Presentation from Aziz Sheikh at CLAHRC WM Scientific Advisory Group meeting, 8th and 9th June 2016

1. Towards a National
Learning Health System
Aziz Sheikh OBE
BSc, MBBS, MD, MSc, FRCGP, FRCP, FRCPE, FFPH, FRSE, FMedSci, FACMI
Professor of Primary Care Research & Development and
Co-Director, Centre for Medical Informatics, The University of Edinburgh
Director, Asthma UK Centre for Applied Research
Visiting Professor of Medicine, Brigham and Women’s Hospital/Harvard Medical School
Birmingham, 9th June 2016
@DrAzizSheikh

2. Disclosures
• Research funding from:
– Agency for Healthcare Research and Quality
– Asthma UK
– British Lung Foundation
– Chief Scientist's Office of the Scottish Government
– Department of Health
– Digital Health Institute
– Horizon 2020
– Medical Research Council
– National Institute of Health Research Applied Programme Grants
– NHS Connecting for Health Evaluation Programme
– The Commonwealth Fund
– Wellcome Trust
– World Health Organization
• Submissions with decisions pending to:
– Engineering and Physical Sciences Research Council
– National Environmental Research Council
– Robert Wood Johnson Foundation

3. Overview
• Current models of healthcare are fatally flawed
• Health systems need to be digitised and the potential of
digital data needs to be unlocked
• Examples of using these digitised datasets to:
– Undertake epidemiological investigations
– Support evaluation of policy interventions
– Increase the clinical efficiency of trials
• The need to move from the current ad-hoc bespoke
researcher-led efforts to a ‘Learning Healthcare System’…
• Building a prototype national asthma learning healthcare
system
• Looking ahead: From a Learning Healthcare System to a
Learning Health System

4. THE BURNING PLATFORM…

5. Major challenges facing health
systems internationally
• Changing demographics: ageing populations
• Increasing numbers of people living with long-
term conditions
• Ongoing concerns about the safety and quality
of healthcare
• Spiralling healthcare costs

6. Increasing UK life expectancy

7. The demographic time bomb:
Forecasts for dependency ratios

8. The exponential rise of multi-morbidity

12. NHS Connecting for Health Evaluation Programme

14. HEALTH IT IS NOT A ‘SILVER BULLET’

15. How best to respond?

16. • Increase taxation: direct and indirect
• Increase retirement age
• Modify pension plans: average salary schemes
• Encourage immigration
• Cut expenditure on public services
• All are however deeply unpopular and make
politicians very wary…
Possible solutions

17. The UK’s National Programme for IT
• Considerable policy interest in Health IT as being
the answer
• 1998: “If I live in Bradford and fall ill in
Birmingham then I want the doctor treating me
to have access to the information he needs to
treat me.” (Rt. Hon. Tony Blair, NHS Conference,
London, July 2, 1998)
• 2002: £12billion ‘vision’ for the National Programme
for IT approved by Tony Blair at an un-minuted 10-
minute briefing in Downing Street with Bill Gates

18. 18 |

22. Systems Optimisation:
Turning data into information

23. Antibiotic - % Missed Doses
Date Intervention
A 15 April 2009 Pause function for doctors
B 04 August 2009 Missed Doses go live on clinical dashboard
C 15 December
2009
Introduction of coloured indicators to show due /
overdue drugs
D
*
24 February
2010
NPSA Rapid Response Alert
D
*
30 March 2010 Chief Executive Missed Dose Root Cause Analysis
meetings
Step change in % missed
doses when information
shared with clinicians /
managers
Further highly
significant change when
CEO started RCA
meetings
Coleman et al. Missed medication doses in hospitalised patients: a descriptive account of
quality improvement measures and time series analysis. Int J Qual Health Care. 2013
Oct;25(5):564-72.

25. UNLOCKING THE POTENTIAL OF EHR-
DERIVED DATA

26. Digital infrastructure

27. EPIDEMIOLOGICAL STUDIES

28. Centre for Medical Informatics, The University of Edinburgh
Observational studies using hospital data:
Hospital Episodes Statistics
Trends in hospital discharges for anaphylaxis, 1991-1995
Alves B, et al.
BMJ 2000; 320; 1441

29. Centre for Medical Informatics, The University of Edinburgh
Observational studies using hospital data:
Hospital Episodes Statistics
Trends in discharge rates for systemic allergic disorders, 1990-2001
Gupta R, et al.
BMJ 2003; 327: 1142-
43

30. GP and nurse consultation rates by sex in those with and without eczema
Simpson CR, et al. JRSM.
2009; 102:108-17
Observational studies using GP data:
QResearch

31. Centre for Medical Informatics, The University of Edinburgh
Constructing birth cohorts: investigating
“the allergic march” in the
General Practice Research Database

32. + asthma (15.4% also had rhinitis)
+ rhinitis (10.0% also had asthma)
n=7608
+ rhinitis (10.0% also had eczema)
+ eczema(11.9% also had rhinitis)
n=3567
+ eczema (12.2% also had asthma)
+ asthma(10.9% also had eczema)
n=1316
Describing numerous variants of “the
allergic march” in GPRD birth cohort
24,112
patients
Punekar Y, et al.
Clin Exp Allergy
2009;39:1889-95

