Ian CreekHead of Projects
Dr Deborah Neal
Training & evaluation lead
Symphony Vanguard Programme
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A little about me…
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He came from the dark side…. or some people call it a ‘Commercial background’
A little about me…
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• Currently working in south Somerset on the NHSE Vanguard programme
• Joined the NHS 3 years ago after 12 years in marketing
• Spoilt in the past by great data and free flowing access
• A data lover not hater!
Background
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• Changing demographic• Financial & workforce challenges• Need to change to less reactive, acute hospital
based system• Widespread stakeholder consultation –
patient, staff, carers• Symphony data set – includes social care,
primary, secondary, community & mental health data.
Proportion of the population
aged 65+ by LSOA - 2033
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0
1
2
3
4
5
6
7
8
£m
Gain share
CC: otherCC: IPMH: I/PMH: O/PSC: otherSC: ResiSC:Home+Day
AC: I/P urgent
AC: I/P elect
AC: O/PAC: A&EPrimary care
GPPrescribing
Socialcare
Mentalhealth
Acutecare
List size 6,380 4,340 660 1,990 630 490 70 30 130 120 10 30 1,010
Cost per patient
£160 £210 £140 £340 £1,940 £2,770 £5,100 £12,500 £2,550 £2,230 £20,600 £9,790 £250
Communitycare
Pote
nti
al t
o b
e g
ain
sh
ared
Pre
scri
bin
g2
Total: £1.02m £0.93m £3.35m £1.02m £0.48m £0.58m £7.38m
1. Mean figures averaging across 19 South Somerset GP practices.2. Prescribing cost is extrapolated from Mar 2015 (HSCIC), with prescribing list for 2013–14 (Symphony data)
Distribution of spending across a typical GP Practice
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Our new care model and
extended teams
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Brief overview of the Symphony care model
The symphony new care model was developed following consultation with
patients, carers and staff and using learning from research conducted and
established care models from other countries – namely IORA health, CareMore
and Alignment Health in the USA but also work in Jönköping in Sweden. The
model is closely aligned to the NHS England General Practice Forward View
and includes but is not limited to:
• Complex care for the highest need patient cohort
• Health Coaches – as a new role into Primary care
• Pharmacists, MSK practitioners and Mental Health Link Workers
• New data flows and data analysis/reporting tools
• MDT meetings and development of the huddle approach
• Patient stratification and identification
• An ethos of person centred, coordinated care, self-care and prevention
Population segments Cost breakdown Care models
Complex patients with many conditionsHigh Cost (over £7k/yr)
Person-centred, holistic coordinated care
→ Complex Care (CC)
Less complex patientswith fewer conditionsModerate cost (£1-7k/yr)
Proactive, person centredcondition management
→ Enhanced Primary Care (EPC)
Mainly healthy patientsLow cost (under £1k/yr)
Efficient primary care, proactive health and well-being services
Population cost pyramid, South Somerset
78%~90k
18%~20k
4%~5k
~15%~£20m
~35%~£55m
~50%~£75m
Previously 85% of resources were used by ~20% of the population, the challenge therefore was to develop new care models that deliver high quality, effective, cost
efficient care for all of the population.
Source: South Somerset Symphony project data 12/13, Oliver Wyman analysis
Note: 1 Community service activity (e.g. district nursing) data not allocated to individual patients, therefore not included here
~£150m total cost; Primary, secondary, community1, mental health and social care
The top 4% of population drive 50% of the cost; the top 22% drive 85% of the cost
Our new care
model and
extended
teams
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Complex Care
Health Coaching
Specialist nursing
Virtual clinics
MSK
Exte
nd
ed t
eam
Mental Health
QIRapid
access to advice
Numbers through the New
Care Model
• The complex care team has worked with 1,030 individuals
• 10,848 people have had contact with a health coach.
• There are also knock on benefits for the remainder of the patients in a practice, even where there is no health coach or complex care involvement, through the greater time and proactive approach which the practice is able to take.
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Data, data, data…
Local data collection by practices & YDH
External evaluation partners – SWAHSN, SWCSU, York university
Three main strands to the evaluation
• Quantitative
• Qualitative
• Econometric
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Data, data, data…
• Quantitative Dashboard: PAM, A&E attendances, Emergency Admissions, Emergency Bed Days and Outpatients.
• Qualitative: Views of patients, Health coaches, GPs and Practice Mangers.
• Econometric: ‘difference in differences’ analysis using Symphony dataset.
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Year to date compared to the previous year, based on CSU data for the South Somerset registered population at all providers
• Non-elective overnight admissions (non-ambulatory care) are down 7.5%
• Non-elective bed days are down 15.2%
• Excess bed days are down 48.9%
• In addition, a year on year historic trend of an increase in non-elective admissions of 4.5% has been avoided, giving a net benefit of 12% compared to the models not being in place.
