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Journal of Accounting and Public Policy 20 (2001) 73±88
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Research Note
Management control and hospital cost
reduction: additional evidence
John H. Evans III a,*, Yuhchang Hwang b,
Nandu J. Nagarajan a
a
The Joseph M. Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA 15260,
USA
b
School of Accountancy, Arizona State University, Tempe, AZ 85253, USA
Abstract
We consider the consequences of the attempts of a particular hospital to control
costs, through reductions in patients' length of stay (LOS), using management accounting control techniques, including physician pro®ling. Building on prior research
discussed in our paper, that documents how pro®ling was more successful in reducing
LOS but less successful in reducing cost, we demonstrate how the economic and social
consequences of the pro®ling program were evaluated. Using data collected from a
hospital as well as other data, we found that both LOS and the number of procedures
performed per patient were signi®cant determinants of monthly hospital costs, but the
physician pro®ling policy was also found to be associated with a signi®cant increase in
the number of procedures performed per patient day. As a result, because the potential
savings from fewer patient days appeared to have been oset by a concurrent increase in
procedures performed per patient day, the pro®ling program did not produce a signi®cant reduction in hospital costs. Ó 2001 Published by Elsevier Science Ltd.
1. Introduction
Hospitals today face an increasingly competitive environment in which
hospital costs have been identi®ed as the single largest component of the
*
Corresponding author. Tel.: +1-412-648-1714; fax: +1-412-648-1693.
E-mail address: jhe@katz.pitt.edu (J.H. Evans III).
0278-4254/01/$ - see front matter Ó 2001 Published by Elsevier Science Ltd.
PII: S 0 2 7 8 - 4 2 5 4 ( 0 0 ) 0 0 0 2 4 - 7
74
J.H. Evans III et al. / Journal of Accounting and Public Policy 20 (2001) 73±88
overall increase in healthcare costs (Newhouse, 1992, p. 7), leading to public
policy and private sector eorts to contain these costs. Our paper analyzes the
economic consequences, particularly the cost implications, of one hospital's
attempt to reduce patients' length of stay (LOS) in the hospital by in¯uencing
physicians' practice patterns. 1 In previous research we analyzed this hospital's
eorts to reduce hospital LOS by means of a physician pro®ling program
(Evans et al., 1995). The physician pro®ling program produced benchmarks in
terms of mean LOS by Diagnosis Related Group (DRG) against which to
compare individual physician's performance in subsequent periods (Evans et
al., 1995, p. 1109). Evans et al. (1995, p. 1107) documented that the pro®ling
program led to an increase in the percentage of physicians achieving the
benchmark LOS target. Further, the percentage of physicians meeting the
benchmark was greater at intermediate levels of patient severity, and in those
DRGs with the greatest economic impact for the hospital (Evans et al., 1995,
p. 1107). Finally, a greater percentage reduction in mean LOS was documented among ``physicians who initially failed to meet the LOS benchmark''
versus physicians who initially did meet the benchmark (Evans et al., 1995,
p. 1107). 2
Next, we provided a brief summary of the economic consequences of the
pro®ling program in Evans et al. (1997a). There we noted (1997a, p. 25) that
the reduction in LOS did not achieve the goal of a signi®cant cost reduction,
possibly because the number of procedures performed per day increased as
hospital stays declined. However, that report did not address the methodological issues which arise from the complexity of the hospital environment,
and which must be understood to properly interpret the previous results from a
social cost and bene®t perspective.
The issue of the duration of inpatient stays in hospitals is of considerable
importance in the ongoing debate on healthcare policy because it has both
economic and social cost implications. First, despite the focus on reducing total
inpatient hospital days to control costs, the empirical evidence relating hospital
LOS to overall lower healthcare is mixed. 3 Next, while the eectiveness of
hospital attempts to control costs through reducing LOS remains an unresolved issue, there is evidence that indigent and chronically ill patients, who
may be less able to substitute other eective forms of care for reduced hospital
1
Another documented example of an attempt by hospital to control LOS and costs by in¯uencing
physician behaviour is provided by Eldenburg (1994).
2
The present study also extended the previously reported analysis (Evans et al., 1995) by adding a
national control sample of hospitals. The results of the physician pro®ling program in terms of
changes in LOS were then compared to corresponding changes during the same time period for the
control sample. The results (not reported in this paper) con®rmed that the pro®ling program did
achieve a statistically signi®cant reduction in LOS.
3
See, for instance, Reinhardt (1996, p. 148) and Eldenburg and Kallapur (2000, p. 99).
J.H. Evans III et al. / Journal of Accounting and Public Policy 20 (2001) 73±88
75
days, can experience adverse medical consequences from early discharge from
hospitals, resulting in an ultimate increase in the long-term social cost of
providing life-cycle patient care (Parisi and Meyer, 1995, p. 1636). For instance, the increased regulatory concern over early discharges following
childbirth re¯ects the social concern that indigent mothers, or those who are
disadvantaged because of lack of education and who may have less access to
important information on maternal day-care of the newborn, may be relatively
disadvantaged in ensuring that their babies stay healthy (Fuchs, 1974, pp. 34±
35). Conversely, medical programs aimed at high-risk mothers have achieved
substantial reductions in neo-natal mortality (Fuchs, 1974, p. 37).
The potentially high social cost of reductions in LOS motivates a careful
examination of the rationale for reductions in LOS. Given that the social costs
from reduction in LOS may be borne disproportionately by certain subgroups,
it becomes important that hospitals and managed care organizations have clear
evidence that reductions in LOS are accompanied by economically signi®cant
cost savings.
Our study contributes to understanding the consequences of hospital cost
control programs in three ways. First, we provide the models and statistical
evidence for the conclusions that physician pro®ling reduced LOS (Evans et al.,
1995, p. 1107), but did not signi®cantly reduce hospital costs (Evans et al.,
1997a, p. 25). In particular, we identify methodological issues that must be
addressed in any analysis of hospital costs in light of the complexity of the
interrelationship among the variables involved. Second, we supply the details
necessary to evaluate the direct cost savings from reductions in LOS, thus
providing a better sense of the trade-os in the social policy debate on delivery
of healthcare, and responding to the call for examining how incentive schemes
for physicians help to achieve societal health goals (Mensah, 1996, pp. 373±
374). Third, we provide interesting evidence on the eciency of non-contractual management control systems such as pro®ling, while also re®ning the
literature on cost drivers in the hospital environment.
2. Background and literature review
The increasingly competitive environment that hospitals face includes insurers and HMOs using capitated contracts to shift cost risk to the hospitals
(Miller and Luft, 1994, p. 1517) and the increasing number of Medicare
patients for whom the hospital receives ®xed, diagnosis-based prospective
payments (Folland et al., 1997, p. 503). At the same time, there is increasing
public concern about managed care's impact on the quality of care and
awareness concerning the relative level of charges and eectiveness of care
across hospitals, including such measures as average patient LOS, and
mortality rates (Gos®eld, 1997, p. 27). There is also evidence that hospitals
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J.H. Evans III et al. / Journal of Accounting and Public Policy 20 (2001) 73±88
care about such disclosures and respond to them (Evans et al., 1997b, pp.
316±317). One consequence has been hospitals responding to such market
pressures by attempting to improve both the quality and technological sophistication of their healthcare services which, in turn, has contributed to
further upward pressure on hospital costs (Teisberg et al., 1994, p. 137).
Thus, reductions in LOS may themselves be fueling other costly actions by
hospitals because of social concerns about the quality of care (Evans et al.,
1997b, pp. 316±317).
Previous healthcare research has addressed the relations among the Medicare prospective payment system, patient LOS, and hospital costs from a
public policy perspective. For example, Sloan et al. (1988, pp. 210±211), suggested that Medicare's prospective payment mechanism has resulted in an increased cost per patient day in the hospital, and Newhouse (1992, p. 11)
reported that the real cost per patient day increased by ``nearly a factor of 4
from 1965 to 1986'', at the same time that mean LOS declined. In seeking to
explain the increase in cost per patient day, Sloan et al. (1988, p. 210) noted
that increasing average inpatient severity could be an important contributing
factor. However, because they (1988) did not control for potentially confounding factors such as severity, the implication of their analysis for the social
policy debate on reductions in LOS was limited. 4 Eldenburg and Kallapur
(2000, p. 99) found that after controlling for changes in cost allocations, the
Medicare prospective payment system, which has been associated with shorter
hospital stays, was not associated with inpatient cost containment. Our study
complements such previous research by incorporating LOS, procedures and
cost per patient day in a framework in which LOS is endogenous, while also
including an explicit control for patient severity.
