The Decision Machine - Advisory · 2016 2030 2016 2030 2016 2030 2016 2030 2016 2030 Australia2 9.6% 14.4% 2010 New Zealand3 6.2% 8.9% 10.6% 15.9% 11.0% 18.0% Canada France United

Health Care IT Advisor

The Decision MachineAnalytics and the Rise of AI

Greg Kuhnen

Senior Research Director

kuhnengadvisorycom

copy2018 Advisory Board bull All Rights Reserved bull advisorycom bull 36516D

2

Manage Your Audio

Use Telephone

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use the dial-in phone number and access

code provided on your GoTo panel

If you select the ldquoMic amp Speakersrdquo

option please be sure to check that your

speakersheadphones are connected

Use Microphone and Speakers

All attendees will be muted during the presentation


3

Manage Your GoTo Panel

Ask a Question

To ask a question please type it into the

ldquoQuestionsrdquo box on the GoTo panel and

press ldquoSendrdquo

Use the orange and white arrow to

minimize and maximize the GoTo panel

Use the monitor button to toggle

between Fullscreen and window mode

Minimize and Maximize Your Screen


4

2018 Health Care IT Advisor Virtual Summit

Beyond Meaningful Use and Operational Excellence

Virtual Summit Agenda

Source Health Care IT Advisor research and analysis

Final session September 6th at 300pm ET

Available on-demand to Advisory Board Members

The Decision Machine Analytics and the Rise of Artificial Intelligence


State of the Union


ITrsquos Role in the New Cost Control Mandate


Digital Health Systems The Innovation Journey Continues


The Era of the Connected Patient Patient-Generated Health Data

and Social Determinants of Health

See the 2018 Virtual Summit



Greg Kuhnen


kuhnengadvisorycom


6

Sources ldquoAnalyticsrdquo Wikipedia last modified March 5 2018

httpsenwikipediaorgwikiAnalytics English Oxford Living Dictionaries Oxford Oxford

University Press 2018 (also available at httpsenoxforddictionariescom) Health Care IT Advisor

research and analysis

Analytics

The discovery interpretation and communication of meaningful

patterns in data

ndashWikipedia

Artificial Intelligence (AI)

The theory and development of computer systems able to

perform tasks normally requiring human intelligencehellip

ndashOxford English Dictionary

Machine Learning (ML)

The field of study that gives computers the ability to learn

without being explicitly programmed

ndashArthur Samuel 1959


ROAD MAP7

The Data Dilemma1

2 AI and the New Machine Age

3 How to Get There


8

Health Economics An Unsustainable Trajectory

Analytics Can Be a Powerful Tool for Bending the Cost Curve

Sources Cottarelli C ldquoMacro-Fiscal Implications of Health Care Reform in Advanced and Emerging Economiesrdquo IMF

Fiscal Affairs Department December 2010 McKinsey amp Company ldquomHealth A new vision for healthcarerdquo 2010

Office of Economic Co-operation and development lsquoHealth Spendingrdquo Health Care IT Advisor research and analysis

1) Global Burden of Disease Health Financing Collaborator Network ldquoFuture and potential spending on

health 2015-40 development assistance for health and government prepaid private and out-of-

pocket health spending in 184 countriesrdquo The Lancet 389 No 10083 (2016) 2) According to the

Australian Institute of Health and Welfare (AIHW) calculations 3) New Zealand Ministry of Health

projections 4) GDP = Gross domestic product

Per Capita Health Care Spending1 Health Expenditures as Percentage of GDP4

2016 20302016 2030

2016 2030 2016 2030

2016 2030

Australia2

96144

2010 2030

New Zealand3

62 89

106159

110

180

FranceCanada

United States United Kingdom

172249

97135

$9237

$5234

$4751

$4576

$4032

$4050

$3749

$3096

$12448

$7799

$6437

$5926

$5606

$5496

$5002

$4245

United States

Netherlands

Belgium

Canada

Australia

New Zealand

UnitedKingdom

Spain

Health Expenditure per Capita 2014



10

Technology Drives the Flood of Data

Smartphones and Genomes and Sensors Oh My


1) IoT = Internet of things

2) RTLS = Real-time locating system

3) ROI = Return on investment

Common Barriers to

Adoption of New Sources

bull Privacy security concerns

bull Immature technology

bull Lack of budget

bull Unclear benefit ROI3

bull Staff already overwhelmed

with data

A Sampling of New Data Sources

Integrated Partners

(eg social services

cross-continuum care)

Data from Outside

Organizations

(eg social)

Environmental Data

(eg IoT1 RTLS2

temperature air

quality)

Genomes Proteomes

Microbiomes

Metabolomes

Clinical Surveillance

(eg public health

outbreak reports)

Remote Patient Monitoring

(eg bedside monitors

remote patient monitoring

consumer wearables)


11

Cognitive Overload and Clinician Burnout

Sources Millett D Exclusive ldquoThree GPs a day seeking help for burnout

official data showrdquo GP Jan 2018 Health Care IT Advisor research and

analysis

1) Obermeyer Z Lee T ldquoLost in Thought ndash The Limits of the Human Mind and

the Future of Medicinerdquo N Engl J Med 377 no 13 (2017)1209-1211

Tim

e R

eq

uir

ed

ldquoMedical thinking has become vastly more complex mirroring changes in

our patients our health care system and medical science The complexity

of medicine now exceeds the capacity of the human mind1rdquo

Ziad Obermeyer MD and Thomas Lee MD

Number of Considerations for Decision Making

Significant

opportunity for

AI and analytics

Augmented Intelligence Reduces Cognitive Burden

Reliance on heuristics rules of thumb ldquogood-enoughrdquo


12

Data Power the Decision Engine


Four Levels of Analytic Capabilities

Data

Inform

Recommend

Decision

Automation

Decision

Support

Descriptive

What happened

How many of our

patients were

readmitted last

month

What is likely to happen

Is this patient likely to

readmit What do we

expect seasonally

Prescriptive

Recommend or

decide the best action

What should we do

to address this

patientrsquos risks

PredictiveDiagnostic

Why did it happen

Rates spiked was there

a change in case mix

Demographics

Decide

Hum

an

Decis

ion

Analy

tics


ROAD MAP13

The Data Dilemma1


3 How to Get There


14

AI Are We There Yet

And if Not When


We May Be at the Start of an Explosive

Growth in AI Capabilities

Incremental Growth

AI continues to make

small advances

delivering value in

niche applicationsAI Winters AI has

already gone through

past phases of hype and

troughs of disillusionment

Exponential Growth

AI rapidly gains

capability and becomes

a mainstream part of

most digital systems

Unpredictable Timing Some

advances never seem to arrive

(conversational systems)

while others take off

unexpectedly (smartphones)

60s 70s 80s 90s 2000 2010 2020 203050s 2040 2050


15

Fuel for the AI Fire


1) EMR = Electronic medical record

2) GPU = Graphics processing unit

3) TPU = Tensor processing unit

AlgorithmsData

bull Internet Everywhere

bull Broad EMR1 Adoption

bull Wearables

bull Real-Time Location

bull IoT Sensors

bull Genomics Proteomics

Microbiome

bull Automated Feature

Extraction

bull Cluster Compute

Platforms (eg

Spark Hadoop)

bull Deep Learning

bull Off-the-Shelf

Libraries for

Common Tasksbull Faster Processors

bull Specialised Processors (GPUs2 TPUs3)

bull Inexpensive Storage

bull Elastic Cloud Computing

Hardware

Combinatorial Advances Delivering

Radically Better Models


16

The AI Winter Is Thawing


1) NASA = National Aeronautics and Space Administration

Microsoft and Alibaba outscore humans in a Stanford reading comprehension test 2018

Google DeepMindrsquos AlphaZero convincingly leads at Chess Shogi Go 2017

Skype launches real-time voice translation 2014

Mass market voice assistants (Apple Siri Google Now Microsoft Cortana) 2012

IBM Watson conclusively beats Jeopardy game show champions 2011

NASArsquos1 Mars Rovers operate semi-autonomously 2004

IBMrsquos Deep Blue beats world chess champion Gary Kasparov 1997

1950 Alan Turing proposes the ldquoTuring Testrdquo for conversational systems

1956 The term ldquoArtificial Intelligencerdquo is coined

1970s First AI Winter

1981 Digital Equipment Corporation order

expert saves company $40M per year

Timeline of AI Successes

From Toys to Tools


17

DeepMindrsquos AlphaZero

Novice to Superhuman in Just Four Hours

DeepMindrsquos AlphaZero Uses lsquoZero Human Knowledgersquo

Sources Vincent J ldquoDeepMindrsquos AI became a superhuman chess player in a few hours just for funrdquo The

Verge December 6 2017 Gershgorn D ldquoDeepMind wants to find the next miracle materialmdashexperts just

donrsquot know theyrsquoll pull it offrdquo Quartz October 25 2017 Health Care IT Advisor research and analysis

By not using this human data by not using

human features or human expertise in any

fashion wersquove actually rem

oved the constraints of human knowledge Itrsquos

able to therefore create knowledge for itselfrdquo

bull AI product of Google

subsidiary DeepMind

bull Provided no prior

knowledge of the

game given only basic

game rules and an

objective (win)

bull Learns through self-play

at an accelerated pace

(lsquoreinforced learningrsquo)

searches 80 thousand

positions per second

bull Variants of AlphaZero also

defeated world champion

Go and Shogi systems

AlphaZero

outperformed chess

world-champion

Stockfish1 in just

4 hours (300k steps)

By not using this human data by not using human

features or human expertise in any fashion wersquove

actually removed the constraints of human knowledge

Itrsquos able to therefore create knowledge for itselfrdquo

David Silver Lead Programmer

DEEPMIND

1) Stockfish is the 2016 Top Chess Engine Championship (TCEC) world-champion

AlphaZero Chess

Performance


18

Need a Radiologist

Therersquos an App for That

Sources Kubota T ldquoStanford algorithm can diagnose pneumonia better than

radiologistsrdquo Stanford News Nov 2017 Health Care IT Advisor research and analysis

1) Stanford University Medical Center

2) NIH = National Institutes of Health

Case in Brief Stanford University1

CheXNet Algorithm

bull Algorithm developed by researchers at

Stanford University in the US can diagnose

14 common pathologies in chest x-rays

bull Trained on ChestX-ray14 a public data set

released by the NIH2 containing 112120

frontal-view chest x-ray images labelled with

the 14 possible pathologies

bull Outperforms previous models from the same

data set for all 14 conditions and diagnoses

pneumonia at an accuracy exceeding the

performance of four control radiologists

bull Produces heatmaps that visualize the areas

of an image most indicative of disease

Development of CheXNet

Sept 26 2017

ChestX-ray14 data set

released along with a

preliminary algorithm

that could detect the

labelled conditionsasymp One week later

CheXNet could

diagnose 10 of the

14 pathologies more

accurately than all

previous algorithmsasymp One month later

CheXNet surpassed

best published results

for all 14 pathologies

and outperformed

Stanford radiologists in

detecting pneumonialdquoI treat the model as a lazy resident Theyrsquore not dumb theyrsquoll

just use every trick they can find to avoid doing hard workrdquo

Dr Matthew Lungren RadiologistStanford University Medical Center


19

Lessons from Stanfordrsquos CheXNet

AI Is Effective on Narrow Tasks

Recent advances in AI have focused on

solving narrowly defined tasks Each

sub-problem (eg pneumonia) is a model

Handle ldquoNormalrdquo Explicitly

Identifying normal conditions versus

anomalies can help recognize when

human review may be needed

Explainable1 AI (XAI)

Models that can be interpreted at least

partially are easier to trust deploy and

govern Explainability is an area of rapid

progress in AI research

1) Also known as interpretable AI

Training Requires a Teacher or Referee

Machine learning needs to be told what

answers are correct or what actions produce

positive outcomes Well-curated data are

critical to the learning process



20

The AI Value Proposition

AI Excels at Decisions That Are Dull Detailed and Dear (Expensive)

Three Key Areas of Benefit

Dear

Provide speed capacity 24x7

availability and consistency

at a fraction of the cost

Although these are well

known benefits of automation

when you apply this to

cognitive and complex tasks

itrsquos a new ball game

Detailed

Evaluate a vastly broader

and deeper set of data to

improve decision quality

The complexity of health

care decision making is

growing due to new data

from genetics IoT devices

wearables better

interoperability and the

growing number of drugs

and treatments

Unlimited capacity

instant response

disruptive economics

Perform at super-human

levels exceeding experts

Serves as an assistant

(watches your back)

Dull

Free human decision makers

to focus on more challenging

ldquotop of licenserdquo activities

Reduce effort spent on

repetitive tasks process

large amounts of complex

information provide

continuous monitoring and

notifications and ensure

ldquostuffrdquo doesnrsquot ldquofall through

the cracksrdquo



21

Where to Start Where Wersquore Going

1) Narrow (Task Weak) AI focuses on narrowly defined problems such as predicting a patientrsquos risk of developing sepsis

2) General (Strong) AI attempts to match the flexibility and versatility of human intelligence

Narr

ow

(T

ask W

ea

k)

AI1

Clinical Decisions

Administrative Decisions

Ge

ne

ral (S

tro

ng

) A

I2

Full Diagnosis

and Treatment Plan

Clinical

Chatbots

Radiology

Interpretation

Acute

Clinical

Risk

Recruiting

Safety Risk Smart

Monitoring

Medication Dosing

Administrative

Chatbots

(Concierge)

Precision

Engagement

Precision

Medicine

Ambient

Documentation

Adaptive User

Interfaces

Worklist

Prioritization

Inevitable with Time

Todayrsquos Opportunity

(growing)

The Computer Will

See You Now

The Uber-ization of

Health Care

An AI Application Landscape

Capacity Staffing

Management

Population

Health Risk

The Self-Driving

HospitalInformation

Security

Targeted

Marketing



22

The Self-Driving Hospital

Combine Predictive Models with Front-Line Activation

Sources ldquoVirtual Air Traffic Control for Emergency Departmentsrdquo Qventus

httpsqventuscomcontentuploads201703QVE-ED-Solution-Briefpdf Health Care

IT Advisor research and analysis

1) EVS = Environmental services

2) LWBS = Left without being seen

3) LOS = Length of stay

Case in Brief

Mercy Hospital Fort Smith

bull 336-bed acute care hospital in Fort

Smith AR

bull Partnered with Qventus to improve

emergency department (ED) patient flow

and patient satisfaction

bull Analytics applies ML algorithms to

anticipate potential capacity shortfalls

process bottlenecks

bull Application sends real-time notifications

(nudges) to care teams via their mobile

phones to trigger specific interventions

bull Emergency department results

ndash LWBS2 rates dropped 30

ndash Door-to-doc time reduced by 20

ndash 24-minute reduction in average LOS3

ndash Annual case capacity increased by 6

See that a radiology order is delayed for a

patient within a few days of discharge and

notify radiology to prioritize the reading

Activate more EVS1 staff when high churn

of beds is anticipated

Send an early warning for difficult patient

placements at discharge

Prioritize inpatient transports depending

on expected bed needs

Sample ldquoDecision Recipesrdquo


23

Applicants Like You Liked This Job

Predictive Models Reduce Turnover Improve Engagement

Case in Brief MultiCare Health System

bull Non-profit system based in Tacoma WA with 16000 employees

bull Partnered with Arena a cloud-based HR solution to predict the

likelihood that a candidate will be retained in a role

bull Arenarsquos machine learning algorithms evaluate applicant

submissions digital interactions and externally sourced data on

the organization (eg employer review sites)

bull Achieved a 40 reduction in RN turnover at 180 days and a

28 reduction in overall turnover at 180 days

MultiCarersquos Hiring Process

Reduced recruiting

expenses

More predictable

staffing levels

More experienced

staff

Realized Benefits

Algorithms predict the likelihood the

applicant will be retained and

engaged in the target role and send

that information to the recruiter

Applicant completes

a 15-20 minute

assessment process

on Arenarsquos platform

Recruiters determine

whether or not to send

the candidate along to

hiring managers

Hiring managers use

Arenarsquos recommendation

as an optional factor in

the hiring decision



24

Accelerating Research

1) Yu KH et al ldquoPredicting non-small cell lung cancer prognosis by fully automated microscopic pathology image featuresrdquo Nature No 12474 (2016)

