Hospital Payment, Nurse
Staffing, and the Outcomes of
Patient Care
Mei Zhao, Ph.D.
University of North Florida
Gloria J. Bazzoli, Ph.D.
Virginia Commonwealth University
Agency for Healthcare Research
and Quality,Grant # R01 HS13094
Rationale for the Study

US hospital payment has been declining during the last
several years

Hospitals have been cutting staffing due to financial
pressures


Studies found that higher nurse staffing ratio is related to
better patient outcomes, but most of these studies are
cross-sectional
Few studies ever examined the relationship among
hospital payment, staffing decisions, and patient
outcomes
Research Questions


What is the effect of hospital payment on
the quality of care and patient safety?
What is the effect of hospital nurse
staffing on the quality of care and patient
safety?
Method



Design
 A panel study design is applied to data
from 1995-2000
Sampling
 Nonfederal short term general hosps from
11 states (AZ, CA, CO, FL, IA, MD, MA, NJ,
NY,WA, and WI), about 1,100 per year
Data Sources
 AHA, MCR, ARF, HCUP (SID) 1995-2000
Key Variables

Payment


Staffing Indicators



Net patient revenue per adjusted patient day
RNs per adjusted patient day
LPNs per adjusted patient day
Patient Outcomes


IQIs (4): AMI, CHF, Stroke, and Pneumonia
PSIs (4): Decubitus ulcer, infections due to medical care,
post-op pulmonary embolism , and sepsis
Analytic Strategies

Panel Analysis


Fixed-effects Models
Outcomeit    1 Re vdayit   2 Staffit   3 Hosp it   4 Patit   5 Mktit   6Tt   i   it
Variable
Hospital Payment
Net revenues per adjusted patient day
Hospital Staffing
RNs per 1000 patient day
LPNs per 1000 patient day
Hospital Organizational Characteristics
1-99 beds
100-299 beds
300-499 beds
Hospital with public ownership
Hospital with church ownership
Hospital with for-profit ownership
Hospital with system affiliation
Hospital with network affiliation
Hospital Medicaid %
Hospital Output
Surgical operations
Number of patients
Hospital Market Characteristics
Herfindahl-Hirschman Index
HMO market share
% of hospitals in market that are for-profit
Per capita income ($1000)
Number of beds per 1000 pop
Patient Characteristics
Age<19
Age 19-64
Female %
Black %
Casemix index
Mortality34 %
Table 2--Variable Definitions and Descriptive Statistics
IQIs Sample
Mean
SD
1063
903
2.320
0.380
1.017
0.397
0.368
0.427
0.145
0.185
0.138
0.156
0.516
0.212
16.483
0.482
0.495
0.352
0.388
0.345
0.363
0.500
0.409
15.006
6108
6644
0.432
0.228
0.156
26.455
10581
0.318
0.211
0.223
8.731
1.588
15.862
41.656
59.665
7.823
1.047
13.792
10.183
13.625
5.470
13.564
0.339
6.727
Table 4.2-Multivariate Analysis of IQIs-- Net Revenues Per 1000 Adjusted Patient Day
Net Revenues Per 1000 Adjusted Patient Day
Hospital Staffing
RNs Per 1000 patient day
LPNs Per 1000 patient day
Hospital Organizational Characteristics
Hospital Medicaid %
Hospital Output
log(Surgical operations)
Number of patients
Hospital Market Characteristics
Herfindahl-Hirschman Index (HHI)
HMO market share
HHI*HMO
% of hospitals in market that are for-profit
Per capita income
Number of beds per 1000 pop
Patient Characteristics
Female %
Black %
Casemix index
Mortality34 %
Time Variables
1996
1997
1998
1999
2000
AMI
Fixed Effects Model
Coeff. Std Error
CHF
Fixed Effects Model
Coeff Std. Error
Stroke
Pneumonia
Fixed Effects Model Fixed Effects Model
Coeff
Std. Error Coeff
Std. Error
.00003
.003
-.003*
.0015
.0002
.003
-.005**
.002
-.912
6.667
1.569
4.300
.094
1.144
0.627
1.523
.256
6.465*
1.303
3.309
.802
1.938
.006
.0001
-.0006** .0003
-.0002** .0001
-.0006** .0002
-.118
4.376**
-.014**
-.0003**
-.0001*** .00002
-.00002***.000007
-.0001***.00003
-.00003*** .000008
0.023** .012
.0191** .008
.0265*** .008
-.0495*** .018
.003** .001
00221*** .0007
0.030*
.0156
0.0166*** .006
-.0273** .0136
0.032***
0.002**
0.0004*
0.002***
-0.0008**
0.078***
-0.002***
-0.001***
-0.002***
-.004
-.024***
-.027***
-.016***
-.013***
------
-.0011*** .0002
Table 4.2-Multivariate Analysis of PSIs-- Net Revenues Per 1000 Adjusted Patient Day
Decubitus ulcer
Infections due to
Post-op PE
medical care
Fixed Effects Model Fixed Effects Model Fixed Effects Model
Coeff. Std Error
Coeff Std. Error
Coeff Std. Error
Net Revenues Per 1000 Adjusted Patient Day 1.42e-04 5.39e-04
-3.52e-05 5.25e-05 -5.97e-05 2.64e-04
Hospital Staffing
RNs Per 1000 patient day
-.0749 .210
-.004 .020
-.017
.107
LPNs Per 1000 patient day
.791 .511
.051 .049
.849*** .271
Hospital Organizational Characteristics
Hospital Medicaid %
.00003 .00004
Hospital Output
Number of patients (%)
1.56e-04 3.58e-04
4.58e-05*** 1.38e-05
Hospital Market Characteristics
Herfindahl-Hirschman Index (HHI)
-.0056** .0022
Number of beds per 1000 pop
.0005*** .0002
Patient Characteristics
Black %
.00014** .00006
.00007** .00003
Mortality34 %
.00065*** .00006
.000026*** 5.19e-06 .00011*** .00003
Time Variables
1996
.0003 .0004
-1997
.0004 .0004
-1998
.002*** .0005
-1999
.002*** .0005
-2000
.004*** .0006
-*p< .10
** p< .05
*** p<.01
Sepsis
Fixed Effects Model
Coeff Std. Error
2.14e-05 5.51e-04
.155
-.336
.230
.562
.00008* .000042
6.68e-04*** 2.03e-04
.00022*** .00007
Conclusions



Hospitals experiencing a decline in payment
may compromise the care they provide to
patients, but the impact is limited
Hospital quality of patient care is only weakly
related to changes in hospital nurse staffing
Patient outcomes are influenced by the
volume of care a hospital provides and its
experience in treating severely ill patients
Significance to Policy and Future
Research



Health policy efforts focused on improving hospital
quality should consider regionalization of certain
types of care more so than implementing minimum
staffing levels or enhancing hospital reimbursement
Even if the relationship between patient outcomes
and changes in staffing levels is minimal, it is
important to investigate how staffing reductions may
affect employee morale and productivity
Many other patient outcome measures were not
included