Informatics, Federation and Big Data

How AHSNs are enabling innovation

The Salford Lung Studies

A new approach to pharmaceutical trials

Professor Martin Gibson

ABPI – The Vision for Real World Data

“The past decade has seen the UK’s share of global commercial clinical trial activity decline significantly…

…A growth in research using real-world data is one way to ensure continued growth of the UK in the research arena. “

September 2011

Data collected outside the controlled constraints of conventional clinical trials to evaluate what is happening in normal clinical practice.

Definition

RWD complements and augments RCT data

• Phase I-IV • Real world data • Hybrid – “Real World Trials”

Real world data can improve performance in all these scenarios

Study Types

• Phase I-IV • Real world data • Hybrid – “Real World Trials”

Real world data can improve performance in all these scenarios

Study Types

• Patient data are held in a number of different formats and systems across the NHS, the main ones being:– Primary care systems (multiple software suppliers)– Diverse hospital systems (+HES data)– Disease registries– Social care systems

• Only rarely are these systems presently integrated

Data Sources

• What do we need?• At HQ

– Protocol design tools– Rapid reliable feasibility across large dataset– The expertise to use these tools across multiple therapeutic

areas

• At local level– Detailed local feasibility with willing and signed up health

practitioners and patients– The ability to recruit from these centres

Implementation

FARSITE

Feasibility And Recruitment System forImprovingTrial Efficiency

OBJECTIVES

Improve feasibility – towards ‘Evidence-based Feasibility’

GPs fully engaged and in control

Protect patient privacy - researchers only access anonymised aggregated data

Improving recruitment while preserving patient privacy

GP retains local control

AnonPatientRecord

s

Trial Protoc

ol Desig

n

Trial Recrui

t

GPResearch Nurse

NHS N3

CCG

Patient

…intelligible…

…and helpful…

…with supported options

…and quick results

Exploring the detail

Investigating site feasibility

The GP view

Example 1 – a study we should not have done

Example 2 – a study that recruited on time and above target

• Phase I-IV • Real world data • Hybrid – “Real World Trials”

Real world data can improve performance in all these scenarios

Study Types

• Globally harsh economic climate• Value for money essential• NICE (and similar governmental bodies world-

wide)• Evidence of effectiveness in a real world setting

not simply efficacy

Why do a ‘real world trial’?

• Inclusive health system free at point of care• GP prescribing• Technical capability improving – linked primary

and secondary care e-records in some areas

Why the UK?

Informatics, Federation and Big Data

How AHSNs are enabling innovation

Gary Leeming

Our role

Greater Manchester Academic Health Science Networkaims to be a catalyst for:

Innovation CollaborationWealth creation

Across Greater Manchester, Cheshire and Lancashire

Linking health andsocial care data in orderto empower individuals,improve treatment andtransform the qualityof care for patients

What is DATAWELL?

Using data to improve health and wellbeing in Greater Manchester

Building on the success of local data integration projects

The Greater Manchester health challenge

Deprivation is higher than the UK average

Obesity and smoking rates are above the national average

High rates of early mortality from cancer, heart disease and stroke

Hospital rates are higher than the English average

Life expectancy is lower than the English average

DATAWELL is part of the solution

The DATAWELL programme

16Provider

Organisations

Linking data across social and healthcare organisations across Greater Manchester, Cheshire and Lancashire

14Commissioners

12Local

Authorities

5Researchpartners

4Universities

Is this relevant to me?The DATAWELL project is directly relevant to:

Community and voluntary organisations

Health andsocial care

professionalsClinicians

Carers

Directorsof finance

IM&T leads

Chief privacy officers

Patient representative

groups

GPs

CCGs

Citizen

Earlydiagnosis

Integrated electronic

patientrecord

Improved patientsafety

Illness prevention

Real-time health

information

Identifyingnew cures

Patients

Why this matters:benefits for the patient

Safer, healthier, smarter

Reduction in readmissions

Reduce number of

re-tests

More accurate prescribing

Reduce duplication

of effort

Wider benefits

Improved service delivery

Improved medical

decision-making

More efficient and

targeted service

Cost savings for health and social care providers

Provides holistic view

of patient care

Inward investing for Greater Manchester

New jobs and wealth

creation

Examples of Clinical Benefits(from Scottish clinical data exchange)