33. Increase in lifetime prevalence rate of COPD in England, 2001- 2005
>5%
4-5%
3-4%
<3%
% increase
Mapping changing COPD prevalence
Simpson C, et al. BJGP
2010;60: 277-84

34. Investigating the relationship between
asthma and exam performance
Sturdy P, et al. PLOS
One 2012; 7:e43977

36. www.qrisk.org/

38. Bhopal R, et al. Eur J
Pub Hlth 2015

39. Sheikh A, et al. BMC Medicine (invited resubmission)

40. QUASI-EXPERIMENTAL STUDIES

41. Centre for Medical Informatics, The University of Edinburgh
Investigating the impact of the Low
Emission Zone on asthma
Griffiths C, et al.
(submitted)

43. 9,536,003
patient-years
GP data
(1997-2012) July 2007
April 2007
April 2007
Maarch 2006

44. Impact of the smoking ban on hospitalisations for
respiratory tract infections in children
Been J, et al.
ERJ 2015

45. Centre for Medical Informatics, The University of Edinburgh
Vaccine effectiveness
in pandemic influenza

46. Preparing for future pandemics…
 Core funding in place and
release of additional funds at
first signs of pandemic
influenza
 This has enabled:
o Creation of data structures
to permit real-time
evaluations
o Permissions and approvals
for data linkage and
analysis
o Development of detailed
analysis and reporting plans

47. SUPPORTING CLINICAL TRIALS

48. t+asthma Abingdon,Oxford
CYMPLA trial
Password protected website
Pinnock et al. BMJ 2012

49. Supporting recruitment
32 practices
(311,926 patients)
Computer searches: 13,101 potentially eligible
1,020 excluded by practice
12,081 postal
invitations
393 eligible and
first visit booked
Expressions of interest: 1,016
623 excluded at pre-screening telephone call
• 470 too well controlled (ACQ<1.5)
• 124 phone/network incompatible
• 29 ‘other’
Attended baseline assessment : 346
58 excluded at baseline assessment
• 37 too well controlled (ACQ<1.5)
• 11 declined
• 10 ‘other’
288 randomised

50. Improving prescribing safety
Base-
line
Results
+
Evidence
+
Consent
letters
Initial meeting
During this
meeting I would
like to feed back
the results of the
searches…..
6 & 12
months
Action
plan
Actions
recorded
GP practiceMy
computer
Simple feedback Pharmacist intervention
(2 days per week for 12 weeks)
+
“Exit” meeting
Data-
base
FTP
FTP
Centre for Medical Infrmatics, The University of Edinburgh

55. In summary…

56. SCALING-UP EFFORTSD: THE FARR
INSTITUTE

57. • Develop UK Health Informatics Research Network Strategy.
• Provide a blueprint for the Network activities which are designed to
harness expertise and engage stakeholders for the coming five
years and beyond.

58. The Farr Institute vision
“To harness health data for patient and
public benefit by setting the
international standard for the safe and
secure use of electronic patient
records and other population-based
datasets for research purposes”

59. Our 10 key activities
1. Collaborative leadership. 6. Enabling datasets
2. Cutting-edge research 7. Harmonized e-infrastructure
3. Public engagement. 8. Industrial partnerships
4. Governance (‘safe havens’) 9. Training and capacity building
5. Methods development 10. Communications
To deliver impact nationally and internationally
eHealth Research Group, The University of Edinburgh

60. Looking ahead: Integration of EHR data
with biomedical data to support
personalised medicine…
• Genetics
• Omics
• Imaging
• Phenotypes

61. Enabling administrative
and social data
Phase 1: Administrative data
Phase 2: Business data
Phase 3: Voluntary sector and
social media data

62. WE NEED TO MOVE FROM THE
CURRENT AD-HOC ARRANGEMENTS TO
A LEARNING HEALTH CARE SYSTEM…

63. The idea of the LHS builds on two era-
defining publications…

64. What is a Learning Healthcare System?
The Institute of Medicine has defined
this as a healthcare system:
• ‘that is designed to generate and
apply the best evidence for the
collaborative healthcare choices of
each patient and provider;
• to drive the process of discovery as
a natural outgrowth of patient care;
• and to ensure innovation, quality,
safety, and value in health care.’