3. Cost analysis data
The cost analysis reported below tests whether the physician pro®ling program was associated with a reduction in the hospital's costs. This analysis uses
monthly total cost data for the treatment of patients across all DRGs because
hospital costs systems typically do not collect cost at the individual patient or
DRG level. Our data are for the 40-month period August 1990±November
1993.
The data for the cost analysis were collected from a hospital (which in this
paper we refer to as Hospital P) as well as from the Healthcare Cost and
4
Because Sloan et al. (1988) do not have a measure of severity for their sample, they rely on
extrapolating from results for an independent sample over the same general time period that found
that patient severity had increased.
J.H. Evans III et al. / Journal of Accounting and Public Policy 20 (2001) 73±88
77
Utilization Project (HCUP) database. 5 From the HCUP data we obtained
TOTPAT (total number of patients in Hospital P by month, based on admissions), as well as the data with which to calculate LOS (average LOS per
patient by month) and MED (the sum of Medicare plus Medicaid patients as a
percentage of all patients in Hospital P by month).
The remaining variables were based on data provided by the hospital. The
dependent variable, cost per inpatient (COST), represents total monthly hospital costs summed across all revenue departments (converted to constant July
1990 dollars using the seasonally adjusted monthly Total Medical Care component of the CPI index for urban consumers) divided by the total number of
patients admitted to the hospital in that month.
Revenue departments are those that charge patients directly for services.
Non-revenue departments are usually service departments; they are initially
assigned their own direct material and labor costs, plus most or all of the
hospital's ®xed costs in the form of depreciation and interest charges associated
with buildings and equipment. As such, the costs in the non-revenue departments are likely to be less amenable to medium term cost control. The exclusion of non-revenue department costs as less controllable in the medium term is
consistent with the conclusions of Noreen and Soderstrom (1997, p. 109), who
found that hospital overhead costs for their sample were primarily ®xed.
Total monthly procedures (PROC) is a measure generated by the hospital of
monthly activity in terms of weighted procedures performed. Here procedures
are de®ned to include all labor and material services for which the patient is
charged. In any month the total weighted procedures performed is calculated
by multiplying the total monthly count of each type of procedure by the corresponding weight for each procedure as determined by the hospital. The
weights for all labor services are calculated by the hospital's management engineers as the average time in minutes required to perform each procedure. The
material weights generally re¯ect the standard cost of the material consumed.
As such, the weights represent a type of complexity measure for each procedure. The mean and standard deviation of the weights for our sample were 157
and 300, respectively, with a minimum of 1 and a maximum of 2400. During
our sample period, the hospital's set of weights for each procedure remained
essentially unchanged.
The exogenous characteristics of the patient pool in a given month are
measured ®rst by the average patient mix (MIX), adjusted for patient admis5
The HCUP inpatient data set is made available by the Agency for Health Care Policy and
Research (AHCPR) of the US Department of Health and Human Services as part of AHCPR's
HCUP. The data set we use is from the HCUP and is referred to as the Nationwide Inpatient
Sample (NIS). These data are obtained from approximately 1000 hospitals in 22 states. The data set
is designed to approximate a 20% sample of US community hospitals and includes records for all
inpatient stays in the sample hospitals.
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J.H. Evans III et al. / Journal of Accounting and Public Policy 20 (2001) 73±88
sion severity (SEV). MIX is the average monthly DRG weight used for
Medicare reimbursement and re¯ects the complexity of care provided. SEV
measures an individual patient's medical stability as of the ®rst week of hospital treatment, based on the MedisGroup ®ve-point scale. SEV is then multiplied by MIX to yield SEVMIX, a measure of the patient pool entering the
hospital each month.
We also control for two other factors that could aect the hospital's operating cost on a monthly basis. The ®rst is the mix of inpatient versus outpatient
services provided, as measured by INPAT, the ratio of net inpatient to net
outpatient revenue. Because net inpatient and outpatient revenues are available
only on an annual basis, each month in the year is assigned the annual average
for that year. The second additional control is OCCUP, the hospital's monthly
occupancy rate (the hospital calculates occupancy on a quarterly basis so we
assign the quarterly value to each month in that quarter).
Panel A of Table 1 reports descriptive statistics for the variables used in the
cost analysis, and Panel B provides a corresponding correlation matrix.
Next, Section 4 provides an analysis of the eect of the physician pro®ling
Relative Performance Information (RPI) program on hospital costs using the
above data to estimate a system of simultaneous equations.
4. Simultaneous determination of costs, LOS, and procedures
This section presents a simultaneous equation model of Hospital P's
monthly costs, LOS, and total procedures performed, each on a per patient
basis. As potential drivers of monthly hospital costs, mean LOS and total
procedures performed per patient can be viewed as intermediate products that
are simultaneously determined by hospital policies and patient characteristics,
and that yield patient services as the ®nal product. Thus, a change in hospital
policy such as introduction of the physician pro®ling RPI program may aect
not only the mean LOS, but also the number and mix of procedures performed
on these patients. For example, as patient LOS is reduced, for some tests and
treatments the total number of procedures will be reduced, while for other tests
and treatments the total may remain unchanged or even increase (Finkler et al.,
1988, pp. 274±275). In turn, the changes in LOS and procedures are expected to
aect hospital costs. Therefore, our analysis employs the system of equations
speci®ed below in which Eq. (1) re¯ects the in¯uence of the intermediate
products (LOS and procedures) on hospital monthly costs, while Eqs. (2) and
(3) represent the determination of these intermediate outcomes.
In the following system of three equations, COST, LOS, and PROC, as well
as RPILOS, the interaction of RPI and LOS, are treated as endogenous. The
exogenous variables in this system are TOTPAT, RPI, MED, OCCUP,
INPAT, and SEVMIX.
Pre-pro®ling period (August 1990±November 1991)
Post-pro®ling period (December 1991±November 1993)
Median
Panel A ± Summary statisticsa
COSTt ± Hospital total
monthly revenue
department cost per patient $2256
in month t
LOSt ± Mean length of stay
per patient in
month t
7.65
PROCt ± Total weighted
procedures per patient
in month t
2927.79
TOTPATt ± Total
1549
inpatients in month t
OCCUPt ± Hospital
84.9%
occupancy rate in month t
SEVMIXt ± Severityweighted patient mix in
month t
1.97
INPATt ± Ratio of
inpatient to outpatient net
revenue in month t
7.24
MEDt ± Percentage of all
patients that are
Medicare or Medicaid in
0.52
month t
Mean
Standard Maximum
deviation
Minimum
Median
Mean
Standard
deviation
Maximum
Minimum
$2252
$88
$2406
$2110
$2012
$1978
208
$2416
$1643
7.74
0.28
8.23
7.30
6.69
6.71
0.56
7.87
5.87
2903.86
1551
163.11
100
3203.15
1686
2545.27
1388
2803.93
1703
2800.61
1698
200.06
98
3239.19
1892
2473.30
1489
85.99%
3.42%
90.3%
80.9%
79.1%
79.58%
5.45%
89.3%
70.3%
1.94
0.11
2.07
1.69
2.13
2.12
0.10
2.30
1.88
7.23
0.01
7.24
7.22
5.87
6.15
0.73
7.21
5.34
0.52
0.03
0.57
0.48
0.60
0.60
0.03
0.64
0.52
J.H. Evans III et al. / Journal of Accounting and Public Policy 20 (2001) 73±88
Table 1
Summary statistics and correlation matrix for the simultaneous equation analysis of monthly hospital costs, mean length of stay per patient and
average procedures per patient (n 40 monthly observations)
79
80
Panel B ± Pearson correlation coecients
COSTt
LOSt
COSTt
LOSt
PROCt
TOTPATt
OCCUPt
SEVMIXt
INPATt
MEDt
1.00
0.82
1.00
PROCt
0.80
0.59
1.00
TOTPATt
)0.72
)0.63
)0.61
1.00
OCCUPt
0.54
0.78
0.41
)0.28
1.00
SEVMIXt INPATt
)0.39
)0.33
)0.61
0.33
)0.18
1.00
)0.72
0.78
0.59
)0.46
0.74
)0.49
1.00
MEDt
)0.66
) 0.83
)0.46
0.55
) 0.74
0.41
0.55
1.00
a
Observations for this table are mean values for each of 40 months in our sample period. For example, LOSt is the mean length of stay across patients
in all DRGs in month t. ** p < 0:05 (two-tailed); * p < 0:10 (two-tailed).
