The innovative use of information to achieve the three AHSN goals
Dr Jeremy Wyatt DM FRCP
Leadership chair in eHealth Research (Health Informatics)
Yorkshire Centre for Health Informatics,
Leeds Institute of Health Sciences www.clahrc-lyb.nihr.ac.uk
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“Use of information & communications technologies to support & improve the delivery of health, social &
self care”
Focus is on:
– Information - both data and knowledge
– Decisions of clinicians, patients, public…
– Communication : appropriate messages, channels and formats www.clahrc-lyb.nihr.ac.uk
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Information: “organised data and knowledge used to
support decisions and actions”
Shortliffe EH: Textbook of Medical Informatics 1 st edition, 1990
Data: the specifics of a case / patient - captured in records
Knowledge: generic information that applies across cases - captured in books / websites / guidelines...
Wyatt & Sullivan, BMJ 2005 www.clahrc-lyb.nihr.ac.uk
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Information cycles in healthcare
Apply knowledge Assemble evidence
Knowledge
Evidence based health & behaviour change
Retrieve data
Patient / self care
Clinical practice, self care
Capture data
Learn & apply lessons
Clinical audit / CQI / research cycle
Insights, evidence
Analyse records www.clahrc-lyb.nihr.ac.uk
Records www.yhahsn.org.uk
www.clahrc-sy.nihr.ac.uk

Aim: to ensure that robust, comprehensive information and evidence are at the heart of decision making
Objectives:
1. Ensure accurate, timely information is delivered to every point of need
2. Improve integration of health databases across sectors, building on existing strengths
3. Bring latest developments in big data, cloud computing and data modelling to healthcare frontline
4. Give health professionals access to analytical / reporting skills www.clahrc-lyb.nihr.ac.uk
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• What are the current information challenges in transforming health care?
• How can research contribute to addressing these challenges?

Kate Pickett on behalf of HeRC Consortium
Leeds, 5 th Mar 2013

Improved Care for
Patients and Communities
(Service)
Link
Value
Science and Industry
(R&D)
Datasets
Link
Ingredients
Methods
Experts
Delivering improved care for patients and communities through large-scale sense-making methodology reusing health data

• CoOP
– “Coproducing observation with patients”
• MOD
– “Missed opportunities detector”
• SEA-3
– “Scalable endotypes of asthma, allergies and andrology”
• DOT
– Diabesity outcomes translator
• FIN
– Trials feasibility improvement network

Steering Group
Director + Management Team
Manchester
Psychology
Manchester
PROMS
& ARUK
Clinical
Epidemiol.
PPI: (ethics) + (communities)
Lancaster
Statistics
Manchester
& Microsoft
Machine
Learning
Manchester
Biostatistics
 Health Informatics 
Liverpool
MRC hub
Trials
Methods
CHIP-SET software tools (and generic platform)
Liverpool
Public
Health
Manchester
Clinical
Epidemiol.
York
Social Epidemiol.
Manchester
Pharmaco
Epidemiol.
Greater
Manchester
NHS trials
Methods
Real-world
Problems and data e-Research

Using Data Linkage to assess the extent of health inequalities and generate data informing a targeted intervention:
Maternal mental health example
• Parental depression can have a profound impact on children’s health, wellbeing and social development
• Problem: Ethnic minority women have a higher rate of depression than white, are just as likely to access care, but less likely to be diagnosed and therefore treated, with consequences for the children
• Understand characteristics of target sample
– What proportion of the variation is due to
• Non-attendance
• Variation in presentation of symptoms
• Coding practices
• Treatment uptake
• Outcome variation
• Comparison with cohort measures (demography, outcomes for mother & child)
• Clustered by area (practice)? Area (geography?) Ethnicity?
• Explore solutions
– Area-based
• GP practice
• Geographical barriers to care
– Treatment based
• Acceptability of treatments
• Outcome variation for different groups
– Target individual packages for those at the tail end of the distribution, or
– Population shifts in health seeking behaviour?

Maternal mental health example
Data linkage, primary care mental health:
Who comes?
For what?
Dx. Coding Treatment F/U
Demographics
Spatial
Coding of complaint
Depression
Physical
Differences Quantity in tx options?
Coding
Outcome
Cohort measures: Mental health, children’s mental health
Extra data collection: e.g. GP practice characteristics, interviews?

