Directory UMM :Data Elmu:jurnal:J-a:Journal of Accounting and Public Policy:Vol20.Issue1.2001:

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, PA15260,}

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 ac-counting 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 o€set by a concurrent increase in procedures performed per patient day, the pro®ling program did not produce a sig-ni®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

Journal of Accounting and Public Policy 20 (2001) 73±88

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*_{Corresponding author. Tel.: +1-412-648-1714; fax: +1-412-648-1693.}

E-mail address:jhe@katz.pitt.edu (J.H. Evans III).

overall increase in healthcare costs (Newhouse, 1992, p. 7), leading to public policy and private sector e€orts 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.1In previous research we analyzed this hospital's e€orts 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 docu-mented 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 method-ological 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 e€ectiveness of hospital attempts to control costs through reducing LOS remains an unre-solved issue, there is evidence that indigent and chronically ill patients, who may be less able to substitute other e€ective 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).}

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 in-stance, 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-o€s 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 eciency of non-contrac-tual 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 in-surers 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 e€ectiveness 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

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 so-phistication 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 Medi-care prospective payment system, patient LOS, and hospital costs from a public policy perspective. For example, Sloan et al. (1988, pp. 210±211), sug-gested that Medicare's prospective payment mechanism has resulted in an in-creased 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 con-founding 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 pro-gram 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.

Utilization Project (HCUP) database.5 From the HCUP data we obtained TOTPAT (total number of patients in Hospital P by month, based on ad-missions), 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 hos-pital costs summed across all revenue departments (converted to constant July 1990 dollars using the seasonally adjusted monthly Total Medical Care com-ponent 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 depart-ments are likely to be less amenable to medium term cost control. The exclu-sion 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. Hereprocedures

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 cor-responding weight for each procedure as determined by the hospital. The weights for all labor services are calculated by the hospital's management en-gineers 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 proce-dure. 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

admis-5

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.

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 hos-pital treatment, based on the MedisGroup ®ve-point scale. SEV is then mul-tiplied 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 a€ect the hospital's oper-ating 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 e€ect 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 a€ect 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 a€ect 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.

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 (nˆ40 monthly observations)

Pre-pro®ling period (August 1990±November 1991) Post-pro®ling period (December 1991±November 1993)

Median Mean Standard

$2256 $2252 $88 $2406 $2110 $2012 $1978 208 $2416 $1643

LOSt± Mean length of stay

per patient in

montht 7.65 7.74 0.28 8.23 7.30 6.69 6.71 0.56 7.87 5.87

PROCt± Total weighted

procedures per patient

in montht 2927.79 2903.86 163.11 3203.15 2545.27 2803.93 2800.61 200.06 3239.19 2473.30

TOTPATt± Total

inpatients in montht

1549 1551 100 1686 1388 1703 1698 98 1892 1489

OCCUPt± Hospital

occupancy rate in montht

84.9% 85.99% 3.42% 90.3% 80.9% 79.1% 79.58% 5.45% 89.3% 70.3%

SEVMIXt±

Severity-weighted patient mix in

montht 1.97 1.94 0.11 2.07 1.69 2.13 2.12 0.10 2.30 1.88

INPATt± Ratio of

inpatient to outpatient net

revenue in montht 7.24 7.23 0.01 7.24 7.22 5.87 6.15 0.73 7.21 5.34

MEDt± Percentage of all

patients that are Medicare or Medicaid in montht

0.52 0.52 0.03 0.57 0.48 0.60 0.60 0.03 0.64 0.52

Table 1 (Continued)

Panel B ± Pearson correlation coecients

COSTt LOSt PROCt TOTPATt OCCUPt SEVMIXt INPATt MEDt

COSTt 1.00 0.82 0.80 )0.72 0.54 )0.39 )0.72 )0.66

LOSt 1.00 0.59 )0.63 0.78 )0.33 0.78 )0.83

PROCt 1.00 )0.61 0.41 )0.61 0.59 )0.46

TOTPATt 1.00 )0.28 0.33 )0.46 0.55

OCCUPt 1.00 )0.18 0.74 )0.74

SEVMIXt 1.00 )0.49 0.41

INPATt 1.00 0.55

MEDt 1.00

a_{Observations for this table are mean values for each of 40 months in our sample period. For example, LOS}

tis the mean length of stay across patients

in all DRGs in montht.**p<0:05 (two-tailed);*p<0:10 (two-tailed).

