Jennifer Dixon: Managing financial risk in the NHS

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© Nuffield Trust Annual Health Strategy Summit Managing financial risk in the NHS March 2011 Twitter: #NTSummit Jennifer Dixon (with thanks to Sian Davies) Nuffield Trust

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Transcript of Jennifer Dixon: Managing financial risk in the NHS

Page 1: Jennifer Dixon: Managing financial risk in the NHS

© Nuffield Trust

Annual Health Strategy Summit

Managing financial risk in the NHS

March 2011 Twitter: #NTSummit

Jennifer Dixon (with thanks to Sian Davies)Nuffield Trust

Page 2: Jennifer Dixon: Managing financial risk in the NHS

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PresentationConceptsHealth and Social Care BillInsurance riskPerson-based resource allocation

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Financial risk: concepts

• Risk of a unit overspending due to circumstances beyond its control

• Insurance risk• Provider risk

• Ex ante risk management• Ex post risk management

March 2011

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Health and Social Care Bill: Insurance risk

• SoS specifies resources to NHS CB in annual mandate• NHS CB allocates resources to consortia• NHS CB commissions specialised services for rare

conditions (SoS decides)• NHS CB and consortia can set jointly or each up a pooled

fund• NHS CB can set up a contingency fund• NHS CB can provide financial assistance• NHS CB specifies matters in standard commissioning

contracts• NHS CB sets structure of pricing• NHS CB can set up a failure regime for consortia

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Health and Social Care Bill: Provider (FT) risk; designated services

• Monitor sets prices• Monitor: core function of setting up a ‘special

administration regime’ in event of provider failure to preserve ‘designated services’

• Commissioners apply for a service to be ‘designated’ (Monitor provides guidance on criteria)

• Monitor can impose additional licence conditions on the designated.

• Can be local modifications of prices for designated services

• Corporate insolvency procedures (undesignated services)• Special administration regime (designated)

March 2011

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Health and Social Care Bill: Provider (FT) risk

• Financial assistance for failing FTs providing designated services could be through:- providers and commissioners being required to set up a

risk pool (powers by Monitor to require commissioners or providers to pay a levy)

- providers being required to purchase their own insurance to cover liabilities as specified by Monitor.

• Taxpayer investment in FTs managed through operationally independent banking function.

March 2011

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Risk map: undesignated services

NHS CB

PCT clusters

Consortia

Practices

Patients

FTs

Practices

Insurance Provider

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Risk map: designated services

NHS CB

PCT clusters

Consortia

Practices

Patients

Monitor

FTs

Practices

Insurance Provider

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Insurance risk

March 2011 © Nuffield Trust

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Insurance risk: strategies

Risk bearing Risk sharing Transferring risk

Source: Ryan, J. Bruce, Healthcare Financial Management 07350732, Jan97, Vol. 51, Issue 1

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Insurance risk: some strategies (ex ante)

Risk bearing

Increasing the risk pool

Spreading risk across

years

Self insurance

Risk sharing

Joining others’ risk

pools

Alliance contracts

Transferring risk

To providers

To other insurance

entity

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Insurance risk: some strategies (ex ante)

Risk bearing

Increasing the risk pool

Spreading risk across

years

Self insurance

Risk sharing

Joining others’ risk

pools

Alliance contracts

Transferring risk

To providers

To other insurance

entity

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Person-based resource allocationPBRA

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Policy context

• NHS Commissioning Board responsible for allocations to GP consortia

• Cover: secondary care, prescribing, community health services• Allocations based on aggregating up practice level budgets

(allows practices to move between consortia)• First allocations to be made for 2013/14• Shadow allocations in 2012/13

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Person-based resource allocation

• To develop a person-based formula for resource allocation to practices for commissioning

• To promote equity of access for equal need

• Provide advice on risk sharing

March 2011

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ExpenditureiNeeds i supplyaNeedsa Other variablesa, , ,( (f

Basic model

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Explanatory variables Prediction variable

2007/08 2009/102008/09

Data

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PBRA model: actual to predicted costs, 2007/8