2) Rajwa B et al ldquoAutomated assessment of disease progression in acute myeloid leukemia by probabilistic analysis of flow cytometry datardquo IEEE Transactions on Biomedical Engineering 64 No 5 (2017) 1089-1098

3) Liu Y et al Detecting cancer metastases on gigapixel pathology images arXiv preprint arXiv1703 No 02442 (2017)

4) Rajpurkar P et al Cardiologist-level arrhythmia detection with convolutional neural networks arXiv preprint arXiv1707 No 01836 (2017)

5) Nielsen S et al A predictive model to identify Parkinson disease from administrative claims data Neurology 89 No 14 (2017) 1448-1456

6) Rajpurkar P et al CheXNet Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning arXiv preprint arXiv 1711 No 05225 (2017)

7) Poplin R et al Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning Nature Biomedical Engineering No 2 (2018)

8) ACC Machine learning techniques show promise for supporting medical decisions By quickly assessing risk new algorithms may help triage patients and inform clinical decisions ScienceDaily 2018

9) Oh J et al ldquoA Generalizable Data-Driven Approach to Predict Daily Risk of Clostridium difficile Infection at Two Large Academic Health Centersrdquo ICHE 39 No 4 (2018) 425-433

Nature1 ndash August 2016

ML algorithm can accurately

differentiate two types of lung

cancers and predict patient

survival time

IEEE2 ndash February 2017

ML model predicts relapse rates for

acute myelogenous leukemia with

90 accuracy

arXiv3 ndash March 2017

ML approach to imaging analytics

can identify metastasized breast

cancer with accuracy rates rivaling

those of pathologists

Neurology5 ndash Sept 2017

Predictive model to identify

Parkinsonrsquos disease from claims

data achieved 73-83 accuracy in

study results

arXiv4 ndash July 2017

ML algorithm can diagnose

heart arrhythmias using data

from wearables sensors with

cardiologist-level accuracy

arXiv6 ndash November 2017

Deep-learning algorithm

outperforms Stanford radiologists

at diagnosing pneumonia from

x-rays

American College of Cardiology8 ndash

Feb 2018

Model predicts heart attack

diagnosis among patients in the ED

with chest pain with ~90 accuracy

Nature7 ndash February 2018

ML algorithm can predict a

personrsquos cardiovascular risk

factors using eye scans

Infection Control Hospital

Epidemiology9 ndash April 2018

ML model predicts Clostridium

difficile infection (CDI) earlier

than current diagnostic methods


25

Driving the Decision Machine

1Acquire Data

bull Ensure all models have

a sponsor and regular

evaluation against goals

bull All models require

tune-ups as the

environment changes

Monitor Performance

and Revisit

42Train or Refine

the Model

bull The more the better

buthellip

bull High-quality well-

governed data pay

enormous dividends

bull Diverse cross-continuum

sources improve

predictions and

robustness of models

bull Decide what features

are important (eg

explainability)

bull Apply practical and

ethical constraints

bull Evaluate under

realistic conditions

(silent mode real-time

incomplete data)

bull Embed insights directly

into applications at key

decision points

bull Reengineer processes

as warranted

bull Consider the ldquo5 rightsrdquo

of decision support

3Incorporate Models

into Workflow

Hint The Analytics Is the Easy Part

Begin with

bull Explicit leadership agreement on outcome goals expected benefit mechanisms

bull Participation from empowered representatives of impacted process

bull External experiencemdashlearn from others



26

New Data Sources

Mainstream Systems Cover Only a Fraction of Predictive Power

Acquire Data

Sources Nash DB ldquoPopulation Health Why It Mattersrdquo Essentials in Population Health Childrenrsquos

Hospital Association Thomas Jefferson University October 2016 Health Care IT Advisor research and

analysis

1) RPM = Remote patient monitoring

EMR(s)

Lab Systems

Pharmacy Systems

Imaging

Medical Claims

Prescription Drug Claims

Billing and Supply Chain

Scheduling Systems

Medical

Care Data

10

Sensors RPM1

mHealth Apps

Patient-Reported

Outcomes

Genetics

Socioeconomic Data

Patient Questionnaires

Human

Biology

Data

20

Lifestyle

and

Behavior

Data

50

Social and

Environmental

Data

20

Data Sources by Influence on Health Outcomes


27

Big Data a Technology and a Philosophy

ldquoTherersquos Gold in Them Thar Hillsrdquo

Acquire Data

Sources Cloudera ldquoInova Translational Medicine Institute Partners with Cloudera to Advance Genome-Based Machine

Learning Initiatives and Save Livesrdquo PR Newswire June 2017 Health Care IT Advisor research and analysis

1) Originally coined by Doug Laney of the Meta Group

Assumes Value in the Data

Based on a premise that there are

valuable insights that can be distilled

from the mountains of data captured

by machines

Characterised by the 3 Vs1

bull Volume

bull Velocity

bull Variety

Places greater emphasis on

unstructured data

Free text images streams of sensor

data are all fair targets for analysis

Big data is the difference

between heart rates and

heart beats

Case in Brief Inova Health System

bull Inovarsquos Translational Medicine Institute

(ITMI) is assembling one of the worldrsquos

largest whole genome sequence

databases connected to patient

information in a health care system

bull Researchers use the Cloudera

machine learning platform to analyze

terabytes of clinical and genomic data

and identify genetic links to diseases

bull Insights are used to develop

personalized treatment plans for

patients in collaboration with the

treating physician

Resource Big Data in Health Care

Strengths challenges and pragmatic

guidelines for those considering the addition

of big data capabilities to their IT portfolio


30

How Machines Learn

Similar Methods Differing Goals

Build the Model

Source Advisory Board research and analysis

Input Data


bull Labeled with key indicators

Adjust the Model

bull Automated trial and error

bull Cross-breeding successful

candidates

Repeat

Supervised Learning

Evaluate models against the ldquocorrectrdquo answers

provided with the data The learning process

focuses on minimizing errors

Unsupervised Learning

Evaluate models against an abstract algorithmic

goal Example Divide this population of diabetics

into a handful of subpopulations that have similar

future risk profiles

Reinforcement Learning

Evaluate models by giving a reward for

desirable outcomes and a penalty for negative

outcomes Models are trained to maximize their

total rewards over timeReady for Review

Evaluate Performance

bull Against what goal

bull Forces explicit

tradeoffs (eg cost

vs quality)


32

Black Boxes Shades of Grey

Differing Views on the Importance of Explainability

Incorporate into Workflow

Mike Wall PharmD MBA

Chief Analytics Officer

The University of Chicago Medicine

The opaqueness of black-box models is

really scary When we canrsquot tell a

clinician why a patient wonrsquot do well on a

certain medication that can be a tough

thing to blindly trust from a clinical

perspective We want to reach a place

where we augment our practice

through AI rather than dictatingrdquo

Nigam Shah MBBS PhD

Associate Professor of Medicine

(Biomedical Informatics)

Stanford University

I am pushing to evaluate these models

via a similar set of mechanisms as RCT1

or AB testing We should evaluate

against the current status-quo and do a

beforeafter studyhellipWe have to convince

ourselves of the utility of these models

We should focus on utility before

explainabilityrdquo

Surgery is the

best option ()

Data

Black Box Model

1) RCT = Randomized controlled trial



33

Workflow Is Key to Adoption


The Right Information

To the Right Person

In the Right Form

By the Right Channel

At the Right Time

Case in Brief Mayo Breast Oncology

Clinic Trials Recruitment

bull Up to 100 concurrent drug therapy clinical trials are

available to Mayo Clinic breast cancer patients making

it challenging to consistently identify candidates

bull New clinical trial matching technology developed in

collaboration with IBM Watson Health evaluates charts

to identify eligible patients

bull Launched in January 2016 as a point-of-care solution

for oncology providers initial adoption was low due to

time pressures and fell to zero by February

bull Rework by health systems engineers resulted in a July

2016 relaunch screening moved to occur in

advance of patient visits so clinicians know of trial

matches before opening a patientrsquos chart

bull New process enrolls significantly more patients in

clinical trials than the prior manual process success led

to additional coordinator funding

The Five Rights of

Clinical Decision Support



34

Health Carersquos Field Agents

Medical Devices Become Intelligent Partners


1) EKG = Electrocardiogram

ME

DT

RO

NIC

MIN

IME

D 6

70G

Case in Brief Artificial Pancreas and Smart Infusion Pumpsmdash

Medtronic MiniMed Connect

bull SMARTGUARD mimics some functions of a healthy pancreas

measures and predicts glucose level drift and adjusts dosing

bull Insulin pump and continuous glucose monitoring can send data directly

to smartphone

bull Trials showed a 44 reduction in hypoglycemia and reduction in

average A1C values from 74 to 69

Case in Brief AliveCor KardiaMobile

bull Portable consumer-grade single-lead EKG1 retails for $100

bull Intended for self-capture of intermittent cardiac events such as atrial

fibrillation (AFIB)

bull Smartphone-based machine learning validates quality of trace

assesses possibility of AFIB classifies normal abnormal sinus rhythm

bull EKGs can be sent to a board-certified cardiologist for interpretation

for $19

Sources alivecorcom Medtroniccom Health Care IT Advisor research and

analysis

AliveC

orK

ard

iaM

obile


35

Who Watches the Machines

Treat Artificial Intelligence Models Like Junior Staff

Monitor Performance and Revisit

Governance is focused on

quantifiable target

outcomes

Automated

processes need

human oversight

ldquoCircuit breakerrdquo

rules can limit

potential harm

Periodically

evaluate processes

against goals

ldquoTo err is human but to really foul things up you need a computerrdquo

William E Vaughan Columnist

Kansas City Star



ROAD MAP36

The Data Dilemma1


3 How to Get There


41

Major Challenges to AI in Health Care

Business Challenges Legal and Ethical Challenges

Complexity Medical issues do not

appear in isolation and coordination

of care is difficult

Threat to Human Jobs Strong fear

associated with technology displacing

human workers

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 We are still in

the midst of installing basic and

foundational systems (eg EMRs)

while addressing regulatory and

other pressing PHM initiatives

Regulation Health IT regulations

are hotly debated at the national

level Finding the right balance of

public health protection and fostering

innovation are 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 Even if AI

approaches are statistically better

there may be liability when it fails

Human Touch How will we interact

with AI How strongly will we require

the human touch and human

compassion in health care

Cost The high costs for

developing testing certifying and

implementing can be a barrier



43

Cut Through the AI Clutter

Is Your Vendor Ready for AI Are You

1) Accuracy is the portion of cases a model correctly predicts

2) Sensitivity also known as recall is the probability a model can detect the condition it was designed to find

3) Specificity also known as precision is the probability a case is truly positive when the model says it is

Ask YourselfhellipAsk Your Vendorhellip

If you arenrsquot listening to what your data tells you about the present

yoursquore not ready to use it to predict the future

Health System ReadinessVendor Readiness

bull What outcomes are we trying to

impact How are they measured

today

bull Do we have access to the data and

talent we need to execute

bull How will AI interact with human

decision makers What workflows

will change

bull Who will oversee the system over

time Are there circuit breakers

bull Has their approach been deployed

with similar data and populations

bull Does using AI improve upon

conventional approaches (eg

statistical models visualization)

bull Who will manage the training and

ongoing validation of models

bull Go beyond accuracy1 get details

on sensitivity2 and specificity3



45

Analytics is an essential tool for improvement of health carersquos

economics quality of clinical care and operational efficiency

New data sources present opportunities for better decision making

but people are already overwhelmed Analytics distills raw data

into actionable insights and machine learning provides deeper

analysis and faster model development

Narrow task-focused administrative and operational

decisions are an immediate opportunity for AIML applications

Task-focused clinical applications are inevitable

Build a strong foundation in governance data-driven decision

culture and skills before you pursue advanced analytical

techniques including artificial intelligence

Analytics only delivers value when coupled with broader workflow

and process changes

Questions amp Key TakeawaysThe Decision Machine

Use our AI Cheat SheetTo explain key concepts and takeaways to non-IT leaders


46

Develop

Market-Leading Strategy

Enhance

Team Effectiveness

Accelerate

Performance Improvement

Letrsquos Keep the Conversation Going

bull Staff training and development

bull News digests and analysis

including the IT Forefront blog

bull Health care cheat sheets

bull Ready-made presentations

bull Presentations on critical IT-

related topics to get IT and

non-IT leaders on the same

page

bull On-call expert consultations

bull Strategic planning tools

bull Executive briefings reports

and presentations

bull Decision guides

bull Tools

bull Templates

ITSuiteEventsadvisorycom or leave a

comment in the survey

To access these resources contact us at

Next Steps Available to Advisory Board Members

To see the latest in your HCITA

membership visit advisorycom


47

Advisors to Our Work

With Sincere Appreciation

The Health Care IT Advisor program would like to express deep gratitude to

the individuals and organizations that shared their insights analysis and time

with us The research team would especially like to recognize the following

contributors for being particularly generous with their time and expertise

ArenaIOBaltimore Maryland

Michael Finn

Michael Rosenbaum

AthenahealthWatertown Massachusetts

Girish Venkatachaliah

AyasdiPalo Alto California

Jonathan Symonds

Childrenrsquos Hospital of

Eastern OntarioOttawa Ontario Canada

Christina Honeywell

CollibraNew York New York

Chris Cooper

Dan Scholler

Dignity Health San Francisco California

Dr Gurmeet Sran

Duke University

Health SystemDurham North Carolina

Dr Mark Sendak

Forecast Health Durham North Carolina

Michael Cousins

Geisinger Health SystemDanville Pennsylvania

Kirk Hanson

Health Catalyst Salt Lake City Utah

Dale Sanders

Levi Thatcher

HealthDataVizWaltham Massachusetts

Sandy Lawson

Kathy Rowell

Intermountain Healthcare Salt Lake City Utah

Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48







Jewish General Hospital Montreacuteal Queacutebec Canada

Dr Lawrence Rosenberg

Phil Troy

Mayo ClinicRochester Minnesota

Dr Tufia Haddad

Memorial Sloan Kettering

Cancer CenterNew York New York

Ari Caroline

Isaac Wagner

Microsoft Seattle Washington

John Doyle

Tom Lawry

Dennis Schmuland

New York PresbyterianNew York New York

Dr Peter Fleischut

Dr David Tsay

PaxataRedwood City California

Nenshad Bardoliwalla

Jayanta Bhowmik

Pieces Technologies Dallas Texas

Dr Ruben Amarasingham

Shannon Kmak

QventusLos Altos California

Mudit Garg

Venkat Mocherla

Royal Free London NHS

Foundation TrustSurrey London UK

Glenn Winteringham

SalesforceSan Francisco California

Jayesh Govindarajan

Stanford University

Medical CenterStanford California

Dr Matthew Lungren

Dr Nigam Shah

Taunton and Somerset NHS

Foundation TrustSomerset England UK

Andrew Forrest


49







The MetroHealth SystemCleveland Ohio

Dr Robert Fergueson

Dr David Kaelber

The University of

Chicago MedicineChicago Illinois

Mike Wall

University Hospital

BirminghamBirmingham England UK

Steven Chilton

Paul Jennings

Barnaby Waters

University Hospitals of

LeicesterLeicester England UK

Andrew Carruthers

University of CambridgeCambridge England UK

Pietro Lio

University of LeedsLeeds England UK

Owen Johnson

University of North

Carolina Healthcare Chapel Hill North Carolina

Jason Burke

University of Pittsburg

Medical Center Pittsburgh Pennsylvania

Terri Mikol

Don Riefner

Dr Rasu Shreshta

Washington University

Medical CenterSt Louis Missouri

Dr Brad Racette


LEGAL CAVEAT

Advisory Board is a division of The Advisory Board Company Advisory Board has

made efforts to verify the accuracy of the information it provides to members This

report relies on data obtained from many sources however and Advisory Board

cannot guarantee the accuracy of the information provided or any analysis based

thereon In addition Advisory Board is not in the business of giving legal medical

accounting or other professional advice and its reports should not be construed

as professional advice In particular members should not rely on any legal

commentary in this report as a basis for action or assume that any tactics

described herein would be permitted by applicable law or appropriate for a given

memberrsquos situation Members are advised to consult with appropriate professionals

concerning legal medical tax or accounting issues before implementing any of

these tactics Neither Advisory Board nor its officers directors trustees

employees and agents shall be liable for any claims liabilities or expenses

relating to (a) any errors or omissions in this report whether caused by Advisory

