This presentation contains the slide deck that Professor Iain Buchan - of Manchester University delivered in Koc University about what can be achieved if clinical health information were captured in a digital format.

1. Making Health Data Work
for Patients and Populations
Iain Buchan
Farr Institute @ HeRC
& University of Manchester
www.herc.ac.uk
Koç University Hospital, 20th March 2017
@profbuchan
#DataSavesLives

2. Ancient, yet Relevant, Public Health Statistics
Plague Of Justinian (541–542)
• 40% Constantinople dead
• 25-50 m dead globally over 200y
Black Death (1338/1346–1353)
• China to Constantinople by 1347
• 30-60% Europe’s population dead
Wagner DM et al. Yersinia pestis and the plague of Justinian 541-543 AD: a genomic analysis.
Lancet Infect Dis. 2014;14(4):319-26.

3. London 1600s: Plague; Fire; Data; Society
Parish (deaths)
Number of hearths (fireplaces) as a proxy for house size and over-crowding
1 2 3 4 5 6 7 8+
St James Clerkenwell (44)
St Botolph without Aldgate (41)
St Dunstan in the West (49)
St Michael Queenhithe (20)
St Saviour Southwark (42)
31.7
39.8
31.2
26.5
15.5
10.7
35.1
24.3
14.2
30.0
20.8
19.3
25.2
33.1
11.7
7.5
24.3
12.9
31.8
33.5
24.7
16.7
22.3
24.4
12.6
7.3
16.2
20.0
28.2
20.1
9.7
7.3
8.3
5.1
20.2
14.3
5.4
7.1
12.4
7.5
2.6
3.9
4.7
3.8
8.6
11.9
5.4
15.0
6.9
3.8
3.2
2.7
4.7
4.1
5.0
10.5
2.7
10.0
4.3
2.9
1.2
1.9
1.5
1.5
4.7
8.7
2.7
2.1
0.7
0.9
6.2
8.4
2.2
1.7
21.7
29.1
8.1
8.5
1.5
1.3
From: Epidemic Disease in London, ed. J.A.I. Champion (Centre for Metropolitan History Working Papers Series, No.1, 1993): pp. 35-52
…any man's death diminishes me, because I am involved in mankind,
and therefore never send to know for whom the bells tolls; it tolls for thee.
Epidemiology and politics preceding the Great Plague of London (1665; 25% population die)
followed by the Great Fire of 1666 then social and structural reform.
John Donne, London 1624

4. Health Data Computation: 1841
Farr
(1807-1883)
HPC for life tables  public health reform
1700s: Bernoulli & DeMoivre introduce
probability theory to quantifying (health) risks
Early 1800s: Laplace then Louis apply
probability theory to showing some treatments to be ineffective
– rebuked by medical profession
– Quetelet’s concept of ‘the average man’ adds fuel to the fire
Letting the data speak computationally…
Babbage
(1791-1871)

5. Evidence Based Care
Mid-late 1800s: Lister uses statistical arguments and
Pasteur’s germ theory to
revolutionise surgery with carbolic spray
Early 1900s: Statistical Movement,
strong in Agriculture and emerging in Medicine
Mid 1900s: Experimental (statistical) discipline into Medicine
and NHS founded (1948)
1970-80s onward: Disciplined implementation
of evidence into practice

6. NHS: Learning System Legacy and Duty
30 years of GPs coding
in routine primary care
Needs-based
Constitution
NHS Computability: 1970s onward
• Administrative consistency: Körner to ICD to HES to QOF
• Clinical utility: GP home-grown IT to patient apps
• Research integration: VAMP to CPRD & registries to Farr

7. 1988 AAH MEDITEL advertisement courtesy of Tim Benson
30 Years of Structured Primary Care Data
Schulz EB, Price C, Brown PJ. Symbolic anatomic knowledge representation in the Read Codes version 3:
structure and application. J Am Med Inform Assoc. 1997 Jan-Feb;4(1):38-48.
UK: 30 years of GPs coding in routine primary care

8. Healthcare Data Analytic Partnerships
MISSED OPPORTUNITIES DETECTOR
Find patients
relevant to
care pathway
Exclude if
target
inappropriate
e.g. A&E asthma terminal illness
Exclude if
target
achieved
Follow-up < 48h
Identify how
care could be
improved
Rx & social review
Integrated Care Record
BLIZZARD OF DATABASES
(Salford: 53 GP offices + 1 Hospital)
Salford Resident Population
Care Quality
Management
Patients’
Decisions
ACTIONABLE INFORMATION
Actionable information
attracts: trust & traction
from patients, public and
practitioners… and better
data quality.
Brown B et al. Missed opportunities
mapping: computable healthcare
quality improvement. Stud Health
Technol Inform. 2013;192:387-91.

