Dominic Cushnan, Ameet Bakhai, Andy Wilkins. https://www.digitalhealthcareshow.com/welcome Wednesday 27th June 2018. 12:20-13:20. Twitter @domcushnan #AICommunity

1. How will Artificial Intelligence in
healthcare will impact patient
outcomes in the future?
Dominic Cushnan, Ameet Bakhai, Andy Wilkins
Twitter: #AICommunity

2. The future is here: it is just not
evenly distributed
William Ford Gibson (born 17 March 1948) is an American-Canadian writer
who has been called the "noir prophet" of the cyberpunk subgenre of
science fiction. Gibson coined the term "cyberspace" in his short story
"Burning Chrome" and later popularized the concept in his debut novel,
Neuromancer (1984).

3. As healthcare professionals, what
does this future hold for us and
our patients?
Write on the paper in front of you.

5. ADMIRALTY LIBRARY, NAVAL HISTORIC BRANCH
Getting evidence in to practice

6. INSTITUTE OF NAVAL MEDICINE
Getting evidence in to practice
It took 200 years before the Royal Navy routinely used
lemon juice to prevent scurvy.
First study 1601

8. The internet has changed the way we do …
everything.
1998 was also the year a little company called Google was born,
although back then, it looked a little different than it does now.

9. Social media has taken over.
One of the most influential applications of the internet is social media.

10. Source:OrganisationalNetworkAnalysisbyInnovisor
Just 3% of people in the
organisation or system typically
influence 85% of the other people

13. What is AI?

15. The maned sloth, also known as the ai,
is a three-toed sloth that lives only in Brazil.

16. What is AI
AI describes a set of advanced technologies that enable machines to do
highly complex tasks effectively – which would require intelligence if a
person were to perform them.
There is, however, “no standard definition of intelligence” and no
single agreed definition of AI.
In addition, the line between AI and other techniques, such as big data
analytics, can be blurred.

17. AI Types
• Weak AI (narrow AI) – non-sentient machine intelligence, typically
focused on a narrow task (narrow AI).
• Strong AI – (hypothetical) sentient machine (with consciousness and
mind).
• Artificial general intelligence (AGI) – (hypothetical) machine with the
ability to apply intelligence to any problem, rather than just one
specific problem, typically meaning "at least as smart as a typical
human".
• Superintelligence – (hypothetical) artificial intelligence far surpassing
that of the brightest and most gifted human minds.

18. Process
optimisation
Pre-clinical
research
Clinical
pathways
Patient
interactions
Public health
applications

22. Augmented
Intelligence

23. AI Organisations & Workstreams in
Healthcare

26. “Despite central government’s enthusiasm for AI,
current applications within the NHS are
piecemeal.”
Reform January 2018

27. Artificial intelligence / digital Health care Impact on professionals
& future of NHS Healthcare
Ameet Bakhai
MBBS, MD, FRCP, FESC
Consultant Cardiologist & Physician
Cardiovascular Research & Development Director
Amore Health Ltd / Royal Free London NHS Trust / Royal National
Orthopeadic Hospital / Barnet CCG
abakhai@gmail.com

28. Patient Access Value Based Care Personalized Digital Health Preventative Medicine Drug Trials and Discovery
data environment is rapidly changing
Healthcare organizations are facing a deluge of rich data that is enabling them to become more efficient, operate
with greater insight and effectiveness, and deliver better service
Advances analytical and computing techniques coupled with the explosion of data in healthcare organizations can help uncover leading clinical practices, shrink research discovery time,
streamline administration, and offer new personalized engagement paradigms at an industrial scale that align people’s decisions and actions in ways that improve outcomes and add value
Sources of the data deluge
Advances in computing power and techniques
Smarter Algorithms Faster Processing Speed Improved Visualization
Patient Centric
Optimal Resource
Structure
Adaptive Organization
* HP Autonomy, Transitioning to a new era of human information, 2013
** Steve Hagan, Big data, cloud computing, spatial databases, 2012
Sensors / DevicesVideosImagesSocial MediaPaper / Text
Documents
EMRsMobile
40-50%
Annual growth in digital data volume*
~9X
of unstructured data vs. structured data by 2020**
62%
Annual growth in unstructured data*