Increase quality and admission to mental health services, especially children and adolescents

Monitor and decrease hospital acquired infections

Create person centric dementia care packages with nominated link worker

Decrease the 4 hour wait from arrival to admissions and discharge

The DatawellEcosystem

Social care

Nurse

GP

Hospital

Pharmacist

R&D PrivateSector

R&DUniversity

Citizen

The DATAWELL programme: guiding principles

The data belongs to the patient

Confidentiality and data security is paramount

Records will be anonymised when required

Robust information governance and consent model

All citizens can access their information

Let’s build on the success of what’s already working

The DATAWELL programme

Local projects (e.g. EPR/GP systems)

Innovation

Local integration

Investment

Signup GM integration

Innovation

GW

Projects

Analytics

DataExchange

InformationGovernance

Projects

Projects

Projects

Projects

Projects

Projects

Projects

GW

GW

The Datawell Roadmap

ConnectionPolicies

BrokerageSystem

AnalyticsPlatform

APIsOpenData sets

Governance

Data

Innovation

Platform

Projects

Citizen

Datawell – Iteration and Integration

Datawell as a Platform

Use of data within a secure, ethical platform using locally owned electronic data sharing agreements

Vendor agnostic to support innovation from multiple sources

Supporting innovative clinical use of large data analytics

Recording of all consent and usage of data to support the patient understanding own information

Datawell – Sharing based on local storage and agreement

Contracts define uses and access rights for queries and audit

The DATAWELL programme: Ongoing Engagement

January Informatics conference

Finance and Technology surveys for all NHS organisations

One to one detailed interviews

Attendance at local meetings of Acute CEOs, Directors of Finance, CCGs, Public Health and Social Services

Workshops

Steering group meetings with NHS, local government, patient representatives and 3rd sector

-

Jan - March 2014Consultation, engagement and business planning

The DATAWELL programme: next steps

– Research into existing information management and technology infrastructure

–  Development of appropriate governance structure for the programme

–  Development of outline business case

–  Work towards Horizon 2020 funding application - April 2014

– Apply for funding to build on the success of local projects

–  Establish DATAWELL programme

Jan - March 2014Consultation, engagement and business planning

April 2014Funding proposal developed

2016DATAWELL programmeestablished

Find out more at: www.gmahsn.org

Making Big Data “Actionable”How can the Farr Institute support population level health improvement?

Presented by John Ainsworth

University of Manchester

www.herc.ac.uk & www.farrinstitute.org

33% 1. Valderas JM, et al. Defining comorbidity: implications for understanding health and health services. Annals of family medicine; 7(4):357–63. 2. Fortin M, et al. Randomized controlled trials: do they have external validity for patients with multiple comorbidities? Annals of family medicine 2006 Jan

ARTEFACTSSimplifying Assumptions Lead To

Assumption: Time Invariance

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

0.02

0.03

0.04

0.05

0.06

0.07

0.08

Year

In-h

ospi

tal m

orta

lity

prop

ortio

n

Observed in UK (SCTS audit)Expected via EuroSCORE I model

Source: G. Hickey 2012

Drifting EuroSCORE Calibration

Assumption: Homogeneity

Hacking BMJ 2011;342:d508

Assumption: Homogeneity

From B. Hanif, GM CLAHRC

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2-3

-2

-1

0

1

2

3

4

Deprivation

Prem

atur

e (<

75) M

orta

lity

(age

-sex

stan

dard

ised

)

Some Salford GP practices havelow premature mortality for deprivation: why?

