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www.egrist.org. Improving care of people with mental health problems using the Galatean Risk and Safety Tool ( GRiST ). The potential for IAPT services. Wolfson College. Cambridge. September 26 th , 2012. Christopher Buckingham Computer Science, Aston University Ann Adams

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slide1
www.egrist.org

Improving care of people with mental health problems using the Galatean Risk and Safety Tool (GRiST)

The potential for IAPT services

Wolfson College

Cambridge

September 26th, 2012

Christopher Buckingham

Computer Science, Aston University

Ann Adams

Medical School, University of Warwick

risks associated with mental health problems
Risks associated with mental health problems
  • Suicide
  • Self harm
  • Harm to others and damage to property
  • Self neglect
  • Vulnerability
  • Risk to dependents

Our research is about better understanding, detection, and management

It is aimed at both clinicians and service users

It feeds into the clinical tool and improved services

some of the research team
Some of the Research Team

Christopher Buckingham,

Ashish Kumar, Abu Ahmed

University of Aston

Ann Adams,

& Christopher Mace

University of Warwick

evidence about mental health risks
Evidence about mental-health risks

Risk

particular cue

combinations

cue clusters

Risk

We know a little

Risk

We know quite a lot

independent cues

cue interactions

specific cue values

occurring together

We hardly know anything

no explicit integration
No explicit integration

Clinical judgement

Risk tool

RISK

ASSESSMENT

need to connect the information sources
Need to connect the information sources

Clinical judgement

Risk tool

holistic

RISK

ASSESSMENT

electronic documents little structure information buried
Electronic documents: little structure, information buried

Yes, this really is an NHS decision support document

data not shared
Data not shared

Mon

RISK

ASSESSMENT

Tue

RISK

ASSESSMENT

or exploit

the semantic

web

Fri

RISK

ASSESSMENT

the solution grist
The solution: GRiST
  • Explicitly models structured clinical judgements
  • Underpinned by a database with sophisticated statistical and pattern recognition tools.
    • linked with empirical evidence
  • Developed from the start to exploit the semantic web
    • universally available
    • ordinary web browsers
  • Designed as an interactive tool with sophisticated interface functionality
  • Provides a common risk language with multiple interfaces
    • collecting information
    • providing advice
  • Supports shared decision making and self-assessment
the solution grist1
The solution: GRiST
  • Versions for different populations
    • older, working age, child and adolescent
    • specialist services (e.g. learning disability, forensic)
  • A whole (health and social care) system approach to risk assessment
slide13
Dissemination

Expertise

Wisdom

eliciting expertise
Eliciting expertise

Knowledge bottleneck

  • Extracting expertise
  • Representational language experts understand
  • Gain agreement between multiple experts
  • Lowest common denominator ……
unstructured interview
Unstructured Interview
  • What factors would you consider important to evaluate in an assessment of someone presenting with mental health difficulties?
    • prompts or probes to explore further
  • 46 multidisciplinary mental-health practitioners
mind map with total numbers of experts results of integrating interview data
Mind map with total numbers of expertsresults of integrating interview data
  • experts
  • identifies relevant service-user data
  • “tree” relates data to risk concepts and top-level risks
  • information profile for service user
slide20
Different risk

screening

tools for

varying

circumstances

and assessors

XSLT

Tree for pruning

Lisp or XSLT

Lisp

Pruned tree

Mind map

mark up

Interview transcripts

Coding

Fully annotated

pruned tree

XSLT

Qs & layers

Data gathering tree

with questions and layers

that organise question priority

Data gathering tree

All trees are implemented as XML

hanging notes on the tree
Hanging notes on the tree
  • Instructions to the computer
  • What tools to produce
  • What target users
iapt demo
IAPT demo

If the person says yes

IAPT version

of Grist

just 6 screening

questions

slide28
An overall risk judgement is made along with supporting comments and risk management information
slide29
Risk reports are generated immediately and can be downloaded as a pdf.

This shows a summary just for suicide

slide31
Interface functionality

comment

gold padlock

silver padlock

red means filled

action/intervention

vision for mygrist
Vision for myGRiST
  • A tool to help service users:
    • Self-monitor and self-manage risk
    • Understand factors in their lives that influence risk
    • Make decisions about how and when to intervene to reduce risk
    • Own their own history and risk profile
    • Communicate with clinicians and others about risk
    • Share in risk management decisions
grist dss in the community
GRiST DSS in the community
  • Service usersuse myGRiST for self assessment
    • with carers
    • reports sent to clinicians prior to consultations
  • Clinicians use GRiST for own assessment
    • compare with consumers
    • support shared assessment and personal safety planning
  • Monitoring in the community
    • service userscontinue to use myGRiST
    • alerts sent to clinicians for high-risk issues
slide39
Primary care

Recovery in

the community

Secondary care

Community

IAPT

myGRiST

myGRiST

  • social care
    • housing
    • police
  • education
  • occupational health
  • general public
  • social care
    • housing
    • police
  • education
  • occupational health
  • general public
  • mental health services
    • acute
    • specialist
    • OATS
communication
Communication
  • GRiST Cloud
    • common data

Data sharing

Data exchange

Data integration

IAPT

MH trusts

myGRiST

PHQ-9 et al

Non-health orgs:

education, work, community

GAD-7

social services

GPs

Private hospitals

current grist database
Current GRiST database
  • 96,040 cases of patient data linked to clinical risk judgements
  • Different risks
  • Different age ranges
  • Precise quantitative input linked with qualitative free text
slide42
How we do it

Transparent

Knowledge and reasoning can be understood

Risk evaluation

Risk data

f(data)

output judgement

input data

  • Black box
  • Can’t see how answer derived
slide43
GRiST cognitive model

Clear explanation for risk judgement

Identifies important risk concepts

Informs interventions

input data

judgement

secure

trusted

risks

RBFN

BBN

neural net

PCA

judgement

input data

Mathematical models

Optimal prediction of judgement

Validation of cognitive model

Evidence base for cues and relationship with risks

remote monitoring and support
Remote monitoring and support

myGRiST assessments

by the service user

Raised risks raise alerts

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