Improving care of people with mental health problems using the Galatean Risk and Safety Tool
(GRiST)
Christopher Buckingham, Computer Science, Aston University
Ann Adams, Medical School, University of Warwick
April 10th, 2013
The potential for IAPT services
www.egrist.org
LUFC Elland Road
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 usersIt feeds into the GRiST clinical tool and improved
services
Some of the Research Team
Ann Adams,& Christopher MaceUniversity of Warwick
Christopher Buckingham,Ashish Kumar, Abu AhmedUniversity of Aston
Evidence about mental-health risksRisk
independent cues
Risk
cue clusters
Risk
cue interactions
specific cue valuesoccurring together
particular cuecombinations
We know quite a lot We know a little
We hardly know anything
No explicit integration
RISKASSESSMENT
Risk tool
Clinical judgement
Need to connect the information sources
RISKASSESSMENT
Risk tool
Clinical judgement
HOLISTIC
Data hard to extract
Electronic documents: little structure, information buried
Yes, this really is an NHS decision support document
Data not shared
RISKASSESSMENT
RISKASSESSMENT
Mon
Tue
Fri
RISKASSESSMENT
or exploitthe semanticweb
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: 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
www.egrist.org
Eliciting expertiseKnowledge bottleneck
– Extracting expertise– Representational language experts understand– Gain agreement between multiple experts– Lowest common denominator ……
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 expertsresults of integrating interview data
12 experts
• identifies relevant service-user data• “tree” relates data to risk concepts and top-level risks• information profile for service user
Interview transcripts
Qs & layers
XSLT
Different riskscreeningtools for varying circumstancesand assessors
Coding
Lisp
Lisp or XSLT
Mind map
Tree for pruning
Pruned tree
Data gathering treeData gathering treewith questions and layers
that organise question priority
Fully annotatedpruned tree
mark up
XS
LT
All trees are implemented as XML
Multiple populations handled by instructions in the tree
• Work on specifying different models done by XML attributes
• End-users access their own simple tree
• What is XML?<family>
<brother “john”/><sister “mary”/><daddy “long legs”/>
</family>
Arboreal sculpture
Complete “universal” tree: multiple overlays
working age
Complete “universal” tree: multiple overlays
CAMHS
Complete “universal” tree: multiple overlays
OlderAdults
Complete “universal” tree: multiple overlays
Service users
Complete “universal” tree: multiple overlays
Carers
Complete “universal” tree: multiple overlays
Friends
Multiple services
• Same idea as populations• Customise service requirements• Difference is that they cover all populations• Services so far:
– IAPT– Primary Care– Forensic
How not to design and develop• Must be able to meet end-user’s changing and
varied requirements
Iterative development for implementing research results into evolving GRiST and
myGRiST
Agile software engineering
IAPT demoIf the person says yes
IAPT versionof Gristjust 6 screeningquestions
Opens up four subsidiary questions for IAPT
If the person says yes
Two more IAPT questions are asked.
Comments and management information can be added to any
questions
An overall risk judgement is made along with supporting comments
and risk management information
Risk reports are generated immediately and can be downloaded
as a pdf.This shows a summary
just for suicide
Each risk has a detailed information profile that explains where the risk judgement came
from.
commentaction/intervention
gold padlock
silver padlock
red means filled
Interface functionality
Manage patient assessments
Service audit data (i)
Service audit data (ii)
myGRiST
myGRiST
Communication
• GRiST Cloud– common data
PHQ-9 et alGAD-7
GPs
IAPT myGRiST MH trusts
Private hospitals
Non-health orgs:education, work,
community
Data sharingData exchangeData integration
social services
Patient-centric web of care
clin
ical
per
spec
tive
Riskcl
inic
al/s
ervi
ce u
ser
Safety se
rvic
e us
erWell-being
Current GRiST database (now twice as big)
• 96,040 cases of patient data linked to clinical risk judgements
• Different risks• Different age ranges• Precise quantitative input linked with
qualitative free text
Wisdom
Expertise
Dissemination
f(data)
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1
22
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How we do itTransparentKnowledge and reasoning can be understood
• Black box• Can’t see how
answer derived
input data
Risk data
output judgement
Risk evaluation
input
data
judgement
input data
GRiST cognitive modelClear explanation for risk judgementIdentifies important risk conceptsInforms interventions
judgemen
t
Mathematical modelsOptimal prediction of judgementValidation of cognitive modelEvidence base for cues and relationship with risks
RBFNBBNneural netPCA
securetrusted
risks
GRiST captures consensus• Preliminary (crude analysis) results for clinical tool
– Correlation > 0.8, R2 = 0.69– 87% of 4000 predictions within 1 of the expert on 11-point scale– No difference if inputs are raw values or membership grades
• So we can model evaluations for different types of user
Clinical Decision Support for Mental Healthwww.eGRiST.org
Galatean Risk Screening ToolResults
Absolute Error in Predicting Judgement
87% of predictions have an error of < +/- 1eg If judgement = 3, 2 < prediction < 4Less than 3% have an error of greater than +/- 2 less than 2
87%
>+/- 113%
less than 2More than 2
No Risk Low Risk Medium Risk High Risk Max Risk
0 to 2 2 to 4 4 to 6 6 to 8 8 to 10
www.egrist.org