9. Theoretical Framework
Perception Acceptance
Desire Action
Clinical performance
Intention
Data
analysis
Message
Data
collection
Interaction
Organisational
Individual patient
Verification Unintended
outcomes
Task Action Audit Message
Patient
Co-
interventions Recipients Organisation
USABILITY/DESIGN
TEAM DELIVERY
ALGORITHM ACCURACY
DATA CREDIBILITY
ACTION PLANNING
NHS: a decade of dashboards
Business intelligence tools
Provider management led
No theoretical framework

10. Connecting Population Analytics with Care
• Audit & Feedback Theory
• Eye Tracking Experiments
• Field Trials in Salford, UK
• Patients Asking for Safety Alerts
• Now Targeting Antimicrobial Resistance

11. Instrumenting Alternative Views
Fraccaro, P et al. "Patients’ online access and interpretation of laboratory test results: a human computer interaction study”.
Digital Health and Care Congress 2016: www.kingsfund.org.uk/events/digital-health-and-care-congress-2016
In search of a better
conversation of healthcare
over shared records,
brokered by informatics
that is context-aware.

12. Reusable Health Analytics: Trials & Audits
National Proteomics Centre:
Stoller Biomarker Discovery
Clinical Audits
and Service Planning
for the local population

13. Evidence Translation Singularity
Evidence
Practice
Evidence
Practice
Implementation
Evidence
(Co-)Practice
System
Asynchronous Healthcare Information Synchronous Translation
More Specialisation
More Underpinning Biology
Precision Medicine?
Ironing out Variation in Care
Real-world Evidence
Managed Self-care
Translation

14. Problem: Big Data & Blunt Evidence
Mental health team…
Primary care team…
Zak… 47y; asthma since early
childhood; schizophrenia since
teenage; overweight; smoker
 Weight
Respiratory team… Evidence needed is the
union not sum of models
~ Mood
Primary Care
Respiratory Medicine
Psychiatry
Smoking cessation;
social support; weight
control; work
Inhaled steroid
adherence
Antipsychotic medication
adherence

15. Over-implementing Blunt Evidence
Current evidence-base predicts < 30% healthcare outcomes: so why try to “iron out variation”?
Primary
Care
Respiratory
Medicine
Better healthcare needs information
on how care works across diseases,
providers and daily-life contexts
Psychiatry
Valderas JM, Starfield B, Sibbald B, Salisbury C, Roland M. Defining comorbidity: implications for
understanding health and health services. Ann. Fam. 2009;7:357–363.

16. Precision Medicine Horizons: Asthma(s)
• Life-course complexity indicates multiple (sub-)diseases
• Usually starts young
• May progress, remit or relapse over life
• Inconsistent gene-environment interactions indicates multiple
(sub-)diseases
• Variable effects of genetic polymorphisms, e.g. CD14
• Variable treatment-setting interactions
C allele associated
T allele associated
No association
CD14 Endotoxin Receptor
Simpson A et al. Endotoxin exposure, CD14, and allergic disease: an interaction between genes and the environment.
Am J Respir Crit Care Med. 2006;174(4):386-92.
50-60% heritability in twin studies but < 2%
phenotype explained by current genomics

17. Seeking Sub-disease Patterns in Data
Mite
Cat
Dog
Pollen
Egg
Milk
Mold
Peanut
Sensitized
Age 1
Sensitized
Age 3
Sensitized
Age 5
Sensitized
Age 8
Skin Test
Age 1
Skin Test
Age 3
Skin Test
Age 5
Skin Test
Age 8
Blood Test
Age 1
Blood Test
Age 3
Blood Test
Age 5
Blood Test
Age 8
Sensitization Groupswitch group
P(Sens’n)
in year 1
P(Gain)
P (Loose)
Sens’n
3 intervals
P(+ skin)
Sens’
P(+ skin)
Not Sens’
P(+ blood)
Sens’
P(+ blood)
Not Sens’
Sens’n state
1,053 Children
8 Allergens
Machine-learning software
& partial statistical models
ATOPY
(allergic tendency)
Crude clinical label
not explained by
genomic studies

18. New Risk Factor for Asthma Discovered
Allergic
sensitisation
patterns
‘learned’
from data
Simpson A, Tan VY, Winn J, Svensén M, Bishop CM, Heckerman DE, Buchan I, Custovic A. Beyond atopy: multiple
patterns of sensitization in relation to asthma in a birth cohort study. Am J Respir Crit Care Med. 2010;181(11):1200-6.

19. Multiplying Analytic Capacity via eLab
MAAS
SEATON
ASHFORD
ALSPAC
IOW
Modelling
Data & Harmonized
Metadata from Cohorts
Data Extracts
Networking:
Ideas, Activities,
Results, Meanings
MRC STELAR and NIH CREW Consortia: www.asthmaelab.org
New US
New Au.
Custovic A et al. The Study Team for Early
Life Asthma Research (STELAR) consortium
'Asthma e-lab': team science bringing
data, methods and investigators together.
Thorax. 2015;70(8):799-801.

