Hospital Ownership Form and Quality Hospital Ownership Form and Quality Changes: Changes in Nurse Staffing Changes: Changes in Nurse Staffing and Failure-to-Rescue following the and Failure-to-Rescue following the

BBA of 1997BBA of 1997

David K. Song, M.D., Ph.D.David K. Song, M.D., Ph.D.Kevin G. Volpp, M.D., Ph.D.Kevin G. Volpp, M.D., Ph.D.

We thank VA HSR&D, the Doris Duke Foundation, We thank VA HSR&D, the Doris Duke Foundation, and The National Bureau of Economic Research for and The National Bureau of Economic Research for

financial support.financial support.

Purpose of the Research AgendaPurpose of the Research Agenda

Do hospitals decrease quality in response to fiscal Do hospitals decrease quality in response to fiscal pressure?pressure?Do NP and FP hospitals respond differently to changes Do NP and FP hospitals respond differently to changes in government reimbursement of medical services?in government reimbursement of medical services?In this presentation, we examine whether FP hospitals In this presentation, we examine whether FP hospitals differed from NP hospitals in the adjustment of nurse differed from NP hospitals in the adjustment of nurse staffing and failure-to-rescue following the passage of staffing and failure-to-rescue following the passage of the Balanced Budget Act of 1997.the Balanced Budget Act of 1997.Caveat: Since quality is multi-dimensional and hard to Caveat: Since quality is multi-dimensional and hard to define, we put our study within the context of other define, we put our study within the context of other attempts to examine multiple outcome measures in attempts to examine multiple outcome measures in various provider settings.various provider settings.

FP vs. NP: Nurse StaffingFP vs. NP: Nurse StaffingRN per adjusted day

33.5

44.5

55.5

66.5

77.5

1996 1997 1998 1999 2000 2001

FP 1

FP 4

RN per adjusted day

33.5

44.5

55.5

66.5

77.5

1996 1997 1998 1999 2000 2001

NP 1

NP 4

The Balanced Budget Act (97) and The Balanced Budget Act (97) and Hospital Operating IncomeHospital Operating Income

By decreasing the growth rate in Medicare By decreasing the growth rate in Medicare reimbursement, the BBA enforced fiscal reimbursement, the BBA enforced fiscal discipline on hospitals.discipline on hospitals.The passage of the law coincided with a nation-The passage of the law coincided with a nation-wide decline in operating income.wide decline in operating income.We find that hospitals, particularly for-profits, We find that hospitals, particularly for-profits, decreased their operating expenses in decreased their operating expenses in proportion to their Medicare exposure, but not proportion to their Medicare exposure, but not enough to offset the Medicare revenue shortfall.enough to offset the Medicare revenue shortfall.Changes in quality may be part of the story.Changes in quality may be part of the story.

Operating IncomeOperating Income

Operating Income in U.S. Hospitals

(600)

(500)

(400)

(300)

(200)

(100)

0

100

2001

99

1

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

$/a

dm

iss

ion

California

Florida

Illinois

New York

Texas

National

HypothesesHypotheses

Hypothesis 1: Hospitals with greater exposures to the BBA tend to Hypothesis 1: Hospitals with greater exposures to the BBA tend to decrease RN staffing and increase FTR to a greater degree.decrease RN staffing and increase FTR to a greater degree.– Hospitals under financial stress may trade off profits and quality Hospitals under financial stress may trade off profits and quality

above profit-maximizing level.above profit-maximizing level.– Evidence from previous studies that 30-day AMI mortality Evidence from previous studies that 30-day AMI mortality

worsened in hospitals more affected by BBA.worsened in hospitals more affected by BBA.Hypothesis 2: NP and FP hospitals in California do not differ in 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 degree of change in RN staffing or FTR for a similar degree of BBA impact.impact.– According to utility-based models of hospital behavior, NP firms According to utility-based models of hospital behavior, NP firms

are more responsive to changes in regulated prices under many are more responsive to changes in regulated prices under many conditions, but this result does not hold for some profit functions.conditions, but this result does not hold for some profit functions.

– Previous work demonstrated no differential impact by FP/NFP Previous work demonstrated no differential impact by FP/NFP hospitals on 30-day AMI mortality.hospitals on 30-day AMI mortality.

Primary Sources of DataPrimary Sources of Data

California hospital characteristics from the California hospital characteristics from the Office of Statewide Health Planning and Office of Statewide Health Planning and Development (OSHPD) from 1995-2001.Development (OSHPD) from 1995-2001.

California patient discharge data to California patient discharge data to examine deaths from surgical examine deaths from surgical complications.complications.

