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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. eHealth / health informatics.

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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

ehealth health informatics
eHealth / health informatics

“Use of information & communications technologies to support & improve the delivery of health, social & self care”

Focus is on:

  • Information - both data and knowledge
  • Decisionsof clinicians, patients, public…
  • Communication: appropriate messages,

channels and formats

what is information
What is information ?

Information: “organised data and knowledge used to support decisions and actions”

Shortliffe EH: Textbook of Medical Informatics 1st 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

information c ycles in healthcare
Information cycles in healthcare

Assemble evidence

Apply knowledge


Evidence based health & behaviour change

Learn & apply lessons

Clinical audit / CQI / research cycle

Retrieve data

Patient / self care

Insights, evidence

Clinical practice,

self care

Capture data

Analyse records


ahsn information theme
AHSN Information theme
  • Aim:to ensure that robust, comprehensive informationand evidenceare at the heart of decision making


  • Ensureaccurate, timely information is delivered to every point of need
  • Improve integration of health databases across sectors, building on existing strengths
  • Bring latest developments in big data, cloud computing and data modelling to healthcare frontline
  • Give health professionals access toanalytical / reporting skills

questions for table discussion
Questions for table discussion
  • What are the current information challenges in transforming health care?
  • How can research contribute to addressing these challenges?
health e research centre herc update

Health e-Research Centre(HeRC) Update

Kate Pickett on behalf of HeRC Consortium

Leeds, 5th Mar 2013

herc mission

Improved Care for Patients and Communities


HeRC Mission



Science and Industry



Data Quality






Delivering improved care for patients and communitiesthrough large-scale sense-making methodology reusing health data

herc research themes
HeRC Research Themes
  • 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
herc operations

Steering Group

HeRC Operations

Director + Management Team

PPI: (ethics) + (communities)



Manchester & Microsoft MachineLearning



MRC hubTrials Methods

Lancaster Statistics


CHIP-SET software tools (and generic platform)







Liverpool Public Health

ManchesterPROMS& ARUKClinical


Greater Manchester NHS trials








and data

YorkSocial Epidemiol.


 Health Informatics 


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
Maternal mental health example

Data linkage, primary care mental health:

Who comes? For what? Dx. Coding Treatment F/U

Demographics Coding of Depression Differences Quantity

Spatial complaint Physical in tx options? Coding


Cohort measures: Mental health, children’s mental health

Extra data collection: e.g. GP practice characteristics, interviews?

using patient data for service improvement an example from stroke


Using patient data for service improvement: an example from stroke

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

clinical information and management system for stroke cimss


Clinical Information and Management System for Stroke (CIMSS)

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

cimss publication trail


Frenchay Activities Index

Subjective Index of Physical & Social Outcomes

Euro QoL

A review of stroke outcome measures valid and reliable for administration by postal survey

(Reviews in Clinical Gerontology 2010)


  • Six Simple Variables Model
  • (but predicts mortality)

A systematic review of case-mix adjustment models for stroke

(Clinical Rehabilitation 2012)

  • The SSV model can severity adjust the SIPSO measure

Predicting patient reported outcomes: a validation of the Six Simple Variable Model

(Cerebrovascular Diseases)

  • The two sub-scale structure (physical & social outcomes) is confirmed

Confirmation of the validity of a two-scale structure for the SIPSO

(Archives of the Phys Med & Rehabilitation)

cimss publication trail1


A cacophony of clinical datasets:

the example of stroke

(Geriatric Medicine)

Overlapping (but different) indicators

Data dictionary approach


NHS IT climate is disjointed and fragmented

“One size fits all” not appropriate

Linking existing systems useful

15 stage IT development plan developed

A point of care electronic stroke data collection system

(Health Technology 2013)

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

what have we learned


What have we learned?

Successful research service improvements based on information innovation requires:

Valid PROM (or PREM) ± process measures

Severity adjustment approaches

Existing IT system > bespoke

Mechanisms for behaviour change

information absent from implementation transformation decision making part ii preferences

information absent from implementation/transformation decision making: Part II - preferences

Carl Thompson


TRiP-LaB, University of York

preferences silent misdiagnosis mulley et al kings fund 2012
Preferences: “Silent misdiagnosis”? (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


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

  • 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…
  • 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.
and for the ahsn
And for the AHSN?
  • 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?

What are the current challenges in using information?

  • How can research contribute to addressing these challenges?