J.H. Evans III et al. / Journal of Accounting and Public Policy 20 (2001) 73±88
Table 1 (Continued)
J.H. Evans III et al. / Journal of Accounting and Public Policy 20 (2001) 73±88
81
COSTt a0 a1 PROCt a2 LOSt a3 TOTPATt a4 OCCUPt e1 ; 1
LOSt b0 b1 RPIt b2 PROCt b3 OCCUPt
b4 INPATt b5 MEDt e2 ;
2
PROCt c0 c1 LOSt c2 RPILOSt c3 OCCUPt
c4 SEVMIXt c5 INPATt e3 ;
3
where COST is the hospital revenue department cost per patient in month t,
PROC the weighted procedures performed per inpatient in month t, LOS the
mean length of stay per inpatient in month t, TOTPAT the total inpatients
admitted in month t, OCCUP the hospital occupancy rate in month t, RPI the
physician pro®ling Relative Performance Information (RPI) dummy variable
(RPI 0 in pre-pro®ling period and RPI 1 in post-pro®ling period), INPAT
the hospital ratio of inpatient to outpatient revenue in month t, MED the
hospital's percentage of total inpatients who are either Medicare or Medicaid
patients in month t, RPILOS interaction (product) of RPI dummy variable
and LOS (RPILOS 0 in pre-pro®ling period and RPILOS LOS in postpro®ling period), SEVMIX is the average patient admission severity in month t
(SEV) weighted by (multiplied by) the average DRG weight in month t (MIX).
The system of equations (1)±(3) treats the decision to implement the physician pro®ling program (RPI) as exogenous. It is exogenous because the decision to implement the pro®ling program was made by hospital management,
while our analysis focuses on how physicians respond to the pro®ling program,
and in turn, how this response in¯uenced the hospital's costs.
We hypothesize that in Eq. (1) monthly hospital revenue department operating cost per patient (COST) is an increasing function of the average
number of weighted procedures performed per patient in that month (PROC)
and of the corresponding mean LOS per patient for the month (LOS). In
addition, to control for the eect of patient volume, we include the total
number of patients admitted in the month (TOTPAT). To capture the potential
in¯uence of capacity constraints on costs, we include the monthly hospital
occupancy rate (OCCUP).
Eq. (2) re¯ects the determination of mean monthly LOS per patient (LOS)
as a function of the hospital's policies and of the characteristics of the patients
treated in that month. Speci®cally, we hypothesize that LOS is a decreasing
function of the pro®ling policy variable, RPI, which equals 1 for all months
after the physician pro®ling RPI policy was implemented, and 0 otherwise.
Patient characteristics include the ratio of inpatient care to outpatient care
(INPAT) and the percentage of Medicare and Medicaid patients (MED). We
included MED because previous cross-sectional studies (e.g., Phelps, 1992, pp.
348±350) have found that hospitals with a greater percentage of Medicare and
Medicaid patients tend to have a shorter LOS as a result of the ®xed rate reimbursement incentive. The hospital occupancy rate (OCCUP) is included to
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J.H. Evans III et al. / Journal of Accounting and Public Policy 20 (2001) 73±88
control for the in¯uence of capacity constraints. Total weighted procedures
performed per patient (PROC) is the second endogenous variable.
Eq. (3) represents the determination of the monthly number of weighted
procedures performed per patient (PROC) as a function of hospital policies
and patient characteristics. The eect of the RPI program on the number of
procedures performed per patient is likely to re¯ect three factors. First, as the
average LOS changes, the number of routine daily procedures is expected to
vary proportionately, so that the expected sign on the LOS variable is positive. On the other hand, to the extent that LOS and procedures are substitutes in providing patient care (e.g., a physician may utilize more tests to
compensate for the patient remaining in the hospital under direct observation
for fewer days), the RPI program will result in more procedures being performed per patient day as LOS falls. Finally, the second eect may also be
reinforced by physicians' personal liability concerns in that defensive medicine
may call for performing additional tests when a patient is released sooner
from the hospital. As both of these two eects produce an increase in procedures performed per patient day, our hypothesis that the RPILOS term will
have a positive sign indicating that the RPI program resulted in the performance of more procedures per patient day. Whether the net eect of the RPI
program is more or fewer procedures per patient depends on the relative
magnitudes of these eects in Eq. (3), which is an empirical question. Finally,
patient characteristics include SEVMIX and INPAT, while OCCUP re¯ects
the potential in¯uence of capacity constraints on the number of procedures
performed.
4.1. Cost estimation results
Three-stage least squares (3SLS) regression results for Hospital P's
monthly cost per patient (Eq. (1)) are reported in Table 2. The corresponding
two-stage least squares (2SLS) results (not reported) are very similar to those
for 3SLS. We focus on the 3SLS results, which explicitly re¯ect the covariance of error terms in estimating coecients across equations, consistent with
the hypothesized simultaneous determination of cost, LOS and procedures.
The 3SLS approach also facilitates the subsequent hypotheses tests in Section
5 that examine the net eects of the RPI policy on the total number of
procedures performed and on the hospital's revenue department costs. In
order to capture the interdependencies among the system of equations (1)±(3),
the hypotheses tests of the net eects of the RPI policy on total procedures
per patient and cost per patient must re¯ect the net eect of two components.
The ®rst is the reduction of procedures due to the reduced LOS, and the
second is the hypothesized increase of procedures per day during the resulting
LOS. The net eect of the RPI policy on total procedures per patient is then
estimated jointly using the corresponding coecients from Eqs. (2) and (3).
J.H. Evans III et al. / Journal of Accounting and Public Policy 20 (2001) 73±88
83
Table 2
Simulation equation analysis of monthly hospital costs, length of stay, and procedures per patient
(n 40 months)a
3SLS Model
Intercept
PROCt
LOSt
TOTPATt
OCCUPt
RPI
INPATt
MEDt
SEVMIXt
RPILOSt
Eq. (1) Hospital
cost per patient
Eq. (2) Length of
stay per patient
COSTt
LOSt
)11.867
()0.160)
Total weighted
0.443
procedures per patient (2.983)
in month t
287.387
Mean length of stay
per patient in month t (2.888)
)0.34
Total inpatients in
month t
()0.1214)
Hospital occupancy
)12.788
rate in month t
()1.557)
Dummy variable for
the physician pro®ling
program, RPI 1 for
December 1991 and
later months, RPI 0
otherwise
Ratio of inpatient to
outpatient net revenue
in month t
Percentage of Medicare plus Medicaid
patients in month t
Severity-weighted
patient mix in month t
Interaction of physician pro®ling program
and LOS in month t
Durbin±Watson
D 1:782
0.221
(0.195)
0.001
(3.306)
Eq. (3) Weighted
procedure(s) per
patient
PROCt
547.2
(0.968)
628.55
(4.803)
0.043
(3.183)
)0.581
()3.998)
)30.25
()2.871)
)0.035
()0.263)
)1.49
()0.023)
)0.047
()0.129)
51.7
(0.971)
51.43
(3.893)
D 1:775
D 1:813
a
t-Statistics in parenthesis (two-tailed); White (1980) ± adjusted for heteroskedasticity); p < 0:01.
System-weighted R2 0:8673; system weighted MSE 32.899 with 103 degrees of freedom;
p < 0:001 (two-tailed).
Chi-squared statistic for Hansen test 0.13; p 0:94 (two-tailed).
Wald Statistic for Hausman test 6.62; p < 0:05 (two-tailed).
The net cost implication of the reduction in LOS is estimated in a similar
manner.
The results for Eq. (1) in Table 2 show that as hypothesized, both total
weighted procedures per patient and mean LOS per patient are positive and
statistically signi®cant. Considered in isolation, this result would indicate that
reducing either weighted procedures per patient or LOS per patient should lead
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J.H. Evans III et al. / Journal of Accounting and Public Policy 20 (2001) 73±88
to a reduction in hospital costs per patient. However, consistent with our
system of simultaneous equations, we expect that the RPI policy will also in¯uence LOS per patient and procedures per patient in Eqs. (2) and (3), respectively. Therefore, we next examine the results for Eqs. (2) and (3) to
estimate the net eect of the pro®ling policy on hospital costs.
In Column 2 of Table 2 the results for mean LOS per patient in Eq. (2) show
that RPI, the dummy variable for the physician pro®ling program, is negative
and statistically signi®cant. Because the cost data for this simultaneous equations approach is aggregated across all DRGs, these results use corresponding
LOS data which are also aggregated across all DRGs.
The results in Column 2 of Table 2 show an estimated coecient of )0.581
for the RPI dummy variable, implying that the RPI pro®ling program resulted
in an estimated 0.581 days reduction in mean LOS. In addition, in Column 2 of
Table 2 the total number of weighted procedures per patient (PROC) and the
hospital's monthly occupancy rate (OCCUP) are both positive and statistically
signi®cant. The latter result is consistent with declining occupancy rates as
patients stay fewer days in the hospital.
