artificial intelligence and health: technology implementation in a broken system
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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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