20. • Progression of allergy
Eczema → Asthma → Rhinitis
• Inferred from population summary →
• Assumed causal link between
eczema – asthma & rhinitis
• Clinical response:
target children with eczema
to reduce progression to asthma
Received Wisdom: Atopic March
Spergel & Paller, 2003
World Allergy Organization, 2014

21. Model-based Machine Learning
Probability
Eczema Age 8
Children (n=9801)
Probability
Eczema Age 5
Probability
Eczema Age 3
Probability
Eczema Age 1
Probability
Eczema Age 11
Probability
Wheeze Age 8
Probability
Wheeze Age 5
Probability
Wheeze Age 3
Probability
Wheeze Age 1
Probability
Wheeze Age 11
Probability
Rhinitis Age 8
Probability
Rhinitis Age 5
Probability
Rhinitis Age 3
Probability
Rhinitis Age 1
Probability
Rhinitis Age 11
Eczema
Class
Wheeze
Class
Rhinitis
Class
Latent Class
Disease Profile
Start with a well-reasoned
(partial) model, not a
‘bucket of data’

22. Ecologic Fallacy Revealed
Belgrave et al. Developmental Profiles of Eczema,
Wheeze, and Rhinitis: Two Population-Based Birth
Cohort Studies.
PloS Medicine 2014;21;11(10):e1001748.
MRC STELAR consortium working at scale
across MAAS and ALSPACS cohorts
Better:
population analytics;
targets for ‘omic research

23. Biology-Behaviour-Environment Interaction
Life course
Developmental genetics
Disease risk environment
Treatment environment
‘Persistent’ genetics
Data
Measurement error
Mechanism knowledge
Missingness
ASTHMA
genes
* environments
 endotypes
Transient early wheeze
Late-onset wheeze
Persistent troublesome wheeze
Persistent controlled wheeze

24. My Health Data Ecosystem
My Health, My Data:
Where are the most predictive data?

25. Rhythms of Life, Health, Disease and Care
Low-cost ubiquitous technologies capturing
digital by-products of the life
High-cost medical devices
(regulated clinical algorithms)
Clinicvisit8
Clinicvisit9
Patterns of disease invisible to
infrequent clinical observation
Precision medicine may need data on
(sub)disease rhythms to realise its
potential
Future? My ‘health avatar’ says no to your care pathway
n-of-1 trials Average patient guidelines

26. • Who self-weighs?
• UK Withings smart-scale users vs. Health Survey for England (2011)
Ubiquitous (Almost) Technology
Difference in Mean BMI
Fatter men use smart-scalesSlimmer women use smart-scales
-2 1 0 1 2
Men point-estimate = 1.26 [95% CI: 0.84,1.69]Women point-estimate = -1.62 [95% CI: -2.22,-1.03]
Sperrin M et al. Who Self-Weighs and What Do They Gain From It? A Retrospective Comparison Between
Smart Scale Users and the General Population in England. J Med Internet Res. 2016;18(1):e17.

27. • What came first, weighing or weight-loss?
Complex Frequent Observation/Intervention
Engagement
Weight Loss
• An additional monthly weighing is associated with
an extra 1kg weight lost over the course of a year
• Recent weight loss encourages subsequent
measurement: a person who has recently lost 1kg is
twice as likely to reweigh on a given day compared
with someone who has remained the same weight
Sperrin M et al. Who Self-Weighs and What Do They Gain From It? A Retrospective Comparison Between
Smart Scale Users and the General Population in England. J Med Internet Res. 2016;18(1):e17.

28. Rethink Experimental Designs
Dwyer T et al. Objectively Measured Daily Steps and Subsequent Long Term All-Cause Mortality:
The Tasped Prospective Cohort Study. PLoS One. 2015;10(11):e0141274.

29. Co-produced Observations & Outcomes
Aim: To Reduce Relapse in Schizophrenia via Smartphone
Drug + behaviour (information * psychological endotype) = outcome
From J. Ainsworth & S. Lewis
Informatics enabled observation
Informatics
intervention
www.clintouch.com
Generic:
• Self-measurement
• Symptom awareness
• Clinical workflow integration
• Self-efficacy / autonomy
• Alert-fatigue avoidance

30. Civic AND Clinical Analytics
Smartphone GPS data
infer social functioning
in patients with
schizophrenia
From Difrancesco et al. Out-of-home
activity recognition from GPS data in
schizophrenic patients. IEEE 29th
International Symposium on
Computer-Based Medical Systems
(CBMS 2016).Sport
Swim
ming
pool
Volley
ball
1. Raw GPS
data
2. Detection of geolocation visited
3. Geolocations
visited
4. Identification of places visited
5. Places
visited
6. Type of places and activities
recognition
7. Out-of-home
activities