Turning to the results for weighted procedures per patient in Eq. (3), Column 3 of Table 2 ®rst shows that LOS is statistically signi®cant with a coef®cient of 628.55. This indicates that each additional day in the hospital is
associated with the performance of approximately 628.55 additional weighted
procedures. Next, RPILOS, the interaction of the RPI program and mean
LOS, has a positive and statistically signi®cant coecient of 51.43, indicating
that introduction of the RPI program was associated with an estimated increase of approximately 51.43 weighted procedures per patient day. The estimated coecient of the average severity-weighted patient mix (SEVMIX) has
the expected positive sign but is not statistically signi®cant at conventional
levels. 6
6
To check the robustness of the Section 4 results, we conducted various sensitivity analyses,
including rerunning the analysis using 2SLS rather than 3SLS, replacing the dependent variable in
Eq. (1), cost per patient in revenue departments, with cost per patient in all departments, and
replacing the total weighted procedures per patient with the corresponding raw total procedures in
Eqs. (1) and (2). Our results are generally robust to these changes except that in the case of the last
change, LOS and OCCUP are no longer statistically signi®cant, consistent with our overall result
that the reduction in LOS did not produce a reduction in cost. We also reran the Section 4 analysis
using a reduced form estimation to avoid the loss of degrees of freedom associated with the
simultaneous equation approach. Again, the ordinary least squares (OLS) regression results are
consistent with the pro®ling policy having no statistically signi®cant eect on the cost per patient
day. Finally, to check the simultaneity speci®cation, we ran the Hausman test (1978, pp. 1264±
1269) of the null hypothesis that all of the variables are exogenous and that the coecients obtained
by using OLS and 2SLS are equal. The Hausman test results for Eqs. (1)±(3) indicate rejection of
the null hypothesis that the variables of concern are exogenous for Eqs. (2) and (3).
J.H. Evans III et al. / Journal of Accounting and Public Policy 20 (2001) 73±88
85
5. Evaluating the net eect of the pro®ling program on hospital costs
We now use the estimated system of equations from Section 4 to test
whether the RPI policy produced a signi®cant change in procedures per patient, and whether the RPI policy produced a signi®cant change in the hospital's monthly costs. Results of testing the ®rst hypothesis are presented in Panel
A of Table 3, where RPIEq: 2 LOSEq: 3 0:581 628:55 365:2; the
estimated change in the number of weighted procedures per patient due to a
reduction of LOS induced by the hospital's RPI policy. The second term,
6:71 RPILOSEq: 3 ; becomes (6.71)(51.43) 345.1 additional procedures
performed per patient after the RPI policy. Summing the two terms
( 365:2 345:1) provides an estimated net reduction of 20.1 procedures per
patient as a result of the hospital's physician pro®ling RPI policy.
We tested whether this reduction was statistically signi®cantly dierent from
zero using a Taylor series approximation to the non-linear expression for the
null hypothesis in Panel A of Table 3 (Greene, 1993, pp. 218±220). The results
reported in Panel A of Table 3 provide no support (z 0:001) for rejecting the
null hypothesis of no change in procedures per patient. These results suggest
that the RPI policy produced two osetting in¯uences on total procedures
performed per patient, with the net eect being no signi®cant change in procedures per patient as a result of the RPI policy. In turn, to the extent that
hospital costs are driven by the number of procedures performed per patient,
the result suggests that the RPI policy may not have produced a signi®cant
reduction in hospital costs during the sample period. We next address this issue.
Given that both LOS and the number of procedures performed are drivers
of revenue department costs, it is important to examine the eects of changes in
patient average LOS and in the number of procedures performed per patient
on hospital operating costs. Following a similar rationale to that used in Panel
A of Table 3, Panel B of Table 3 reports results for the null hypothesis that the
RPI policy had no eect on the hospital's monthly revenue department costs.
The ®rst bracketed term in Panel B is again the estimated reduction of 20.1
Table 3
Hypotheses tests involving coecients of the system of equations (1)±(3) (Wald Test)
Panel A ± Net eect of the RPI pro®ling program on total procedures performed per patient
H0 : fRPIEq: 2 LOSEq: 3 6:71 RPILOSEq: 3 g 0
Z statistic 0.001; P-value 0.999
Panel B ± Net eect of the RPI pro®ling program on hospital cost per patient
H0 : fRPIEq: 2 LOSEq: 3 6:71 RPILOSEq: 3 g PROCEq: 1 fRPIEq: 2
LOSEq: 1 g 0
Z statistic 0.001; P-value 0.999.
*
Two-tailed Wald test; Taylor series approximation is used to re¯ect testing non-linear restrictions
(Greene, 1993, pp. 218±220).
86
J.H. Evans III et al. / Journal of Accounting and Public Policy 20 (2001) 73±88
procedures per patient, which is then multiplied by the estimated cost of $0:443
per procedure, which is the coecient of PROC from Eq. (1) of Table 2. The
result for the ®rst bracketed term is an estimated reduction of $8:904 per
patient in hospital cost.
The remaining term from Panel B of Table 3 multiplies RPIEq: 2 , the estimated reduction of 0.581 days in LOS per patient from the RPI policy, by
$287:388, the estimated cost per patient day, which is the coecient of
LOSEq: 1 from Eq. (1) in Column 1 of Table 2. The result is an estimated cost
reduction of $166:972 per patient. Combining the two terms yields an estimated net cost reduction from the RPI policy of $175:876 per patient. However, this estimated eect is not signi®cantly dierent from zero in light of the
variances associated with the estimates involved. That is, the results in Panel B
of Table 3 indicate no support (z 0:001) for rejecting the null hypothesis that
the RPI policy had no eect on hospital costs. Alternatively, if one takes the
social costs of reducing LOS (and thereby medical care) as given, reductions in
LOS creating reductions in cost can be taken as the null hypothesis, for which
our data provides no support.
6. Conclusion
This paper provides empirical evidence that disclosing relative performance
information through physician pro®ling in¯uenced physicians in one hospital
to reduce average LOS and charges per patient, even in the absence of contractual incentives. To this extent, the physician pro®ling program was successful in achieving its immediate objective.
We next explored the relation between the average LOS, the number of
procedures performed per patient, and the hospital's monthly costs. The average number of procedures performed per patient and the patient's average
LOS are documented as drivers of hospital costs. However, the reduction in
average LOS is not associated with a signi®cant decrease in monthly hospital
operating costs. This result appears to stem from the fact that reducing average
LOS is associated with an increase in procedures per patient day. In turn, the
cost saved by having fewer patient days appears to be oset by the increased
cost of performing more procedures per day. The net eect is that the physician
pro®ling program is associated with a signi®cant reduction in average LOS, but
not in hospital operating costs. Our study has implications for the social policy
debate on the value of attempts by hospitals and managed care organizations
to control health care costs by reducing LOS. The lack of support for cost
reduction as a rationale for reducing LOS suggests that careful attention be
devoted to the social costs of premature discharges, particularly to the extent
that these costs are borne disproportionately by certain segments of the population.
J.H. Evans III et al. / Journal of Accounting and Public Policy 20 (2001) 73±88
87
Another implication of our study is that to be meaningful, cost control efforts in hospitals must focus not only on LOS, but also on managing the
number and mix of procedures performed during the patient's hospital stay.
Thus, hospitals may potentially be better o by investing greater resources in
process improvement initiatives such as critical pathways (see e.g. Evans et al.,
1997a, pp. 26±27).
Finally, our study suggests the value of a portfolio of performance measures
in evaluating the impact of policy changes. LOS and procedures constitute
non-®nancial measures of performance while costs and charges are ®nancial
measures of performance. Focusing exclusively on one type of performance
measure might lead to an incorrect view of hospital performance in general or
the success or failure of a policy initiative in particular. Combining costs and
charges with internal process measures such as LOS and procedures, as well as
customer outcome measures such as mortality, morbidity and patient satisfaction indices, can be the basis for a balanced scorecard approach to measuring hospital performance.
Acknowledgements
We acknowledge excellent research assistance from Andy Leone. We also
acknowledge helpful comments from G.G. Hegde, Chuan Yang Hwang, YuhChia Hwang, Dave Larcker, Ken Lehn, Lance Lieberman, Robert Melby,
Michael Rutigliano, seminar participants at New York University, Ohio State
University, Santa Clara University, the Industrial Engineering Department
and the Katz Graduate School of Business of the University of Pittsburgh, two
anonymous referees, various participants at the 1994 American Accounting
Association's Management Accounting Research Conference, and the 1994
AAA Annual Meeting in New York (Evans et al., 1994). We also acknowledge
a research grant from the Institute of Management Accounts. In an earlier
version the paper was entitled, ``Can Hospital Costs be Controlled by Inducing
Physicians to Reduce Patients' LOS? An Empirical Analysis'' (see Evans et al.,
1994).