Board or any of its employees or agents or sources or other third parties (b) any

recommendation or graded ranking by Advisory Board or (c) failure of member

and its employees and agents to abide by the terms set forth herein

The Advisory Board Company and the ldquoArdquo logo are registered trademarks of The

Advisory Board Company in the United States and other countries Members are

not permitted to use these trademarks or any other trademark product name

service name trade name and logo of Advisory Board without prior written consent

of Advisory Board All other trademarks product names service names trade

names and logos used within these pages are the property of their respective

holders Use of other company trademarks product names service names trade

names and logos or images of the same does not necessarily constitute (a) an

endorsement by such company of Advisory Board and its products and services or

(b) an endorsement of the company or its products or services by Advisory Board

Advisory Board is not affiliated with any such company

IMPORTANT Please read the following

Advisory Board has prepared this report for the exclusive use of its members

Each member acknowledges and agrees that this report and the information

contained herein (collectively the ldquoReportrdquo) are confidential and proprietary to

Advisory Board By accepting delivery of this Report each member agrees to

abide by the terms as stated herein including the following

1 Advisory Board owns all right title and interest in and to this Report Except

as stated herein no right license permission or interest of any kind in this

Report is intended to be given transferred to or acquired by a member

Each member is authorized to use this Report only to the extent expressly

authorized herein

2 Each member shall not sell license republish or post online or otherwise this

Report in part or in whole Each member shall not disseminate or permit the

use of and shall take reasonable precautions to prevent such dissemination or

use of this Report by (a) any of its employees and agents (except as stated

below) or (b) any third party

3 Each member may make this Report available solely to those of its employees

and agents who (a) are registered for the workshop or membership program of

which this Report is a part (b) require access to this Report in order to learn

from the information described herein and (c) agree not to disclose this Report

to other employees or agents or any third party Each member shall use and

shall ensure that its employees and agents use this Report for its internal use

only Each member may make a limited number of copies solely as adequate

for use by its employees and agents in accordance with the terms herein

4 Each member shall not remove from this Report any confidential markings

copyright notices andor other similar indicia herein

5 Each member is responsible for any breach of its obligations as stated herein

by any of its employees or agents

6 If a member is unwilling to abide by any of the foregoing obligations then

such member shall promptly return this Report and all copies thereof to

Advisory Board


Research DirectorGreg Kuhnen

kuhnengadvisorycom

Research TeamJacqueline Beltejar

Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan

Program LeadershipJim Adams

Design ConsultantKevin Matovich

2445 M Street NW Washington DC 20037

P 2022665600 F 2022665700 advisorycom


2

Manage Your Audio

Use Telephone

If you select the ldquoTelephonerdquo option please

use the dial-in phone number and access

code provided on your GoTo panel

If you select the ldquoMic amp Speakersrdquo

option please be sure to check that your

speakersheadphones are connected

Use Microphone and Speakers

All attendees will be muted during the presentation


3


Ask a Question










4









State of the Union











Greg Kuhnen


kuhnengadvisorycom


6





Analytics


patterns in data

ndashWikipedia










ROAD MAP7

The Data Dilemma1


3 How to Get There


8












2016 20302016 2030

2016 2030 2016 2030

2016 2030

Australia2

96144

2010 2030

New Zealand3

62 89

106159

110

180

FranceCanada


172249

97135

$9237

$5234

$4751

$4576

$4032

$4050

$3749

$3096

$12448

$7799

$6437

$5926

$5606

$5496

$5002

$4245

United States

Netherlands

Belgium

Canada

Australia

New Zealand

UnitedKingdom

Spain




10







Common Barriers to




bull Lack of budget



with data


Integrated Partners

(eg social services


Data from Outside

Organizations

(eg social)

Environmental Data

(eg IoT1 RTLS2

temperature air

quality)

Genomes Proteomes

Microbiomes

Metabolomes


(eg public health

outbreak reports)




consumer wearables)


11




analysis



Tim

e R

eq

uir

ed






Significant

opportunity for

AI and analytics




12




Data

Inform

Recommend

Decision

Automation

Decision

Support

Descriptive

What happened

How many of our

patients were

readmitted last

month



readmit What do we

expect seasonally

Prescriptive

Recommend or


What should we do

to address this

patientrsquos risks


Why did it happen



Demographics

Decide

Hum

an

Decis

ion

Analy

tics


ROAD MAP13

The Data Dilemma1


3 How to Get There


14

AI Are We There Yet

And if Not When




Incremental Growth


small advances

delivering value in





Exponential Growth

AI rapidly gains









60s 70s 80s 90s 2000 2010 2020 203050s 2040 2050


15






AlgorithmsData



bull Wearables


bull IoT Sensors


Microbiome


Extraction


Platforms (eg

Spark Hadoop)

bull Deep Learning

bull Off-the-Shelf

Libraries for





Hardware




16

















From Toys to Tools


17













subsidiary DeepMind


knowledge of the


game rules and an

objective (win)









AlphaZero

outperformed chess

world-champion

Stockfish1 in just







DEEPMIND


AlphaZero Chess

Performance


18

Need a Radiologist







CheXNet Algorithm















Sept 26 2017






CheXNet could

diagnose 10 of the

14 pathologies more

accurately than all


CheXNet surpassed



and outperformed






19























20




Dear









Detailed








wearables better



and treatments

Unlimited capacity

instant response





(watches your back)

Dull







information provide




the cracksrdquo



21




Narr

ow

(T

ask W

ea

k)

AI1

Clinical Decisions


Ge

ne

ral (S

tro

ng

) A

I2

Full Diagnosis

and Treatment Plan

Clinical

Chatbots

Radiology

Interpretation

Acute

Clinical

Risk

Recruiting

Safety Risk Smart

Monitoring

Medication Dosing

Administrative

Chatbots

(Concierge)

Precision

Engagement

Precision

Medicine

Ambient

Documentation

Adaptive User

Interfaces

Worklist

Prioritization



(growing)

The Computer Will

See You Now

The Uber-ization of

Health Care


Capacity Staffing

Management

Population

Health Risk

The Self-Driving

HospitalInformation

Security

Targeted

Marketing



22









Case in Brief



Smith AR






process bottlenecks




















23













Reduced recruiting

expenses

More predictable

staffing levels

More experienced

staff

Realized Benefits





Applicant completes

a 15-20 minute

assessment process





hiring managers

Hiring managers use



the hiring decision



24















survival time




90 accuracy










study results










x-rays


Feb 2018














25


1Acquire Data





tune-ups as the

environment changes

Monitor Performance

and Revisit

42Train or Refine

the Model


buthellip


governed data pay

enormous dividends


sources improve

predictions and



are important (eg

explainability)


ethical constraints

bull Evaluate under



incomplete data)



decision points


as warranted


of decision support

3Incorporate Models

into Workflow


Begin with






26

New Data Sources


Acquire Data



analysis


EMR(s)

Lab Systems

Pharmacy Systems

Imaging

Medical Claims



Scheduling Systems

Medical

Care Data

10

Sensors RPM1

mHealth Apps

Patient-Reported

Outcomes

Genetics

Socioeconomic Data


Human

Biology

Data

20

Lifestyle

and

Behavior

Data

50

Social and

Environmental

Data

20



27



Acquire Data








by machines


bull Volume

bull Velocity

bull Variety


unstructured data





heart beats














treating physician






30

How Machines Learn


Build the Model


Input Data



Adjust the Model



candidates

Repeat

Supervised Learning

















tradeoffs (eg cost

vs quality)


32















Nigam Shah MBBS PhD



Stanford University








explainabilityrdquo

Surgery is the

best option ()

Data

Black Box Model




33




To the Right Person

In the Right Form


At the Right Time



















The Five Rights of




34





ME

DT

RO

NIC

MIN

IME

D 6

70G






to smartphone






fibrillation (AFIB)




for $19


analysis

AliveC

orK

ard

iaM

obile


35





quantifiable target

outcomes

Automated

processes need

human oversight


rules can limit

potential harm

Periodically

evaluate processes

against goals



Kansas City Star



ROAD MAP36

The Data Dilemma1


3 How to Get There


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


3


Ask a Question










4









State of the Union











Greg Kuhnen


kuhnengadvisorycom


6





Analytics


patterns in data

ndashWikipedia










ROAD MAP7

The Data Dilemma1


3 How to Get There


8












2016 20302016 2030

2016 2030 2016 2030

2016 2030

Australia2

96144

2010 2030

New Zealand3

62 89

106159

110

180

FranceCanada


172249

97135

$9237

$5234

$4751

$4576

$4032

$4050

$3749

$3096

$12448

$7799

$6437

$5926

$5606

$5496

$5002

$4245

United States

Netherlands

Belgium

Canada

Australia

New Zealand

UnitedKingdom

Spain




10







Common Barriers to




bull Lack of budget



with data


Integrated Partners

(eg social services


Data from Outside

Organizations

(eg social)

Environmental Data

(eg IoT1 RTLS2

temperature air

quality)

Genomes Proteomes

Microbiomes

Metabolomes


(eg public health

outbreak reports)




consumer wearables)


11




analysis



Tim

e R

eq

uir

ed






Significant

opportunity for

AI and analytics




12




Data

Inform

Recommend

Decision

Automation

Decision

Support

Descriptive

What happened

How many of our

patients were

readmitted last

month



readmit What do we

expect seasonally

Prescriptive

Recommend or


What should we do

to address this

patientrsquos risks


Why did it happen



Demographics

Decide

Hum

an

Decis

ion

Analy

tics


ROAD MAP13

The Data Dilemma1


3 How to Get There


14

AI Are We There Yet

And if Not When




Incremental Growth


small advances

delivering value in





Exponential Growth

AI rapidly gains









60s 70s 80s 90s 2000 2010 2020 203050s 2040 2050


15






AlgorithmsData



bull Wearables


bull IoT Sensors


Microbiome


Extraction


Platforms (eg

Spark Hadoop)

bull Deep Learning

bull Off-the-Shelf

Libraries for





Hardware




16

















From Toys to Tools


17













subsidiary DeepMind


knowledge of the


game rules and an

objective (win)









AlphaZero

outperformed chess

world-champion

Stockfish1 in just







DEEPMIND


AlphaZero Chess

Performance


18

Need a Radiologist







CheXNet Algorithm















Sept 26 2017






CheXNet could

diagnose 10 of the

14 pathologies more

accurately than all


CheXNet surpassed



and outperformed






19























20




Dear









Detailed








wearables better



and treatments

Unlimited capacity

instant response





(watches your back)

Dull







information provide




the cracksrdquo



21




Narr

ow

(T

ask W

ea

k)

AI1

Clinical Decisions


Ge

ne

ral (S

tro

ng

) A

I2

Full Diagnosis

and Treatment Plan

Clinical

Chatbots

Radiology

Interpretation

Acute

Clinical

Risk

Recruiting

Safety Risk Smart

Monitoring

Medication Dosing

Administrative

Chatbots

(Concierge)

Precision

Engagement

Precision

Medicine

Ambient

Documentation

Adaptive User

Interfaces

Worklist

Prioritization



(growing)

The Computer Will

See You Now

The Uber-ization of

Health Care


Capacity Staffing

Management

Population

Health Risk

The Self-Driving

HospitalInformation

Security

Targeted

Marketing



22









Case in Brief



Smith AR






process bottlenecks




















23













Reduced recruiting

expenses

More predictable

staffing levels

More experienced

staff

Realized Benefits





Applicant completes

a 15-20 minute

assessment process





hiring managers

Hiring managers use



the hiring decision



24















survival time




90 accuracy










study results










x-rays


Feb 2018














25


1Acquire Data





tune-ups as the

environment changes

Monitor Performance

and Revisit

42Train or Refine

the Model


buthellip


governed data pay

enormous dividends


sources improve

predictions and



are important (eg

explainability)


ethical constraints

bull Evaluate under



incomplete data)



decision points


as warranted


of decision support

3Incorporate Models

into Workflow


Begin with






26

New Data Sources


Acquire Data



analysis


EMR(s)

Lab Systems

Pharmacy Systems

Imaging

Medical Claims



Scheduling Systems

Medical

Care Data

10

Sensors RPM1

mHealth Apps

Patient-Reported

Outcomes

Genetics

Socioeconomic Data


Human

Biology

Data

20

Lifestyle

and

Behavior

Data

50

Social and

Environmental

Data

20



27



Acquire Data








by machines


bull Volume

bull Velocity

bull Variety


unstructured data





heart beats














treating physician






30

How Machines Learn


Build the Model


Input Data



Adjust the Model



candidates

Repeat

Supervised Learning

















tradeoffs (eg cost

vs quality)


32















Nigam Shah MBBS PhD



Stanford University








explainabilityrdquo

Surgery is the

best option ()

Data

Black Box Model




33




To the Right Person

In the Right Form


At the Right Time



















The Five Rights of




34





ME

DT

RO

NIC

MIN

IME

D 6

70G






to smartphone






fibrillation (AFIB)




for $19


analysis

AliveC

orK

ard

iaM

obile


35





quantifiable target

outcomes

Automated

processes need

human oversight


rules can limit

potential harm

Periodically

evaluate processes

against goals



Kansas City Star



ROAD MAP36

The Data Dilemma1


3 How to Get There


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


4









State of the Union











Greg Kuhnen


kuhnengadvisorycom


6





Analytics


patterns in data

ndashWikipedia










ROAD MAP7

The Data Dilemma1


3 How to Get There


8












2016 20302016 2030

2016 2030 2016 2030

2016 2030

Australia2

96144

2010 2030

New Zealand3

62 89

106159

110

180

FranceCanada


172249

97135

$9237

$5234

$4751

$4576

$4032

$4050

$3749

$3096

$12448

$7799

$6437

$5926

$5606

$5496

$5002

$4245

United States

Netherlands

Belgium

Canada

Australia

New Zealand

UnitedKingdom

Spain




10







Common Barriers to




bull Lack of budget



with data


Integrated Partners

(eg social services


Data from Outside

Organizations

(eg social)

Environmental Data

(eg IoT1 RTLS2

temperature air

quality)

Genomes Proteomes

Microbiomes

Metabolomes


(eg public health

outbreak reports)




consumer wearables)


11




analysis



Tim

e R

eq

uir

ed






Significant

opportunity for

AI and analytics




12




Data

Inform

Recommend

Decision

Automation

Decision

Support

Descriptive

What happened

How many of our

patients were

readmitted last

month



readmit What do we

expect seasonally

Prescriptive

Recommend or


What should we do

to address this

patientrsquos risks


Why did it happen



Demographics

Decide

Hum

an

Decis

ion

Analy

tics


ROAD MAP13

The Data Dilemma1


3 How to Get There


14

AI Are We There Yet

And if Not When




Incremental Growth


small advances

delivering value in





Exponential Growth

AI rapidly gains









60s 70s 80s 90s 2000 2010 2020 203050s 2040 2050


15






AlgorithmsData



bull Wearables


bull IoT Sensors


Microbiome


Extraction


Platforms (eg

Spark Hadoop)

bull Deep Learning

bull Off-the-Shelf

Libraries for





Hardware




16

















From Toys to Tools


17













subsidiary DeepMind


knowledge of the


game rules and an

objective (win)









