1 a cost-effectiveness framework for profiling hospital efficiency justin timbie academyhealth...

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A cost-effectiveness framework for profiling hospital efficiency

Justin Timbie

AcademyHealth Annual Research Meeting

June 5, 2007

Walt Disney World “Dolphin”

2

Acknowledgements

Sharon-Lise Normand1,2

Joe Newhouse1

Meredith Rosenthal3

1Department of Health Care Policy, Harvard Medical School2Department of Biostatistics, Harvard School of Public

Health3Department of Health Policy & Management, Harvard

School of Public Health

3

Context

• Interest in efficiency measurement following growth of P4P.– 42% of commercial HMOs use cost information

(Rosenthal, 2005)

• DRA of 2005 requires Medicare to implement value based purchasing for hospital services by FY’09.– Efficiency measures to be included in FY’10-11.

• Measuring appropriateness and efficiency are both challenges.

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Examples of efficiency metrics

• Dartmouth Atlas: population-based efficiency:– Medicare spending (last 2 years of life)– Resource inputs: beds, physician FTE inputs – Utilization: hospital/ICU days, physician visits

• Leapfrog Group: risk-adjusted LOS, readmission rates within 14 days.

• National Quality Forum: focusing on LOS and readmission.

• Medicare: MEDPAC considering publicly reporting hospital readmission rates.

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Measurement challenges

• Defining efficiency: Focus on payment or resource use (LOS, readmission rates, RVUs).

– DRG-based payment makes hospital efficiency profiling different.

– Limited ability to measure inpatient resource use.

• Duration of efficiency, quality measurement.

– Longer duration is desired.

– Causes attribution difficulties (PAC providers).

• Weighting of cost vs. quality.

– Binary (threshold) scoring approaches weight domains equally.

– Measuring performance continuously allows tradeoffs.

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Study design

• Objective: Compare efficiency of hospital care following acute myocardial infarction (AMI).

• Motivation: Channeling patients to high-value hospitals for specific conditions.

• Outcomes: In-hospital survival, hospital costs. • Data source: Massachusetts all payer data.

– 69 hospitals (11,259 patients) in FY’03.

Efficiency = Health benefit relative to cost

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Methods - Cost measurement

• Used total hospital charges and global cost-to-charge ratios. – Costs derived from charge data remove price

variation.– Use of global cost-to-charge ratios may confound

estimates due to differential markup across revenue centers.

• Used in-hospital outcomes, although 30-day outcomes are preferred.

• Lacking post-acute care costs, costs of procedures.

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Methods - Estimation

• Link inter-hospital transfers to create inpatient “episodes.”

• Estimate “predicted” outcomes.– Fit hierarchical models.– Condition on hospital-specific effect, risk factors.

• Estimate “expected” outcomes.– Condition on population mean effect, risk factors.

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Methods - Combining measures

• Incremental outcomes:

ΔEi = Predicted survivali – Expected survivaliΔCi = Predicted costi – Expected costi

• Incremental Net Health Benefits (INHB):

• Estimate P(INHB > 0)• Identify efficient hospitals using relative or absolute

threshold.

INHBi = ΔEi – ΔCi/

where = WTP/ΔE = $5M/Life saved

10

15000 20000 25000 30000 35000

87

88

89

90

91

92

93

94

Standardized Cost (Dollars)

Sta

nd

ard

ize

d S

urv

iva

l (%

)

91.34

17,846

Results – Threshold Scoring

Standardized Cost (dollars)

Sta

nd

ard

ized

Su

rviv

al (

%)

15,000 20,000 25,000 30,000 35,000

88

9

0

9

2

94

)YY,YP(Y (C)(C)(S)(S)

)YY,YP(Y (C)(C)(S)(S)

1115000 20000 25000 30000 35000

87

88

89

90

91

92

93

94

Standardized Cost (Dollars)

Sta

nd

ard

ize

d S

urv

iva

l (%

)

91.34

17,846

Results - Cost-effectiveness

Standardized Cost (dollars)

Sta

nd

ard

ized

Su

rviv

al (

%)

15,000 20,000 25,000 30,000 35,000

88

9

0

9

2

94

12

Sensitivity of INHB estimates to 0

.00

.20

.40

.60

.81

.0

Willingness to Pay (Million $/Life Saved)

P(I

NH

B >

0)

0 1 2 3 4 5

Willingness to Pay Threshold

(Million $/Life Saved)

0 1 2 3 4 5

P (

INH

B >

0)

0.0

0.2

0.4

0.6

0.8

1

.0

0

λi

ii

ΔCΔEP0INHBP

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Summary

• Proposed an economic approach to measuring efficiency using a composite measure.

• Theoretically strong and objective weighting mechanism.

• Results will differ from threshold model due to ability to incorporate tradeoffs.

• Difficult to agree on single WTP value.– LY and QALY measures of benefit are more

promising.

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Future work

• Longitudinal analysis.• Inclusion of AMI process measures, quality of

life.• Developing willingness to pay values that reflect

multiple outputs (benefits).• Refining cost measure to include RVUs.• Exploring a composite measure of hospital

efficiency.