31. Health & Care Context: Place
Typical health & care data landscape: Greater Manchester
• £6 billion annual care budget
• >7000 care provider databases
• Local bye-law “duty to share” equal to “duty to protect”
• 2.7M people with low life expectancy and high inequalities

32. Data Sharing: Diameter of Trust Actionable
information for
health system
optimisation
National/Large Population
Audits/Registers/Monitoring
NOT SCALABLE
Excellence provider benchmarking
e.g. strokeaudit.org but no learning
across disease areas and not
integrated with clinical workflows
SCALABLE
Payer evidence, quality
management, public health
intelligence and research share
data, infrastructure and expertise
Large enough for
economy of scale
Small enough for
a conversation with
the citizenry
about data sharing
www.herc.ac.uk/get-involved/citizens-jury/

33. Trust in Predictive Analytics
Academia rewards
publishing papers on the
same topic every 10y or so
Law sees algorithms as
medical devices
(EU Directive 2007/47)
Industry has no trusted 3rd
party lab for validating
algorithms/models
EuroScore prediction
Calibration drift:
Typical of many
published models
Observed death rate
Hickey GL et al. Dynamic trends in cardiac surgery:
why the logistic EuroSCORE is no longer suitable for
contemporary cardiac surgery and implications for
future risk models. Eur J Cardiothorac Surg. 2013
Jun;43(6):1146-52.

34. Civic Health Data Analytics
Data
Public
sector
encounters
Services
Targeted
by need
Targeting
Tools
Ark
Involved Citizens
Problem Owners
Data Managers
Public Health Analysts
Care Service Analysts
Statisticians
Informaticians
Social Scientists
Health Economists
Health Service
Researchers
Communications Experts
Service
Planning
and PolicyInsights
SME Global Corp.
Which services and how?
Spin-in/out Laboratory
Farr Institute & NIHR Centres
Connected Health Cities
Pilots 2016-9
North England
twitter.com/hashtag/datasaveslives
Ainsworth J, Buchan I. Combining Health Data Uses to Ignite Health System Learning.
Methods Inf Med. 2015 Nov 27;54(6):479-87.
www.connectedhealthcities.org
Time
Data Production
Analysis
Data ProductionData-Action Latency
Insight
Action
Data
Preparation

35. Stroke Pathway Learning
1 3
2
Better decision support
tools for paramedics:
Recognise ‘stroke mimics’
Faster, more accurate triage
and improve access to
neurosurgery when needed
Enhanced workflows e.g.
medication vs. BP reviews to
prevent another stroke

36. • Smartwatch detects atrial fibrillation over a week,
otherwise missed by a GP in a 10 minute consultation:
then supports anticoagulant medication.
• Virtual rehab assistant (voice/AI appliance) and smart electricity meter
data alert rehab team to a change in daily living patterns.
• Subsidised public transport after cardiovascular risk screening
makes it cheaper to walk/tram than take the car to work:
increased physical activity sustained where exercise prescriptions fail.
Stroke Prevention: Civic Extensions

37. Borrowing Insights
De-identified
Records
Identified
Records
Study
Protocol
/Assessment
Study
Recruit
Clinician
Researcher
Commons of Metadata and Information Governance (Clinical & Research)
Clinical Care
Patient
Research Safe Haven
Encrypted (SHA1 & AES256);
Certified (ISO 27001)
System 1
System 3
System 2
System 4
Linkable Data
Providers
Analytic
Objects
RAPID REPLICATION
• Study/audit protocol
• Codes for the data
• Statistical scripts
• Results in progress
• Report
• Slides etc.
Bechofer S, Buchan I et al. Why linked data is not enough for scientists.
Future Generation Computer Systems 2013;29(2):599–611.
Ainsworth J, Buchan I. e-Labs and
Work Objects: Towards Digital
Health Economies. Lecture Notes
of the Institute for Computer
Sciences, Social Informatics and
Telecommunications Engineering.
Springer, Berlin Heidelberg
2009;16:206-216.
Routine
Randomisation

38. “Learning Healthcare Systems”
are an illusion if restricted to
provider organisations.
Health(care) can’t be
optimised outside the civic
context of health.
Civic Imperative
@profbuchan
#DataSavesLives

39. • Grasp patient-led healthcare records/apps for self-care and clinical
workflow improvement: don’t focus on legacy clinical IT.
• Allow cities/regions to self-organise around healthcare workflow
optimisation, and join the global Connected Health Cities: prepare for
streams of place-based data that affect health and care.
• Exploit the diversity of biology-behaviour-environment interactions as a
global science and technology innovation asset as Turkish digital health
data start to join up.
Thoughts for Turkey’s Digital Health