AlphaZero

outperformed chess

world-champion

Stockfish1 in just







DEEPMIND


AlphaZero Chess

Performance


18

Need a Radiologist







CheXNet Algorithm















Sept 26 2017






CheXNet could

diagnose 10 of the

14 pathologies more

accurately than all


CheXNet surpassed



and outperformed






19























20




Dear









Detailed








wearables better



and treatments

Unlimited capacity

instant response





(watches your back)

Dull







information provide




the cracksrdquo



21




Narr

ow

(T

ask W

ea

k)

AI1

Clinical Decisions


Ge

ne

ral (S

tro

ng

) A

I2

Full Diagnosis

and Treatment Plan

Clinical

Chatbots

Radiology

Interpretation

Acute

Clinical

Risk

Recruiting

Safety Risk Smart

Monitoring

Medication Dosing

Administrative

Chatbots

(Concierge)

Precision

Engagement

Precision

Medicine

Ambient

Documentation

Adaptive User

Interfaces

Worklist

Prioritization



(growing)

The Computer Will

See You Now

The Uber-ization of

Health Care


Capacity Staffing

Management

Population

Health Risk

The Self-Driving

HospitalInformation

Security

Targeted

Marketing



22









Case in Brief



Smith AR






process bottlenecks




















23













Reduced recruiting

expenses

More predictable

staffing levels

More experienced

staff

Realized Benefits





Applicant completes

a 15-20 minute

assessment process





hiring managers

Hiring managers use



the hiring decision



24















survival time




90 accuracy










study results










x-rays


Feb 2018














25


1Acquire Data





tune-ups as the

environment changes

Monitor Performance

and Revisit

42Train or Refine

the Model


buthellip


governed data pay

enormous dividends


sources improve

predictions and



are important (eg

explainability)


ethical constraints

bull Evaluate under



incomplete data)



decision points


as warranted


of decision support

3Incorporate Models

into Workflow


Begin with






26

New Data Sources


Acquire Data



analysis


EMR(s)

Lab Systems

Pharmacy Systems

Imaging

Medical Claims



Scheduling Systems

Medical

Care Data

10

Sensors RPM1

mHealth Apps

Patient-Reported

Outcomes

Genetics

Socioeconomic Data


Human

Biology

Data

20

Lifestyle

and

Behavior

Data

50

Social and

Environmental

Data

20



27



Acquire Data








by machines


bull Volume

bull Velocity

bull Variety


unstructured data





heart beats














treating physician






30

How Machines Learn


Build the Model


Input Data



Adjust the Model



candidates

Repeat

Supervised Learning

















tradeoffs (eg cost

vs quality)


32















Nigam Shah MBBS PhD



Stanford University








explainabilityrdquo

Surgery is the

best option ()

Data

Black Box Model




33




To the Right Person

In the Right Form


At the Right Time



















The Five Rights of




34





ME

DT

RO

NIC

MIN

IME

D 6

70G






to smartphone






fibrillation (AFIB)




for $19


analysis

AliveC

orK

ard

iaM

obile


35





quantifiable target

outcomes

Automated

processes need

human oversight


rules can limit

potential harm

Periodically

evaluate processes

against goals



Kansas City Star



ROAD MAP36

The Data Dilemma1


3 How to Get There


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom



Greg Kuhnen


kuhnengadvisorycom


6





Analytics


patterns in data

ndashWikipedia










ROAD MAP7

The Data Dilemma1


3 How to Get There


8












2016 20302016 2030

2016 2030 2016 2030

2016 2030

Australia2

96144

2010 2030

New Zealand3

62 89

106159

110

180

FranceCanada


172249

97135

$9237

$5234

$4751

$4576

$4032

$4050

$3749

$3096

$12448

$7799

$6437

$5926

$5606

$5496

$5002

$4245

United States

Netherlands

Belgium

Canada

Australia

New Zealand

UnitedKingdom

Spain




10







Common Barriers to




bull Lack of budget



with data


Integrated Partners

(eg social services


Data from Outside

Organizations

(eg social)

Environmental Data

(eg IoT1 RTLS2

temperature air

quality)

Genomes Proteomes

Microbiomes

Metabolomes


(eg public health

outbreak reports)




consumer wearables)


11




analysis



Tim

e R

eq

uir

ed






Significant

opportunity for

AI and analytics




12




Data

Inform

Recommend

Decision

Automation

Decision

Support

Descriptive

What happened

How many of our

patients were

readmitted last

month



readmit What do we

expect seasonally

Prescriptive

Recommend or


What should we do

to address this

patientrsquos risks


Why did it happen



Demographics

Decide

Hum

an

Decis

ion

Analy

tics


ROAD MAP13

The Data Dilemma1


3 How to Get There


14

AI Are We There Yet

And if Not When




Incremental Growth


small advances

delivering value in





Exponential Growth

AI rapidly gains









60s 70s 80s 90s 2000 2010 2020 203050s 2040 2050


15






AlgorithmsData



bull Wearables


bull IoT Sensors


Microbiome


Extraction


Platforms (eg

Spark Hadoop)

bull Deep Learning

bull Off-the-Shelf

Libraries for





Hardware




16

















From Toys to Tools


17













subsidiary DeepMind


knowledge of the


game rules and an

objective (win)









AlphaZero

outperformed chess

world-champion

Stockfish1 in just







DEEPMIND


AlphaZero Chess

Performance


18

Need a Radiologist







CheXNet Algorithm















Sept 26 2017






CheXNet could

diagnose 10 of the

14 pathologies more

accurately than all


CheXNet surpassed



and outperformed






19























20




Dear









Detailed








wearables better



and treatments

Unlimited capacity

instant response





(watches your back)

Dull







information provide




the cracksrdquo



21




Narr

ow

(T

ask W

ea

k)

AI1

Clinical Decisions


Ge

ne

ral (S

tro

ng

) A

I2

Full Diagnosis

and Treatment Plan

Clinical

Chatbots

Radiology

Interpretation

Acute

Clinical

Risk

Recruiting

Safety Risk Smart

Monitoring

Medication Dosing

Administrative

Chatbots

(Concierge)

Precision

Engagement

Precision

Medicine

Ambient

Documentation

Adaptive User

Interfaces

Worklist

Prioritization



(growing)

The Computer Will

See You Now

The Uber-ization of

Health Care


Capacity Staffing

Management

Population

Health Risk

The Self-Driving

HospitalInformation

Security

Targeted

Marketing



22









Case in Brief



Smith AR






process bottlenecks




















23













Reduced recruiting

expenses

More predictable

staffing levels

More experienced

staff

Realized Benefits





Applicant completes

a 15-20 minute

assessment process





hiring managers

Hiring managers use



the hiring decision



24















survival time




90 accuracy










study results










x-rays


Feb 2018














25


1Acquire Data





tune-ups as the

environment changes

Monitor Performance

and Revisit

42Train or Refine

the Model


buthellip


governed data pay

enormous dividends


sources improve

predictions and



are important (eg

explainability)


ethical constraints

bull Evaluate under



incomplete data)



decision points


as warranted


of decision support

3Incorporate Models

into Workflow


Begin with






26

New Data Sources


Acquire Data



analysis


EMR(s)

Lab Systems

Pharmacy Systems

Imaging

Medical Claims



Scheduling Systems

Medical

Care Data

10

Sensors RPM1

mHealth Apps

Patient-Reported

Outcomes

Genetics

Socioeconomic Data


Human

Biology

Data

20

Lifestyle

and

Behavior

Data

50

Social and

Environmental

Data

20



27



Acquire Data








by machines


bull Volume

bull Velocity

bull Variety


unstructured data





heart beats














treating physician






30

How Machines Learn


Build the Model


Input Data



Adjust the Model



candidates

Repeat

Supervised Learning

















tradeoffs (eg cost

vs quality)


32















Nigam Shah MBBS PhD



Stanford University








explainabilityrdquo

Surgery is the

best option ()

Data

Black Box Model




33




To the Right Person

In the Right Form


At the Right Time



















The Five Rights of




34





ME

DT

RO

NIC

MIN

IME

D 6

70G






to smartphone






fibrillation (AFIB)




for $19


analysis

AliveC

orK

ard

iaM

obile


35





quantifiable target

outcomes

Automated

processes need

human oversight


rules can limit

potential harm

Periodically

evaluate processes

against goals



Kansas City Star



ROAD MAP36

The Data Dilemma1


3 How to Get There


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


6





Analytics


patterns in data

ndashWikipedia










ROAD MAP7

The Data Dilemma1


3 How to Get There


8












2016 20302016 2030

2016 2030 2016 2030

2016 2030

Australia2

96144

2010 2030

New Zealand3

62 89

106159

110

180

FranceCanada


172249

97135

$9237

$5234

$4751

$4576

$4032

$4050

$3749

$3096

$12448

$7799

$6437

$5926

$5606

$5496

$5002

$4245

United States

Netherlands

Belgium

Canada

Australia

New Zealand

UnitedKingdom

Spain




10







Common Barriers to




bull Lack of budget



with data


Integrated Partners

(eg social services


Data from Outside

Organizations

(eg social)

Environmental Data

(eg IoT1 RTLS2

temperature air

quality)

Genomes Proteomes

Microbiomes

Metabolomes


(eg public health

outbreak reports)




consumer wearables)


11




analysis



Tim

e R

eq

uir

ed






Significant

opportunity for

AI and analytics




12




Data

Inform

Recommend

Decision

Automation

Decision

Support

Descriptive

What happened

How many of our

patients were

readmitted last

month



readmit What do we

expect seasonally

Prescriptive

Recommend or


What should we do

to address this

patientrsquos risks


Why did it happen



Demographics

Decide

Hum

an

Decis

ion

Analy

tics


ROAD MAP13

The Data Dilemma1


3 How to Get There


14

AI Are We There Yet

And if Not When




Incremental Growth


small advances

delivering value in





Exponential Growth

AI rapidly gains









60s 70s 80s 90s 2000 2010 2020 203050s 2040 2050


15






AlgorithmsData



bull Wearables


bull IoT Sensors


Microbiome


Extraction


Platforms (eg

Spark Hadoop)

bull Deep Learning

bull Off-the-Shelf

Libraries for





Hardware




16

















From Toys to Tools


17













subsidiary DeepMind


knowledge of the


game rules and an

objective (win)









AlphaZero

outperformed chess

world-champion

Stockfish1 in just







DEEPMIND


AlphaZero Chess

Performance


18

Need a Radiologist







CheXNet Algorithm















Sept 26 2017






CheXNet could

diagnose 10 of the

14 pathologies more

accurately than all


CheXNet surpassed



and outperformed






19























20




Dear









Detailed








wearables better



and treatments

Unlimited capacity

instant response





(watches your back)

Dull







information provide




the cracksrdquo



21




Narr

ow

(T

ask W

ea

k)

AI1

Clinical Decisions


Ge

ne

ral (S

tro

ng

) A

I2

Full Diagnosis

and Treatment Plan

Clinical

Chatbots

Radiology

Interpretation

Acute

Clinical

Risk

Recruiting

Safety Risk Smart

Monitoring

Medication Dosing

Administrative

Chatbots

(Concierge)

Precision

Engagement

Precision

Medicine

Ambient

Documentation

Adaptive User

Interfaces

Worklist

Prioritization



(growing)

The Computer Will

See You Now

The Uber-ization of

Health Care


Capacity Staffing

Management

Population

Health Risk

The Self-Driving

HospitalInformation

Security

Targeted

Marketing



22









Case in Brief



Smith AR






process bottlenecks




















23













Reduced recruiting

expenses

More predictable

staffing levels

More experienced

staff

Realized Benefits





Applicant completes

a 15-20 minute

assessment process





hiring managers

Hiring managers use



the hiring decision



24















survival time




90 accuracy










study results










x-rays


Feb 2018














25


1Acquire Data





tune-ups as the

environment changes

Monitor Performance

and Revisit

42Train or Refine

the Model


buthellip


governed data pay

enormous dividends


sources improve

predictions and



are important (eg

explainability)


ethical constraints

bull Evaluate under



incomplete data)



decision points


as warranted


of decision support

3Incorporate Models

into Workflow


Begin with






26

New Data Sources


Acquire Data



analysis


EMR(s)

Lab Systems

Pharmacy Systems

Imaging

Medical Claims



Scheduling Systems

Medical

Care Data

10

Sensors RPM1

mHealth Apps

Patient-Reported

Outcomes

Genetics

Socioeconomic Data


Human

Biology

Data

20

Lifestyle

and

Behavior

Data

50

Social and

Environmental

Data

20



27



Acquire Data








by machines


bull Volume

bull Velocity

bull Variety


unstructured data





heart beats














treating physician






30

How Machines Learn


Build the Model


Input Data



Adjust the Model



candidates

Repeat

Supervised Learning

















tradeoffs (eg cost

vs quality)


32















Nigam Shah MBBS PhD



Stanford University








explainabilityrdquo

Surgery is the

best option ()

Data

Black Box Model




33




To the Right Person

In the Right Form


At the Right Time



















The Five Rights of




34





ME

DT

RO

NIC

MIN

IME

D 6

70G






to smartphone






fibrillation (AFIB)




for $19


analysis

AliveC

orK

ard

iaM

obile


35





quantifiable target

outcomes

Automated

processes need

human oversight


rules can limit

potential harm

Periodically

evaluate processes

against goals



Kansas City Star



ROAD MAP36

The Data Dilemma1


3 How to Get There


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


ROAD MAP7

The Data Dilemma1


3 How to Get There


8












2016 20302016 2030

2016 2030 2016 2030

2016 2030

Australia2

96144

2010 2030

New Zealand3

62 89

106159

110

180

FranceCanada


172249

97135

$9237

$5234

$4751

$4576

$4032

$4050

$3749

$3096

$12448

$7799

$6437

$5926

$5606

$5496

$5002

$4245

United States

Netherlands

Belgium

Canada

Australia

New Zealand

UnitedKingdom

Spain




10







Common Barriers to




bull Lack of budget



with data


Integrated Partners

(eg social services


Data from Outside

Organizations

(eg social)

Environmental Data

(eg IoT1 RTLS2

temperature air

quality)

Genomes Proteomes

Microbiomes

Metabolomes


(eg public health

outbreak reports)




consumer wearables)


11




analysis



Tim

e R

eq

uir

ed






Significant

opportunity for

AI and analytics




12




Data

Inform

Recommend

Decision

Automation

Decision

Support

Descriptive

What happened

How many of our

patients were

readmitted last

month



readmit What do we

expect seasonally

Prescriptive

Recommend or


What should we do

to address this

patientrsquos risks


Why did it happen



Demographics

Decide

Hum

an

Decis

ion

Analy

tics


ROAD MAP13

The Data Dilemma1


3 How to Get There


14

AI Are We There Yet

And if Not When




Incremental Growth


small advances

delivering value in





Exponential Growth

AI rapidly gains









60s 70s 80s 90s 2000 2010 2020 203050s 2040 2050


15






AlgorithmsData



bull Wearables


bull IoT Sensors


Microbiome


Extraction


Platforms (eg

Spark Hadoop)

bull Deep Learning

bull Off-the-Shelf

Libraries for





Hardware




16

















From Toys to Tools


17













subsidiary DeepMind


knowledge of the


game rules and an

objective (win)









AlphaZero

outperformed chess

world-champion

Stockfish1 in just







DEEPMIND


AlphaZero Chess

Performance


18

Need a Radiologist







CheXNet Algorithm















Sept 26 2017






CheXNet could

diagnose 10 of the

14 pathologies more

accurately than all


CheXNet surpassed



and outperformed






19























20




Dear









Detailed








wearables better



and treatments

Unlimited capacity

instant response





(watches your back)

