HOW AI CAN CHANGE THE FUTURE OF HEALTHCARE · Rules-based, non-learning AI ”learns” by ingesting data, predicts and observes outcomes for a very specific job Applying “intelligence”

HOW AI CAN CHANGE THE FUTURE OF HEALTHCARE Kaveh Safavi, M.D., J.D. Senior Managing Director, Accenture’s Global Health Practice

Copyright © 2018 Accenture. All rights reserved. 2

Joseph Campbell American writer and Mythologist

IF YOU WANT TO CHANGE THE WORLD, YOU HAVE TO CHANGE THE METAPHOR.


AUSTRALIA 6.5% 1.3%

CANADA 5.4% 1.4%

GERMANY 4.6% 0.7%

UK 7.0% 2.9%

FRANCE 4.7% 0.8%

NORWAY 6.0% 0.8%

USA 5.8% 1.7%

OECD 5.5% 1.1%

COUNTRY AVERAGE ANNUAL NHE GROWTH (2001-18) NHE GROWTH ABOVE GDP

RISING NATIONAL HEALTHCARE EXPENDITURES Developed countries have historically seen their NHE grow +1% to 3% faster than GDP

NHE VS GDP GROWTH COMPARISON [2001-2018]

Source: Accenture analysis from OECD data


Gap between most and least

productive service companies

3X

HEALTHCARE HAS BEEN LOSING PRODUCTIVITY Service sector declining since 1980s, especially healthcare

Source: Brookings Institution; WSJ, While the Services Sector Booms, Productivity Remains Elusive, November 2016

— Average annual rate of change 1987-2014 —

Productivity by selected sectors

-1.0% -0.5% 0.0% 0.5% 1.0% 1.5%

Retail

Information

Manufacturing

Finance

Services

Hospital/long-term care


SOURCES OF GDP GROWTH BY TYPE OF INDUSTRY

% of U.S. growth, 2001 - 2016

Source: Bureau of Economic Analysis; Bureau of Labor Statistics; McKinsey analysis

-13

26

-53

35

99

60

66

58

14

14

87

7

Healthcare

Services

Goods

Federal, state and local government

Growth attributed to labor

99% Healthcare

VS

25% Overall U.S. economy

Labor (workforce)

Capital (assets)

Multifactor productivity (technology/innovation)


COMPETITION AND EXPECTATIONS Coming from everywhere

DIRECT COMPETITORS

Sell products or services that directly compete with ours

EXPERIMENTAL COMPETITORS

Sell experiences that replace ours

PERCEPTUAL COMPETITORS

Change expectations our customers have


THE OPPORTUNITY: AI IN HEALTHCARE

Solves Problems

Thinks + Pays for Itself Start Here Work

After AI


ARTIFICIAL INTELLIGENCE

Automation of routine vs. judgement tasks U.S. employment by type of work

What Can’t be Automated?

Source: Economist, March of the Machines, 2016

“Abstract Manual” perception, manipulation, dexterity, physical adaptability

Creative Intelligence ideation, critical thinking, problem solving

Social Intelligence teamwork, persuasion, intuition, empathy, resilience


ARTIFICIAL INTELLIGENCE A constellation of technologies

Applications

AI capabilities

Machine learning


WHAT IS AI? What is called “AI” is mostly weak or non-AI uses

Characteristics

Examples

Time to Maturity

Rules-based, non-learning AI ”learns” by ingesting data, predicts and observes outcomes for a very specific job

Applying “intelligence” to any problem vs. the one AI is trained to solve (any job)

Robotic process automation, hypothesis-based analytics

Video analytics, self-generating hypothesis analytics, natural language processing, policy advisory

Cross-domain problem solving (e.g. Turing Test, Coffee Test)

KEY INITIATIVES Non-AI KEY INITIATIVES Weak / Specialized AI KEY INITIATIVES Strong / Generalized AI

0-2 years 2-5 years 10+ years


AI: A FRAMEWORK OF CAPABILITIES

Source: Accenture, AI Explained: A Guide for Executives, 2018

AI is the ability for technology to sense, comprehend, act and learn in a way that mimics human intelligence.

