how science, technology and innovation will disrupt what we do...climate change ecological system...
TRANSCRIPT
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How science, technology and innovation
will disrupt what we do
Keith Ison Head of Medical Physics, Guy’s and St Thomas’
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The scale of the future challenge • All developed healthcare systems hitting resource problems*
• Difficult discussions on limits and rationing to come
• Growing global burden of non-communicable disease
• Growing global population
• Poverty and inequality
• Huge sustainability gap
* ‘The world will be short of 12.9 million healthcare workers by 2035’ [WHO, 2013]
Expectations and resources…
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Death
Famine
War
Conquest
The Four Horsemen of the Apocalypse... Brexit
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More of the same...
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Climate
change
Famine and
water shortages
War and
conflict
Disease and
chronic illness
Artificial
Intelligence
... and a fifth ?
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E
P S
T
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SO
CIA
L
POLITICAL
EN
VIR
ON
ME
NTA
L
Climate change
Ecological system collapse
Food and water shortages
War & conflict More disease and
chronic illness
Population growth
Inequality
ECONOMIC
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Climate change
Ecological system collapse
Food and water shortages
War & conflict More disease and
chronic illness
Population growth
Inequality
SO
CIA
L
POLITICAL
EN
VIR
ON
ME
NTA
L
In a complex system, what if anything can
TECHNOLOGY fix?
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Technology
1. The pace of change
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Doubling of “capacity” every 2 years from 1965 to 2015 (exponential)
Moore’s Law
from 1975
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1983 2000 2008 1973
10 years 17 years 8 years
2015
7 years
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But Moore’s Law no longer applies because of:
Global interconnections between
people and organisations
Technology improves tools
as well as outcomes
More scientists/engineers
means more competition
Greater accessibility of
knowledge
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2040-50
2000 Doubling time...
Rate of technological
change increasing
as a Reciprocal, not an
Exponential:
y = 1
x
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2040-50 2000
This is
now
SINGULARITY
2016
That was
when you
grew up…
This is 10
years time…
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accessed 15/5/2016
Wetterstrand KA. DNA Sequencing Costs: Data from the NHGRI Genome Sequencing Program (GSP)
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Technology
2. Genomics
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Five babies – five different treatments
KCNJ11 p.V59M
Permanent diabetes
and developmental
delay
FOXP3
c.227delT
IPEX
syndrome
EIF2AK3 p.E371*
Wolcott Rallison
Syndrome
Bone Marrow
Transplant
Liver
Transplant
Sulphonylurea
therapy
STAT3 p.T716M
Multi-organ
autoimmune
disease
GATA6 c.1448-1455del
Syndromic pancreatic
agenesis
Insulin and
exocrine
supplements
? STAT3
inhibitor
EXAMPLE: Genomic diagnosis guides clinical management
- mutations in >25 different genes cause neonatal diabetes
- genomic technology has found 5 new genetic subtypes
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Siblings with the same genetic defect
4 years to diagnose
genetic methionine
handling defect
Treated from birth with
a dietary supplement
costing 40p a day
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Rapid viral diagnosis • DNA technology to diagnose viral infection
from a nasal swab
• Machines the size of a microwave (already outdated 1yr on)
• Fast enough to guide nursing strategies and avoid infection of vulnerable patients
• Multiple further tests and miniaturisation in development
• How best to benefit from this technology and when and how to invest ?
15 minutes
1 hour
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Technology
3. Personalised medicine
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Precision & personalised medicine
• ‘One size fits all’ treatment
based on symptoms and
averaged responses to
drugs and procedures
• Treat underlying cause & personal response
Now Emerging
‘One size fits all’ Individually-tailored approach
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Precision & personalised medicine ...a fundamental revolution in medical practice...
• Organ/ speciality organisation
of services and professions
• Limited use of
genomic/molecular markers
• Diagnostic & other clinical data
not linked
• New taxonomy of medicine based on underlying cause & personal response
• Comprehensive linked diagnostics to give full picture
• Tailored therapies for better outcomes
• Integrated clinical services taking a ‘whole body’ approach
Now 2020s?
‘One size fits all’ treatments & intervention
Individually-tailored approach
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Personalisation: Warfarin management • 600,000 Warfarin users in UK
• Wide response range – 40x dose
variability
• Major cause of adverse reactions
• Current ‘trial and error’ method
in outpatient INR clinics
• Now understand the genetics behind
significant element of variation
• Genotype-based protocols identify the
right dose sooner, with fewer adverse
effects
• Improves patient outcome and
experience and reduces costs
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You feel ill...
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You go to the doctor...
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Who runs some tests...
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Which show you need some medicine...
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The pharmacist looks up your genomic
and biological profile...