Dull







information provide




the cracksrdquo



21




Narr

ow

(T

ask W

ea

k)

AI1

Clinical Decisions


Ge

ne

ral (S

tro

ng

) A

I2

Full Diagnosis

and Treatment Plan

Clinical

Chatbots

Radiology

Interpretation

Acute

Clinical

Risk

Recruiting

Safety Risk Smart

Monitoring

Medication Dosing

Administrative

Chatbots

(Concierge)

Precision

Engagement

Precision

Medicine

Ambient

Documentation

Adaptive User

Interfaces

Worklist

Prioritization



(growing)

The Computer Will

See You Now

The Uber-ization of

Health Care


Capacity Staffing

Management

Population

Health Risk

The Self-Driving

HospitalInformation

Security

Targeted

Marketing



22









Case in Brief



Smith AR






process bottlenecks




















23













Reduced recruiting

expenses

More predictable

staffing levels

More experienced

staff

Realized Benefits





Applicant completes

a 15-20 minute

assessment process





hiring managers

Hiring managers use



the hiring decision



24















survival time




90 accuracy










study results










x-rays


Feb 2018














25


1Acquire Data





tune-ups as the

environment changes

Monitor Performance

and Revisit

42Train or Refine

the Model


buthellip


governed data pay

enormous dividends


sources improve

predictions and



are important (eg

explainability)


ethical constraints

bull Evaluate under



incomplete data)



decision points


as warranted


of decision support

3Incorporate Models

into Workflow


Begin with






26

New Data Sources


Acquire Data



analysis


EMR(s)

Lab Systems

Pharmacy Systems

Imaging

Medical Claims



Scheduling Systems

Medical

Care Data

10

Sensors RPM1

mHealth Apps

Patient-Reported

Outcomes

Genetics

Socioeconomic Data


Human

Biology

Data

20

Lifestyle

and

Behavior

Data

50

Social and

Environmental

Data

20



27



Acquire Data








by machines


bull Volume

bull Velocity

bull Variety


unstructured data





heart beats














treating physician






30

How Machines Learn


Build the Model


Input Data



Adjust the Model



candidates

Repeat

Supervised Learning

















tradeoffs (eg cost

vs quality)


32















Nigam Shah MBBS PhD



Stanford University








explainabilityrdquo

Surgery is the

best option ()

Data

Black Box Model




33




To the Right Person

In the Right Form


At the Right Time



















The Five Rights of




34





ME

DT

RO

NIC

MIN

IME

D 6

70G






to smartphone






fibrillation (AFIB)




for $19


analysis

AliveC

orK

ard

iaM

obile


35





quantifiable target

outcomes

Automated

processes need

human oversight


rules can limit

potential harm

Periodically

evaluate processes

against goals



Kansas City Star



ROAD MAP36

The Data Dilemma1


3 How to Get There


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


8












2016 20302016 2030

2016 2030 2016 2030

2016 2030

Australia2

96144

2010 2030

New Zealand3

62 89

106159

110

180

FranceCanada


172249

97135

$9237

$5234

$4751

$4576

$4032

$4050

$3749

$3096

$12448

$7799

$6437

$5926

$5606

$5496

$5002

$4245

United States

Netherlands

Belgium

Canada

Australia

New Zealand

UnitedKingdom

Spain




10







Common Barriers to




bull Lack of budget



with data


Integrated Partners

(eg social services


Data from Outside

Organizations

(eg social)

Environmental Data

(eg IoT1 RTLS2

temperature air

quality)

Genomes Proteomes

Microbiomes

Metabolomes


(eg public health

outbreak reports)




consumer wearables)


11




analysis



Tim

e R

eq

uir

ed






Significant

opportunity for

AI and analytics




12




Data

Inform

Recommend

Decision

Automation

Decision

Support

Descriptive

What happened

How many of our

patients were

readmitted last

month



readmit What do we

expect seasonally

Prescriptive

Recommend or


What should we do

to address this

patientrsquos risks


Why did it happen



Demographics

Decide

Hum

an

Decis

ion

Analy

tics


ROAD MAP13

The Data Dilemma1


3 How to Get There


14

AI Are We There Yet

And if Not When




Incremental Growth


small advances

delivering value in





Exponential Growth

AI rapidly gains









60s 70s 80s 90s 2000 2010 2020 203050s 2040 2050


15






AlgorithmsData



bull Wearables


bull IoT Sensors


Microbiome


Extraction


Platforms (eg

Spark Hadoop)

bull Deep Learning

bull Off-the-Shelf

Libraries for





Hardware




16

















From Toys to Tools


17













subsidiary DeepMind


knowledge of the


game rules and an

objective (win)









AlphaZero

outperformed chess

world-champion

Stockfish1 in just







DEEPMIND


AlphaZero Chess

Performance


18

Need a Radiologist







CheXNet Algorithm















Sept 26 2017






CheXNet could

diagnose 10 of the

14 pathologies more

accurately than all


CheXNet surpassed



and outperformed






19























20




Dear









Detailed








wearables better



and treatments

Unlimited capacity

instant response





(watches your back)

Dull







information provide




the cracksrdquo



21




Narr

ow

(T

ask W

ea

k)

AI1

Clinical Decisions


Ge

ne

ral (S

tro

ng

) A

I2

Full Diagnosis

and Treatment Plan

Clinical

Chatbots

Radiology

Interpretation

Acute

Clinical

Risk

Recruiting

Safety Risk Smart

Monitoring

Medication Dosing

Administrative

Chatbots

(Concierge)

Precision

Engagement

Precision

Medicine

Ambient

Documentation

Adaptive User

Interfaces

Worklist

Prioritization



(growing)

The Computer Will

See You Now

The Uber-ization of

Health Care


Capacity Staffing

Management

Population

Health Risk

The Self-Driving

HospitalInformation

Security

Targeted

Marketing



22









Case in Brief



Smith AR






process bottlenecks




















23













Reduced recruiting

expenses

More predictable

staffing levels

More experienced

staff

Realized Benefits





Applicant completes

a 15-20 minute

assessment process





hiring managers

Hiring managers use



the hiring decision



24















survival time




90 accuracy










study results










x-rays


Feb 2018














25


1Acquire Data





tune-ups as the

environment changes

Monitor Performance

and Revisit

42Train or Refine

the Model


buthellip


governed data pay

enormous dividends


sources improve

predictions and



are important (eg

explainability)


ethical constraints

bull Evaluate under



incomplete data)



decision points


as warranted


of decision support

3Incorporate Models

into Workflow


Begin with






26

New Data Sources


Acquire Data



analysis


EMR(s)

Lab Systems

Pharmacy Systems

Imaging

Medical Claims



Scheduling Systems

Medical

Care Data

10

Sensors RPM1

mHealth Apps

Patient-Reported

Outcomes

Genetics

Socioeconomic Data


Human

Biology

Data

20

Lifestyle

and

Behavior

Data

50

Social and

Environmental

Data

20



27



Acquire Data








by machines


bull Volume

bull Velocity

bull Variety


unstructured data





heart beats














treating physician






30

How Machines Learn


Build the Model


Input Data



Adjust the Model



candidates

Repeat

Supervised Learning

















tradeoffs (eg cost

vs quality)


32















Nigam Shah MBBS PhD



Stanford University








explainabilityrdquo

Surgery is the

best option ()

Data

Black Box Model




33




To the Right Person

In the Right Form


At the Right Time



















The Five Rights of




34





ME

DT

RO

NIC

MIN

IME

D 6

70G






to smartphone






fibrillation (AFIB)




for $19


analysis

AliveC

orK

ard

iaM

obile


35





quantifiable target

outcomes

Automated

processes need

human oversight


rules can limit

potential harm

Periodically

evaluate processes

against goals



Kansas City Star



ROAD MAP36

The Data Dilemma1


3 How to Get There


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


10







Common Barriers to




bull Lack of budget



with data


Integrated Partners

(eg social services


Data from Outside

Organizations

(eg social)

Environmental Data

(eg IoT1 RTLS2

temperature air

quality)

Genomes Proteomes

Microbiomes

Metabolomes


(eg public health

outbreak reports)




consumer wearables)


11




analysis



Tim

e R

eq

uir

ed






Significant

opportunity for

AI and analytics




12




Data

Inform

Recommend

Decision

Automation

Decision

Support

Descriptive

What happened

How many of our

patients were

readmitted last

month



readmit What do we

expect seasonally

Prescriptive

Recommend or


What should we do

to address this

patientrsquos risks


Why did it happen



Demographics

Decide

Hum

an

Decis

ion

Analy

tics


ROAD MAP13

The Data Dilemma1


3 How to Get There


14

AI Are We There Yet

And if Not When




Incremental Growth


small advances

delivering value in





Exponential Growth

AI rapidly gains









60s 70s 80s 90s 2000 2010 2020 203050s 2040 2050


15






AlgorithmsData



bull Wearables


bull IoT Sensors


Microbiome


Extraction


Platforms (eg

Spark Hadoop)

bull Deep Learning

bull Off-the-Shelf

Libraries for





Hardware




16

















From Toys to Tools


17













subsidiary DeepMind


knowledge of the


game rules and an

objective (win)









AlphaZero

outperformed chess

world-champion

Stockfish1 in just







DEEPMIND


AlphaZero Chess

Performance


18

Need a Radiologist







CheXNet Algorithm















Sept 26 2017






CheXNet could

diagnose 10 of the

14 pathologies more

accurately than all


CheXNet surpassed



and outperformed






19























20




Dear









Detailed








wearables better



and treatments

Unlimited capacity

instant response





(watches your back)

Dull







information provide




the cracksrdquo



21




Narr

ow

(T

ask W

ea

k)

AI1

Clinical Decisions


Ge

ne

ral (S

tro

ng

) A

I2

Full Diagnosis

and Treatment Plan

Clinical

Chatbots

Radiology

Interpretation

Acute

Clinical

Risk

Recruiting

Safety Risk Smart

Monitoring

Medication Dosing

Administrative

Chatbots

(Concierge)

Precision

Engagement

Precision

Medicine

Ambient

Documentation

Adaptive User

Interfaces

Worklist

Prioritization



(growing)

The Computer Will

See You Now

The Uber-ization of

Health Care


Capacity Staffing

Management

Population

Health Risk

The Self-Driving

HospitalInformation

Security

Targeted

Marketing



22









Case in Brief



Smith AR






process bottlenecks




















23













Reduced recruiting

expenses

More predictable

staffing levels

More experienced

staff

Realized Benefits





Applicant completes

a 15-20 minute

assessment process





hiring managers

Hiring managers use



the hiring decision



24















survival time




90 accuracy










study results










x-rays


Feb 2018














25


1Acquire Data





tune-ups as the

environment changes

Monitor Performance

and Revisit

42Train or Refine

the Model


buthellip


governed data pay

enormous dividends


sources improve

predictions and



are important (eg

explainability)


ethical constraints

bull Evaluate under



incomplete data)



decision points


as warranted


of decision support

3Incorporate Models

into Workflow


Begin with






26

New Data Sources


Acquire Data



analysis


EMR(s)

Lab Systems

Pharmacy Systems

Imaging

Medical Claims



Scheduling Systems

Medical

Care Data

10

Sensors RPM1

mHealth Apps

Patient-Reported

Outcomes

Genetics

Socioeconomic Data


Human

Biology

Data

20

Lifestyle

and

Behavior

Data

50

Social and

Environmental

Data

20



27



Acquire Data








by machines


bull Volume

bull Velocity

bull Variety


unstructured data





heart beats














treating physician






30

How Machines Learn


Build the Model


Input Data



Adjust the Model



candidates

Repeat

Supervised Learning

















tradeoffs (eg cost

vs quality)


32















Nigam Shah MBBS PhD



Stanford University








explainabilityrdquo

Surgery is the

best option ()

Data

Black Box Model




33




To the Right Person

In the Right Form


At the Right Time



















The Five Rights of




34





ME

DT

RO

NIC

MIN

IME

D 6

70G






to smartphone






fibrillation (AFIB)




for $19


analysis

AliveC

orK

ard

iaM

obile


35





quantifiable target

outcomes

Automated

processes need

human oversight


rules can limit

potential harm

Periodically

evaluate processes

against goals



Kansas City Star



ROAD MAP36

The Data Dilemma1


3 How to Get There


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


11




analysis



Tim

e R

eq

uir

ed






Significant

opportunity for

AI and analytics




12




Data

Inform

Recommend

Decision

Automation

Decision

Support

Descriptive

What happened

How many of our

patients were

readmitted last

month



readmit What do we

expect seasonally

Prescriptive

Recommend or


What should we do

to address this

patientrsquos risks


Why did it happen



Demographics

Decide

Hum

an

Decis

ion

Analy

tics


ROAD MAP13

The Data Dilemma1


3 How to Get There


14

AI Are We There Yet

And if Not When




Incremental Growth


small advances

delivering value in





Exponential Growth

AI rapidly gains









60s 70s 80s 90s 2000 2010 2020 203050s 2040 2050


15






AlgorithmsData



bull Wearables


bull IoT Sensors


Microbiome


Extraction


Platforms (eg

Spark Hadoop)

bull Deep Learning

bull Off-the-Shelf

Libraries for





Hardware




16

















From Toys to Tools


17













subsidiary DeepMind


knowledge of the


game rules and an

objective (win)









AlphaZero

outperformed chess

world-champion

Stockfish1 in just







DEEPMIND


AlphaZero Chess

Performance


18

Need a Radiologist







CheXNet Algorithm















Sept 26 2017






CheXNet could

diagnose 10 of the

14 pathologies more

accurately than all


CheXNet surpassed



and outperformed






19























20




Dear









Detailed








wearables better



and treatments

Unlimited capacity

instant response





(watches your back)

Dull







information provide




the cracksrdquo



21




Narr

ow

(T

ask W

ea

k)

AI1

Clinical Decisions


Ge

ne

ral (S

tro

ng

) A

I2

Full Diagnosis

and Treatment Plan

Clinical

Chatbots

Radiology

Interpretation

Acute

Clinical

Risk

Recruiting

Safety Risk Smart

Monitoring

Medication Dosing

Administrative

Chatbots

(Concierge)

Precision

Engagement

Precision

Medicine

Ambient

Documentation

Adaptive User

Interfaces

Worklist

Prioritization



(growing)

The Computer Will

See You Now

The Uber-ization of

Health Care


Capacity Staffing

Management

Population

Health Risk

The Self-Driving

HospitalInformation

Security

Targeted

Marketing



22









Case in Brief



Smith AR






process bottlenecks




















23













Reduced recruiting

expenses

More predictable

staffing levels

More experienced

staff

Realized Benefits





Applicant completes

a 15-20 minute

assessment process





hiring managers

Hiring managers use



the hiring decision



24















survival time




90 accuracy










study results










x-rays


Feb 2018














25


1Acquire Data





tune-ups as the

environment changes

Monitor Performance

and Revisit

42Train or Refine

the Model


buthellip


governed data pay

enormous dividends


sources improve

predictions and



are important (eg

explainability)


ethical constraints

bull Evaluate under



incomplete data)



decision points


as warranted


of decision support

3Incorporate Models

into Workflow


Begin with






26

New Data Sources


Acquire Data



analysis


EMR(s)

Lab Systems

Pharmacy Systems

Imaging

Medical Claims



Scheduling Systems

Medical

Care Data

10

Sensors RPM1

mHealth Apps

Patient-Reported

Outcomes

Genetics

Socioeconomic Data


Human

Biology

Data

20

Lifestyle

and

Behavior

Data

50

Social and

Environmental

Data

20



27



Acquire Data








by machines


bull Volume

bull Velocity

bull Variety


unstructured data





heart beats














treating physician






30

How Machines Learn


Build the Model


Input Data



Adjust the Model



candidates

Repeat

Supervised Learning

















tradeoffs (eg cost

vs quality)


32















Nigam Shah MBBS PhD



Stanford University








explainabilityrdquo

Surgery is the

best option ()

