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Outcome Orientated Approaches
Sami Timimi
Tuesday, 3 September 13
Washing Machines and all that
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Quality control.
Standardise all components.
Remove the impact of human
variation and fallibility.
Predicting outcomes
Patient/Extratherapeutic/Factors/(87%)/
Feedback/Effects/
//15A31%/
Treatment/Effects/
///13%//
Alliance/Effects/
///////38A54%//
Model/Technique.
............8%.
Therapist/Effects/
/////////46A69%/
Duncan, B. (2010). On becoming a better therapist.
Washington DC: American Psychological Association
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Model/Technique/Delivered:/
Expectancy/Model/Technique/Delivered:/
Expectancy/Allegiance/
Rationale/Ritual/(General/Effects)/
//////////////30A?%/
/
Key findings from research
• Research finds therapy is effective for mental health problems in
short and medium term. Poor long term outcomes.
•
• Extra-therapeutic factors such as social circumstances and
Model or technique has a minimal impact on outcomes.
motivation have biggest impact on outcomes
• Quality of therapeutic alliance important.
• Regular monitoring of progress and alliance improves outcomes
(10 RCTs).
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Move away from
• A focus on a formal diagnosis.
• A focus on ‘pathology’ and ‘dysfunction’
Blaming
and/or
berating
people.
•
• Acting as the ‘expert’.
• Encouraging dependency on services.
• Using outcome measures as a ‘tick list’.
Paperwork
with
no
positive
evidence.
•
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Move toward
• De-centring importance of our treatments.
Fostering
meaningful
therapeutic
relations.
•
• Believe everyone is capable of change.
• Using patient’s ideas about treatment.
• Fostering patient autonomy.
Appreciating
the
‘messiness’
of
mental
health
work.
•
• Encouraging clinician diversity.
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Partners for Change Outcome Management Systems (PCOMS)
• Norway couples therapy. Feedback v
TAU; Both persons reliable or sig.
change 50.5% v. 22.6%. FU v
TAU-34.2% v. 18.4% Feedback sep./
divorce rate.
• 2 independent US based studies.
Feedback group doubled controls (10.4
vs. 5.1 points)
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PCOMS in real clinical settings
•Community Health and Counselling Services in Maine: Number of
patients seen for more than one year from 655 (pre-PCOMS) to
321 (post-PCOMS). Number patients seen more than two years
from 227 (pre-PCOMS) to 94 (post-PCOMS). ‘No Shows’ down
by 30%.
•Southwest Behavioral Health Services, Arizona: Average length of an
episode of care in children’s’ services from 315 days to 188. Length
of stay in adult 322 days to 158. No shows down by 47%.
•Center for Family Service, Florida.‘. Using 40% fewer sessions to
achieve program goals. ‘No shows’ down by 25%.
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Adapted American Partners for Change
Outcome Management systems (PCOMS)
• Innovation award grant for Outcome Orientated Child and Adolescent
Mental Health Services (OO-CAMHS) Jan 2011-Jan 2012.
• Health and Education Innovation Clusters award for OO-AMHS elearning August 2011-June 2012.
• Implementation grant award for OO-AMHS with NSPCC July 2012.
• Whole service implementation across Lincolnshire CAMHS and a variety
of accompanying projects locally, nationally, internationally.
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Data comparison April-Oct. 2011
OO-CAMHS team
Non-OO-CAMHS team
104 patients
168 patients
• Over 2 years: 9%
• Over 1 year: 28%
1% (1 pt.)
• Tier 4:
7%
• DNA:
• Cancellation: 11%
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• Over 2 years:
• Over 1 year:
• Tier 4:
• DNA:
• Cancellation:
34%
58%
9% (15 pt.)
11%
10%
The problem of branding/commodifying
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http://www.oocamhs.com
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www.innovationforlearning.com/LPFT
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Service Transformation Toolkit
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QIPP case study
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Peer reviewed and other
publications and awards
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What sort of service do you want?
• Patient at the centre, involved in all decisions: A recovery and user
empowering approach.
• Challenge the dominance of models that are ‘expert’ led and emphasize
the technical (e.g. medical model).
• Sees mental health as part of social justice.
Tackles
stigma
and
celebrates
diversity.