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www.elsevier.com/locate/jaccpubpol
Research Note
Management control and hospital cost
reduction: additional evidence
John H. Evans III a,*, Yuhchang Hwang b,
Nandu J. Nagarajan a
a
The Joseph M. Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA 15260,
USA
b
School of Accountancy, Arizona State University, Tempe, AZ 85253, USA
Abstract
We consider the consequences of the attempts of a particular hospital to control
costs, through reductions in patients' length of stay (LOS), using management accounting control techniques, including physician pro®ling. Building on prior research
discussed in our paper, that documents how pro®ling was more successful in reducing
LOS but less successful in reducing cost, we demonstrate how the economic and social
consequences of the pro®ling program were evaluated. Using data collected from a
hospital as well as other data, we found that both LOS and the number of procedures
performed per patient were signi®cant determinants of monthly hospital costs, but the
physician pro®ling policy was also found to be associated with a signi®cant increase in
the number of procedures performed per patient day. As a result, because the potential
savings from fewer patient days appeared to have been oset by a concurrent increase in
procedures performed per patient day, the pro®ling program did not produce a signi®cant reduction in hospital costs. Ó 2001 Published by Elsevier Science Ltd.
1. Introduction
Hospitals today face an increasingly competitive environment in which
hospital costs have been identi®ed as the single largest component of the
*
Corresponding author. Tel.: +1-412-648-1714; fax: +1-412-648-1693.
E-mail address: jhe@katz.pitt.edu (J.H. Evans III).
0278-4254/01/$ - see front matter Ó 2001 Published by Elsevier Science Ltd.
PII: S 0 2 7 8 - 4 2 5 4 ( 0 0 ) 0 0 0 2 4 - 7
74
J.H. Evans III et al. / Journal of Accounting and Public Policy 20 (2001) 73±88
overall increase in healthcare costs (Newhouse, 1992, p. 7), leading to public
policy and private sector eorts to contain these costs. Our paper analyzes the
economic consequences, particularly the cost implications, of one hospital's
attempt to reduce patients' length of stay (LOS) in the hospital by in¯uencing
physicians' practice patterns. 1 In previous research we analyzed this hospital's
eorts to reduce hospital LOS by means of a physician pro®ling program
(Evans et al., 1995). The physician pro®ling program produced benchmarks in
terms of mean LOS by Diagnosis Related Group (DRG) against which to
compare individual physician's performance in subsequent periods (Evans et
al., 1995, p. 1109). Evans et al. (1995, p. 1107) documented that the pro®ling
program led to an increase in the percentage of physicians achieving the
benchmark LOS target. Further, the percentage of physicians meeting the
benchmark was greater at intermediate levels of patient severity, and in those
DRGs with the greatest economic impact for the hospital (Evans et al., 1995,
p. 1107). Finally, a greater percentage reduction in mean LOS was documented among ``physicians who initially failed to meet the LOS benchmark''
versus physicians who initially did meet the benchmark (Evans et al., 1995,
p. 1107). 2
Next, we provided a brief summary of the economic consequences of the
pro®ling program in Evans et al. (1997a). There we noted (1997a, p. 25) that
the reduction in LOS did not achieve the goal of a signi®cant cost reduction,
possibly because the number of procedures performed per day increased as
hospital stays declined. However, that report did not address the methodological issues which arise from the complexity of the hospital environment,
and which must be understood to properly interpret the previous results from a
social cost and bene®t perspective.
The issue of the duration of inpatient stays in hospitals is of considerable
importance in the ongoing debate on healthcare policy because it has both
economic and social cost implications. First, despite the focus on reducing total
inpatient hospital days to control costs, the empirical evidence relating hospital
LOS to overall lower healthcare is mixed. 3 Next, while the eectiveness of
hospital attempts to control costs through reducing LOS remains an unresolved issue, there is evidence that indigent and chronically ill patients, who
may be less able to substitute other eective forms of care for reduced hospital
1
Another documented example of an attempt by hospital to control LOS and costs by in¯uencing
physician behaviour is provided by Eldenburg (1994).
2
The present study also extended the previously reported analysis (Evans et al., 1995) by adding a
national control sample of hospitals. The results of the physician pro®ling program in terms of
changes in LOS were then compared to corresponding changes during the same time period for the
control sample. The results (not reported in this paper) con®rmed that the pro®ling program did
achieve a statistically signi®cant reduction in LOS.
3
See, for instance, Reinhardt (1996, p. 148) and Eldenburg and Kallapur (2000, p. 99).
J.H. Evans III et al. / Journal of Accounting and Public Policy 20 (2001) 73±88
75
days, can experience adverse medical consequences from early discharge from
hospitals, resulting in an ultimate increase in the long-term social cost of
providing life-cycle patient care (Parisi and Meyer, 1995, p. 1636). For instance, the increased regulatory concern over early discharges following
childbirth re¯ects the social concern that indigent mothers, or those who are
disadvantaged because of lack of education and who may have less access to
important information on maternal day-care of the newborn, may be relatively
disadvantaged in ensuring that their babies stay healthy (Fuchs, 1974, pp. 34±
35). Conversely, medical programs aimed at high-risk mothers have achieved
substantial reductions in neo-natal mortality (Fuchs, 1974, p. 37).
The potentially high social cost of reductions in LOS motivates a careful
examination of the rationale for reductions in LOS. Given that the social costs
from reduction in LOS may be borne disproportionately by certain subgroups,
it becomes important that hospitals and managed care organizations have clear
evidence that reductions in LOS are accompanied by economically signi®cant
cost savings.
Our study contributes to understanding the consequences of hospital cost
control programs in three ways. First, we provide the models and statistical
evidence for the conclusions that physician pro®ling reduced LOS (Evans et al.,
1995, p. 1107), but did not signi®cantly reduce hospital costs (Evans et al.,
1997a, p. 25). In particular, we identify methodological issues that must be
addressed in any analysis of hospital costs in light of the complexity of the
interrelationship among the variables involved. Second, we supply the details
necessary to evaluate the direct cost savings from reductions in LOS, thus
providing a better sense of the trade-os in the social policy debate on delivery
of healthcare, and responding to the call for examining how incentive schemes
for physicians help to achieve societal health goals (Mensah, 1996, pp. 373±
374). Third, we provide interesting evidence on the eciency of non-contractual management control systems such as pro®ling, while also re®ning the
literature on cost drivers in the hospital environment.
2. Background and literature review
The increasingly competitive environment that hospitals face includes insurers and HMOs using capitated contracts to shift cost risk to the hospitals
(Miller and Luft, 1994, p. 1517) and the increasing number of Medicare
patients for whom the hospital receives ®xed, diagnosis-based prospective
payments (Folland et al., 1997, p. 503). At the same time, there is increasing
public concern about managed care's impact on the quality of care and
awareness concerning the relative level of charges and eectiveness of care
across hospitals, including such measures as average patient LOS, and
mortality rates (Gos®eld, 1997, p. 27). There is also evidence that hospitals
76
J.H. Evans III et al. / Journal of Accounting and Public Policy 20 (2001) 73±88
care about such disclosures and respond to them (Evans et al., 1997b, pp.
316±317). One consequence has been hospitals responding to such market
pressures by attempting to improve both the quality and technological sophistication of their healthcare services which, in turn, has contributed to
further upward pressure on hospital costs (Teisberg et al., 1994, p. 137).
Thus, reductions in LOS may themselves be fueling other costly actions by
hospitals because of social concerns about the quality of care (Evans et al.,
1997b, pp. 316±317).
Previous healthcare research has addressed the relations among the Medicare prospective payment system, patient LOS, and hospital costs from a
public policy perspective. For example, Sloan et al. (1988, pp. 210±211), suggested that Medicare's prospective payment mechanism has resulted in an increased cost per patient day in the hospital, and Newhouse (1992, p. 11)
reported that the real cost per patient day increased by ``nearly a factor of 4
from 1965 to 1986'', at the same time that mean LOS declined. In seeking to
explain the increase in cost per patient day, Sloan et al. (1988, p. 210) noted
that increasing average inpatient severity could be an important contributing
factor. However, because they (1988) did not control for potentially confounding factors such as severity, the implication of their analysis for the social
policy debate on reductions in LOS was limited. 4 Eldenburg and Kallapur
(2000, p. 99) found that after controlling for changes in cost allocations, the
Medicare prospective payment system, which has been associated with shorter
hospital stays, was not associated with inpatient cost containment. Our study
complements such previous research by incorporating LOS, procedures and
cost per patient day in a framework in which LOS is endogenous, while also
including an explicit control for patient severity.