Construction of the BBA VariableConstruction of the BBA Variable

Prior to BBA’s implementation, the AHA provided a forecast for Prior to BBA’s implementation, the AHA provided a forecast for every hospital’s Medicare revenues from 1998-2000 under two every hospital’s Medicare revenues from 1998-2000 under two scenarios: (1) BBA implementation; (2) reimbursement without BBA scenarios: (1) BBA implementation; (2) reimbursement without BBA The difference in revenue under these two scenarios provides us The difference in revenue under these two scenarios provides us with a plausibly exogenous source of variation in the change in with a plausibly exogenous source of variation in the change in hospital revenuehospital revenueForecast focuses on changes in reimbursement per admission, not Forecast focuses on changes in reimbursement per admission, not “secondary” effects such as firm’s strategic responses “secondary” effects such as firm’s strategic responses To assess the impact of BBA on patient outcomes, we scale to To assess the impact of BBA on patient outcomes, we scale to level of institution, i.e. 1% expected decrease in total firm revenue, level of institution, i.e. 1% expected decrease in total firm revenue, rather than $1 expected decrease in Medicare revenue. Hence, rather than $1 expected decrease in Medicare revenue. Hence, our leading measure is: our leading measure is:

BBA).-(pre 1997in hospital for the revenues Medicare of proportion by the scaled

,BBA]-nonunder 2000 to1998 from revenues d[Forecaste

|BBA-nonunder 2000 to1998 from revenues Forecasted -BBA under 2000 to1998 from revenues Forecasted|

Empirical Strategy Empirical Strategy

1999-1997 Change Regression of the following form:1999-1997 Change Regression of the following form:

If c = 1 for revenue change regressions, the impact on actual revenues is very 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 strong. If c is less than 0.5, one major explanation is that firms responded with top-line measures.top-line measures.If c = 1 for operating expense regressions, firms responded with aggressive If c = 1 for operating expense regressions, firms responded with aggressive cost-cutting measures.cost-cutting measures.

Figure 5: BBA and Hospital Revenue Response

Dependent Variable---------------> Log [Revenue]

Log [Revenue]

% [Revenue]

% [Revenue]

Revenue / Admission97

Revenue / Admission97

Explanatory Variable (1) (2) (3) (4) (5) (6) log[BBA/non-BBA]a 0.587362 ** 0.428141 (0.22324) (0.44169) FP*log[BBA/non-BBA] 0.140814 (0.35490) [BBA change]/[non-BBA]b 0.613107 ** 0.5036518 (0.30363) (0.53629) FP*[BBA change]/[non-BBA] 0.092948 (0.40947) [BBA change]/[Admissions1997]c 0.254696 ** 0.1623242 (0.13002) (0.12299) FP*[BBA change]/[Admissions1997] 1.421067 (1.11632) R-squared: 0.02 0.02 0.01 0.01 0.05 0.10 N: 329 a 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 b 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 c 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

BBA and Hospital Operating Expenses

Dependent Variable--------------->

Log [Operating Expenses]

Log [Operating Expenses]

% [Operating Expenses]

% [Operating Expenses]

Operating Expenses / Admission97

Operating Expenses / Admission97

Column # 1 2 3 4 5 6 Explanatory Variable log[BBA/non-BBA]a 0.2397437 * -0.333348 (0.14336) (0.29203) FP*log[BBA/non-BBA] 0.5325457 ** (0.23585)

[BBA change]/[non-BBA]b 0.4970544 ** -

0.0160219 (0.19824) (0.32526) FP*[BBA change]/[non-BBA] 0.4877474 ** (0.24436) [BBA change]/[Admissions1997]c -0.0040325 -0.6890022 (0.57416) (0.67179) FP*[BBA change]/[Admissions1997] 1.467882 ** (0.67462) R-squared: 0.01 0.03 0.02 0.04 0.01 0.02 N: 329

Empirical StrategyEmpirical Strategy

For patient outcomes, we estimate linear probability For patient outcomes, we estimate linear probability models of the following form (the form is similar for models of the following form (the form is similar for hospital-level nurse staffing):hospital-level nurse staffing):

We use dummy variables that proxy for the degree of We use dummy variables that proxy for the degree of BBA impact. This is based on division of the BBA impact. This is based on division of the magnitude of BBA impact into three groups, low magnitude of BBA impact into three groups, low (excluded), middle, and high impact. Results are (excluded), middle, and high impact. Results are qualitatively similar to the results involving the qualitatively similar to the results involving the continuous variable.continuous variable. Robust errors for clustering.Robust errors for clustering.

iiiiii

iiiiiii

POSTBBAOWNERPOSTOWNER

OWNERPOSTBBAPOSTBBAY

)***()*(*

)(*)*(***'