•
• Uses feedback throughout treatment in a way that is easy for busy
clinicians to learn and use, achieves good outcomes, discharges nearly
all, few in long term treatment, low rates of referral to tier 4/specialist
services.
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OO-CAMHS CORE principles
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CONSULTATION: pay attention to extra-therapeutic
factors
OUTCOME: Monitor outcome session-by-session. If no
change by session 5, review with patient and MDT.
RELATIONSHIP: Monitor the alliance session-bysession.
ETHICS OF CARE: Develop a whole team ethos. Teams
are the drivers of change.
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Consultation
• Patient/extra-therapeutic = 40-87% variance of outcome.
• Who is best placed to have a meaningful relationship with patient? Multi-agency
consultation.
• Are circumstances favourable for treatment? How stable is the extra-therapeutic
context.
• Is more than one agency working on same problem? Avoid duplication. Complex
cases are often created.
• Match clinician to patient from first appointment.
• Avoid long term treatment with no discernible or measurable benefit.
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Outcomes
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Outcomes
• Regular feedback can by itself improve outcomes.
• PROM measure should be simple and feasible otherwise it won’t
engage clinicians.
• Keep a record of the outcome score session by session and plot a
graph that is discussed at the beginning of each session.
• If no improvement has occurred after 5 sessions discuss with the
patient and/or their carer and/or the Multidisciplinary team, as up
to 90% chance of no improvement in that treatment episode..
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Outcome Rating Scale
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Relationship
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Relationship
• Alliance as rated by the patient strongest factor, from within treatment,
associated with improved outcomes.
• Use simple and feasible PROM measure.
• From the first session create a culture of strong interest in patient
feedback.
• Building strong alliances is important particularly early in treatment.
• Address and discuss alliance issues before the patient and/or their carer
leave the session.
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Ethics of care
• Most ‘transformation’ projects fail at implementation and then maintenance phases.
• Like patients, clinicians work better when they feel valued, listened to, and taken
seriously.
• Create culture that is interested in outcomes and values patients’ perspective.
• Build strong culture of clinical feedback and supervision.
• Be prepared to fail ‘successfully’
• Evidence base everything!
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Feedback
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Team/peer supervision
• Cases for discussion.
• Generate new ideas.
• Encourage different perspectives.
• Review outcome scores from first session.
• Consider change of clinician.
• Bring team data.
• Celebrate team accomplishments.
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5 Session clinic
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Listening, respecting, resourcing
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Learning from feedback
• Local learning trumps model fidelity.
• Data will support learning (Practice Based Evidence).
• Supporting whole team implementation drives real change.
• Assume everyone has resources.
• Assume everyone can make good recovery.
• Aim for discharge as soon as feasible.
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Whole service implementation
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Service reporting Templates
NA/
Standard
Criterion
No.
1
Yes
No
!
!
Exceptions
!
Is there evidence that another agency (e.g. social
services) is involved in addressing the problems the
patient is referred for?
• If YES, have multi-agency discussions/consultations
taken place?
(Data source: patient record)
2
!
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!
Is there evidence the Outcome Rating Scale (ORS) has
been administered at each appointment and the score
recorded for:
a.
The young person?
!
b.
Parent/carer?
!
!
•
If NO – is the reason for not using the ORS
documented in the patient notes?
Has a graph been used been used to plot the ORS scores
so that client progress can be observed?
(Data source: patient record/online database)
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3
Is there evidence that Session Rating Scale (SRS) is
being administered each appointment with:
a.
b.
The young person?
Parent/carer?
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If NO – is the reason for not using the SRS
documented in the patient notes?
Is there evidence of SRS scores of 36 or below?
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If YES, is there a record that this has been
discussed with the person(s) who did the rating?
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Successful transformations
• Clinician buy in: easy to use, leadership, teams are the drivers
of change.
• Data integrity: Aim to get data. Focus in implementation
phase of just getting back the data, that way you know clinicians
are trying it.
• Data management: Clear policies on who sees what and
how it is used. In particular clinician specific data should not be
available to managers in a position to hire and fire.
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Learning from feedback
• Local learning trumps model fidelity.
• Data will support learning (Practice Based Evidence).
• Supporting whole team implementation drives real change - use whole team data.
• Everyone has resources.
• Everyone can make good recovery.
• Aim for discharge as soon as feasible.
• Evaluate projects and developments as well as on-going team data.
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