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COSTtˆa0‡a1PROCt‡a2LOSt‡a3TOTPATt‡a4OCCUPt‡e1;…1†

LOStˆb0‡b1RPIt‡b2PROCt‡b3OCCUPt

‡b4INPATt‡b5MEDt‡e₂; …2† PROCtˆc0‡c1LOSt‡c2RPILOSt‡c3OCCUPt

‡c4SEVMIXt‡c5INPATt‡e₃; …3†

where COST is the hospital revenue department cost per patient in montht, PROC the weighted procedures performed per inpatient in montht, LOS the mean length of stay per inpatient in month t, TOTPAT the total inpatients admitted in montht, OCCUP the hospital occupancy rate in montht, RPI the physician pro®ling Relative Performance Information (RPI) dummy variable (RPIˆ0 in pre-pro®ling period and RPIˆ1 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 montht, RPILOS ˆ interaction (product) of RPI dummy variable and LOS (RPILOSˆ0 in pre-pro®ling period and RPILOSˆLOS in post-pro®ling period), SEVMIX is the average patient admission severity in montht

(SEV) weighted by (multiplied by) the average DRG weight in montht(MIX). The system of equations (1)±(3) treats the decision to implement the phy-sician pro®ling program (RPI) as exogenous. It is exogenous because the de-cision 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 op-erating 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 e€ect 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 re-imbursement incentive. The hospital occupancy rate (OCCUP) is included to

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 e€ect 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 posi-tive. On the other hand, to the extent that LOS and procedures are substi-tutes 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 per-formed per patient day as LOS falls. Finally, the second e€ect 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 e€ects produce an increase in pro-cedures performed per patient day, our hypothesis that the RPILOS term will have a positive sign indicating that the RPI program resulted in the perfor-mance of more procedures per patient day. Whether the net e€ect of the RPI program is more or fewer procedures per patient depends on the relative magnitudes of these e€ects 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 covari-ance of error terms in estimating coecients 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 e€ects 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 e€ects of the RPI policy on total procedures per patient and cost per patient must re¯ect the net e€ect 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 e€ect of the RPI policy on total procedures per patient is then estimated jointly using the corresponding coecients from Eqs. (2) and (3).

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

Table 2

Simulation equation analysis of monthly hospital costs, length of stay, and procedures per patient (nˆ40 months)a

LOSt Mean length of stay

per patient in montht

INPATt Ratio of inpatient to

outpatient net revenue

t-Statistics in parenthesis (two-tailed); White (1980) ± adjusted for heteroskedasticity);_p<0:01. System-weighted R2_{ˆ0:8673; system weighted MSEˆ32.899 with 103 degrees of freedom;}

p<0:001 (two-tailed).

Chi-squared statistic for Hansen testˆ0.13;pˆ0:94 (two-tailed). Wald Statistic for Hausman testˆ6.62;p<0:05 (two-tailed).

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), re-spectively. Therefore, we next examine the results for Eqs. (2) and (3) to estimate the net e€ect 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 equa-tions 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 coecient 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), Col-umn 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 coecient of 51.43, indicating that introduction of the RPI program was associated with an estimated in-crease of approximately 51.43 weighted procedures per patient day. The esti-mated coecient 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 e€ect 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 coecients 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).

5. Evaluating the net e€ect 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 pa-tient, and whether the RPI policy produced a signi®cant change in the hospi-tal's monthly costs. Results of testing the ®rst hypothesis are presented in Panel A of Table 3, where RPIEq:…2†LOSEq:…3†ˆ … 0:581†…628:55† ˆ 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†Š; becomes (6.71)(51.43)ˆ345.1 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 di€erent 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 (zˆ0:001) for rejecting the null hypothesis of no change in procedures per patient. These results suggest that the RPI policy produced two o€setting in¯uences on total procedures performed per patient, with the net e€ect being no signi®cant change in pro-cedures 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 e€ects 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 e€ect 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 coecients of the system of equations (1)±(3) (Wald Test)

Panel A± Net e€ect of the RPI pro®ling program on total procedures performed per patient H0:fRPIEq:…2†LOSEq:…3†‡6:71RPILOSEq:…3†g ˆ0

Zstatisticˆ0.001;P-valueˆ0.999

Panel B ± Net e€ect of the RPI pro®ling program on hospital cost per patient H0:fRPIEq:…2†LOSEq:…3†‡6:71RPILOSEq:…3†g PROCEq:…1†‡ fRPIEq:…2† LOSEq:…1†g ˆ0

Zstatisticˆ0.001;P-valueˆ0.999.

*_{Two-tailed Wald test; Taylor series approximation is used to re¯ect testing non-linear restrictions}

(Greene, 1993, pp. 218±220).

procedures per patient, which is then multiplied by the estimated cost of$_0:443 per procedure, which is the coecient 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

esti-mated 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 coecient 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 esti-mated net cost reduction from the RPI policy of$175:876 per patient. How-ever, this estimated e€ect is not signi®cantly di€erent 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 (zˆ0:001) for rejecting the null hypothesis that the RPI policy had no e€ect 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 con-tractual incentives. To this extent, the physician pro®ling program was suc-cessful 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 av-erage number of procedures performed per patient and the patient's avav-erage 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 o€set by the increased cost of performing more procedures per day. The net e€ect 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 pop-ulation.

Another implication of our study is that to be meaningful, cost control ef-forts 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 satis-faction indices, can be the basis for a balanced scorecard approach to mea-suring hospital performance.

Acknowledgements

We acknowledge excellent research assistance from Andy Leone. We also acknowledge helpful comments from G.G. Hegde, Chuan Yang Hwang, Yuh-Chia 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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