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 September 26 th, 2012 The potential for IAPT services Wolfson College Cambridge

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 Ann Adams, & Christopher Mace University of Warwick Christopher Buckingham, Ashish Kumar, Abu Ahmed University of Aston

Evidence about mental-health risks Risk independent cues Risk cue clusters Risk cue interactions specific cue values occurring together particular cue combinations We know quite a lot We know a little We hardly know anything

No explicit integration RISK ASSESSMENT Risk tool Clinical judgement

Need to connect the information sources RISK ASSESSMENT Risk tool Clinical judgement

Data hard to extract

Electronic documents: little structure, information buried Yes, this really is an NHS decision support document

Data not shared RISK ASSESSMENT RISK ASSESSMENT Mon Tue Fri RISK ASSESSMENT or exploit the semantic web

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

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 12experts 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 risk screening tools for varying circumstances and assessors Coding Lisp Lisp or XSLT Mind map Tree for pruning Pruned tree Data gathering tree with questions and layers that organise question priority Fully annotated pruned tree mark up XSLT All trees are implemented as XML

Hanging notes on the tree Instructions to the computer What tools to produce What target users

IAPT demo If the person says yes IAPT version of Grist just 6 screening questions

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.

comment action/intervention gold padlock silver padlock red means filled Interface functionality

Manage patient assessments

Service audit data (i)

Service audit data (ii)

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

myGRiST

GRiST DSS in the community Service users use 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 users continue to use myGRiST –alerts sent to clinicians for high-risk issues

Community Primary care IAPT Secondary care Recovery in the community social care –housing –police education occupational health general public mental health services –acute –specialist –OATS myGRiST social care –housing –police education occupational health general public

Communication GRiST Cloud –common data PHQ-9 et al GAD-7 Data sharing Data exchange Data integration

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

f(data) How we do it Transparent Knowledge 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 model Clear explanation for risk judgement Identifies important risk concepts Informs interventions judgement Mathematical models Optimal prediction of judgement Validation of cognitive model Evidence base for cues and relationship with risks RBFN BBN neural net PCA secure trusted risks

Remote monitoring and support myGRiST assessments by the service user Raised risks raise alerts