Christopher Buckingham, Computer Science, Aston University

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


CAMHS


OlderAdults


Service users


Carers


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

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)

c

kkk ztztwJ

1

22

21)(

21)(

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

http://www.egrist.org/

www.egrist.org

Christopher Buckingham, Computer Science, Aston University

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