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...designs and 3D prints a matching pill
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...designs and 3D prints a matching pill
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Technology
4. Imaging and visualisation
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Development cine sequences
for foetal MR imaging
Information aids
clinical and parental
decision making
when serious
problems are found
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• Detailed 3D models derived
from imaging: CT, MR, PET
etc
• Model used directly during the
ablation procedure
• Anatomic/functional model can
be fused to a catheter model
• Leads to more effective and
safer ablation
Interventional Example – Ventricular Tachycardia Ablation
Model showing main cardiac
structures including left ventricular
wall thickness. These are used to
guide the ablation procedure.
SHEFFIELD
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3D printing Initial use for highly accurate surgical guides and
true-to-life medical device prototypes
MR scan
of heart
Image
Software
Segment Print
Other 3D technologies for
personalised implants, organs…
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Surgical Planning Example
Pre-operative planning
makes more effective
use of time and skills
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Imaging Genomics
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Linking imaging and gene expression data for primary liver tumors
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Systematic relationship between imaging
appearance and gene expression
Could reduce the need
for invasive biopsies
and use to monitor
treatment progress
• A large fraction of gene expression can be reconstructed
from only a small number of imaging traits
• 9 traits account for 50% of tumour genetic variation
• For each gene, need on average three and no more than
four traits to predict variation
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Technology
5. IT and ‘Big Data’
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Impact of Computing power From dedicated models and
Expert Systems trying to
replicate human thinking...
.....to extracting evidence
from massive data sets and
machine learning...
Cognitive computing
Person AND machine > Person OR Machine
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200 million digital pages
2,880 processors
80 teraflops
US Jeopardy game show 2011
Human Human
Computer IBM’s Watson supercomputer
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The power of predictive analytics
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Watson Paths 2013
Cleveland Clinic
“When I look ahead into the era of cognitive computing, I see a
revolution unfolding before my eyes. For the first time,
computers will adapt to the way we want to do things, rather
than vice versa. That will be a remarkable change.”
Michael Barborak, IBM Research
Integrating data and
inference to guide
complex diagnosis…
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“What's unique about Apple’s ResearchKit is that it works
from the consumer-in. [An open source software framework],
it empowers consumers rather than providers and in the
process changes the healthcare research model.” March 2015
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New algorithms and big data
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What will doctors do when
equipment and IT systems
know more than they do?
Scientists and engineers will
programme the new systems...
But will anyone know how they
work?
And who is in control?
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Technology
6. Putting it all together
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Integrated diagnostic view Individual
problem
Person and
diagnosis:
genotype
to
phenotype
Targeted
treatment
Images
Lab results
Tissue samples
Tests
Genomics
Self-monitoring
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The Big Data Challenge – putting it all together
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7. Behaviours, Expectations
and Professional futures
S
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Behaviours: what connected
humans do
• Communicate
• Research
• Share
• Co-operate
• Build Communities
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Expectations: what connected
humans want
• Online help and access to knowledge
• Selection of the best expertise and techniques
• Personalisation and customisation of care
• Realisation of latent demand
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Patterns and trends of change • The end of an era
– From reactive patients to proactive citizens
– From treatment to improvement and prevention
– The more-for-less challenge
• Transformation by technology
– Smartphones and the internet
• Demystification
– From exercise of a craft to operation of a system
– Clarity of outcomes and effectiveness
• Professional work reconfigured
– Routinised / Decomposed / Reallocated
– New ways to access knowledge and care
– New ways of working
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Individual
Craft Standardisation Systematization
Increasing spread & commoditisation of knowledge
The evolution of professional knowledge
Procedural
Structural Guilds
Relational?
Democratisation?
Time
Verbal Written Printed Internet ?
Experts
Organisations
Networks?
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Charge
(commercial)
No Charge
(charitable)
Commons
(cooperative)
Craft Standardisation Systematization
Commoditisation
The evolution of professional knowledge
Medicine as a profession is
very resistant to change...why?
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8. Changes by 2025 ?
... in technologies and
roles
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Professions and healthcare now...
DIFFICULT
PROBLEMS
DEVELOPED
KNOWLEDGE
EXPERIENCE
PREPARATION
Expert
Public and patients
Specialist
Assistant
Trainee
Limited capacity
Care pushed
towards patients
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Professions and healthcare... in future
DIFFICULT
PROBLEMS
ROUTINE
TREATMENT
UNDERLYING
CAUSES
NEW PARADIGMS
Public and patients
Specialists
Experts
Para professionals
Systems engineers
Expert Systems
Enhanced capacity
Patients pushing
towards clinicians
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Analysts
Future structuring of medical work ?