29. Technology – GAME CHANGERS / DISRUPTORS
https://hbr.org/2011/01/reinvent-your-business-before-its-too-late
http://marketingio.com/2016/11/07/martecs-law-the-greatest-management-challenge-of-the-21st-century-chief-marketing-technologist/

30. Technology Medicine
• Gartner Hype Cycle for Telemedicine, 2014

31. Deep Learning
Open Access
Google Inc et al.
May 2018
216K Adults
46K Million data
24 hours
AUROC: (predict)
0.93 death
0.85 re-admit
Beat clinical scores

32. 32
Example: Project Genesis
• Scope of Computer Systems
• Clinical
• Power Chart - Orders and results
• Clin Doc - Clinical documentation
• PharmNet - Pharmacy
• FirstNet: Emergency Dept.
• RadNet: Radiology Dept.
• SurgiNet: Operating Room
• Inet: ICU
• Profile - HIM application
• EMPI
• CPOE
• Electronic Record
- Clinical functions by pt. type
- Current clinical
documentation forms
Implementation Readiness
Process Requires 20-24 Months
•People
•Process
•Technology
Implementation Readiness
Major Learning: Realizing clinical benefits of
transformational change is a function of time

33. COGNITIVE LOAD THEORY: WHY USING YOUR ELECTRONIC HEALTH RECORD
IS SO PAINFUL AND HOW TO FIX IT
Michael Zimmerman, MD Temescal Creek Medicine

36. Amongst the Oldest Health Information Technologies
• Original digital health device
• Conceived and designed to solve
a healthcare need, not fill a
market gap
• Strengths:
• Fits into existing lifestyle
• Passive (no patient action
necessary)
• It works really, really well
• Weaknesses to overcome with
new technologies
• Security
• One size does not fit all; QoL
• Expense
• Designed to be replaced – profit at
the price of complications
Oh, and by the way, making a great device is
good business: 2014 WW sales > $6 billion
https://globenewswire.com/news-release/2016/01/07/800112/0/en/Cardiac-Pacemaker-Market-
to-Reach-US-12-85-bn-by-2023-Rising-Geriatric-Population-is-High-impact-Driver-Transparency-
Market-Research.html

37. 37
Cognitive Technologies

38. Cardiovascular Risk rnoh

39. More Patients, Intelligently
Navigated through surgery, with
Better Outcomes, Shorter Stays,
Better Staff Morale & Respectful
Teamwork !

40. Care Algorithms / Decision Pathways:
The Slide Towards AI in Healthcare
Useful
• Speed up decision making
• Project sense of normality
• Demonstrate predictability
• Allow forecasting
• Project an evidence base
• Reduce variation in
practice
• Reduce ‘fud’
• Avoids senior input
(GP + AI = Specialist?)
Challenge
• Speed up nature - impatience
• Assume NO human error
• Reduce checks
• Debase impact of issue (MI)
• Generalisability to individual
• Divert individualised care
• Reduce responsibility
• Reduce experience base
(GP – AI = Medical Novice?)
MI = Myocardial Infarction – now only a 72 hr issue

41. When Big Data goes wrong:
UK breast cancer screening IT error
• All women aged 50 to 70 in the UK who are registered with a
GP are automatically invited for breast cancer screening
every three years
• A “computer algorithm failure”, which dated back to 2009,
meant an estimated 450,000 women aged between 68 and
71 were not invited to their final breast screening between
2009 and the start of 2018
• Initial estimates have suggested that between 135 and 270
women may have “had their lives shortened as a result”
• If you don’t get the programming right, systems won’t work
correctly
Available from: https://www.digitalhealth.net/2018/05/14000-women-contact-breast-cancer-helpline/

42. AI: friend or foe?
“What does your organization primarily plan to do in terms of
employees that have been replaced with technology?”
• Retrain them into a new role/area of the organization 34%
• Redeploy within the same area of the organization 42%
• Make them redundant 24%
• Need to carefully consider how AI deployment could affect the
workforce and ensure that the proper ethical checks for autonomous
systems are in place.
• AI will exist to support people in their jobs. For instance, AI will
optimize clinical processes, such as recording patients’ vital signs or
analysing scans and samples, but the doctor will decide the final line
of treatment.
• The purpose of AI will be to augment natural intelligence, and its role
will always be subordinate to the human’s.
Available from: https://www.infosys.com/smart-automation/Documents/ai-healthcare.pdf

