Peter Smith: Allocating healthcare budgets to general practices
1. Allocating Healthcare Budgets to
General Practices
Peter C. Smith on behalf of PBRA team
Imperial College Business School &
Centre for Health Policy
http://www.nuffieldtrust.org.uk/projects/index.aspx?id=338
2. The Person‐based resource allocation
(PBRA) project
( )
• Led by Jennifer Dixon (Nuffield Trust) from
Led by Jennifer Dixon (Nuffield Trust) from
2007
• Initial purpose was to develop budgets for
Initial purpose was to develop budgets for
practice based commissioning based on
individual patient data
individual patient data
• Coverage: secondary care, prescribing,
community health services
i h lh i
3. Reviews of resource allocation in English NHS
Hospital and Community Health Services , 1976‐
Hospital and Community Health Services 1976‐ today
Year Name Allocations Approximate Years applied
to population
population
size
1976 RAWP 14 RHAs 3m 77/78 – 90/91
1980 RoR
R R 14 RHAs
14 RHA 3m
3 91/92 –
91/92 94/95
1993 University of 14 RHAs 3m 95/96 – 01/02
York 192 DHAs 250,000
2001 AREA 303 PCTs 175,000 02/03 – 06/07
2006 CARAN 152 PCTs 350,000 07/08 –
Drawn from Bevan, and Bevan and Van der Ven
Drawn from Bevan, and Bevan and Van der Ven
Note: RAWP = Resource Allocation Working Party
RoR = Review of RAWP
AREA = Allocation of Resources to English Areas
CARAN = Combining Age Related Additional Needs (9)
3
4. PBRA modelling principles
PBRA modelling principles
• Use of individual‐level data on both users and
non‐users of health care services (entire English
population)
• Use of data from past NHS encounters to
Use of data from past NHS encounters to
measure morbidity directly (via ICD chapters)
• Predict future expenditure at an individual level.
Predict future expenditure at an individual level.
• Developed on samples of 5 million patients
registered within GP practices – models validated
on separate sample of 5 million patients.
l f 5 illi i
• Models further assessed by performance of
predictions at practice level
predictions at practice level
6. Modelling principles
d lli i i l
Explanatory variables Prediction variable
2005/06 2006/07 2007/08
Samples drawn from patients registered 1 April 2007
6
7. Modelling
• Hospital‐based expenditure excluding maternity and
mental illness
• Modelled hospital expenditure in year t as a function
of:
– Age and sex (36)
– Diagnostic categories from hospital utilization in years t‐1
and t‐2
and t‐2 (152)
– Attributed GP and small area needs characteristics (135)
– Attributed small area supply characteristics (63)
– PCT (152)
( )
• Note: did not consider variables with potentially
adverse incentive effects, eg number of encounters
g
8. Summary results of a set of five models, predicting
costs for 2007/08 using data from 2005/06 & 2006/07
t f 2007/08 i d t f 2005/06 & 2006/07
MODEL R2 individual R2 practice
Model 1: age and gender 0.0366 0.3444
Model 2 ‐ ADD:
152 morbidity markers 0.1223 0.6084
Model 3 ‐ ADD:
152 PCT dummies 0.1227 0.7437
Model 4 ‐ ADD:
135 attributed needs & 63 supply
135 tt ib t d d & 63 l 0.1230
0 1230 0.7851
0 7851
Model 5 ‐ REDUCE TO:
7 attributed needs & 3 supply
7 attributed needs & 3 supply 0.1229
0 1229 0.7735
0 7735
9. Type of Variable name
variable
Individual • Age and gender
• 157 ICD‐10 groups
Attributed
Attributed • Persons in social rented housing
Persons in social rented housing
needs
• All disability allowance claimants
• Persons aged 16 74 with no qualifications (age standardised)
Persons aged 16‐74 with no qualifications (age standardised)
• Mature city professionals
• Proportion of students in the population
• Whether the person had a privately funded inpatient episode of care
provided by the NHS in previous two years
• Asthma prevalence rate
Attributed • Quality of stroke care (primary and secondary care), by weighted
supply population
• Accessibility to MRI scanner
• Catchment population of the hospital trust that supplied the practice
with the largest number of inpatient admissions
10. Using the formula to allocate to
practices
• ‘Freeze’ supply variables at national levels
pp y
• For each individual, calculate predicted NHS
hospital costs
• For each practice calculate average costs in each
age/sex category
• Assign age/sex specific averages to all individuals
Assign age/sex specific averages to all individuals
in practice
– To address data lags and changes in registration
• Share out PCT budget according to practices’ total
predicted expenditure
11. Distance from target and practice size
for the new model and practices with more than 500 patients
2
DF index: relative to England mean
1.5
o
.5 1
FT
0
0 10000 20000 30000 40000
practice size: number of patients
Excludes the 16 practices with a DFT index > 2.
