Hospital Ownership Form and Quality
Changes: Changes in Nurse Staffing
and Failure-to-Rescue following the
BBA of 1997
David K. Song, M.D., Ph.D.
Kevin G. Volpp, M.D., Ph.D.
We thank VA HSR&D, the Doris Duke Foundation,
and The National Bureau of Economic Research
for financial support.
Purpose of the Research Agenda
Do hospitals decrease quality in response to fiscal
pressure?
Do NP and FP hospitals respond differently to changes
in government reimbursement of medical services?
In this presentation, we examine whether FP hospitals
differed from NP hospitals in the adjustment of nurse
staffing and failure-to-rescue following the passage of
the Balanced Budget Act of 1997.
Caveat: Since quality is multi-dimensional and hard to
define, we put our study within the context of other
attempts to examine multiple outcome measures in
various provider settings.
FP vs. NP: Nurse Staffing
RN per adjusted day
7.5
7
6.5
6
5.5
5
4.5
4
3.5
3
FP 1
FP 4
1996
1997
1998
1999
2000
2001
RN per adjusted day
7.5
7
6.5
6
5.5
5
4.5
4
3.5
3
NP 1
NP 4
1996
1997
1998
1999
2000
2001
The Balanced Budget Act (97) and
Hospital Operating Income
By decreasing the growth rate in Medicare
reimbursement, the BBA enforced fiscal
discipline on hospitals.
The passage of the law coincided with a nationwide decline in operating income.
We find that hospitals, particularly for-profits,
decreased their operating expenses in
proportion to their Medicare exposure, but not
enough to offset the Medicare revenue shortfall.
Changes in quality may be part of the story.
Operating Income
Operating Income in U.S. Hospitals
200
100
2000
1999
1998
1997
1996
1995
1994
1993
1992
(100)
1991
$/admission
0
California
Florida
(200)
Illinois
New York
(300)
Texas
National
(400)
(500)
(600)
Hypotheses
Hypothesis 1: Hospitals with greater exposures to the BBA tend to
decrease RN staffing and increase FTR to a greater degree.
– Hospitals under financial stress may trade off profits and quality
above profit-maximizing level.
– Evidence from previous studies that 30-day AMI mortality
worsened in hospitals more affected by BBA.
Hypothesis 2: NP and FP hospitals in California do not differ in
degree of change in RN staffing or FTR for a similar degree of BBA
impact.
– According to utility-based models of hospital behavior, NP firms
are more responsive to changes in regulated prices under many
conditions, but this result does not hold for some profit functions.
– Previous work demonstrated no differential impact by FP/NFP
hospitals on 30-day AMI mortality.
Primary Sources of Data
California hospital characteristics from the
Office of Statewide Health Planning and
Development (OSHPD) from 1995-2001.
California patient discharge data to
examine deaths from surgical
complications.
Construction of the BBA Variable
Prior to BBA’s implementation, the AHA provided a forecast for
every hospital’s Medicare revenues from 1998-2000 under two
scenarios: (1) BBA implementation; (2) reimbursement without BBA
The difference in revenue under these two scenarios provides us
with a plausibly exogenous source of variation in the change in
hospital revenue
Forecast focuses on changes in reimbursement per admission, not
“secondary” effects such as firm’s strategic responses
To assess the impact of BBA on patient outcomes, we scale to level
of institution, i.e. 1% expected decrease in total firm revenue, rather
than $1 expected decrease in Medicare revenue. Hence, our
leading measure is:
| Forecasted revenues from 1998 to 2000 under BBA - Forecasted revenues from 1998 to 2000 under non - BBA |
,
[Forecaste d revenues from 1998 to 2000 under non - BBA]
scaled by the proportion of Medicare revenues for the hospital in 1997 (pre - BBA).
Empirical Strategy
1999-1997 Change Regression of the following form:
If c = 1 for revenue change regressions, the impact on actual revenues is very
strong. If c is less than 0.5, one major explanation is that firms responded with
top-line measures.
If c = 1 for operating expense regressions, firms responded with aggressive
cost-cutting measures.