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Impact on Emergency Admissions
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Impact on Non-Elective Bed days
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Change in PAM scores
• Health coaches and the complex care team record PAM scores at the start of an intervention and then repeat the score 6 months later
• More activated patients are more likely to adhere to treatment protocols, to eat healthily and to have fewer hospital admissions and A&E attendances.
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Evaluation of impact of health coach role
Admission avoidance
• Helped to avoid demand on hospital services
• Improved communicationbetween primary and secondary care
Health outcomes
• Supported patient weight loss
• Supported diabetes self-management with reductions in HbA1c level, due to support of health coaches to attend exercise and diet support.
• Confidence to self-manage improved
• Positive general lifestyle change
• Reduction in social isolation
• Patient resilience improved
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Evaluation of impact of health coach role
Impact on primary care
• Allowed GPs to change their appointment structure with longer appointments
• Helped to improve GPs’ working day & reduce GP stress
• Could repeat/relay GP advice to patients & support continuity of care
• Could engage in other work to support the practice
• Contributed to care plan development
• Prepared to engage with ‘difficult cases’
• Act as the GPs eyes and ears
Patient experience
• Carers felt supported
• Reduction in patients bouncing around the system
• Positive and tailored outcomes
• Identifying unseen problems (detective role)
• Health coaches easy to access and flexible providing multiple roles; Signposting, Empowering, Liaising, Activating, Persuading and Patient advocate.
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Matching process - CCT
• Data Preparation• Identify patients in data every year 2012/13 to
2015/16, i.e. 4 years
• Split CCT cases into 6m periods by enrolment date: 5 cohorts
• Generate covariates
• Two types of matching• Matching by propensity score
• Nearest neighbour, no replacement
• Coarsened exact Matching
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20132012 2014 2015 2016 2017
MAY 2015(cohort midpoint)
1
COHORT 122-Feb-15 to 22-Aug-15
MAY 2012
Count of emergency admissions each year
y3 y2 y1
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20132012 2014 2015 2016 2017
MAY 2015(cohort midpoint)
MAY 2012
1
COHORT 122-Feb-15 to 22-Aug-15
Count of emergency admissions each year
y3 y2 y1
2
COHORT 222-Aug-15 to 22-Nov-15
Count of emergency admissions each year
y3 y2 y1
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Covariates used for matching cases & controls
• Utilisation• 3 x lagged years of emergency admission counts• Readmissions for LTC• Risk ratio
• Clinical• Count of 8 comorbidities used for eligibility
assessment
• Demographics• 4 age groups (<18s are excluded)• Deprivation quintiles• Gender
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Balance graphs – by enrolment cohort
-100 0 100200300Standardized % bias across covariates
IMD_quint5male
IMD_quint3agecat3
IMD_quint4IMD_quint2
agecat5agecat4
ac_ip_cnt_lag3ac_ip_cnt_lag2
readmac_ip_cnt_lag1
rratiosumcond8
-50 0 50100150200Standardized % bias across covariates
IMD_quint5IMD_quint3
agecat3IMD_quint4
agecat4IMD_quint2
malereadm
ac_ip_cnt_lag3ac_ip_cnt_lag2ac_ip_cnt_lag1
agecat5rratio
sumcond8
0 50 100150200Standardized % bias across covariates
IMD_quint4IMD_quint5
agecat3IMD_quint3
maleIMD_quint2
agecat4ac_ip_cnt_lag2
agecat5readm
ac_ip_cnt_lag3ac_ip_cnt_lag1
sumcond8rratio
-50 0 50 100150Standardized % bias across covariates
agecat3IMD_quint5IMD_quint4
maleIMD_quint3IMD_quint2
agecat4readm
ac_ip_cnt_lag3ac_ip_cnt_lag2
agecat5ac_ip_cnt_lag1
sumcond8rratio
-50 0 50 100 150Standardized % bias across covariates
agecat3IMD_quint5IMD_quint4
maleIMD_quint3IMD_quint2
agecat4readm
ac_ip_cnt_lag3ac_ip_cnt_lag2
agecat5rratio
sumcond8ac_ip_cnt_lag1
From cohort 1 (top left) across then down to 5 (bottom centre)
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Challenges
• Information Governance
• Challenges in getting an appropriate not just accurately matched cohort
• Patterns of utilisation rather than average utilisation in previous year.
• Trigger for intervention is often a change in social context. Limited social care data has been available as yet to be used for matching purposes due to IG.
• Low patient activation impacts significantly on utilisation of health and social care services .There is PAM data for the cohort group but very limited PAM data for controls.
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Questions
• ?
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