43. AI in healthcare: peer-reviewed evidence
PubMed search for clinical trials with the keywords “artificial intelligence” and “NHS” published in the last 5 years

44. Duty of candour: a level playing field?
• Aim of the regulation: to ensure that providers are open and
transparent with people who use services in relation to care and
treatment.
• Contains specific requirements that providers must follow when things
go wrong with care and treatment, including:
• informing people about the incident
• providing reasonable support
• providing truthful information and an apology.
• Providers must promote a culture that encourages candour, openness
and honesty at all levels.
• What about companies who provide healthcare IT solutions?
Available from: http://www.cqc.org.uk/sites/default/files/20150327_duty_of_candour_guidance_final.pdf

45. FDA report
In 2010, 260 HIT reports, 44 injuries, 6 deaths in 2
years – Voluntary reporting system – likely
underreported..

46. Example: Babylon Health to power NHS 111
with ‘AI triage’ bot
https://www.digitalhealth.net/2017/01/babylon-health-to-power-nhs-111-with-ai-triage-bot/
• A chatbot to answer NHS non-emergency inquiries
from more than a million Londoners as a new way
to manage the growing health burden.
• Driven by clinically based algorithms that triage
patients without human intervention based on
reported symptoms.
• Based on the symptoms and its own algorithms,
the app could refer the patient to hospital or
recommend a GP appointment the next day.
• Doctors have already expressed concerns about
the reliance on algorithms and self-reported
symptoms for determining the severity of a
person’s illness. However, ? published evidence...

47. AI has huge potential
Feasibility study of a randomised controlled trial to
investigate the effectiveness of using a humanoid
robot to improve the social skills of children with
autism spectrum disorder (Kaspar RCT): a study
protocol
Mengoni SE, Irvine K, Thakur D, et al. BMJ Open 2017;7:e017376. doi:10.1136/bmjopen-2017-017376

48. Immediate example in cardiology
management?
• Research presented at the 2017 AHA Congress on pairing machine-
learning algorithms with the Apple Watch’s heart-rate sensor and
step counter to predict hypertension or sleep apnoea
• Apple says it’s working on a study with Stanford that will test the
gadget’s ability to detect atrial fibrillation
• There are many theoretical possibilities for how AI could assist us
with managing patients with or at risk from thromboembolic disease
Available from: https://www.wired.com/story/ai-can-help-apple-watch-predict-high-blood-pressure-sleep-apnea/

49. Kintzugi – Japanese Art of Appreciating that which has Broken
– for its wisdom & sacrifice. By Repairing it with Gold.
The NHS may face disruption before it’s evolves to a new model of care.
Schwartz Rounds
- prior Barnet Clinical Lead
- Compassion for the Caregivers

50. Disclosures
Ameet Bakhai, Consultant Cardiologist & Physician / Cardiovascular R&D Director
• I am employed by the NHS, CCG and work with AHSNs – UCL Partners,
Imperial Health
• Founder Amore Health Ltd
• An ambassador for Digital Health London
• I have advised pharma, device, advisory, strategy, health technology
appraisal, government policy, commissioning groups and technology
firms on innovations in healthcare from drugs, devices, diagnostics,
decision pathways to digital technologies
• Past & Present Committees: Research & Development, Governance
and Risk, 18 week Pathway Champion, Medicine Management, D&TC,
Thrombosis, Audit, Clinical Excellence Awards, Physician Associate
Project, Work Experience, Education & Partnership for Cardiac & Stroke
Network, Surrey Heath CCG Board Member, Task Forces in Cardiology
for UCLp & Imperial Health, Horizon Scanning for NICE, End of Life Care
for NHS England – London, Cardiovascular Research North Thames
CRN, Education Standards ABPI
• Studied Decision Analysis Modelling & Health Economics @ Harvard
School of Public Health
Advisor / Lecturer / Appraiser / Committee
- Health technology appraisal groups
- Economic modelling teams
- Pharma, Device & Strategy companies
- IT and Digital Health companies
- ABPI, NICE, UCLp

51. Scope & purpose of report
51
A 10-15 year vision for AI powered
person-centred public healthcare
Andy Wilkins – Report Lead Author
On behalf of the Royal Free Charity
The Digital Healthcare Show
27th June 2018

52. 52
Royal Free North Central London
New models of care
• Integrated care
• Whole person care
• Population health
New capabilities
• New medical
breakthroughs
• New clinical & digital
technologies
Fog of Uncertainty
The challenge – how to invest for the future when so much is
changing?