Data

Black Box Model




33




To the Right Person

In the Right Form


At the Right Time



















The Five Rights of




34





ME

DT

RO

NIC

MIN

IME

D 6

70G






to smartphone






fibrillation (AFIB)




for $19


analysis

AliveC

orK

ard

iaM

obile


35





quantifiable target

outcomes

Automated

processes need

human oversight


rules can limit

potential harm

Periodically

evaluate processes

against goals



Kansas City Star



ROAD MAP36

The Data Dilemma1


3 How to Get There


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


12




Data

Inform

Recommend

Decision

Automation

Decision

Support

Descriptive

What happened

How many of our

patients were

readmitted last

month



readmit What do we

expect seasonally

Prescriptive

Recommend or


What should we do

to address this

patientrsquos risks


Why did it happen



Demographics

Decide

Hum

an

Decis

ion

Analy

tics


ROAD MAP13

The Data Dilemma1


3 How to Get There


14

AI Are We There Yet

And if Not When




Incremental Growth


small advances

delivering value in





Exponential Growth

AI rapidly gains









60s 70s 80s 90s 2000 2010 2020 203050s 2040 2050


15






AlgorithmsData



bull Wearables


bull IoT Sensors


Microbiome


Extraction


Platforms (eg

Spark Hadoop)

bull Deep Learning

bull Off-the-Shelf

Libraries for





Hardware




16

















From Toys to Tools


17













subsidiary DeepMind


knowledge of the


game rules and an

objective (win)









AlphaZero

outperformed chess

world-champion

Stockfish1 in just







DEEPMIND


AlphaZero Chess

Performance


18

Need a Radiologist







CheXNet Algorithm















Sept 26 2017






CheXNet could

diagnose 10 of the

14 pathologies more

accurately than all


CheXNet surpassed



and outperformed






19























20




Dear









Detailed








wearables better



and treatments

Unlimited capacity

instant response





(watches your back)

Dull







information provide




the cracksrdquo



21




Narr

ow

(T

ask W

ea

k)

AI1

Clinical Decisions


Ge

ne

ral (S

tro

ng

) A

I2

Full Diagnosis

and Treatment Plan

Clinical

Chatbots

Radiology

Interpretation

Acute

Clinical

Risk

Recruiting

Safety Risk Smart

Monitoring

Medication Dosing

Administrative

Chatbots

(Concierge)

Precision

Engagement

Precision

Medicine

Ambient

Documentation

Adaptive User

Interfaces

Worklist

Prioritization



(growing)

The Computer Will

See You Now

The Uber-ization of

Health Care


Capacity Staffing

Management

Population

Health Risk

The Self-Driving

HospitalInformation

Security

Targeted

Marketing



22









Case in Brief



Smith AR






process bottlenecks




















23













Reduced recruiting

expenses

More predictable

staffing levels

More experienced

staff

Realized Benefits





Applicant completes

a 15-20 minute

assessment process





hiring managers

Hiring managers use



the hiring decision



24















survival time




90 accuracy










study results










x-rays


Feb 2018














25


1Acquire Data





tune-ups as the

environment changes

Monitor Performance

and Revisit

42Train or Refine

the Model


buthellip


governed data pay

enormous dividends


sources improve

predictions and



are important (eg

explainability)


ethical constraints

bull Evaluate under



incomplete data)



decision points


as warranted


of decision support

3Incorporate Models

into Workflow


Begin with






26

New Data Sources


Acquire Data



analysis


EMR(s)

Lab Systems

Pharmacy Systems

Imaging

Medical Claims



Scheduling Systems

Medical

Care Data

10

Sensors RPM1

mHealth Apps

Patient-Reported

Outcomes

Genetics

Socioeconomic Data


Human

Biology

Data

20

Lifestyle

and

Behavior

Data

50

Social and

Environmental

Data

20



27



Acquire Data








by machines


bull Volume

bull Velocity

bull Variety


unstructured data





heart beats














treating physician






30

How Machines Learn


Build the Model


Input Data



Adjust the Model



candidates

Repeat

Supervised Learning

















tradeoffs (eg cost

vs quality)


32















Nigam Shah MBBS PhD



Stanford University








explainabilityrdquo

Surgery is the

best option ()

Data

Black Box Model




33




To the Right Person

In the Right Form


At the Right Time



















The Five Rights of




34





ME

DT

RO

NIC

MIN

IME

D 6

70G






to smartphone






fibrillation (AFIB)




for $19


analysis

AliveC

orK

ard

iaM

obile


35





quantifiable target

outcomes

Automated

processes need

human oversight


rules can limit

potential harm

Periodically

evaluate processes

against goals



Kansas City Star



ROAD MAP36

The Data Dilemma1


3 How to Get There


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


ROAD MAP13

The Data Dilemma1


3 How to Get There


14

AI Are We There Yet

And if Not When




Incremental Growth


small advances

delivering value in





Exponential Growth

AI rapidly gains









60s 70s 80s 90s 2000 2010 2020 203050s 2040 2050


15






AlgorithmsData



bull Wearables


bull IoT Sensors


Microbiome


Extraction


Platforms (eg

Spark Hadoop)

bull Deep Learning

bull Off-the-Shelf

Libraries for





Hardware




16

















From Toys to Tools


17













subsidiary DeepMind


knowledge of the


game rules and an

objective (win)









AlphaZero

outperformed chess

world-champion

Stockfish1 in just







DEEPMIND


AlphaZero Chess

Performance


18

Need a Radiologist







CheXNet Algorithm















Sept 26 2017






CheXNet could

diagnose 10 of the

14 pathologies more

accurately than all


CheXNet surpassed



and outperformed






19























20




Dear









Detailed








wearables better



and treatments

Unlimited capacity

instant response





(watches your back)

Dull







information provide




the cracksrdquo



21




Narr

ow

(T

ask W

ea

k)

AI1

Clinical Decisions


Ge

ne

ral (S

tro

ng

) A

I2

Full Diagnosis

and Treatment Plan

Clinical

Chatbots

Radiology

Interpretation

Acute

Clinical

Risk

Recruiting

Safety Risk Smart

Monitoring

Medication Dosing

Administrative

Chatbots

(Concierge)

Precision

Engagement

Precision

Medicine

Ambient

Documentation

Adaptive User

Interfaces

Worklist

Prioritization



(growing)

The Computer Will

See You Now

The Uber-ization of

Health Care


Capacity Staffing

Management

Population

Health Risk

The Self-Driving

HospitalInformation

Security

Targeted

Marketing



22









Case in Brief



Smith AR






process bottlenecks




















23













Reduced recruiting

expenses

More predictable

staffing levels

More experienced

staff

Realized Benefits





Applicant completes

a 15-20 minute

assessment process





hiring managers

Hiring managers use



the hiring decision



24















survival time




90 accuracy










study results










x-rays


Feb 2018














25


1Acquire Data





tune-ups as the

environment changes

Monitor Performance

and Revisit

42Train or Refine

the Model


buthellip


governed data pay

enormous dividends


sources improve

predictions and



are important (eg

explainability)


ethical constraints

bull Evaluate under



incomplete data)



decision points


as warranted


of decision support

3Incorporate Models

into Workflow


Begin with






26

New Data Sources


Acquire Data



analysis


EMR(s)

Lab Systems

Pharmacy Systems

Imaging

Medical Claims



Scheduling Systems

Medical

Care Data

10

Sensors RPM1

mHealth Apps

Patient-Reported

Outcomes

Genetics

Socioeconomic Data


Human

Biology

Data

20

Lifestyle

and

Behavior

Data

50

Social and

Environmental

Data

20



27



Acquire Data








by machines


bull Volume

bull Velocity

bull Variety


unstructured data





heart beats














treating physician






30

How Machines Learn


Build the Model


Input Data



Adjust the Model



candidates

Repeat

Supervised Learning

















tradeoffs (eg cost

vs quality)


32















Nigam Shah MBBS PhD



Stanford University








explainabilityrdquo

Surgery is the

best option ()

Data

Black Box Model




33




To the Right Person

In the Right Form


At the Right Time



















The Five Rights of




34





ME

DT

RO

NIC

MIN

IME

D 6

70G






to smartphone






fibrillation (AFIB)




for $19


analysis

AliveC

orK

ard

iaM

obile


35





quantifiable target

outcomes

Automated

processes need

human oversight


rules can limit

potential harm

Periodically

evaluate processes

against goals



Kansas City Star



ROAD MAP36

The Data Dilemma1


3 How to Get There


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


14

AI Are We There Yet

And if Not When




Incremental Growth


small advances

delivering value in





Exponential Growth

AI rapidly gains









60s 70s 80s 90s 2000 2010 2020 203050s 2040 2050


15






AlgorithmsData



bull Wearables


bull IoT Sensors


Microbiome


Extraction


Platforms (eg

Spark Hadoop)

bull Deep Learning

bull Off-the-Shelf

Libraries for





Hardware




16

















From Toys to Tools


17













subsidiary DeepMind


knowledge of the


game rules and an

objective (win)









AlphaZero

outperformed chess

world-champion

Stockfish1 in just







DEEPMIND


AlphaZero Chess

Performance


18

Need a Radiologist







CheXNet Algorithm















Sept 26 2017






CheXNet could

diagnose 10 of the

14 pathologies more

accurately than all


CheXNet surpassed



and outperformed






19























20




Dear









Detailed








wearables better



and treatments

Unlimited capacity

instant response





(watches your back)

Dull







information provide




the cracksrdquo



21




Narr

ow

(T

ask W

ea

k)

AI1

Clinical Decisions


Ge

ne

ral (S

tro

ng

) A

I2

Full Diagnosis

and Treatment Plan

Clinical

Chatbots

Radiology

Interpretation

Acute

Clinical

Risk

Recruiting

Safety Risk Smart

Monitoring

Medication Dosing

Administrative

Chatbots

(Concierge)

Precision

Engagement

Precision

Medicine

Ambient

Documentation

Adaptive User

Interfaces

Worklist

Prioritization



(growing)

The Computer Will

See You Now

The Uber-ization of

Health Care


Capacity Staffing

Management

Population

Health Risk

The Self-Driving

HospitalInformation

Security

Targeted

Marketing



22









Case in Brief



Smith AR






process bottlenecks




















23













Reduced recruiting

expenses

More predictable

staffing levels

More experienced

staff

Realized Benefits





Applicant completes

a 15-20 minute

assessment process





hiring managers

Hiring managers use



the hiring decision



24















survival time




90 accuracy










study results










x-rays


Feb 2018














25


1Acquire Data





tune-ups as the

environment changes

Monitor Performance

and Revisit

42Train or Refine

the Model


buthellip


governed data pay

enormous dividends


sources improve

predictions and



are important (eg

explainability)


ethical constraints

bull Evaluate under



incomplete data)



decision points


as warranted


of decision support

3Incorporate Models

into Workflow


Begin with






26

New Data Sources


Acquire Data



analysis


EMR(s)

Lab Systems

Pharmacy Systems

Imaging

Medical Claims



Scheduling Systems

Medical

Care Data

10

Sensors RPM1

mHealth Apps

Patient-Reported

Outcomes

Genetics

Socioeconomic Data


Human

Biology

Data

20

Lifestyle

and

Behavior

Data

50

Social and

Environmental

Data

20



27



Acquire Data








by machines


bull Volume

bull Velocity

bull Variety


unstructured data





heart beats














treating physician






30

How Machines Learn


Build the Model


Input Data



Adjust the Model



candidates

Repeat

Supervised Learning

















tradeoffs (eg cost

vs quality)


32















Nigam Shah MBBS PhD



Stanford University








explainabilityrdquo

Surgery is the

best option ()

Data

Black Box Model




33




To the Right Person

In the Right Form


At the Right Time



















The Five Rights of




34





ME

DT

RO

NIC

MIN

IME

D 6

70G






to smartphone






fibrillation (AFIB)




for $19


analysis

AliveC

orK

ard

iaM

obile


35





quantifiable target

outcomes

Automated

processes need

human oversight


rules can limit

potential harm

Periodically

evaluate processes

against goals



Kansas City Star



ROAD MAP36

The Data Dilemma1


3 How to Get There


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


15






AlgorithmsData



bull Wearables


bull IoT Sensors


Microbiome


Extraction


Platforms (eg

Spark Hadoop)

bull Deep Learning

bull Off-the-Shelf

Libraries for





Hardware




16

















From Toys to Tools


17













subsidiary DeepMind


knowledge of the


game rules and an

objective (win)









AlphaZero

outperformed chess

world-champion

Stockfish1 in just







DEEPMIND


AlphaZero Chess

Performance


18

Need a Radiologist







CheXNet Algorithm















Sept 26 2017






CheXNet could

diagnose 10 of the

14 pathologies more

accurately than all


CheXNet surpassed



and outperformed






19























20




Dear









Detailed








wearables better



and treatments

Unlimited capacity

instant response





(watches your back)

Dull







information provide




the cracksrdquo



21




Narr

ow

(T

ask W

ea

k)

AI1

Clinical Decisions


Ge

ne

ral (S

tro

ng

) A

I2

Full Diagnosis

and Treatment Plan

Clinical

Chatbots

Radiology

Interpretation

Acute

Clinical

Risk

Recruiting

Safety Risk Smart

Monitoring

Medication Dosing

Administrative

Chatbots

(Concierge)

Precision

Engagement

Precision

Medicine

Ambient

Documentation

Adaptive User

Interfaces

Worklist

Prioritization



(growing)

The Computer Will

See You Now

The Uber-ization of

Health Care


Capacity Staffing

Management

Population

Health Risk

The Self-Driving

HospitalInformation

Security

Targeted

Marketing



22









Case in Brief



Smith AR






process bottlenecks




















23













Reduced recruiting

expenses

More predictable

staffing levels

More experienced

staff

Realized Benefits





Applicant completes

a 15-20 minute

assessment process





hiring managers

Hiring managers use



the hiring decision



24















survival time




90 accuracy










study results










x-rays


Feb 2018














25


1Acquire Data





tune-ups as the

environment changes

Monitor Performance

and Revisit

42Train or Refine

the Model


buthellip


governed data pay

enormous dividends


sources improve

predictions and



are important (eg

explainability)


ethical constraints

bull Evaluate under



incomplete data)



decision points


as warranted


of decision support

3Incorporate Models

into Workflow


Begin with






26

New Data Sources


Acquire Data



analysis


EMR(s)

Lab Systems

Pharmacy Systems

Imaging

Medical Claims



Scheduling Systems

Medical

Care Data

10

Sensors RPM1

mHealth Apps

Patient-Reported

Outcomes

Genetics

Socioeconomic Data


Human

Biology

Data

20

Lifestyle

and

Behavior

Data

50

Social and

Environmental

Data

20



27



Acquire Data








by machines


bull Volume

bull Velocity

bull Variety


unstructured data





heart beats














treating physician






30

How Machines Learn


Build the Model


Input Data



Adjust the Model



candidates

Repeat

Supervised Learning

















tradeoffs (eg cost

vs quality)


32















Nigam Shah MBBS PhD



Stanford University








explainabilityrdquo

Surgery is the

best option ()

Data

Black Box Model




33




To the Right Person

In the Right Form


At the Right Time



















The Five Rights of




34





ME

DT

RO

NIC

MIN

IME

D 6

70G






to smartphone






fibrillation (AFIB)




for $19


analysis

AliveC

orK

ard

iaM

obile


35





quantifiable target

outcomes

Automated

processes need

human oversight


rules can limit

potential harm

Periodically

evaluate processes

against goals



Kansas City Star



ROAD MAP36

The Data Dilemma1


3 How to Get There


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


16

















From Toys to Tools


17













subsidiary DeepMind


knowledge of the


game rules and an

objective (win)









