GRiST – 10th April 2013

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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
LUFC
Elland Road
April 10th, 2013
Christopher Buckingham, Computer Science, Aston University
Ann Adams, Medical School, University of Warwick
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 GRiST clinical tool and improved
services
Some of the
Christopher Buckingham,
Ashish Kumar, Abu Ahmed
University of Aston
Ann Adams,
& Christopher Mace
University of Warwick
Research Team
Risk
Evidence about
mental-health risks
independent cues
We know quite a lot
particular cue
combinations
Risk
Risk
cue clusters
We know a little
cue interactions
specific cue values
occurring together
We hardly know anything
No explicit integration
Clinical judgement
Risk tool
RISK
ASSESSMENT
Need to connect the information sources
Clinical judgement
Risk tool
RISK
ASSESSMENT
Data hard to extract
Electronic documents: little structure, information buried
Yes, this really is an NHS decision support document
Data not shared
Mon
Tue
RISK
ASSESSMENT
RISK
ASSESSMENT
Fri
RISK
ASSESSMENT
or exploit
the semantic
web
The solution:
•
•
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
•
–
–
•
GRiST
collecting information
providing advice
Supports shared decision making and self-assessment
The solution:
•
Versions for different populations
–
–
•
GRiST
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 expertise
Knowledge 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 experts
results 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
Tree for pruning
Lisp or XSLT
Pruned tree
mark up
Mind map
Interview
transcripts
XSLT
Different risk
screening
tools for
varying
circumstances
and assessors
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
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
Older
Adults
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 demo
If the person says yes
IAPT version
of Grist
just 6 screening
questions
If the person says yes
Opens up four subsidiary questions
for IAPT
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.
Interface functionality
gold padlock
comment
action/intervention
silver padlock
red means filled
Manage patient assessments
Service audit data (i)
Service audit data (ii)
myGRiST
myGRiST
Communication
• GRiST Cloud
– common data
PHQ-9 et al
GAD-7
Data sharing
Data exchange
Data integration
clinical perspective
Risk
clinical/service user
Safety
service user
Patient-centric web of care
Wellbeing
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
How we do it
Transparent
Knowledge and reasoning can be understood
Risk evaluation
Risk data
input data
• Black box
• Can’t see how
answer derived
f(data)
output judgement
1 c
1
J ( w)   (tk  zk ) 2  t  z
2 k 1
2
2
GRiST cognitive model
Clear explanation for risk judgement
Identifies important risk concepts
Informs interventions
risks
RBFN
BBN
neural net
PCA
Mathematical models
Optimal prediction of judgement
Validation of cognitive model
Evidence base for cues and relationship with risks
secure
trusted
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
Galatean Risk Screening Tool
Results
>+/- 1
13%
Absolute Error in
Predicting Judgement
87% of predictions have
an error of < +/- 1
eg If judgement = 3,
2 < prediction < 4
Less than 3% have an
error of greater than +/- 2
less than 2
More than 2
less than 2
87%
No Risk
Low Risk
Medium Risk
0 to 2
2 to
4
4 to
6
6 to 8
Clinical
Decision
Support
for Mental
Health
www.eGRiST.org
High Risk
Max Risk
8 to 10
www.egrist.org
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