3. Cost analysis data
The cost analysis reported below tests whether the physician pro®ling program was associated with a reduction in the hospital's costs. This analysis uses
monthly total cost data for the treatment of patients across all DRGs because
hospital costs systems typically do not collect cost at the individual patient or
DRG level. Our data are for the 40-month period August 1990±November
1993.
The data for the cost analysis were collected from a hospital (which in this
paper we refer to as Hospital P) as well as from the Healthcare Cost and
4
Because Sloan et al. (1988) do not have a measure of severity for their sample, they rely on
extrapolating from results for an independent sample over the same general time period that found
that patient severity had increased.
J.H. Evans III et al. / Journal of Accounting and Public Policy 20 (2001) 73±88
77
Utilization Project (HCUP) database. 5 From the HCUP data we obtained
TOTPAT (total number of patients in Hospital P by month, based on admissions), as well as the data with which to calculate LOS (average LOS per
patient by month) and MED (the sum of Medicare plus Medicaid patients as a
percentage of all patients in Hospital P by month).
The remaining variables were based on data provided by the hospital. The
dependent variable, cost per inpatient (COST), represents total monthly hospital costs summed across all revenue departments (converted to constant July
1990 dollars using the seasonally adjusted monthly Total Medical Care component of the CPI index for urban consumers) divided by the total number of
patients admitted to the hospital in that month.
Revenue departments are those that charge patients directly for services.
Non-revenue departments are usually service departments; they are initially
assigned their own direct material and labor costs, plus most or all of the
hospital's ®xed costs in the form of depreciation and interest charges associated
with buildings and equipment. As such, the costs in the non-revenue departments are likely to be less amenable to medium term cost control. The exclusion of non-revenue department costs as less controllable in the medium term is
consistent with the conclusions of Noreen and Soderstrom (1997, p. 109), who
found that hospital overhead costs for their sample were primarily ®xed.
Total monthly procedures (PROC) is a measure generated by the hospital of
monthly activity in terms of weighted procedures performed. Here procedures
are de®ned to include all labor and material services for which the patient is
charged. In any month the total weighted procedures performed is calculated
by multiplying the total monthly count of each type of procedure by the corresponding weight for each procedure as determined by the hospital. The
weights for all labor services are calculated by the hospital's management engineers as the average time in minutes required to perform each procedure. The
material weights generally re¯ect the standard cost of the material consumed.
As such, the weights represent a type of complexity measure for each procedure. The mean and standard deviation of the weights for our sample were 157
and 300, respectively, with a minimum of 1 and a maximum of 2400. During
our sample period, the hospital's set of weights for each procedure remained
essentially unchanged.
The exogenous characteristics of the patient pool in a given month are
measured ®rst by the average patient mix (MIX), adjusted for patient admis5
The HCUP inpatient data set is made available by the Agency for Health Care Policy and
Research (AHCPR) of the US Department of Health and Human Services as part of AHCPR's
HCUP. The data set we use is from the HCUP and is referred to as the Nationwide Inpatient
Sample (NIS). These data are obtained from approximately 1000 hospitals in 22 states. The data set
is designed to approximate a 20% sample of US community hospitals and includes records for all
inpatient stays in the sample hospitals.
78
J.H. Evans III et al. / Journal of Accounting and Public Policy 20 (2001) 73±88
sion severity (SEV). MIX is the average monthly DRG weight used for
Medicare reimbursement and re¯ects the complexity of care provided. SEV
measures an individual patient's medical stability as of the ®rst week of hospital treatment, based on the MedisGroup ®ve-point scale. SEV is then multiplied by MIX to yield SEVMIX, a measure of the patient pool entering the
hospital each month.
We also control for two other factors that could aect the hospital's operating cost on a monthly basis. The ®rst is the mix of inpatient versus outpatient
services provided, as measured by INPAT, the ratio of net inpatient to net
outpatient revenue. Because net inpatient and outpatient revenues are available
only on an annual basis, each month in the year is assigned the annual average
for that year. The second additional control is OCCUP, the hospital's monthly
occupancy rate (the hospital calculates occupancy on a quarterly basis so we
assign the quarterly value to each month in that quarter).
Panel A of Table 1 reports descriptive statistics for the variables used in the
cost analysis, and Panel B provides a corresponding correlation matrix.
Next, Section 4 provides an analysis of the eect of the physician pro®ling
Relative Performance Information (RPI) program on hospital costs using the
above data to estimate a system of simultaneous equations.
4. Simultaneous determination of costs, LOS, and procedures
This section presents a simultaneous equation model of Hospital P's
monthly costs, LOS, and total procedures performed, each on a per patient
basis. As potential drivers of monthly hospital costs, mean LOS and total
procedures performed per patient can be viewed as intermediate products that
are simultaneously determined by hospital policies and patient characteristics,
and that yield patient services as the ®nal product. Thus, a change in hospital
policy such as introduction of the physician pro®ling RPI program may aect
not only the mean LOS, but also the number and mix of procedures performed
on these patients. For example, as patient LOS is reduced, for some tests and
treatments the total number of procedures will be reduced, while for other tests
and treatments the total may remain unchanged or even increase (Finkler et al.,
1988, pp. 274±275). In turn, the changes in LOS and procedures are expected to
aect hospital costs. Therefore, our analysis employs the system of equations
speci®ed below in which Eq. (1) re¯ects the in¯uence of the intermediate
products (LOS and procedures) on hospital monthly costs, while Eqs. (2) and
(3) represent the determination of these intermediate outcomes.
In the following system of three equations, COST, LOS, and PROC, as well
as RPILOS, the interaction of RPI and LOS, are treated as endogenous. The
exogenous variables in this system are TOTPAT, RPI, MED, OCCUP,
INPAT, and SEVMIX.
Pre-pro®ling period (August 1990±November 1991)
Post-pro®ling period (December 1991±November 1993)
Median
Panel A ± Summary statisticsa
COSTt ± Hospital total
monthly revenue
department cost per patient $2256
in month t
LOSt ± Mean length of stay
per patient in
month t
7.65
PROCt ± Total weighted
procedures per patient
in month t
2927.79
TOTPATt ± Total
1549
inpatients in month t
OCCUPt ± Hospital
84.9%
occupancy rate in month t
SEVMIXt ± Severityweighted patient mix in
month t
1.97
INPATt ± Ratio of
inpatient to outpatient net
revenue in month t
7.24
MEDt ± Percentage of all
patients that are
Medicare or Medicaid in
0.52
month t
Mean
Standard Maximum
deviation
Minimum
Median
Mean
Standard
deviation
Maximum
Minimum
$2252
$88
$2406
$2110
$2012
$1978
208
$2416
$1643
7.74
0.28
8.23
7.30
6.69
6.71
0.56
7.87
5.87
2903.86
1551
163.11
100
3203.15
1686
2545.27
1388
2803.93
1703
2800.61
1698
200.06
98
3239.19
1892
2473.30
1489
85.99%
3.42%
90.3%
80.9%
79.1%
79.58%
5.45%
89.3%
70.3%
1.94
0.11
2.07
1.69
2.13
2.12
0.10
2.30
1.88
7.23
0.01
7.24
7.22
5.87
6.15
0.73
7.21
5.34
0.52
0.03
0.57
0.48
0.60
0.60
0.03
0.64
0.52
J.H. Evans III et al. / Journal of Accounting and Public Policy 20 (2001) 73±88
Table 1
Summary statistics and correlation matrix for the simultaneous equation analysis of monthly hospital costs, mean length of stay per patient and
average procedures per patient (n 40 monthly observations)
79
80
Panel B ± Pearson correlation coecients
COSTt
LOSt
COSTt
LOSt
PROCt
TOTPATt
OCCUPt
SEVMIXt
INPATt
MEDt
1.00
0.82
1.00
PROCt
0.80
0.59
1.00
TOTPATt
)0.72
)0.63
)0.61
1.00
OCCUPt
0.54
0.78
0.41
)0.28
1.00
SEVMIXt INPATt
)0.39
)0.33
)0.61
0.33
)0.18
1.00
)0.72
0.78
0.59
)0.46
0.74
)0.49
1.00
MEDt
)0.66
) 0.83
)0.46
0.55
) 0.74
0.41
0.55
1.00
a
Observations for this table are mean values for each of 40 months in our sample period. For example, LOSt is the mean length of stay across patients
in all DRGs in month t. ** p < 0:05 (two-tailed); * p < 0:10 (two-tailed).