SENSE

Perceive the world by acquiring and processing images, sounds, speech, text and other data.

COMPREHEND

Analyze and understand the information collected by adding meaning and insights.

ACT

Take action in the physical world based on comprehension and understanding.

LEARN

Improve performance (quality, consistency, and accuracy) based on real world experiences.


COST REDUCTION

OUTCOME IMPROVEMENT

CONSUMER EXPERIENCE

AI CAN POWER AMBITIONS IN VARIOUS WAYS

• Operational efficiency

•  Payer management

•  Cybersecurity

•  Fraud detection

•  Claims payment

Optimize talent and streamline processes

Accelerate discovery and increase accuracy

•  Virtual nurse assistant

•  Preliminary diagnosis

•  Provider + customer service

•  Digital engagement

•  Connected machines

Increase personalization and convenience

•  Robotic assisted surgery

•  Automated image diagnosis

•  Next-gen pathways

•  Dosage error reduction

•  Clinical trial matching


APPLICATION POTENTIAL ANNUAL VALUE BY 2026 KEY DRIVERS FOR ADOPTION

Robot-assisted surgery Technological advances in robotic solutions for more types of surgery

Virtual nursing assistants Increasing pressure caused by medical labor shortage

Administrative workflow Easier integration with existing technology infrastructure

Fraud detection Need to address increasingly complex service and payment fraud attempts

Dosage Error Reduction Prevalence of medical errors, which leads to tangible penalties

Connection machines Proliferation of connected machines/devices

Clinical trial participation Patent cliff; plethora of data; outcomes-driven approach

Preliminary diagnosis Interoperability/data architecture to enhance accuracy

Automated image diagnosis Storage capacity; greater trust in AI technology

Cybersecurity Increase in breaches; pressure to protect health data

AI THINKS AND PAYS FOR ITSELF $55 – 150B in potential savings by 2026

2

3

5

13

14

16

17

18

20

$40B

Source: Accenture Analysis, 2017

10 AI APPLICATIONS THAT COULD CHANGE HEALTH CARE


Top 4 Big Gains have transformational impacts and are all operationally focused

Perceived Value of AI:

BIG GAINS EXPECTED Transformative benefits more likely operationally focused

Transformative benefit Substantial benefit

#1

10%

3%

12%

9%

13%

11%

30%

21%

33%

45%

42%

51%

46%

50%

48%

50%

49%

65%

56%

46%

Better access to care

Better clinical outcomes

Expanded patient reach

Labor savings/reallocation

Patient satisfaction

Lower cost of care

Improved analytical capabilities

Cost savings (e.g. appts)

Operational efficiency

Increased cybersecurity

2

3

4

Source: Accenture, C-Suite Survey: Uses of AI Among Six Country Health Systems , 2018


AUTOMATION SUBSTITUTES TASKS—NOT JOBS Routine tasks likely to be automated

Source: Brookings analysis of BLS, Census, EMSI, Moodys, January 2019

POTENTIAL FOR AUTOMATION TYPES OF JOBS (% of tasks automated)

HIGH >70% of tasks (across 25% of jobs)

MEDIUM 30-70% of tasks

(across 36% of jobs)

LOW <30% of tasks

(across 39% of U.S. jobs)

HIGH Food prep/serving (81%) HIGH Production (79%) MED Office and admin (60%) MED Transportation and material moving (55%) MED Sales (45%) MED Healthcare support (40%) MED Computer/mathematical (37%) MED Personal care and service (34%) MED Healthcare practitioners/technical (33%) MED Community and social services (22%) LOW Art, design, entertainment and media (20%) LOW Education (18%) LOW Business and financial operations (14%)

About 40% of non-clinical healthcare support worker tasks and 33% of clinical worker tasks can be shifted to machines by automating tasks


WILL CLINICIANS BE AUTOMATED? Probability of automation of select professions

Source: Economist, March of the Machines, 2016; Frey, C. Osborne, M. The Future of Employment, 2013