65

4321Xβ

ResultsResults

Failure-to-rescueFailure-to-rescue Nurse StaffingNurse Staffing Variable Coefficient Year99 -0.0064 Year00 -0.00034 BBA_MID -0.00213 BBA_MID * Year99 -0.00416 BBA_MID * Year00 -0.0152 * BBA_HI -0.00225 BBA_HI * Year99 0.0048 BBA_HI * Year00 -0.01329 FP 0.00649 FP * BBA_HI -0.01822 FP * BBA_MID -0.01559 FP * BBA_HI * Year99 0.020114 FP * BBA_MID * Year99 0.036133 FP * BBA_HI * Year00 0.039951 * FP * BBA_MID * Year00 0.055005 * FP * Year99 -0.02353 FP * Year00 -0.03322 * Constant 0.078946 ** R-squared 0.1948 N 130889

Variable CoefficientYear99 -0.46723Year00 0.02446BBA_MID -0.46211BBA_MID*Year99 0.441297BBA_MID*Year00 -0.27738BBA_HI -0.82616 *BBA_HI*Year99 0.623277BBA_HI*Year00 -0.03178FP -1.31556FP*BBA_MID 1.413058FP*BBA_HI 0.892203FP*BBA_MID*Year99 -2.24392 *FP*BBA_HI*Year99 -2.4639 *FP*BBA_MID*Year00 -1.33034FP*BBA_HI*Year00 -1.84781FP*Year99 1.873551FP*Year00 1.540503Constant 6.59583 **R-squared 0.2001N 879

Results (2)Results (2)

FRFR RNRNYear99 0.178249Year00 0.486571BBA_MID -0.02614BBA_MID*Year99 -0.24126BBA_MID*Year00 -0.67702 **BBA_HI -0.25372BBA_HI*Year99 -0.20321BBA_HI*Year00 -0.6347 *FP -0.12838FP*BBA_MID 0.238267FP*BBA_HI -0.75254FP*Year99 -0.16496FP*Year00 0.122821Constant 6.119017 **N 879R-sq 0.2

Year99 -0.0093Year00 -0.01401 **BBA_MID 0.001415BBA_MID*Year99 0.005527BBA_MID*Year00 0.005784BBA_HI -0.00356BBA_HI*Year99 -0.00792BBA_HI*Year00 -0.00793FP 0.012831FP*BBA_HI -0.00655FP*BBA_MID -0.0222FP*Year99 0.000646FP*Year00 0.003902Constant 0.076642 **N 130889R-squared 0.19

DiscussionDiscussion

We find a relationship between the expected revenue change under We find a relationship between the expected revenue change under BBA and changes in revenue, operating expenses, and operating BBA and changes in revenue, operating expenses, and operating income.income.– FP’s more aggressively cut operating expenses relative to NP’s.FP’s more aggressively cut operating expenses relative to NP’s.

FP hospitals, relative to NP hospitals, have associated increases in FP hospitals, relative to NP hospitals, have associated increases in the probability of failure to rescue for a given expected decrease in the probability of failure to rescue for a given expected decrease in patient revenue.patient revenue.Nurse staffing in FP hospitals with higher BBA revenue impact Nurse staffing in FP hospitals with higher BBA revenue impact decreased to a greater degree than nurse staffing in NP hospitals decreased to a greater degree than nurse staffing in NP hospitals for similar changes in revenue from the BBA implementation.for similar changes in revenue from the BBA implementation.Some evidence that nurse staffing changes were related to revenue Some evidence that nurse staffing changes were related to revenue changes for the California hospital sample.changes for the California hospital sample.In spite of FP-NP differences, not much evidence that failure rate In spite of FP-NP differences, not much evidence that failure rate changes were related to revenue changes overall.changes were related to revenue changes overall.

Future WorkFuture Work

Future work will examine NP-FP differences across multiple states Future work will examine NP-FP differences across multiple states and different outcomes, further testing the robustness of our and different outcomes, further testing the robustness of our conclusions.conclusions.Examination of the impact of the BBA on charity care.Examination of the impact of the BBA on charity care.Examination of NP-FP differences in firm strategies following the Examination of NP-FP differences in firm strategies following the BBA.BBA.More work on financial distress: e.g. BSM pricing models in publicly 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 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 by others on distress in non-profit firms in general has clarified the direction of research in financially distressed hospitals.direction of research in financially distressed hospitals.Market-level analysis to assess further the welfare implications of Market-level analysis to assess further the welfare implications of the BBA. For example, patients may have been able to identify the BBA. For example, patients may have been able to identify hospitals least affected by the BBA, and so the law could have had hospitals least affected by the BBA, and so the law could have had a minimally detrimental effect on quality within the market.a minimally detrimental effect on quality within the market.