[Missed] OpportunityIs real-world evidence a

Dormant DataBody Mass Index (BMI) trend in Wirral 3y-olds from 1988 to 2003

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

Mar-88 Jul-89 Nov-90 Apr-92 Aug-93 Jan-95 May-96 Sep-97 Feb-99 Jun-00 Nov-01 Mar-03 Aug-04Month of measurement by Health Visitor

Th

ree-

mo

nth

ly r

olli

ng

ave

rag

e B

MI S

DS

SDS = standard deviation score from 1990 British Growth Reference charts – adjusts for age and sex of the child

Clues Actions

Trapped Data• Narrative

– Vioxx risk of MI detectable pre-2004• By text mining 154k RA patient notes• Not by ICD-9 codes from same notes• Repeated for 2 other risks

– LePendu et al. 2012, J BioMed Sem 3(1):S5

• Rubrics– Code for type 2 diabetes annotated “DM r/o”– Access blocked by some ethics committees

Data from Tim Doran, University of Manchester

1999 2000 2001 2002 2003 2004 20050%

1%

2%

3%

4%

5%

GPRD - diagnostic codes GPRD - all codes THINQ-research QOF

Year

Estim

ated

UK

Prev

alen

ce o

f Dia

bete

sMiscoded DataUK Diabetes Prevalence from Different Databases

Anaemia at lower levels of kidney

impairment than commonly thought

Clinical (audit) question leading to scientific finding:required local metadata (assay change) not in national datasets

Data Warehouses are not the Solution• Need contextual metadata continuously adding value to big data

• Need engaged communities adding the metadata

• Local ownership, local control, for the benefit of the population served

• OMOP lessons– Current big data only for crude effects– Different answers from different datasets & methods

‘Faux Big’ Healthcare DataDATA DEPOSIT > USE DIGITAL DUST

Finance Public HealthClinical

Regional Healthcare Data Tomb

Research

Deposit Use

Big Data Climate for HealthcareDATA EXPERTISEMETHODS & MODELS

Vast data volume, velocity, variety

TSUNAMI

Supra-linear growth in papers & tools

BLIZZARD

Similar number of analysts

DROUGHT

SPECIALIST SILOSDiabetesCancerSilo k…

OLD PIPELINESCommissioning

AuditResearch…

x

Data queries: From this…select distinct PatientID, GPPracticeCode from Patients where DeathDate is null and PatientID in (select distinct PatientID from Patients where Dob<=1991 and PatientID in (select distinct PatientID from Patients where Dob>=1931 ) ) and PatientID in (select distinct PatientID from Patients where PatientID in (select distinct j.PatientID from Journal j where j.ReadCode in ('.C21.','.C2A.','.C2D.','.C2G2','C1021','C109.','C1094','C1095','C1097','C1099','C109D','C109F','C109G','C109H','C109J','C10F.','C10F4','C10F5','C10F7','C10F9','C10FD','C10FF','C10FG','C10FH','C10FJ','L1806','X40J5','X40J6','X40Jk','XaELQ','XaFn7','XaFn8','XaFn9','XaFWI','XSETp','XU70f','XU71F','XUKO0','XULXc','XUPHn','XUSbx') and j.EntryDate<@p4DateLimit1 ) ) and PatientID not in (select distinct PatientID from Patients where PatientID in (select distinct j.PatientID from Journal j where j.ReadCode in 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andj.EntryDate>@p5DateLimit1 ) ) and PatientID in (select distinct PatientID from Patients where PatientID in (select distinct j.PatientID from Journal j inner join (select PatientID, MAX(EntryDate) as max_entrydate from Journal where ReadCode in 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and EntryDate<@p6DateLimit1 group by PatientID) x on j.PatientID = x.PatientID and j.EntryDate=x.max_entrydate where j.ReadCode in 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and j.EntryDate<@p6DateLimit1 ) ) and PatientID in (select distinct PatientID from Patients where PatientID in (select distinct j.PatientID from Journal j inner join (select PatientID, MAX(EntryDate) as max_entrydate from Journal where ReadCode in ('.22K.','22K..','X76CN','X76CO','X76CP','XUHPV','XUSxA') group by PatientID) x on j.PatientID = x.PatientID and j.EntryDate=x.max_entrydate where j.ReadCode in ('.22K.','22K..','X76CN','X76CO','X76CP','XUHPV','XUSxA') and j.CodeValue<=@p7CodeValue1 ) ) and PatientID not in (select distinct PatientID from Patients where PatientID in (select distinct j.PatientID from Journal j where j.ReadCode in ('Xa5sa','Xa5sb','Xa5sc','Xa5sd','Xa5se','Xa5sf','Xa5sg','Xa5sh','Xa5sH','Xa5si','Xa5sI','Xa5sj','Xa5sJ','Xa5sk','Xa5sK','Xa5sl','Xa5sL','Xa5sm','Xa5sM','Xa5sn','Xa5sN','Xa5so','Xa5sO','Xa5sp','Xa5sP','Xa5sq','Xa5sQ','Xa5sR','Xa5sS','Xa5sT','Xa5sU','Xa5sV','Xa5sW','Xa5sX','Xa5sY','Xa5sZ') ) )