AlphaZero

outperformed chess

world-champion

Stockfish1 in just







DEEPMIND


AlphaZero Chess

Performance


18

Need a Radiologist







CheXNet Algorithm















Sept 26 2017






CheXNet could

diagnose 10 of the

14 pathologies more

accurately than all


CheXNet surpassed



and outperformed






19























20




Dear









Detailed








wearables better



and treatments

Unlimited capacity

instant response





(watches your back)

Dull







information provide




the cracksrdquo



21




Narr

ow

(T

ask W

ea

k)

AI1

Clinical Decisions


Ge

ne

ral (S

tro

ng

) A

I2

Full Diagnosis

and Treatment Plan

Clinical

Chatbots

Radiology

Interpretation

Acute

Clinical

Risk

Recruiting

Safety Risk Smart

Monitoring

Medication Dosing

Administrative

Chatbots

(Concierge)

Precision

Engagement

Precision

Medicine

Ambient

Documentation

Adaptive User

Interfaces

Worklist

Prioritization



(growing)

The Computer Will

See You Now

The Uber-ization of

Health Care


Capacity Staffing

Management

Population

Health Risk

The Self-Driving

HospitalInformation

Security

Targeted

Marketing



22









Case in Brief



Smith AR






process bottlenecks




















23













Reduced recruiting

expenses

More predictable

staffing levels

More experienced

staff

Realized Benefits





Applicant completes

a 15-20 minute

assessment process





hiring managers

Hiring managers use



the hiring decision



24















survival time




90 accuracy










study results










x-rays


Feb 2018














25


1Acquire Data





tune-ups as the

environment changes

Monitor Performance

and Revisit

42Train or Refine

the Model


buthellip


governed data pay

enormous dividends


sources improve

predictions and



are important (eg

explainability)


ethical constraints

bull Evaluate under



incomplete data)



decision points


as warranted


of decision support

3Incorporate Models

into Workflow


Begin with






26

New Data Sources


Acquire Data



analysis


EMR(s)

Lab Systems

Pharmacy Systems

Imaging

Medical Claims



Scheduling Systems

Medical

Care Data

10

Sensors RPM1

mHealth Apps

Patient-Reported

Outcomes

Genetics

Socioeconomic Data


Human

Biology

Data

20

Lifestyle

and

Behavior

Data

50

Social and

Environmental

Data

20



27



Acquire Data








by machines


bull Volume

bull Velocity

bull Variety


unstructured data





heart beats














treating physician






30

How Machines Learn


Build the Model


Input Data



Adjust the Model



candidates

Repeat

Supervised Learning

















tradeoffs (eg cost

vs quality)


32















Nigam Shah MBBS PhD



Stanford University








explainabilityrdquo

Surgery is the

best option ()

Data

Black Box Model




33




To the Right Person

In the Right Form


At the Right Time



















The Five Rights of




34





ME

DT

RO

NIC

MIN

IME

D 6

70G






to smartphone






fibrillation (AFIB)




for $19


analysis

AliveC

orK

ard

iaM

obile


35





quantifiable target

outcomes

Automated

processes need

human oversight


rules can limit

potential harm

Periodically

evaluate processes

against goals



Kansas City Star



ROAD MAP36

The Data Dilemma1


3 How to Get There


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


17













subsidiary DeepMind


knowledge of the


game rules and an

objective (win)









AlphaZero

outperformed chess

world-champion

Stockfish1 in just







DEEPMIND


AlphaZero Chess

Performance


18

Need a Radiologist







CheXNet Algorithm















Sept 26 2017






CheXNet could

diagnose 10 of the

14 pathologies more

accurately than all


CheXNet surpassed



and outperformed






19























20




Dear









Detailed








wearables better



and treatments

Unlimited capacity

instant response





(watches your back)

Dull







information provide




the cracksrdquo



21




Narr

ow

(T

ask W

ea

k)

AI1

Clinical Decisions


Ge

ne

ral (S

tro

ng

) A

I2

Full Diagnosis

and Treatment Plan

Clinical

Chatbots

Radiology

Interpretation

Acute

Clinical

Risk

Recruiting

Safety Risk Smart

Monitoring

Medication Dosing

Administrative

Chatbots

(Concierge)

Precision

Engagement

Precision

Medicine

Ambient

Documentation

Adaptive User

Interfaces

Worklist

Prioritization



(growing)

The Computer Will

See You Now

The Uber-ization of

Health Care


Capacity Staffing

Management

Population

Health Risk

The Self-Driving

HospitalInformation

Security

Targeted

Marketing



22









Case in Brief



Smith AR






process bottlenecks




















23













Reduced recruiting

expenses

More predictable

staffing levels

More experienced

staff

Realized Benefits





Applicant completes

a 15-20 minute

assessment process





hiring managers

Hiring managers use



the hiring decision



24















survival time




90 accuracy










study results










x-rays


Feb 2018














25


1Acquire Data





tune-ups as the

environment changes

Monitor Performance

and Revisit

42Train or Refine

the Model


buthellip


governed data pay

enormous dividends


sources improve

predictions and



are important (eg

explainability)


ethical constraints

bull Evaluate under



incomplete data)



decision points


as warranted


of decision support

3Incorporate Models

into Workflow


Begin with






26

New Data Sources


Acquire Data



analysis


EMR(s)

Lab Systems

Pharmacy Systems

Imaging

Medical Claims



Scheduling Systems

Medical

Care Data

10

Sensors RPM1

mHealth Apps

Patient-Reported

Outcomes

Genetics

Socioeconomic Data


Human

Biology

Data

20

Lifestyle

and

Behavior

Data

50

Social and

Environmental

Data

20



27



Acquire Data








by machines


bull Volume

bull Velocity

bull Variety


unstructured data





heart beats














treating physician






30

How Machines Learn


Build the Model


Input Data



Adjust the Model



candidates

Repeat

Supervised Learning

















tradeoffs (eg cost

vs quality)


32















Nigam Shah MBBS PhD



Stanford University








explainabilityrdquo

Surgery is the

best option ()

Data

Black Box Model




33




To the Right Person

In the Right Form


At the Right Time



















The Five Rights of




34





ME

DT

RO

NIC

MIN

IME

D 6

70G






to smartphone






fibrillation (AFIB)




for $19


analysis

AliveC

orK

ard

iaM

obile


35





quantifiable target

outcomes

Automated

processes need

human oversight


rules can limit

potential harm

Periodically

evaluate processes

against goals



Kansas City Star



ROAD MAP36

The Data Dilemma1


3 How to Get There


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


18

Need a Radiologist







CheXNet Algorithm















Sept 26 2017






CheXNet could

diagnose 10 of the

14 pathologies more

accurately than all


CheXNet surpassed



and outperformed






19























20




Dear









Detailed








wearables better



and treatments

Unlimited capacity

instant response





(watches your back)

Dull







information provide




the cracksrdquo



21




Narr

ow

(T

ask W

ea

k)

AI1

Clinical Decisions


Ge

ne

ral (S

tro

ng

) A

I2

Full Diagnosis

and Treatment Plan

Clinical

Chatbots

Radiology

Interpretation

Acute

Clinical

Risk

Recruiting

Safety Risk Smart

Monitoring

Medication Dosing

Administrative

Chatbots

(Concierge)

Precision

Engagement

Precision

Medicine

Ambient

Documentation

Adaptive User

Interfaces

Worklist

Prioritization



(growing)

The Computer Will

See You Now

The Uber-ization of

Health Care


Capacity Staffing

Management

Population

Health Risk

The Self-Driving

HospitalInformation

Security

Targeted

Marketing



22









Case in Brief



Smith AR






process bottlenecks




















23













Reduced recruiting

expenses

More predictable

staffing levels

More experienced

staff

Realized Benefits





Applicant completes

a 15-20 minute

assessment process





hiring managers

Hiring managers use



the hiring decision



24















survival time




90 accuracy










study results










x-rays


Feb 2018














25


1Acquire Data





tune-ups as the

environment changes

Monitor Performance

and Revisit

42Train or Refine

the Model


buthellip


governed data pay

enormous dividends


sources improve

predictions and



are important (eg

explainability)


ethical constraints

bull Evaluate under



incomplete data)



decision points


as warranted


of decision support

3Incorporate Models

into Workflow


Begin with






26

New Data Sources


Acquire Data



analysis


EMR(s)

Lab Systems

Pharmacy Systems

Imaging

Medical Claims



Scheduling Systems

Medical

Care Data

10

Sensors RPM1

mHealth Apps

Patient-Reported

Outcomes

Genetics

Socioeconomic Data


Human

Biology

Data

20

Lifestyle

and

Behavior

Data

50

Social and

Environmental

Data

20



27



Acquire Data








by machines


bull Volume

bull Velocity

bull Variety


unstructured data





heart beats














treating physician






30

How Machines Learn


Build the Model


Input Data



Adjust the Model



candidates

Repeat

Supervised Learning

















tradeoffs (eg cost

vs quality)


32















Nigam Shah MBBS PhD



Stanford University








explainabilityrdquo

Surgery is the

best option ()

Data

Black Box Model




33




To the Right Person

In the Right Form


At the Right Time



















The Five Rights of




34





ME

DT

RO

NIC

MIN

IME

D 6

70G






to smartphone






fibrillation (AFIB)




for $19


analysis

AliveC

orK

ard

iaM

obile


35





quantifiable target

outcomes

Automated

processes need

human oversight


rules can limit

potential harm

Periodically

evaluate processes

against goals



Kansas City Star



ROAD MAP36

The Data Dilemma1


3 How to Get There


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


19























20




Dear









Detailed








wearables better



and treatments

Unlimited capacity

instant response





(watches your back)

Dull







information provide




the cracksrdquo



21




Narr

ow

(T

ask W

ea

k)

AI1

Clinical Decisions


Ge

ne

ral (S

tro

ng

) A

I2

Full Diagnosis

and Treatment Plan

Clinical

Chatbots

Radiology

Interpretation

Acute

Clinical

Risk

Recruiting

Safety Risk Smart

Monitoring

Medication Dosing

Administrative

Chatbots

(Concierge)

Precision

Engagement

Precision

Medicine

Ambient

Documentation

Adaptive User

Interfaces

Worklist

Prioritization



(growing)

The Computer Will

See You Now

The Uber-ization of

Health Care


Capacity Staffing

Management

Population

Health Risk

The Self-Driving

HospitalInformation

Security

Targeted

Marketing



22









Case in Brief



Smith AR






process bottlenecks




















23













Reduced recruiting

expenses

More predictable

staffing levels

More experienced

staff

Realized Benefits





Applicant completes

a 15-20 minute

assessment process





hiring managers

Hiring managers use



the hiring decision



24















survival time




90 accuracy










study results










x-rays


Feb 2018














25


1Acquire Data





tune-ups as the

environment changes

Monitor Performance

and Revisit

42Train or Refine

the Model


buthellip


governed data pay

enormous dividends


sources improve

predictions and



are important (eg

explainability)


ethical constraints

bull Evaluate under



incomplete data)



decision points


as warranted


of decision support

3Incorporate Models

into Workflow


Begin with






26

New Data Sources


Acquire Data



analysis


EMR(s)

Lab Systems

Pharmacy Systems

Imaging

Medical Claims



Scheduling Systems

Medical

Care Data

10

Sensors RPM1

mHealth Apps

Patient-Reported

Outcomes

Genetics

Socioeconomic Data


Human

Biology

Data

20

Lifestyle

and

Behavior

Data

50

Social and

Environmental

Data

20



27



Acquire Data








by machines


bull Volume

bull Velocity

bull Variety


unstructured data





heart beats














treating physician






30

How Machines Learn


Build the Model


Input Data



Adjust the Model



candidates

Repeat

Supervised Learning

















tradeoffs (eg cost

vs quality)


32















Nigam Shah MBBS PhD



Stanford University








explainabilityrdquo

Surgery is the

best option ()

Data

Black Box Model




33




To the Right Person

In the Right Form


At the Right Time



















The Five Rights of




34





ME

DT

RO

NIC

MIN

IME

D 6

70G






to smartphone






fibrillation (AFIB)




for $19


analysis

AliveC

orK

ard

iaM

obile


35





quantifiable target

outcomes

Automated

processes need

human oversight


rules can limit

potential harm

Periodically

evaluate processes

against goals



Kansas City Star



ROAD MAP36

The Data Dilemma1


3 How to Get There


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


20




Dear









Detailed








wearables better



and treatments

Unlimited capacity

instant response





(watches your back)

Dull







information provide




the cracksrdquo



21




Narr

ow

(T

ask W

ea

k)

AI1

Clinical Decisions


Ge

ne

ral (S

tro

ng

) A

I2

Full Diagnosis

and Treatment Plan

Clinical

Chatbots

Radiology

Interpretation

Acute

Clinical

Risk

Recruiting

Safety Risk Smart

Monitoring

Medication Dosing

Administrative

Chatbots

(Concierge)

Precision

Engagement

Precision

Medicine

Ambient

Documentation

Adaptive User

Interfaces

Worklist

Prioritization



(growing)

The Computer Will

See You Now

The Uber-ization of

Health Care


Capacity Staffing

Management

Population

Health Risk

The Self-Driving

HospitalInformation

Security

Targeted

Marketing



22









Case in Brief



Smith AR






process bottlenecks




















23













Reduced recruiting

expenses

More predictable

staffing levels

More experienced

staff

Realized Benefits





Applicant completes

a 15-20 minute

assessment process





hiring managers

Hiring managers use



the hiring decision



24















survival time




90 accuracy










study results










x-rays


Feb 2018














25


1Acquire Data





tune-ups as the

environment changes

Monitor Performance

and Revisit

42Train or Refine

the Model


buthellip


governed data pay

enormous dividends


sources improve

predictions and



are important (eg

explainability)


ethical constraints

bull Evaluate under



incomplete data)



decision points


as warranted


of decision support

3Incorporate Models

into Workflow


Begin with






26

New Data Sources


Acquire Data



analysis


EMR(s)

Lab Systems

Pharmacy Systems

Imaging

Medical Claims



Scheduling Systems

Medical

Care Data

10

Sensors RPM1

mHealth Apps

Patient-Reported

Outcomes

Genetics

Socioeconomic Data


Human

Biology

Data

20

Lifestyle

and

Behavior

Data

50

Social and

Environmental

Data

20



27



Acquire Data








by machines


bull Volume

bull Velocity

bull Variety


unstructured data





heart beats














treating physician






30

How Machines Learn


Build the Model


Input Data



Adjust the Model



candidates

Repeat

Supervised Learning

















tradeoffs (eg cost

vs quality)


32















Nigam Shah MBBS PhD



Stanford University








explainabilityrdquo

Surgery is the

best option ()

Data

Black Box Model




33




To the Right Person

In the Right Form


At the Right Time



















The Five Rights of




34





ME

DT

RO

NIC

MIN

IME

D 6

70G






to smartphone






fibrillation (AFIB)




for $19


analysis

AliveC

orK

ard

iaM

obile


35





quantifiable target

outcomes

Automated

processes need

human oversight


rules can limit

potential harm

Periodically

evaluate processes

against goals



Kansas City Star



ROAD MAP36

The Data Dilemma1


3 How to Get There


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


21




Narr

ow

(T

ask W

ea

k)

AI1

Clinical Decisions


Ge

ne

ral (S

tro

ng

) A

I2

Full Diagnosis

and Treatment Plan

Clinical

Chatbots

Radiology

Interpretation

Acute

Clinical

Risk

Recruiting

Safety Risk Smart

Monitoring

Medication Dosing

Administrative

Chatbots

(Concierge)

Precision

Engagement

Precision

Medicine

Ambient

Documentation

Adaptive User

Interfaces

Worklist

Prioritization



(growing)