65. Engineering new models of health care

66. BUILDING A PROTOTYPE NATIONAL
ASTHMA LEARNING HEALTH CARE
SYSTEM

67. 6
7
Our Vision
To create a world-class centre and
associated UK network that will:
Reduce
asthma
hospital
admissions
Improve
asthma
control
Reduce asthma deaths

72. UK Asthma Observatory Platform
Framework Goals
• To identify and utilise relevant
data on asthma across the UK in
order to create a UK-wide
repertoire:
– For interactive monitoring of
real-time estimates of the
burden of asthma
– As a hub for the various
AUKCAR research and policy
outputs
– Repository for AUKCAR
research data
– Other asthma activities in the
UK

73. 7
3

74. Number of UK inpatient episodes with
asthma as the primary diagnosis
7
40
20,000
40,000
60,000
80,000
100,000
120,000
2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

75. Number of UK deaths with asthma as the
underlying condition
7
5
0
200
400
600
800
1000
1200
1400
1600
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

78. Improving
asthma
outcomes

79. Engaging academics, physicians and patients
AUKCAR/Farr Frontiers Asthma Meeting, Edinburgh, 27th October 2015
Asthma patients, clinicians, policy makers, asthma charities,
pharma industry, university researchers…
“You cannot
know too
much about
asthma…”
Michael Bang,
Patient Representative
AUKCAR Advisory Group

80. .
Potential Low-hanging Targets
Some other immediate priorities…
Each can increase the risk of asthma exacerbations
and current practice is ***highly variable***

81. Pharma
81
Patients
Payers
Researchers
Government
/Public Health
Local
Healthcare
Delivery
Systems
(Patient journey)
Research
Institutes
Biomedical
Data
Tech Industry
Devices
Network requirement: Coherent analysis by
heterogeneous source data transformation
COHERENT RESPONSE
UK LHS DATA MODEL
RESEARCH QUESTION/QUERY
TRUSTWORTHY
RESEARCH
ENVIRONMENTS
3rd Sector
LOCAL DATA
REQUIRES
TRANSFORMATION
TO A COMMON
MODEL
& DISSEMINATION AT SCALE
LOCAL
STAKEHOLDER
DATA IS
HETEROGENEOUS

82. Network requirement: Real-time bidirectional flow
using common interface to/from stakeholder systems
CHI (Community Health Index)
EXAMPLE PATIENT JOURNEY DATA SOURCES
HEALTH
OUTCOME
S
(including
patient
reported)
Compute Infrastructure
Analysis
LEARNING
CYCLE
UK LHS Data ModelFARR
SCOTLAND
SAFE
HAVEN
Linkage
TRUSTWORTHY
RESEARCH
ENVIRONMENTS
 COMMON
INTERFACE

83. Working with the Farr @Scotland Safe Haven
FUNCTIONS
• Data linkage
• Anonymisation (or at source)
• Pseudonymisation (or at source)
• Study management for
medical/clinical
social research
• Analytical services
• Specialist compute services
Data from Scotland’s
Source Systems via
Contract reporting (ESCRO,
Structured, Flat-file, Imaging)
Standard interfaces
with other Farr TRE’s
 Common data model
to develop interoperability
and cross-site search
Hardware/software evaluation to
fit use cases or research questions
Trustworthy Research EnvironmentTRUSTWORTHY
RESEARCH
ENVIRONMENT
Analysis
Data Enclave with Credentialed Access

84. The scalability challenge
2-7m
Patient
Population
Networks
Agreed patient benefit use cases
can drive and synchronize cross-site
work
The power of scale - cannot achieve
clinical goals in single or few centres
Cross-site interoperability can be
achieved through Farr Infrastructure
group coordination
Its non-trivial, no complacency, but…

86. LOOKING AHEAD…

87. From ‘Learning Healthcare System’ to
‘Learning Health System’

89. The ‘triple aim’

90. Macro-level
• Thinking about health cross-sectorally
• Giving policymakers the tools and information
they need to support decision making
– Burden of disease estimates
– Considering options and modelling their impact
– Prioritisation exercises for candidate
interventions
– Programmatic evaluations of the impact of policy
interventions

92. Meso-level

93. Members of the National Advisory Group on
Health Information Technology in England
• Robert Wachter (Chair)
• Julia Adler-Milstein
• David Brailer
• Sir David Dalton
• Dave deBronkart “e-
Patient Dave”
• Mary Dixon-Woods
• Rollin (Terry) Fairbanks
• John Halamka
• Crispin Hebron
• Tim Kelsey
• Richard Lilford
• Christian Nohr
• Aziz Sheikh
• Christine Sinsky
• Ann Slee
• Lynda Thomas
• Wai Keong Wong
• Harpreet Sood

95. Micro-level

96. Conclusions
• Healthcare needs to be reengineered
• New models will need to be more patient-centred,
focused on preventive and ambulatory care, and aligned
to the needs of the very large sections of our population
now living with long-term non-communicable disorders
• The concepts of a ‘Learning Healthcare System’ and in
particular a ‘Learning Health System’ provides a
framework to begin to conceptualise future health
systems
• Building such systems rank amongst the greatest
challenges of the 21st Century…

97. Towards a National
Learning Health System
Aziz Sheikh OBE
BSc, MBBS, MD, MSc, FRCGP, FRCP, FRCPE, FFPH, FRSE, FMedSci, FACMI
Professor of Primary Care Research & Development and
Co-Director, Centre for Medical Informatics, The University of Edinburgh
Director, Asthma UK Centre for Applied Research
Visiting Professor of Medicine, Brigham and Women’s Hospital/Harvard Medical School
Birmingham, 9th June 2016
@DrAzizSheikh