47% of U.S. employment at risk for automation

< 1% •  Audiologist •  Choreographer •  Dentist •  Elementary teacher •  Physical therapist •  Physician, surgeon •  Psychologist •  Public relations •  Social worker

> 98% •  Brokerage clerk •  Insurance underwriter •  Legal secretary •  Loan officer •  Procurement clerk •  Referee, sports official •  Tax preparer •  Telemarketer •  Watch repairer


PROJECTED TALENT EQUILIBRIUM

SELF-CARE to meet up to

WORKFLOW AUTOMATION accounts for

IMPACT OF TECHNOLOGY ON THE TALENT SHORTAGE

Source: Accenture Strategy 2030 Healthcare Workforce Research, 2017

Talent Equilibrium

2017 2030

FTE

CLI

NIC

IAN

S

Clinician Supply

of patients’ services demand

of clinicians’ working time

Demand (with AI)

Demand (without AI)


HEALTHCARE IS ALREADY BECOMING MORE PRODUCTIVE Medical management nurses shift to more satisfying work

Source: Accenture 2017

RULES-BASED AUTOMATION (ACHIEVED) •  Transactions have been automated •  Using rules-based minibots and robotic-process automation (RPA)

RULES-BASED AUTOMATION (OPPORTUNITY) •  Could be considered for RPA •  Or, may not be capable of automation

JUDGEMENT-BASED PROCESSES •  Can be augmented by AI and analytics

EXPERIENTIAL KNOWLEDGE NEEDED •  Remaining 45% to be reviewed for AI •  However, about 30% will likely require experiential or knowledge workers

20%

20%

15%

45%


REIMAGINING THE FINANCIAL WORKFORCE

Role TECHNOLOGY

(AI/Robots) HUMAN FUTURE

EXPERTISE

ACCOUNTANTS RPA MANAGERS

BUDGET ANALYSTS

SCENARIO MODELERS

FINANCIAL ANALYSTS

DATA SCIENTISTS

TAX EXAMINERS AND PREPARERS

ADVANCED ANALYTICS

AUDITORS EXCEPTION HANDLERS

TREASURERS CASH FLOW OPTIMIZERS

50%

50%

30%

60%

70%

40%

50%

50%

70%

40%

30%

60%

Adaptive Workforce Fixed Workforce AI/RPA Source: Accenture Organizational Insights, 2017


WHERE DO YOU GO FROM HERE?

Workforce

Strategy Process Data

Pick the right problem

Reimagine processes

Use data + AI to solve to previously unsolvable problems

Transform human + machine relationship across the workforce


HUMANS

Source: Economist, For robots to work with people, they must understand people, Aug. 17, 2017

COBOTS:

+

MACHINES


Redirecting time to distinctly human tasks (interpersonal, decision-making, creativity)

Asking the right questions of AI to

retrieve the insights needed

FUSION SKILLS FOR THE MISSING MIDDLE

Humans complement machines

AI gives humans superpowers

TRAIN EXPLAIN SUSTAIN AMPLIFY INTERACT EMBODY

Rehumanizing time Intelligent interrogation

Responsible normalizing Bot-based empowerment

Judgement integration Holistic melding

Reciprocal apprenticing

Relentless reimagining

HYBRID ACTIVITIES

Source: Paul Daugherty, H. James Wilson, “Human + Machines: Reimagining Work in the Age of AI,” 2017

+


Dr. William W. Mayo

THE AIM OF MEDICINE IS TO PREVENT DISEASE AND PROLONG LIFE; THE IDEAL OF MEDICINE IS TO ELIMINATE THE NEED FOR A PHYSICIAN.


Kaveh Safavi, MD JD

+1 312 693 1541

[email protected]

@drkavehsafavi @AccentureHealth

kavehsafavi

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HOW AI CAN CHANGE THE FUTURE OF HEALTHCARE · Rules-based, non-learning AI ”learns” by ingesting data, predicts and observes outcomes for a very specific job Applying “intelligence”