12. Distance from target
Distance from target
Percentage of practices more than
Percentage of practices more than
x% away from target
> +/‐ 5% > +/‐ 10% > +/‐ 20%
DFT relative to PCT
mean 61.1 34.6 14.0
DFT relative to national
mean 72.5 48.9 20.9
12
13. Phase III Objectives: in progress
Phase III Objectives: in progress
• Refresh existing PBRA model using more
Refresh existing PBRA model using more
recent data (for allocations 2011/12)
• Develop improved PBRA model (for allocations
Develop improved PBRA model (for allocations
2012/13)
• Model a variety of risk sharing arrangements
Model a variety of risk sharing arrangements
(to inform shadow GP Consortia and NHS
Commissioning Board)
• Develop a final PBRA formula (for allocations
2013/14)
14. Basic model
Explanatory variables Prediction variable
2007/08 2008/09 2009/10
16. GP budgets and risk:
we’ve been here before
’ b h b f
• GP fundholding c.1991
g
• Total fundholding c.1995
• ‘Primary Care Groups’ c.1998
• Practice based commissioning c.2002
Martin, S., Rice, N. and Smith, P. (1998), “Risk and the general
practitioner budget holder”, Social Science and Medicine, 47(10),
1547‐1554.
Smith, P. (1999), “Setting budgets for general practice in the New NHS”,
British Medical Journal, 318, 776‐779.
17. Fundholding
• Relatively generous budgets
Relatively generous budgets
• Limited set of elective conditions plus
prescribing covered
prescribing covered
• Per patient limit £6000
• Overspends largely borne by Health Authority
• Underspends kept by practice for patient
p p yp p
services
• A very ‘soft’ budget
A very soft budget
18. Decomposing the variation in practice
expenditure
d
• The formula captures average clinical responses
p g p
to measured patient and area characteristics.
Therefore any variation from the formula will be
due to:
due to:
– Variations in clinical practice;
– Variations in the prices of treatments used by the
practice;
– Imperfections in the formula caused by known patient
characteristics that are not captured in the formula;
p ;
– Random (chance) variations in levels of sickness
within the practice population.
19. High cost cases
High cost cases
practices
umber of p
Nu
Percentage of cases over £20K per person per year 19
20. Sampled from patients (10m) within a 20% random sample of all patients
100 replications for each consortium size
Consortium size increased in units of 10,000
40 Consortia risk profile
p
sortium risk per capita(£)
Upper 95% C.I.
20
14
0
Average risk
‐13.5
-20
-
Cons
Lower 95% C.I.
-40
0 100000 200000 300000 400000 500000
Consortium list size
Average risk Lower CI
Upper CI
Simulations from all data
Risk smoothed over time - predicted versus actual expenditure
21. Consortia risk profile
Consortium size Consortium size
10000 100000
.6
.6
.4
.4
.2
.2
0
0
ability
0 2 4 6 8 10 0 2 4 6 8 10
Proba
Consortium size Consortium size
300000 500000
.6
.6
.4
4
.4
4
.2
.2
0
0
0 2 4 6 8 10 0 2 4 6 8 10
Percentage Variation
Simulations from all data
Probability f
P b bilit of more th an X percent variation f
than t i ti from annual b d t
l budget
Acknowledgement: Nigel Rice and Hugh Gravelle
22. Consortia risk profile
Consortium size Consortium size
10000 50000
.6
.6
.4
.4
.2
.2
0
0
obability
0 2 4 6 8 10 0 2 4 6 8 10
Consortium size Consortium size
Pro
100000 150000
.6
.6
.4
.4
.2
2
.2
2
0
0
0 2 4 6 8 10 0 2 4 6 8 10
Percentage Variation
Omit £100k Omit £150k
Probability of more than an X percent variation from annual budget
Simulations omitting hi h cost patients f
Si l ti itti high t ti t from practice li t
ti lists
Acknowledgement: Nigel Rice and Hugh Gravelle
23. Some possible consequences of ‘hard’
budget constraints
b d
• Practices that perceive that their expenditure will fall below their
budget may “spend up” in order to protect their budgetary position
b d t “ d ”i d t t t th i b d t iti
in future years;
• Practices that perceive that their expenditure will exceed their
budget may be thrown into crisis as they seek to conform to the
budget may be thrown into crisis as they seek to conform to the
budget;
• Patients may be treated inequitably. Different practices will be
under different budgetary pressures, and so may adopt different
treatment practices.
• Within a practice, choice of treatment may vary over the course of
a year if the practice’s perception of its budgetary position changes.
• G
General practices may adopt a variety of defensive stratagems, such
l i d i fd f i h
as cream skimming patients they perceive to be healthier than
implied by their capitation payment.
24. Some budgetary risk management
strategies
• Pooling practices
• Pooling years
• Excluding predictably expensive patients
• ‘Carving out’ certain procedures or services
Carving out certain procedures or services
• Analysis of reasons for variations from budgets
• Allowing some reinsurance of risk
– Limiting liability on individual episode
– Limiting liability on individual patient
– Risk sharing
– Retention of a contingency fund
– Etc
• Making sanctions and rewards proportionate
g p p
