, Health

Sales, AccountingTech Support

Food Service,Personal Assistance

US Employment (‘000 jobs)

Under Threat

Impacting job mix & wage distribution: More ‘Human Service’ More Sales / Customization Less Manufacturing, ‘Business’

Entertainment

But, Job market health key, & tech speed uncertain.

AI will indirectly impact psychological health through jobs:

Preliminary Illustration

And, AI May Expand Current ‘Internet-Driven’ Stress

Loss of human connection: “Antisocial networking” “Connected but alone” “Broad but shallow”

We are selectively exposed to info with which we agree

‘Our community’ shrinksWe become hypersensitive

Family BreakdownSocial Breakdown

Failing Trust

35% of older adults lonely, vs only 20% 10 years earlier.

Suicide among 15-24 Yr Olds, tripled since 1970

+

Understanding the health impact of AI is about understanding the complex human and the health system.

• Despite limited examples to date, AI will impact Lifestyle Choice:Influencing us more deeply than our best friend

• Human quirks and psychology could constrain impact

• Effective persuasive AI may lead to UNhealthy choices, due to:Business Drive for ProfitInformation Problems

• Employment problems & other stresses could outweigh benefits

Conclusion: Part 1– AI’s Impact on Lifestyle Choices

Healthcare Process Impacted by Artificial Intelligence

Collect Symptoms & Diagnose Problem(s)

Select Treatment

Physical Treatment

Develop New Treatments

Select Treatment Strategy

Execute Treatment

Psychological Support

Develop New Treatments

Program AdherenceCoordination Assess Outcomes

Patient Research, Community

Collect Symptoms & Diagnose

US Med. SchoolRead JournalsAnalyze DataDiagnose

OncologistUS Med. School

Diagnose

OncologistSloan Kettering Hospital

Read JournalsAnalyze DataDiagnose

Artificial Intelligence

Diagnose

Oncologist

(US AI model)

Artificial Intelligence

Thai Hospital

Past With AI

Sent To

Supports Potential:• 95% humans do not have ac-cess to US physicians

• 50% of US physicians are below average

• 10-20% of current diagnoses incorrect, relative to data in the patient’s record

• Data in a patient’s record is thin relative to potential

Hurdles:• e-Iatrogenesis: 8 errors / patient file Software bugs Missed Human Visual Cues Pasted Irrelevant; Cut Valuable• Misunderstood Algorithms• Non-comparable data• Regulation*• We like humans; Stats may have improved to > human years ago

• Data Overload* Example: FDA guidelines for 501K submissions for machine-learning based systems that parse image- based radiological data include recommendations that the submission explain exactly how algorithm fine points work, including geo-metrical patterns used to identify & classify suspicious shapes

Treatment Selection

Prescription Vanderbilt University, St. Judes

Past With AI

Patient Trial

Suc-cess

Patient Dr. visit

Recommend pharmaceuticals

better aligned with patient’s DNA

66% recommendations followed

Supports Potential:• Fact-based comparison improves selection

• Massive database empowers less-deeply trained

• Only 55% of recommended treatments for common illnesses provided today

Hurdles:Incentives: Poor system for low- cost (e.g., nutrition, herbal)Varied beliefs: re ‘good medicine’• More than 50% of 2nd opinions

materially change treatment• Believed impact of colon cancer screening on mortality:

Uniform from 3 – 95%Human curation drives systemsPhysician + Patient Choice is a cornerstone of US health policyData Overload: 52% chance life- threatening DDI warning ignored

Treatment Development

Select Molecular TargetBerg Pharmaceuticals

Past Present

$1-6 Billion12-14 Years99% Failure

Drug Development

$0.7 Billion7 Years

Drug Development

Projected

Screen Compounds

Develop Drug

Test Drug

Develop Detailed AI Map of Diseased, Healthy Systems

Translate Difference Healthy, Diseased into Drug

Test Drug

Supports Potential:• Data illustrated to optimize treatment (e.g., stroke)

• Pharmaceutical development will be one of the 5 largest AI uses, through 2021

• Strong progress in ‘personalized medicine’ (genetics-driven)

• Increasing discussion of complete biological models

Hurdles:METHODOLOGY, INCENTIVES: More technology doesn’t fix technology abuse, limits• Most published research findings likely false

• 35-40% of New England Journal of Med. results to be reversed

Personalized medicine reduces relevant population size

Artificial Intelligence is amazing! BUT, it is a tool like any other– to get the outcomes you want, you MUST manage systemic issues.

Conclusion: Part 2– AI’s Impact on Healthcare

Data Overload

System Weakness  (mis-aligned incentives, organized labor, complexity, regulation / ethics,...)

Diagnosis X XTreatment Selection X X

Strategy Development

Physical Treatment   XPsychological Support   XProgram Adherence   XTreatment Development   X

ExecutionCoordination X X

Exacerbated Challenges

Act

ivity

• The Highly-Complex, Inefficient US Health System

• AI: A uniquely powerful– yet delicate– technology

• How to Create the Best Outcomes Recognize The System & Its Complexity Lifestyle Choice Healthcare Apply the Right Lens to Optimize the System

Agenda

NOT amenable to Root cause analysis Linear methods, models

Systemic Problems do NOT Have a ‘Root Cause’

Solutions Derive from•Unmasking Complexity

•Dissolving Systems of Problems

•Organization Design:

process, systems, structures

•Changing Goals & Man-aging

Systems

Systemic Problems do Have a Systemic Solution

1. A Human PurposeFacilitating a long, healthy life– or treat sick?

2. Appropriate Business ArchitectureWhat architecture executes mission, effectively integrates tech, collaborates well? 

3. Professional Standards Backbone: Via which institutions (AMA, IEEE, +) come system-supporting design standards? What are they? 

4. Investor / Technologist PowerHow to architect a complete technology portfolio? And ensure effectively designed businesses? 

5. Effective Government SupportHow to architect for an ecosystem supporting healthier lifestyle choices? (E.g., HUD, HHS) 

6. Quality Education & TrainingHow to train professionals to do the right thing for system, vs excelling at an activity? 

7. Replace or Heal our Institutions?

A Productive, Controlled Human Health System is Aligned

Regulation

Economics

Technology

Culture

Materials Tools Skills Methods

Feasible Productivity

Shared Purpose

VBH

Answering Strategic Question About a Broken Health System

Architecture Evaluation• What are current value-adding systems, processes, & structures?

•Are they aligned?•Is strategy executed, objectives

achieved?

Strategy Identification• Systems available to you– when?• Strategic option simulation• Integrated Strategy development: Process, System, Structure

Implementation

Fixing a soccer ‘meatball’? Describe meatball. Clarify what you want. How to get there. How to stay there.

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