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LYBRA - CLAHRC
John Young
Professor of Elderly Care Medicine Head
Academic Unit of Elderly Care and Rehabilitation
Bradford Hospitals Trust and University of Leeds on behalf of CIMSS team

LYBRA - CLAHRC
A novel IT supported approach to improving stroke care through the collection of high quality data as part of routine care
Calderdale; Leeds; Bradford; Airedale and Harrogate

LYBRA - CLAHRC
A review of stroke outcome measures valid and reliable for administration by postal survey
(Reviews in Clinical Gerontology 2010)
Frenchay Activities Index
Subjective Index of Physical &
Social Outcomes
Euro QoL
A systematic review of case-mix adjustment models for stroke
(Clinical Rehabilitation 2012)
Six Simple Variables Model
(but predicts mortality)
Predicting patient reported outcomes: a validation of the Six Simple Variable Model
(Cerebrovascular Diseases)
Confirmation of the validity of a two-scale structure for the SIPSO
(Archives of the Phys Med & Rehabilitation)
The SSV model can severity adjust the SIPSO measure
The two sub-scale structure
(physical & social outcomes) is confirmed

LYBRA - CLAHRC
A cacophony of clinical datasets: the example of stroke
(Geriatric Medicine)
Overlapping (but different) indicators
Data dictionary approach
A point of care electronic stroke data collection system
(Health Technology 2013)
NHS IT climate is disjointed and fragmented
“One size fits all” not appropriate
Linking existing systems useful
15 stage IT development plan developed
Agile development of an electronic data collection system for stroke
(BMC Medicine)
How CIMSS was developed
Source codes
(The role of Diffusion Fellows in Service
Improvement)
CLAHRC Diffusion Fellow

LYBRA - CLAHRC
Successful research service improvements based on information innovation requires:
1.
Valid PROM (or PREM) ± process measures
2.
Severity adjustment approaches
3.
Existing IT system > bespoke
4.
Mechanisms for behaviour change

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Carl Thompson
Professor
TRiP-LaB, University of York

(Mulley et al. Kings Fund 2012)
Health care may be the only industry in which giving customers what they really want would save money. Well-informed patients consume less medicine… much less.
Wanless* estimated the potential annual savings at £30 billion, or 16 per cent of the projected budget by 2022 (Wanless 2002).
* Based on maximum patient engagement

Aims
Elicit public preferences for innovations
Elicit WTPs for innovations
Investigate similarities and differences among the respondents

Discrete Choice Experiment with latent class modelling
Innovations viewed as a “bundle” of characteristics (cost, evidence base, target groups, time to implementation….) online and paper-pen surveys in West Yorkshire, UK in 2011 stratified random sampling of 3600 people using “Electoral Roll”
Register + Bradford NHS Foundation Trust membership list
Public Voice in Health Service Innovation Investment Decision: A Discrete Choice Experiment

Results
 3 Latent Classes:
Class-1 (57%), Class-2 (25%), Class-3 (18%)
 Everyone prefers
• Implementing innovations to not.
• ‘scientifically’ proven, relatively cheap, innovations with clear health benefits, and are quick to implement.
 people are unwilling to pay for innovations
• that are scientifically unproven, take ‘more than a year’ to implement, and result in only ‘moderate’ health benefits.
• And those targeting ‘drug users’, ‘obese people’, and the ‘elderly’: the
“unpopular”
 The differences…

Results
 Class-1 (57%): Value ‘health gain’, less sensitive to costs, and like innovations targeting people with cancer. Science and expert opinion valued more than others; more likely to be satisfied with the quality of their health care.
 Class-2 (25%): dislike spending on ‘unpopular’ groups. Willing to pay twice for ‘best’ health (100%) than ‘good’ health (50%), and do not value the speed of implementation. more likely to be male, full-time employed, and less satisfied with the quality of health care services available to them.
 Class-3 (18%): accepting of ‘unpopular’ target groups. believe that decisions on the prioritisation of innovation options should not be based on the age and time-to-implementation.

Public &
Patient
Involvement
Involving the public: beyond the focus group

• Chance to design services that reflect public preferences
– realise some of Wanless’ £30 billion budget impact?
• improving the sampling, response rate, attribute
“bundle” size (applicability).
– MRC methods grant still in the game
• Prioritisation decision support
– more innovation than you can fund: how do you choose?

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