J.H. Evans III et al. / Journal of Accounting and Public Policy 20 (2001) 73±88
Table 1 (Continued)
J.H. Evans III et al. / Journal of Accounting and Public Policy 20 (2001) 73±88
81
COSTt a0 a1 PROCt a2 LOSt a3 TOTPATt a4 OCCUPt e1 ; 1
LOSt b0 b1 RPIt b2 PROCt b3 OCCUPt
b4 INPATt b5 MEDt e2 ;
2
PROCt c0 c1 LOSt c2 RPILOSt c3 OCCUPt
c4 SEVMIXt c5 INPATt e3 ;
3
where COST is the hospital revenue department cost per patient in month t,
PROC the weighted procedures performed per inpatient in month t, LOS the
mean length of stay per inpatient in month t, TOTPAT the total inpatients
admitted in month t, OCCUP the hospital occupancy rate in month t, RPI the
physician pro®ling Relative Performance Information (RPI) dummy variable
(RPI 0 in pre-pro®ling period and RPI 1 in post-pro®ling period), INPAT
the hospital ratio of inpatient to outpatient revenue in month t, MED the
hospital's percentage of total inpatients who are either Medicare or Medicaid
patients in month t, RPILOS interaction (product) of RPI dummy variable
and LOS (RPILOS 0 in pre-pro®ling period and RPILOS LOS in postpro®ling period), SEVMIX is the average patient admission severity in month t
(SEV) weighted by (multiplied by) the average DRG weight in month t (MIX).
The system of equations (1)±(3) treats the decision to implement the physician pro®ling program (RPI) as exogenous. It is exogenous because the decision to implement the pro®ling program was made by hospital management,
while our analysis focuses on how physicians respond to the pro®ling program,
and in turn, how this response in¯uenced the hospital's costs.
We hypothesize that in Eq. (1) monthly hospital revenue department operating cost per patient (COST) is an increasing function of the average
number of weighted procedures performed per patient in that month (PROC)
and of the corresponding mean LOS per patient for the month (LOS). In
addition, to control for the eect of patient volume, we include the total
number of patients admitted in the month (TOTPAT). To capture the potential
in¯uence of capacity constraints on costs, we include the monthly hospital
occupancy rate (OCCUP).
Eq. (2) re¯ects the determination of mean monthly LOS per patient (LOS)
as a function of the hospital's policies and of the characteristics of the patients
treated in that month. Speci®cally, we hypothesize that LOS is a decreasing
function of the pro®ling policy variable, RPI, which equals 1 for all months
after the physician pro®ling RPI policy was implemented, and 0 otherwise.
Patient characteristics include the ratio of inpatient care to outpatient care
(INPAT) and the percentage of Medicare and Medicaid patients (MED). We
included MED because previous cross-sectional studies (e.g., Phelps, 1992, pp.
348±350) have found that hospitals with a greater percentage of Medicare and
Medicaid patients tend to have a shorter LOS as a result of the ®xed rate reimbursement incentive. The hospital occupancy rate (OCCUP) is included to
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J.H. Evans III et al. / Journal of Accounting and Public Policy 20 (2001) 73±88
control for the in¯uence of capacity constraints. Total weighted procedures
performed per patient (PROC) is the second endogenous variable.
Eq. (3) represents the determination of the monthly number of weighted
procedures performed per patient (PROC) as a function of hospital policies
and patient characteristics. The eect of the RPI program on the number of
procedures performed per patient is likely to re¯ect three factors. First, as the
average LOS changes, the number of routine daily procedures is expected to
vary proportionately, so that the expected sign on the LOS variable is positive. On the other hand, to the extent that LOS and procedures are substitutes in providing patient care (e.g., a physician may utilize more tests to
compensate for the patient remaining in the hospital under direct observation
for fewer days), the RPI program will result in more procedures being performed per patient day as LOS falls. Finally, the second eect may also be
reinforced by physicians' personal liability concerns in that defensive medicine
may call for performing additional tests when a patient is released sooner
from the hospital. As both of these two eects produce an increase in procedures performed per patient day, our hypothesis that the RPILOS term will
have a positive sign indicating that the RPI program resulted in the performance of more procedures per patient day. Whether the net eect of the RPI
program is more or fewer procedures per patient depends on the relative
magnitudes of these eects in Eq. (3), which is an empirical question. Finally,
patient characteristics include SEVMIX and INPAT, while OCCUP re¯ects
the potential in¯uence of capacity constraints on the number of procedures
performed.
4.1. Cost estimation results
Three-stage least squares (3SLS) regression results for Hospital P's
monthly cost per patient (Eq. (1)) are reported in Table 2. The corresponding
two-stage least squares (2SLS) results (not reported) are very similar to those
for 3SLS. We focus on the 3SLS results, which explicitly re¯ect the covariance of error terms in estimating coecients across equations, consistent with
the hypothesized simultaneous determination of cost, LOS and procedures.
The 3SLS approach also facilitates the subsequent hypotheses tests in Section
5 that examine the net eects of the RPI policy on the total number of
procedures performed and on the hospital's revenue department costs. In
order to capture the interdependencies among the system of equations (1)±(3),
the hypotheses tests of the net eects of the RPI policy on total procedures
per patient and cost per patient must re¯ect the net eect of two components.
The ®rst is the reduction of procedures due to the reduced LOS, and the
second is the hypothesized increase of procedures per day during the resulting
LOS. The net eect of the RPI policy on total procedures per patient is then
estimated jointly using the corresponding coecients from Eqs. (2) and (3).
J.H. Evans III et al. / Journal of Accounting and Public Policy 20 (2001) 73±88
83
Table 2
Simulation equation analysis of monthly hospital costs, length of stay, and procedures per patient
(n 40 months)a
3SLS Model
Intercept
PROCt
LOSt
TOTPATt
OCCUPt
RPI
INPATt
MEDt
SEVMIXt
RPILOSt
Eq. (1) Hospital
cost per patient
Eq. (2) Length of
stay per patient
COSTt
LOSt
)11.867
()0.160)
Total weighted
0.443
procedures per patient (2.983)
in month t
287.387
Mean length of stay
per patient in month t (2.888)
)0.34
Total inpatients in
month t
()0.1214)
Hospital occupancy
)12.788
rate in month t
()1.557)
Dummy variable for
the physician pro®ling
program, RPI 1 for
December 1991 and
later months, RPI 0
otherwise
Ratio of inpatient to
outpatient net revenue
in month t
Percentage of Medicare plus Medicaid
patients in month t
Severity-weighted
patient mix in month t
Interaction of physician pro®ling program
and LOS in month t
Durbin±Watson
D 1:782
0.221
(0.195)
0.001
(3.306)
Eq. (3) Weighted
procedure(s) per
patient
PROCt
547.2
(0.968)
628.55
(4.803)
0.043
(3.183)
)0.581
()3.998)
)30.25
()2.871)
)0.035
()0.263)
)1.49
()0.023)
)0.047
()0.129)
51.7
(0.971)
51.43
(3.893)
D 1:775
D 1:813
a
t-Statistics in parenthesis (two-tailed); White (1980) ± adjusted for heteroskedasticity); p < 0:01.
System-weighted R2 0:8673; system weighted MSE 32.899 with 103 degrees of freedom;
p < 0:001 (two-tailed).
Chi-squared statistic for Hansen test 0.13; p 0:94 (two-tailed).
Wald Statistic for Hausman test 6.62; p < 0:05 (two-tailed).
The net cost implication of the reduction in LOS is estimated in a similar
manner.
The results for Eq. (1) in Table 2 show that as hypothesized, both total
weighted procedures per patient and mean LOS per patient are positive and
statistically signi®cant. Considered in isolation, this result would indicate that
reducing either weighted procedures per patient or LOS per patient should lead
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J.H. Evans III et al. / Journal of Accounting and Public Policy 20 (2001) 73±88
to a reduction in hospital costs per patient. However, consistent with our
system of simultaneous equations, we expect that the RPI policy will also in¯uence LOS per patient and procedures per patient in Eqs. (2) and (3), respectively. Therefore, we next examine the results for Eqs. (2) and (3) to
estimate the net eect of the pro®ling policy on hospital costs.
In Column 2 of Table 2 the results for mean LOS per patient in Eq. (2) show
that RPI, the dummy variable for the physician pro®ling program, is negative
and statistically signi®cant. Because the cost data for this simultaneous equations approach is aggregated across all DRGs, these results use corresponding
LOS data which are also aggregated across all DRGs.
The results in Column 2 of Table 2 show an estimated coecient of )0.581
for the RPI dummy variable, implying that the RPI pro®ling program resulted
in an estimated 0.581 days reduction in mean LOS. In addition, in Column 2 of
Table 2 the total number of weighted procedures per patient (PROC) and the
hospital's monthly occupancy rate (OCCUP) are both positive and statistically
signi®cant. The latter result is consistent with declining occupancy rates as
patients stay fewer days in the hospital.