53. 53
The answer – look beyond the “fog” to shine a light on a vision of the long
term future

54. A 10-15 year vision for person-centred public healthcare
Scope & purpose of report
54
New major report coming soon!!
Report sets out a future that delivers:
1. Transformational improvements in
the nation’s levels of health and
wellbeing
2. A pathway to building a sustainable,
person-centred 21st Century public
health and care system
3. An engine for economic growth and
social renewal
The report describes a vision based on the transformational new capabilities arriving in the next 10-
15 years

55. 55
We rolled forward 6 key trends…
to imagine a world where they
had all “landed” at scale
We asked ourselves:
1. What would health look like for the
individual
2. What would this mean for the future
delivery of health & care?

56. 56
The 21st Century health challenges call for a wider
framing of the healthcare landscape

57. 57
A new generation of sensors will enable revolutionary new
sources of data and possibilities to improve care

58. 58
Real time data will make it possible to dynamically simulate
health

59. 59
A Digital health coach enables always-on personalised care
support
My health
context
Holistic care support
Decision
Support
Integrated
Care Teams

60. 60
The three core elements of the Vision
1. Personalised Health and Wellbeing2. AI Mediated Health Coaching 3. Future Health and Care System

61. 61
As PoC technologies miniaturise and are
combined with AI powered Decision
support systems then:
1. Care becomes more integrated
2. LTC care becomes more health/ life
coaching based and moves into
community settings
3. Digital health coaches manage day to
day and moment to moment support
What impact will this have on the management of chronic
disease?

62. 62
Size of the prize
= better quality of
life + sustainable
healthcare system

63. Building the vision will require a phased transformation
journey

64. System Framing
 Making prevention and population health a
priority
 Integrating prevention, healthcare and social
care into a unified care system
 Taking a longer term investment perspective
Public engagement
 Making the case for self-management of health
and wellbeing
 Acceptance of sensors
 Acceptance of sharing of data
 Acceptance of a AI powered digital health
coach
New healthcare ecosystem
 New data skills and capabilities
 New medical and wellbeing skills
 Transformational funding
 New clinical care models
 New funding and governance
models
 New clinical organisational
structures
 Person-centred data strategy
 Radical new innovation
15 Building Blocks – challenges to overcome not reasons to hold
back

65. 65
The next stage in our journey – creating a movement for change!
2. Working with Junior Doctors at Barnet
Hospital on a Story Platform – CPD
opportunity
1. Created a cross sector steering
group to plan the provocation
3. Exploring joint initiatives with the RSM
4. Engaging with Politicians, Healthcare leaders & Industry
on the need for a new vision for 21st Century healthcare

66. 66
We’d love you to be involved…
go to www.beyondthefog.org to stay in touch

67. Snowstorm
Write down one key thing you have learnt or
will think about when you go back to your
organisation?

68. Snowstorm
• Write down one key thing you have learnt or will think
about when you go back to your organisation?
• Screw the paper up
• On the signal, throw your snowball in the air.

69. Snowstorm
• Write down one key thing you have learnt or will think
about when you go back to your organisation?
• Screw the paper up

70. Snowstorm
• Write down one key thing you have learnt or will think
about when you go back to your organisation?
• Screw the paper up
• On the signal, throw your snowball in the air.
• Pick up a snowball and read it the person next to you

71. Snowstorm
• Write down one key thing you have learnt or will think
about when you go back to your organisation?
• Screw the paper up
• On the signal, throw your snowball in the air.
• Pick up a snowball and read it the person next to you
• On your way out please hand the snowballs to us.

72. Thank you
Dominic Cushnan, Ameet Bakhai, Andy Wilkins