…to this

Social Machine of Healthe-Research

Local Community Integrated

Health Record

De-identified records

Unified local health intelligence

platform

Provider 1

ReusableObject

University 1‘Big data’ and ‘big sense-making’:

Better science

Provider 2

University 2

Multiply analyst capacity:Inform service development

• Query• Dataset• Statistical code• Report• Slide deck…

Toward Sense-making Networks

“Users who selected the variables in your basket also selected these variables and

these models…”

Different uses of healthcare care data borrowing strength from one another

and engaging ‘linkable’ expertsClinical Trial:1 vs. 2 per day dosing adherence (* deprivation)

Clinical Audit:Depression vs. readmission

Public Health:Recalibrated deprivation score

Primary Care Secondary Care

Public Health Self Care

Specialist A Specialist B

Diabetology:Glucose focusNephrology:

Blood pressure focus

Linked Self-Care:Weight control

Physical activity…

Building usefully complex models of health & careneeds a much larger scale of research, ‘in the wild’.

Weight Blood pressure

Grand Challenge

MiteCat

DogPollen

EggMilk

MoldPeanut

Data-intensive Hypothesis-formation

Sensitized

Age 1

Sensitized

Age 3

Sensitized

Age 5

Sensitized

Age 8

Skin TestAge 1

Skin TestAge 3

Skin TestAge 5

Skin TestAge 8

Blood Test

Age 1

Blood Test

Age 3

Blood Test

Age 5

Blood Test

Age 8

Sensitization Classswitch class

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 patternsof allergic sensitisation

Atopy ‘Stratified’Important new risk group for asthma discovered

MobilisedThe revolution will be

mHealth• Computing power• Large display• Usable• Short range

connectivity• Always on• Always connected• Always with you• Familiar

Patient + Mobile = n-of-1 TrialsReducing Relapse in Schizophrenia via Smartphone

Drug + behaviour (information * psychological endotype) = outcome

From J. Ainsworth & S. Lewis

Who is Farr?

“Diseases are more easily prevented than cured and the first step to their prevention is the discovery of their

exciting causes.”

William Farr

Farr Institute

UCL Partners

UCL, LSHTM, Queen Mary, Public Health England

Scotland

Dundee, Glasgow, Edinburgh, St Andrews, Aberdeen, Strathclyde, MRC HGU, NHS NSS

CIPHER

Swansea, Bristol, Cardiff, Exeter, Leicester, Sussex, NWIS, Public Health Wales

HeRC

Manchester, York, Lancaster, Liverpool, Sheffield, AHSNs

Map Source: www.m62.net

Health Informatics Research Centres

• Promoting collaborative leadership• Performing cutting edge research• Strengthening public engagement• Advancing proportionate governance• Developing innovative methods• Enabling Datasets• Harmonising e-Infrastructure• Building industrial partnerships• Developing training and career development opportunities• Engaging in regular communication

Our Key Activities

• Farr will lead the UK Health Informatics Research Network

• Farr will develop the Network’s 5-year strategy plan and provide a blueprint for its activities

• The Network aims to strengthen the UK’s capability in health informatics research by harnessing the expertise in the Farr and the wider UK research community

• The Network is open to all members of the research community

Farr and the UK Network

Conclusion

• Current healthcare evidence predicts less than 30% of true outcomes

• Smart networking of data, models and expertise would provide a world leading engine for health system adaptation

• Self-management and remote monitoring technologies will provide data for high resolution care