A cost-effectiveness framework for
profiling hospital efficiency
Justin Timbie
AcademyHealth Annual Research Meeting
June 5, 2007
Walt Disney World “Dolphin”
1
Acknowledgements
Sharon-Lise Normand1,2
Joe Newhouse1
Meredith Rosenthal3
1Department
of Health Care Policy, Harvard Medical School
2Department of Biostatistics, Harvard School of Public
Health
3Department of Health Policy & Management, Harvard
School of Public Health
2
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.
3
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.
4
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.5
Study design
• Objective: Compare efficiency of hospital care
following acute myocardial infarction (AMI).
• Motivation: Channeling patients to high-value
hospitals for specific conditions.
Efficiency = Health benefit relative to cost
• Outcomes: In-hospital survival, hospital costs.
• Data source: Massachusetts all payer data.
– 69 hospitals (11,259 patients) in FY’03.
6
Methods - Cost measurement
• Used total hospital charges and global cost-tocharge 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.
7
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.
8
Methods - Combining measures
• Incremental outcomes:
ΔEi = Predicted survivali – Expected survivali
ΔCi = Predicted costi – Expected costi
• Incremental Net Health Benefits (INHB):
INHBi = ΔEi – ΔCi/
where  = WTP/ΔE = $5M/Life saved
• Estimate P(INHB > 0)
• Identify efficient hospitals using relative or
absolute threshold.
9
93
92
92
88
88
89
90
90
91
91.34
P(Y(S)  Y(S) , Y(C)  Y(C) )
87
(%)
Survival
Standardized
Standardized
Survival (%)
94
94
Results – Threshold Scoring
P(Y(S)  Y(S) , Y(C)  Y(C) )
17,846
15000
15,000
20000
20,000
25000
25,000
Standardized Cost (Dollars)
30000
30,000
Standardized Cost (dollars)
35000
35,000
10
93
92
92
88
88
89
90
90
91
91.34
87
Standardized Survival (%)
Standardized Survival (%)
94
94
Results - Cost-effectiveness
17,846
15000
15,000
20000
20,000
25000
25,000
30000
30,000
Standardized
Cost (Dollars)
Standardized
Cost
(dollars)
35000
35,000
11
0.6
0.6
0.2
0.2
0.4
0.4
ΔCi


PINHB i  0   P ΔEi 
 0
λ


0.0
0.0
> 0)> 0)
P (INHBP(INHB
0.8
0.8
1.0
1.0
Sensitivity of INHB estimates to 
00
11
22
33
44
55
Willingness to Pay (Million $/Life Saved)
Willingness to Pay Threshold
(Million $/Life Saved)
12
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.
13
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.
14