Turning to the results for weighted procedures per patient in Eq. (3), Column 3 of Table 2 ®rst shows that LOS is statistically signi®cant with a coef®cient of 628.55. This indicates that each additional day in the hospital is
associated with the performance of approximately 628.55 additional weighted
procedures. Next, RPILOS, the interaction of the RPI program and mean
LOS, has a positive and statistically signi®cant coecient of 51.43, indicating
that introduction of the RPI program was associated with an estimated increase of approximately 51.43 weighted procedures per patient day. The estimated coecient of the average severity-weighted patient mix (SEVMIX) has
the expected positive sign but is not statistically signi®cant at conventional
levels. 6
6
To check the robustness of the Section 4 results, we conducted various sensitivity analyses,
including rerunning the analysis using 2SLS rather than 3SLS, replacing the dependent variable in
Eq. (1), cost per patient in revenue departments, with cost per patient in all departments, and
replacing the total weighted procedures per patient with the corresponding raw total procedures in
Eqs. (1) and (2). Our results are generally robust to these changes except that in the case of the last
change, LOS and OCCUP are no longer statistically signi®cant, consistent with our overall result
that the reduction in LOS did not produce a reduction in cost. We also reran the Section 4 analysis
using a reduced form estimation to avoid the loss of degrees of freedom associated with the
simultaneous equation approach. Again, the ordinary least squares (OLS) regression results are
consistent with the pro®ling policy having no statistically signi®cant eect on the cost per patient
day. Finally, to check the simultaneity speci®cation, we ran the Hausman test (1978, pp. 1264±
1269) of the null hypothesis that all of the variables are exogenous and that the coecients obtained
by using OLS and 2SLS are equal. The Hausman test results for Eqs. (1)±(3) indicate rejection of
the null hypothesis that the variables of concern are exogenous for Eqs. (2) and (3).
J.H. Evans III et al. / Journal of Accounting and Public Policy 20 (2001) 73±88
85
5. Evaluating the net eect of the pro®ling program on hospital costs
We now use the estimated system of equations from Section 4 to test
whether the RPI policy produced a signi®cant change in procedures per patient, and whether the RPI policy produced a signi®cant change in the hospital's monthly costs. Results of testing the ®rst hypothesis are presented in Panel
A of Table 3, where RPIEq: 2 LOSEq: 3 0:581 628:55 365:2; the
estimated change in the number of weighted procedures per patient due to a
reduction of LOS induced by the hospital's RPI policy. The second term,
6:71 RPILOSEq: 3 ; becomes (6.71)(51.43) 345.1 additional procedures
performed per patient after the RPI policy. Summing the two terms
( 365:2 345:1) provides an estimated net reduction of 20.1 procedures per
patient as a result of the hospital's physician pro®ling RPI policy.
We tested whether this reduction was statistically signi®cantly dierent from
zero using a Taylor series approximation to the non-linear expression for the
null hypothesis in Panel A of Table 3 (Greene, 1993, pp. 218±220). The results
reported in Panel A of Table 3 provide no support (z 0:001) for rejecting the
null hypothesis of no change in procedures per patient. These results suggest
that the RPI policy produced two osetting in¯uences on total procedures
performed per patient, with the net eect being no signi®cant change in procedures per patient as a result of the RPI policy. In turn, to the extent that
hospital costs are driven by the number of procedures performed per patient,
the result suggests that the RPI policy may not have produced a signi®cant
reduction in hospital costs during the sample period. We next address this issue.
Given that both LOS and the number of procedures performed are drivers
of revenue department costs, it is important to examine the eects of changes in
patient average LOS and in the number of procedures performed per patient
on hospital operating costs. Following a similar rationale to that used in Panel
A of Table 3, Panel B of Table 3 reports results for the null hypothesis that the
RPI policy had no eect on the hospital's monthly revenue department costs.
The ®rst bracketed term in Panel B is again the estimated reduction of 20.1
Table 3
Hypotheses tests involving coecients of the system of equations (1)±(3) (Wald Test)
Panel A ± Net eect of the RPI pro®ling program on total procedures performed per patient
H0 : fRPIEq: 2 LOSEq: 3 6:71 RPILOSEq: 3 g 0
Z statistic 0.001; P-value 0.999
Panel B ± Net eect of the RPI pro®ling program on hospital cost per patient
H0 : fRPIEq: 2 LOSEq: 3 6:71 RPILOSEq: 3 g PROCEq: 1 fRPIEq: 2
LOSEq: 1 g 0
Z statistic 0.001; P-value 0.999.
*
Two-tailed Wald test; Taylor series approximation is used to re¯ect testing non-linear restrictions
(Greene, 1993, pp. 218±220).
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J.H. Evans III et al. / Journal of Accounting and Public Policy 20 (2001) 73±88
procedures per patient, which is then multiplied by the estimated cost of $0:443
per procedure, which is the coecient of PROC from Eq. (1) of Table 2. The
result for the ®rst bracketed term is an estimated reduction of $8:904 per
patient in hospital cost.
The remaining term from Panel B of Table 3 multiplies RPIEq: 2 , the estimated reduction of 0.581 days in LOS per patient from the RPI policy, by
$287:388, the estimated cost per patient day, which is the coecient of
LOSEq: 1 from Eq. (1) in Column 1 of Table 2. The result is an estimated cost
reduction of $166:972 per patient. Combining the two terms yields an estimated net cost reduction from the RPI policy of $175:876 per patient. However, this estimated eect is not signi®cantly dierent from zero in light of the
variances associated with the estimates involved. That is, the results in Panel B
of Table 3 indicate no support (z 0:001) for rejecting the null hypothesis that
the RPI policy had no eect on hospital costs. Alternatively, if one takes the
social costs of reducing LOS (and thereby medical care) as given, reductions in
LOS creating reductions in cost can be taken as the null hypothesis, for which
our data provides no support.
6. Conclusion
This paper provides empirical evidence that disclosing relative performance
information through physician pro®ling in¯uenced physicians in one hospital
to reduce average LOS and charges per patient, even in the absence of contractual incentives. To this extent, the physician pro®ling program was successful in achieving its immediate objective.
We next explored the relation between the average LOS, the number of
procedures performed per patient, and the hospital's monthly costs. The average number of procedures performed per patient and the patient's average
LOS are documented as drivers of hospital costs. However, the reduction in
average LOS is not associated with a signi®cant decrease in monthly hospital
operating costs. This result appears to stem from the fact that reducing average
LOS is associated with an increase in procedures per patient day. In turn, the
cost saved by having fewer patient days appears to be oset by the increased
cost of performing more procedures per day. The net eect is that the physician
pro®ling program is associated with a signi®cant reduction in average LOS, but
not in hospital operating costs. Our study has implications for the social policy
debate on the value of attempts by hospitals and managed care organizations
to control health care costs by reducing LOS. The lack of support for cost
reduction as a rationale for reducing LOS suggests that careful attention be
devoted to the social costs of premature discharges, particularly to the extent
that these costs are borne disproportionately by certain segments of the population.
J.H. Evans III et al. / Journal of Accounting and Public Policy 20 (2001) 73±88
87
Another implication of our study is that to be meaningful, cost control efforts in hospitals must focus not only on LOS, but also on managing the
number and mix of procedures performed during the patient's hospital stay.
Thus, hospitals may potentially be better o by investing greater resources in
process improvement initiatives such as critical pathways (see e.g. Evans et al.,
1997a, pp. 26±27).
Finally, our study suggests the value of a portfolio of performance measures
in evaluating the impact of policy changes. LOS and procedures constitute
non-®nancial measures of performance while costs and charges are ®nancial
measures of performance. Focusing exclusively on one type of performance
measure might lead to an incorrect view of hospital performance in general or
the success or failure of a policy initiative in particular. Combining costs and
charges with internal process measures such as LOS and procedures, as well as
customer outcome measures such as mortality, morbidity and patient satisfaction indices, can be the basis for a balanced scorecard approach to measuring hospital performance.
Acknowledgements
We acknowledge excellent research assistance from Andy Leone. We also
acknowledge helpful comments from G.G. Hegde, Chuan Yang Hwang, YuhChia Hwang, Dave Larcker, Ken Lehn, Lance Lieberman, Robert Melby,
Michael Rutigliano, seminar participants at New York University, Ohio State
University, Santa Clara University, the Industrial Engineering Department
and the Katz Graduate School of Business of the University of Pittsburgh, two
anonymous referees, various participants at the 1994 American Accounting
Association's Management Accounting Research Conference, and the 1994
AAA Annual Meeting in New York (Evans et al., 1994). We also acknowledge
a research grant from the Institute of Management Accounts. In an earlier
version the paper was entitled, ``Can Hospital Costs be Controlled by Inducing
Physicians to Reduce Patients' LOS? An Empirical Analysis'' (see Evans et al.,
1994).
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