Peter Smith: Allocating healthcare budgets to general practices
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
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
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 VenNote: RAWP = Resource Allocation Working Party RoR = Review of RAWP AREA = Allocation of Resources to English Areas CARAN = Combining Age Related Additional Needs (9) 3
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
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
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
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 & 2006/07MODEL R2 individual R2 practiceModel 1: age and gender 0.0366 0.3444Model 2 ‐ ADD: 152 morbidity markers 0.1223 0.6084Model 3 ‐ ADD: 152 PCT dummies 0.1227 0.7437Model 4 ‐ ADD: 135 attributed needs & 63 supply 135 tt ib t d d & 63 l 0.1230 0 1230 0.7851 0 7851Model 5 ‐ REDUCE TO: 7 attributed needs & 3 supply 7 attributed needs & 3 supply 0.1229 0 1229 0.7735 0 7735
Type of Variable namevariableIndividual • Age and gender • 157 ICD‐10 groupsAttributed Attributed • Persons in social rented housing Persons in social rented housingneeds • 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 rateAttributed • 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
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
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 FT0 0 10000 20000 30000 40000 practice size: number of patients Excludes the 16 practices with a DFT index > 2.
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 14.0DFT relative to national mean 72.5 48.9 20.9 12
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)
Basic model Explanatory variables Prediction variable2007/08 2008/09 2009/10
Data lag2007/08 2008/09 2009/10 2010/11 2011/12 2012/13
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.2002Martin, 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.
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
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.
High cost cases High cost cases practices umber of pNu Percentage of cases over £20K per person per year 19
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
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 10Proba 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
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 sizePro 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
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.
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