The Computer Will

See You Now

The Uber-ization of

Health Care


Capacity Staffing

Management

Population

Health Risk

The Self-Driving

HospitalInformation

Security

Targeted

Marketing



22









Case in Brief



Smith AR






process bottlenecks




















23













Reduced recruiting

expenses

More predictable

staffing levels

More experienced

staff

Realized Benefits





Applicant completes

a 15-20 minute

assessment process





hiring managers

Hiring managers use



the hiring decision



24















survival time




90 accuracy










study results










x-rays


Feb 2018














25


1Acquire Data





tune-ups as the

environment changes

Monitor Performance

and Revisit

42Train or Refine

the Model


buthellip


governed data pay

enormous dividends


sources improve

predictions and



are important (eg

explainability)


ethical constraints

bull Evaluate under



incomplete data)



decision points


as warranted


of decision support

3Incorporate Models

into Workflow


Begin with






26

New Data Sources


Acquire Data



analysis


EMR(s)

Lab Systems

Pharmacy Systems

Imaging

Medical Claims



Scheduling Systems

Medical

Care Data

10

Sensors RPM1

mHealth Apps

Patient-Reported

Outcomes

Genetics

Socioeconomic Data


Human

Biology

Data

20

Lifestyle

and

Behavior

Data

50

Social and

Environmental

Data

20



27



Acquire Data








by machines


bull Volume

bull Velocity

bull Variety


unstructured data





heart beats














treating physician






30

How Machines Learn


Build the Model


Input Data



Adjust the Model



candidates

Repeat

Supervised Learning

















tradeoffs (eg cost

vs quality)


32















Nigam Shah MBBS PhD



Stanford University








explainabilityrdquo

Surgery is the

best option ()

Data

Black Box Model




33




To the Right Person

In the Right Form


At the Right Time



















The Five Rights of




34





ME

DT

RO

NIC

MIN

IME

D 6

70G






to smartphone






fibrillation (AFIB)




for $19


analysis

AliveC

orK

ard

iaM

obile


35





quantifiable target

outcomes

Automated

processes need

human oversight


rules can limit

potential harm

Periodically

evaluate processes

against goals



Kansas City Star



ROAD MAP36

The Data Dilemma1


3 How to Get There


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


22









Case in Brief



Smith AR






process bottlenecks




















23













Reduced recruiting

expenses

More predictable

staffing levels

More experienced

staff

Realized Benefits





Applicant completes

a 15-20 minute

assessment process





hiring managers

Hiring managers use



the hiring decision



24















survival time




90 accuracy










study results










x-rays


Feb 2018














25


1Acquire Data





tune-ups as the

environment changes

Monitor Performance

and Revisit

42Train or Refine

the Model


buthellip


governed data pay

enormous dividends


sources improve

predictions and



are important (eg

explainability)


ethical constraints

bull Evaluate under



incomplete data)



decision points


as warranted


of decision support

3Incorporate Models

into Workflow


Begin with






26

New Data Sources


Acquire Data



analysis


EMR(s)

Lab Systems

Pharmacy Systems

Imaging

Medical Claims



Scheduling Systems

Medical

Care Data

10

Sensors RPM1

mHealth Apps

Patient-Reported

Outcomes

Genetics

Socioeconomic Data


Human

Biology

Data

20

Lifestyle

and

Behavior

Data

50

Social and

Environmental

Data

20



27



Acquire Data








by machines


bull Volume

bull Velocity

bull Variety


unstructured data





heart beats














treating physician






30

How Machines Learn


Build the Model


Input Data



Adjust the Model



candidates

Repeat

Supervised Learning

















tradeoffs (eg cost

vs quality)


32















Nigam Shah MBBS PhD



Stanford University








explainabilityrdquo

Surgery is the

best option ()

Data

Black Box Model




33




To the Right Person

In the Right Form


At the Right Time



















The Five Rights of




34





ME

DT

RO

NIC

MIN

IME

D 6

70G






to smartphone






fibrillation (AFIB)




for $19


analysis

AliveC

orK

ard

iaM

obile


35





quantifiable target

outcomes

Automated

processes need

human oversight


rules can limit

potential harm

Periodically

evaluate processes

against goals



Kansas City Star



ROAD MAP36

The Data Dilemma1


3 How to Get There


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


23













Reduced recruiting

expenses

More predictable

staffing levels

More experienced

staff

Realized Benefits





Applicant completes

a 15-20 minute

assessment process





hiring managers

Hiring managers use



the hiring decision



24















survival time




90 accuracy










study results










x-rays


Feb 2018














25


1Acquire Data





tune-ups as the

environment changes

Monitor Performance

and Revisit

42Train or Refine

the Model


buthellip


governed data pay

enormous dividends


sources improve

predictions and



are important (eg

explainability)


ethical constraints

bull Evaluate under



incomplete data)



decision points


as warranted


of decision support

3Incorporate Models

into Workflow


Begin with






26

New Data Sources


Acquire Data



analysis


EMR(s)

Lab Systems

Pharmacy Systems

Imaging

Medical Claims



Scheduling Systems

Medical

Care Data

10

Sensors RPM1

mHealth Apps

Patient-Reported

Outcomes

Genetics

Socioeconomic Data


Human

Biology

Data

20

Lifestyle

and

Behavior

Data

50

Social and

Environmental

Data

20



27



Acquire Data








by machines


bull Volume

bull Velocity

bull Variety


unstructured data





heart beats














treating physician






30

How Machines Learn


Build the Model


Input Data



Adjust the Model



candidates

Repeat

Supervised Learning

















tradeoffs (eg cost

vs quality)


32















Nigam Shah MBBS PhD



Stanford University








explainabilityrdquo

Surgery is the

best option ()

Data

Black Box Model




33




To the Right Person

In the Right Form


At the Right Time



















The Five Rights of




34





ME

DT

RO

NIC

MIN

IME

D 6

70G






to smartphone






fibrillation (AFIB)




for $19


analysis

AliveC

orK

ard

iaM

obile


35





quantifiable target

outcomes

Automated

processes need

human oversight


rules can limit

potential harm

Periodically

evaluate processes

against goals



Kansas City Star



ROAD MAP36

The Data Dilemma1


3 How to Get There


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


24















survival time




90 accuracy










study results










x-rays


Feb 2018














25


1Acquire Data





tune-ups as the

environment changes

Monitor Performance

and Revisit

42Train or Refine

the Model


buthellip


governed data pay

enormous dividends


sources improve

predictions and



are important (eg

explainability)


ethical constraints

bull Evaluate under



incomplete data)



decision points


as warranted


of decision support

3Incorporate Models

into Workflow


Begin with






26

New Data Sources


Acquire Data



analysis


EMR(s)

Lab Systems

Pharmacy Systems

Imaging

Medical Claims



Scheduling Systems

Medical

Care Data

10

Sensors RPM1

mHealth Apps

Patient-Reported

Outcomes

Genetics

Socioeconomic Data


Human

Biology

Data

20

Lifestyle

and

Behavior

Data

50

Social and

Environmental

Data

20



27



Acquire Data








by machines


bull Volume

bull Velocity

bull Variety


unstructured data





heart beats














treating physician






30

How Machines Learn


Build the Model


Input Data



Adjust the Model



candidates

Repeat

Supervised Learning

















tradeoffs (eg cost

vs quality)


32















Nigam Shah MBBS PhD



Stanford University








explainabilityrdquo

Surgery is the

best option ()

Data

Black Box Model




33




To the Right Person

In the Right Form


At the Right Time



















The Five Rights of




34





ME

DT

RO

NIC

MIN

IME

D 6

70G






to smartphone






fibrillation (AFIB)




for $19


analysis

AliveC

orK

ard

iaM

obile


35





quantifiable target

outcomes

Automated

processes need

human oversight


rules can limit

potential harm

Periodically

evaluate processes

against goals



Kansas City Star



ROAD MAP36

The Data Dilemma1


3 How to Get There


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


25


1Acquire Data





tune-ups as the

environment changes

Monitor Performance

and Revisit

42Train or Refine

the Model


buthellip


governed data pay

enormous dividends


sources improve

predictions and



are important (eg

explainability)


ethical constraints

bull Evaluate under



incomplete data)



decision points


as warranted


of decision support

3Incorporate Models

into Workflow


Begin with






26

New Data Sources


Acquire Data



analysis


EMR(s)

Lab Systems

Pharmacy Systems

Imaging

Medical Claims



Scheduling Systems

Medical

Care Data

10

Sensors RPM1

mHealth Apps

Patient-Reported

Outcomes

Genetics

Socioeconomic Data


Human

Biology

Data

20

Lifestyle

and

Behavior

Data

50

Social and

Environmental

Data

20



27



Acquire Data








by machines


bull Volume

bull Velocity

bull Variety


unstructured data





heart beats














treating physician






30

How Machines Learn


Build the Model


Input Data



Adjust the Model



candidates

Repeat

Supervised Learning

















tradeoffs (eg cost

vs quality)


32















Nigam Shah MBBS PhD



Stanford University








explainabilityrdquo

Surgery is the

best option ()

Data

Black Box Model




33




To the Right Person

In the Right Form


At the Right Time



















The Five Rights of




34





ME

DT

RO

NIC

MIN

IME

D 6

70G






to smartphone






fibrillation (AFIB)




for $19


analysis

AliveC

orK

ard

iaM

obile


35





quantifiable target

outcomes

Automated

processes need

human oversight


rules can limit

potential harm

Periodically

evaluate processes

against goals



Kansas City Star



ROAD MAP36

The Data Dilemma1


3 How to Get There


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


26

New Data Sources


Acquire Data



analysis


EMR(s)

Lab Systems

Pharmacy Systems

Imaging

Medical Claims



Scheduling Systems

Medical

Care Data

10

Sensors RPM1

mHealth Apps

Patient-Reported

Outcomes

Genetics

Socioeconomic Data


Human

Biology

Data

20

Lifestyle

and

Behavior

Data

50

Social and

Environmental

Data

20



27



Acquire Data








by machines


bull Volume

bull Velocity

bull Variety


unstructured data





heart beats














treating physician






30

How Machines Learn


Build the Model


Input Data



Adjust the Model



candidates

Repeat

Supervised Learning

















tradeoffs (eg cost

vs quality)


32















Nigam Shah MBBS PhD



Stanford University








explainabilityrdquo

Surgery is the

best option ()

Data

Black Box Model




33




To the Right Person

In the Right Form


At the Right Time



















The Five Rights of




34





ME

DT

RO

NIC

MIN

IME

D 6

70G






to smartphone






fibrillation (AFIB)




for $19


analysis

AliveC

orK

ard

iaM

obile


35





quantifiable target

outcomes

Automated

processes need

human oversight


rules can limit

potential harm

Periodically

evaluate processes

against goals



Kansas City Star



ROAD MAP36

The Data Dilemma1


3 How to Get There


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


27



Acquire Data








by machines


bull Volume

bull Velocity

bull Variety


unstructured data





heart beats














treating physician






30

How Machines Learn


Build the Model


Input Data



Adjust the Model



candidates

Repeat

Supervised Learning

















tradeoffs (eg cost

vs quality)


32















Nigam Shah MBBS PhD



Stanford University








explainabilityrdquo

Surgery is the

best option ()

Data

Black Box Model




33




To the Right Person

In the Right Form


At the Right Time



















The Five Rights of




34





ME

DT

RO

NIC

MIN

IME

D 6

70G






to smartphone






fibrillation (AFIB)




for $19


analysis

AliveC

orK

ard

iaM

obile


35





quantifiable target

outcomes

Automated

processes need

human oversight


rules can limit

potential harm

Periodically

evaluate processes

against goals



Kansas City Star



ROAD MAP36

The Data Dilemma1


3 How to Get There


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


30

How Machines Learn


Build the Model


Input Data



Adjust the Model



candidates

Repeat

Supervised Learning

















tradeoffs (eg cost

vs quality)


32















Nigam Shah MBBS PhD



Stanford University








explainabilityrdquo

Surgery is the

best option ()

Data

Black Box Model




33




To the Right Person

In the Right Form


At the Right Time



















The Five Rights of




34





ME

DT

RO

NIC

MIN

IME

D 6

70G






to smartphone






fibrillation (AFIB)




for $19


analysis

AliveC

orK

ard

iaM

obile


35





quantifiable target

outcomes

Automated

processes need

human oversight


rules can limit

potential harm

Periodically

evaluate processes

against goals



Kansas City Star



ROAD MAP36

The Data Dilemma1


3 How to Get There


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


32















Nigam Shah MBBS PhD



Stanford University








explainabilityrdquo

Surgery is the

best option ()

Data

Black Box Model




33




To the Right Person

In the Right Form


At the Right Time



















The Five Rights of




34





ME

DT

RO

NIC

MIN

IME

D 6

70G






to smartphone






fibrillation (AFIB)




for $19


analysis

AliveC

orK

ard

iaM

obile


35





quantifiable target

outcomes

Automated

processes need

human oversight


rules can limit

potential harm

Periodically

evaluate processes

against goals



Kansas City Star



ROAD MAP36

The Data Dilemma1


3 How to Get There


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


33




To the Right Person

In the Right Form


At the Right Time



















The Five Rights of




34





ME

DT

RO

NIC

MIN

IME

D 6

70G






to smartphone






fibrillation (AFIB)




for $19


analysis

AliveC

orK

ard

iaM

obile


35





quantifiable target

outcomes

Automated

processes need

human oversight


rules can limit

potential harm

Periodically

evaluate processes

against goals



Kansas City Star



ROAD MAP36

The Data Dilemma1


3 How to Get There


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


34





ME

DT

RO

NIC

MIN

IME

D 6

70G






to smartphone






fibrillation (AFIB)




for $19


analysis

AliveC

orK

ard

iaM

obile


35





quantifiable target

outcomes

Automated

processes need

human oversight


rules can limit

potential harm

Periodically

evaluate processes

against goals



Kansas City Star



ROAD MAP36

The Data Dilemma1


3 How to Get There


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


35





quantifiable target

outcomes

Automated

processes need

human oversight


rules can limit

potential harm

Periodically

evaluate processes

against goals



Kansas City Star



ROAD MAP36

The Data Dilemma1


3 How to Get There


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


ROAD MAP36

The Data Dilemma1


3 How to Get There


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


41








human workers














innovation are key




not achieved














43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


43












today





will change














45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


45














and process changes




46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


46

Develop


Enhance

Team Effectiveness

Accelerate











page




and presentations


bull Tools

bull Templates








47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


47








Michael Finn

Michael Rosenbaum




Jonathan Symonds



Christina Honeywell


Chris Cooper

Dan Scholler


Dr Gurmeet Sran

Duke University


Dr Mark Sendak


Michael Cousins


Kirk Hanson


Dale Sanders

Levi Thatcher


Sandy Lawson

Kathy Rowell


Dr Nathan Dean

Jeffrey Ferraro

Lonny Northrup


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


48









Phil Troy


Dr Tufia Haddad



Ari Caroline

Isaac Wagner


John Doyle

Tom Lawry

Dennis Schmuland


Dr Peter Fleischut

Dr David Tsay



Jayanta Bhowmik



Shannon Kmak


Mudit Garg

Venkat Mocherla



Glenn Winteringham


Jayesh Govindarajan

Stanford University


Dr Matthew Lungren

Dr Nigam Shah



Andrew Forrest


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


49








Dr Robert Fergueson

Dr David Kaelber

The University of


Mike Wall

University Hospital


Steven Chilton

Paul Jennings

Barnaby Waters



Andrew Carruthers


Pietro Lio


Owen Johnson

University of North


Jason Burke



Terri Mikol

Don Riefner

Dr Rasu Shreshta



Dr Brad Racette


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


LEGAL CAVEAT







































authorized herein




















Advisory Board



kuhnengadvisorycom


Bethany Jones

Nouran Ragaban

Sophie Ranen

Andrew Rebhan




P 2022665600 F 2022665700 advisorycom


P 2022665600 F 2022665700 advisorycom

Documents

The Decision Machine - Advisory · 2016 2030 2016 2030 2016 2030 2016 2030 2016 2030 Australia2 9.6% 14.4% 2010 New Zealand3 6.2% 8.9% 10.6% 15.9% 11.0% 18.0% Canada France United