Table 4 Actual compared to predicted cost for the basic set of models, predicting costs for 2007/08 -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Model Set of variables Validation sample 2 Individuals=5,445,559 Practices=797 -------------------------------- -------------------------------- Percentage of practices where (actual-predicted)/predicted cost -------------------------------- -------------------------------- -10<%<0 -5<%<0 -3<%<0 0<%<3 0<%<5 0<%<10 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Model 1: age and gender 21 10 5 7 12 21 Model 2: age and gender morbidity markers 26 14 8 8 14 25 Model 3: age and gender morbidity markers 152 PCT dummies 34 16 11 11 18 31 Model 4: age and gender morbidity markers 152 PCT dummies 135 attributed needs & 63 supply 37 22 13 12 19 31 Model 5 age and gender morbidity markers 152 PCT dummies 7 attributed needs & 3 supply 35 19 11 12 19 33

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0.5000

1.0000

1.5000

2.0000

2.5000

0 5000 10000 15000 20000 25000 30000 35000 40000

Comparison Observed and Expected Costs at Practice level

List size

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Risk sharing

Measures include: (actual-predicted)/predicted cost

Size of practice/group of practices/consortia

Various ‘risk’ arrangements:• Service ‘carve outs’ eg specialised commissioning• Per capita limit per annum (stop loss)• Extended ‘break even’ period

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Approach: Pseudo-Monte Carlo simulation

• Dataset of 10million patients with all relevant information to predict expenditures (for 2006/07) using

Nuffield model

• Randomly sample from dataset repeatedly for a given GP consortium size to assess risk:

• Example

• start with GP consortium of size = 10,000

• Sample 10,000 from the available 10m

• Generate the model predicted level of expenditure for each individual

• Compare predicted expenditure to known actual expenditure

• Compute difference (risk) at individual level and at aggregate consortium level

• Repeat above for different sizes of consortia from 10,000 to 500,000 in increments of 10,000

• Summarise results - done graphically

• Can repeat for different assumptions about composition of consortia and/or risk sharing arrangements

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Sampled from patients (10m) within a 20% random sample of all patients100 replications for each consortium sizeConsortium size increased in units of 10,000

-40

-20

020

40C

onso

rtium

risk

per

cap

ita(£

)

0 100000 200000 300000 400000 500000Consortium list size

Average risk Lower CIUpper CI

Simulations from all dataRisk smoothed over time - predicted versus actual expenditure

Consortia risk profile

Upper 95% C.I.

Lower 95% C.I.

Average risk

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-40

-20

020

40C

onso

rtium

risk

per

cap

ita(£

)

0 100000 200000 300000 400000 500000Consortium list size

Average risk Lower CIUpper CI

Simulations from all dataRisk smoothed over time - predicted versus actual expenditure

Consortia risk profile

Upper 95% C.I.

Lower 95% C.I.

Average risk

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-40

-20

020

40Co

nsor

tium

risk p

er ca

pita(

£)

0 100000 200000 300000 400000 500000Consortium list size

Average risk Lower CIUpper CI

Simulations from all dataRisk smoothed over time - predicted versus actual expenditure

Consortia risk profile

14

-13.5

Upper 95% C.I.

Lower 95% C.I.

Average risk

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Sampled from patients (10m) within a 20% random sample of all patients100 replications for each consortium sizeConsortium size increased in units of 10,000

-40

-20

020

40C

onso

rtium

risk

per

cap

ita(£

)

0 100000 200000 300000 400000 500000Consortium list size

Average risk Lower CIUpper CI

Simulations from all dataRisk smoothed over time - predicted versus actual expenditure

Consortia risk profile

£4

£4

Upper 95% C.I.

Lower 95% C.I.

Average risk

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Sampled from patients (10m) within a 20% random sample of all patients100 replications for each consortium sizeConsortium size increased in units of 10,000

-40

-20

020

40C

onso

rtium

risk

per

cap

ita(£

)

0 100000 200000 300000 400000 500000Consortium list size

Average risk Lower CIUpper CI

Simulations from all dataRisk smoothed over time - predicted versus actual expenditure

Consortia risk profile

£8

£8

Upper 95% C.I.

Lower 95% C.I.

Average risk

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ConclusionComprehensive strategy to manage insurance risk needs developingRecent empirical advances in risk adjustment helpEx post risk management needs to be more explicit

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Thank you

March 2011

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