Clinical
Experts
Para professional
Technical
Experts
Procedural expert
Patient research cohorts Knowledge
generation and
systemisation Research
Experts
ROUTINE AND SPECIALIST CARE
DEVELOPMENT ENVIRONMENT
Empathiser
Care integrator
Engineers
Data
scientists
Designers
Machine knowledge
Diagnostics Therapies
Ongoing care and management
Patients
DELIVERY AGENTS
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PERSONAL
CARE INTERVENTION
DIAGNOSIS
MANAGEMENT
What goes on round the patient?
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How does it improve?
2. Standardise
procedures 1. Personalise
care
3. Learn &
improvement
4. Disrupt the
status quo
PERSONAL
CARE INTERVENTION
DIAGNOSIS
MANAGEMENT
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Intervention experts:
doctors, technicians,
therapists, scientists
Expert diagnostic teams:
doctors, technicians, scientists
Management experts: doctors,
nurses, therapists, patients
Personal carers:
nurses, therapists,
carers
Who
does it?
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Intervention experts:
doctors, technicians,
therapists, scientists
Expert diagnostic teams:
doctors, technicians, scientists
Management experts: doctors,
nurses, therapists, patients
Personal carers:
nurses, therapists,
carers
Who
does it?
Supported
by......
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Intervention
robotics
Diagnostic
Expert systems
Management
Expert systems
Rehabilitation
robotics
What
supports it?
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9. Some implications for
NHS investment
E
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Innovation: the ‘S’ curve
Time
Development
and Take Up
rate
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Innovation: the ‘S’ curve
Time
Development
rate
Testing ideas
First prototypes
Trials and refinements
Early
production
Marketed
technology
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Number of CT scanners installed in the US between 1973 and 1980
Bronzino 1992
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Investment example: patient monitors
Time
Take Up
rate
Pioneers
Early
adopters
Late
adopters
Laggards
When to invest?
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Innovation: patient monitoring
Time
Development
maturity
‘Wearables’
‘Big data’ analysis
HARDWARE
SOFTWARE ‘Apps’ Smartphone
based
‘Wireless’
‘Big data’ enterprise-wide integration
‘Expert’ systems
Mobile
monitors
What to invest in?
Fixed devices
‘Intelligent’ alarms
Data
collection
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CLINICAL: Necessary Appropriate Inappropriate Futile
ECONOMIC: High Low Zero Negative
BENEFIT
HARM
+
-
VALUE
0
Resources
Measure balance of benefit and harm for each
procedure, each patient and every population
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BENEFIT
HARM
+
-
VALUE
0
Resources
Measure balance of benefit and harm for each
procedure, each patient and every population
DO MORE OF
DO LESS OF
CLINICAL: Necessary Appropriate Inappropriate Futile
ECONOMIC: High Low Zero Negative
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10. Some implications for
future jobs
E
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What will happen to jobs?
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Current business is based on a Linear Economy
Linear economy is a ‘take-make-dispose’ economy:
Take; get raw materials from the planet’s natural resources
Make; create products to consume and use
Dispose; throw away the waste generated
Current recovery of consumer goods:
• 2% Recovered for reuse
• 18% Recovered for decomposition
• 80% not recovered
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The Circular Economy builds and retains value
• Refurbish
• Remanufacture
• Parts harvesting
• Materials collection & reuse
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Enablers
Design
Collaboration Reverse Logistics
New Business Models
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What will happen to jobs? EXAMPLE: Switchboard operators
120,000 working for the Post Office pre-automation, none afterwards
Where are these jobs now?
• Supporting telephone networks
• Making phones
• Selling mobile phones and services
Overall, more people than ever are employed in this sector…
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What will happen to jobs?
EXAMPLE: Self driving vehicles
• 750,000 drivers currently working in the UK
• There could be none in 20 years time
• Fewer vehicles needed – 20h operating day
Implications:
• Fewer manufacturing jobs
• No taxi drivers
• No parking attendants
Could remove 5 to 10% of jobs from the workforce
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What will happen to jobs? Strategy Directors...
• More uncertainty
• Better technology forecasting
• Targeted investment
• Listen to patients and workforce
• Use systems thinking approaches
Key question:
• What is going to add value in the future?
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2. Complicated Sense – Analyse –
Respond
GOOD PRACTICE
1. Simple Sense – Categorise –
Respond
BEST PRACTICE
3. Complex Probe – Sense –
Respond
EMERGENT PRACTICE
4. Chaotic Act – Sense – Respond
NOVEL PRACTICE
Four categories of system behaviour
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Finally, some tactics to consider: • Develop focus on rare conditions and domains of expertise
• Link research with knowledge generation & support systems
• Horizon scanning essential before making investments
• Investment and procedures guided by value analysis
• Standardise rigorously on best practice
• Radical changes coming in workforce roles and numbers
• Patient involvement will be demanded if not provided
Understanding impact of science and
technology will be central to future success
Technology