Figure 5: BBA and Hospital Revenue Response
Dependent Variable--------------->
Explanatory Variable
log[BBA/non-BBA]a
Log
[Revenue] 
(1)
0.587362
(0.22324)
FP*log[BBA/non-BBA]
Log
%
[Revenue]  [Revenue]
(2)
**
(3)
%
[Revenue]
 Revenue /
Admission97
 Revenue /
Admission97
(4)
(5)
(6)
0.428141
(0.44169)
0.140814
(0.35490)
[BBA change]/[non-BBA]b
0.613107
(0.30363)
FP*[BBA change]/[non-BBA]
**
0.5036518
(0.53629)
0.092948
(0.40947)
[BBA change]/[Admissions1997]c
0.254696
(0.13002)
**
FP*[BBA change]/[Admissions1997]
R-squared:
N: 329
a
b
c
*
**
0.02
0.02
0.01
0.01
0.05
Difference in the natural logarithm of the forecasted revenue under the BBA policy and the log of the forecasted revenue
under the non-BBA policy
Difference in the forecasted revenue under the BBA policy and the forecasted revenue under the non-BBA policy, divided
by the forecasted revenue under the non-BBA policy
Difference in the forecasted revenue under the BBA policy and the forecasted revenue under the non-BBA policy, divided
by baseline admissions (year 1997)
Significant at the 10% level or lower
Significant at the 5% level or lower
0.1623242
(0.12299)
1.421067
(1.11632)
0.10
BBA and Hospital Operating Expenses
Dependent Variable--------------->
Column #
Explanatory Variable
log[BBA/non-BBA]a
Log
[Operating
Expenses] 
1
Log
[Operating
Expenses] 
2
0.2397437 *
(0.14336)
-0.333348
(0.29203)
0.5325457 **
(0.23585)
FP*log[BBA/non-BBA]
[BBA change]/[non-BBA]b
%
[Operating
Expenses]
3
0.4970544 **
(0.19824)
FP*[BBA change]/[non-BBA]
%
[Operating
Expenses]
4
 Operating
Expenses /
Admission97
5
0.0160219
(0.32526)
0.4877474 **
(0.24436)
[BBA change]/[Admissions1997]c
-0.0040325
(0.57416)
FP*[BBA change]/[Admissions1997]
R-squared:
N: 329
0.01
0.03
0.02
 Operating
Expenses /
Admission97
6
0.04
0.01
-0.6890022
(0.67179)
1.467882 **
(0.67462)
0.02
Empirical Strategy
For patient outcomes, we estimate linear probability
models of the following form (the form is similar for
hospital-level nurse staffing):
Yi    β'X i   1 * BBAi   2 * POSTi   3 * ( BBAi * POSTi )   4 * (OWNERi )
  5 * (OWNERi * POSTi )  6*(OWNERi * BBAi * POSTi )   i
We use dummy variables that proxy for the degree of
BBA impact. This is based on division of the
magnitude of BBA impact into three groups, low
(excluded), middle, and high impact. Results are
qualitatively similar to the results involving the
continuous variable.
Robust errors for clustering.
Results
Failure-to-rescue
Variable
Year99
Year00
BBA_MID
BBA_MID * Year99
BBA_MID * Year00
BBA_HI
BBA_HI * Year99
BBA_HI * Year00
FP
FP * BBA_HI
FP * BBA_MID
FP * BBA_HI * Year99
FP * BBA_MID * Year99
FP * BBA_HI * Year00
FP * BBA_MID * Year00
FP * Year99
FP * Year00
Constant
R-squared
N
Coefficient
-0.0064
-0.00034
-0.00213
-0.00416
-0.0152
-0.00225
0.0048
-0.01329
0.00649
-0.01822
-0.01559
0.020114
0.036133
0.039951
0.055005
-0.02353
-0.03322
0.078946
0.1948
130889
Nurse Staffing
*
*
*
*
**
Variable
Year99
Year00
BBA_MID
BBA_MID*Year99
BBA_MID*Year00
BBA_HI
BBA_HI*Year99
BBA_HI*Year00
FP
FP*BBA_MID
FP*BBA_HI
FP*BBA_MID*Year99
FP*BBA_HI*Year99
FP*BBA_MID*Year00
FP*BBA_HI*Year00
FP*Year99
FP*Year00
Constant
R-squared
N
Coefficient
-0.46723
0.02446
-0.46211
0.441297
-0.27738
-0.82616 *
0.623277
-0.03178
-1.31556
1.413058
0.892203
-2.24392 *
-2.4639 *
-1.33034
-1.84781
1.873551
1.540503
6.59583 **
0.2001
879
Results (2)
FR
Year99
Year00
BBA_MID
BBA_MID*Year99
BBA_MID*Year00
BBA_HI
BBA_HI*Year99
BBA_HI*Year00
FP
FP*BBA_HI
FP*BBA_MID
FP*Year99
FP*Year00
Constant
N
R-squared
RN
-0.0093
-0.01401 **
0.001415
0.005527
0.005784
-0.00356
-0.00792
-0.00793
0.012831
-0.00655
-0.0222
0.000646
0.003902
0.076642 **
130889
0.19
Year99
Year00
BBA_MID
BBA_MID*Year99
BBA_MID*Year00
BBA_HI
BBA_HI*Year99
BBA_HI*Year00
FP
FP*BBA_MID
FP*BBA_HI
FP*Year99
FP*Year00
Constant
N
R-sq
0.178249
0.486571
-0.02614
-0.24126
-0.67702 **
-0.25372
-0.20321
-0.6347 *
-0.12838
0.238267
-0.75254
-0.16496
0.122821
6.119017 **
879
0.2
Discussion
We find a relationship between the expected revenue change under
BBA and changes in revenue, operating expenses, and operating
income.
– FP’s more aggressively cut operating expenses relative to NP’s.
FP hospitals, relative to NP hospitals, have associated increases in
the probability of failure to rescue for a given expected decrease in
patient revenue.
Nurse staffing in FP hospitals with higher BBA revenue impact
decreased to a greater degree than nurse staffing in NP hospitals for
similar changes in revenue from the BBA implementation.
Some evidence that nurse staffing changes were related to revenue
changes for the California hospital sample.
In spite of FP-NP differences, not much evidence that failure rate
changes were related to revenue changes overall.
Future Work
Future work will examine NP-FP differences across multiple states
and different outcomes, further testing the robustness of our
conclusions.
Examination of the impact of the BBA on charity care.
Examination of NP-FP differences in firm strategies following the
BBA.
More work on financial distress: e.g. BSM pricing models in publicly
traded hospital chains, and Ohlson’s for NP and FP’s. Recent work
by others on distress in non-profit firms in general has clarified the
direction of research in financially distressed hospitals.
Market-level analysis to assess further the welfare implications of
the BBA. For example, patients may have been able to identify
hospitals least affected by the BBA, and so the law could have had a
minimally detrimental effect on quality within the market.