Hospital Quality, Efficiency, and
Input Slack Differentials
Vivian G. Valdmanis, Michael D. Rosko, and Ryan L. Mutter
Objective. To use an advance in data envelopment analysis (DEA) called congestion
analysis to assess the trade-offs between quality and efficiency in U.S. hospitals.
Study Setting. Urban U.S. hospitals in 34 states operating in 2004.
Study Design and Data Collection. Input and output data from 1,377 urban hos-
pitals were taken from the American Hospital Association Annual Survey and the
Medicare Cost Reports. Nurse-sensitive measures of quality came from the application
ofthe Patient Safety Indicator(PSI) module ofthe Agencyfor Healthcare Research and
Quality (AHRQ) Quality Indicator software to State Inpatient Databases (SID) provided
by the Healthcare Cost and Utilization Project (HCUP).
Data Analysis. In the first step of the study, hospitals’ relative output-based efficiency
tothe occurrence ofpatientsafety events). The outputs wereadjustedto account for this
productivity loss, and a second DEA was performed to obtain input slack values.
Differences in slack values between unadjusted and adjusted outputs were used to
measure either relative inefficiency or a need for quality improvement.
Principal Findings. Overall, the hospitals in our sample could increase the total
amount of outputs produced by an average of 26 percent by eliminating inefficiency.
About 3 percent of this inefficiency can be attributed to congestion. Analysis of sub-
samples showed that teaching hospitals experienced no congestion loss. We found that
quality of care could be improved by increasing the number of labor inputs in
low-quality hospitals, whereas high-quality hospitals tended to have slack on personnel.
Conclusions. Results suggest that reallocation of resources could increase the relative
need not be achieved as a result of higher costs or through reduced access to health
Key Words. Hospital efficiency, data envelopment analysis, congestion, patient
safety, nurse-sensitive outcomes
Much is yet to be learned about the interaction of cost, efficiency, and quality.
It is not clear that cost containment and quality improvement are mutually
consistent objectives. Quality improvement can result in greater resource use
because it may require more or better resources. Yet, proponents of total
No claim to original U.S. government worksrHealth Research and Educational Trust
quality management (TQM) argue that it is possible to reduce costs and
increase quality simultaneously through efficiency gains.
In this study, we use an advance in data envelopment analysis (DEA),
termed congestion analysis, to ascertain whether some hospitals might be
experiencing poor quality that could be corrected by changing the number
and mix of inputs. Unlike the basic DEA model or stochastic frontier analysis
(SFA), the most prominent frontier methods of estimating hospital efficiency,
congestion analysis explicitly considers the possibility that some health care
outputs can be undesirable (e.g., patient safety events). An advantage of our
approach is that we can measure the percent of total outputs that could be
increased, as well as how poor quality affects productivity. Further, by con-
and do not add to quality, as well as which inputs should be increased (and by
how much) to enhance quality. In addition to assessing quality and efficiency
simultaneously, we can also categorize hospitals into peer groups based on
efficiency. Our results have the potential to be actionable by providers and
The literature provides mixed findings about the relationship among costs,
efficiency, and quality. For example, it has been consistently demonstrated
that increases in registered nurses (RNs) in the nursing mix of hospitals are
positively associated with lower rates of certain patient safety events. See
Haberfelde, Bedecarre, and Buffum (2005) for a review.
The use of technologically advanced capital and services has also been
associated with higher quality care (Dranove and White 1998; Picone et al.
2003). However, increases in RNs and sophisticated technology also should
increase costs, ceteris paribus; therefore, it is possible that higher costs (and
perhaps higher inefficiency) and higher reservation quality and/or higher
McKay (2006) tested this relationship empirically but in both studies, higher
Address correspondence to Ryan L. Mutter, Ph.D., Agency for Healthcare Research and Quality,
Center for Delivery, Organization and Markets, 540 Gaither Road, Rockville, MD 20850; e-mail:
Health, University of the Sciences in Philadelphia, PA. Michael D. Rosko, Ph.D., is with the
Graduate Program in Health and Medical Services Administration, School of Business Admin-
istration, One University Place, Widener University, Chester, PA.
Hospital Quality, Efficiency, and Input Slack Differentials1831
cost or cost inefficiency was associated with increases in poor quality, as mea-
sured by risk-adjusted mortality rates. However, the authors of both these
studies suggest that their results may be due to inadequate controls for case-
The works of McCloskey (1998), Blegen, Goode, and Reed (1998), and
Blegen and Vaughan (1998) offer additional insight into the relationship be-
tween quality and inefficiency. They found that increasing the numberof RNs
is quality enhancing, but only to a point, after which it becomes efficiency
decreasing. Therefore, it may be assumed that too much of labor and capital
inputs lead to inefficiency, not to higher quality. Indeed, even though Picone
et al. (2003) found that slack resources and capacity are inputs to quality,
too much slack or capacity will lead to inefficiency as well.
Recognizing the importance of this topic, McKay and Deily (2005) call
for more research on the trade-off between quality and efficiency. They spe-
cifically focus on the issue of nurse staffing. They write, ‘‘Recent studies have
highlighted the role of nursing quality as a major determinant of high quality
care. An unanswered question, however, is how staffing quality affects per-
formance when defined in terms of both quality and efficiency’’ (p. 357).
In the next section we describe DEA and congestion analysis. We then
proceed to a description of the data and the results. The paper concludes with
a discussion of the practical implications of our findings for providers and
The two major frontier techniques that have been used most frequently to
estimate inefficiency at the hospital level are DEA and SFA. DEA is a de-
terministic approach based on the works by Debreu (1951) and Farrell (1957)
and updated in terms of economic efficiency and productivity by Fa ¨re, Gross-
kopf, and Lovell (1994). Coelli et al. (2005) and Aaronson et al. (2006) suggest
that contextual circumstances should determine which technique is selected
when a choice between DEA and SFA has to be made. As our focus is on
patient safety in a multi-input, multi-output framework, we opt for a DEA
approach that specifically incorporates undesirable outputs. SFA is incapable
of doing this.
DEA derives a best practice frontier by solving linear programming
problems, which identify those hospitals maximizing outputs given inputs.
Individual hospital performance is a proportional measure gauged relative to
1832 HSR: Health Services Research 43:5, Part II (October 2008)
the frontier that is defined by hospitals deemed to be the best performers. A
score of 1.00 indicates that a hospital is operating on the best practice frontier
(i.e., one that is efficient). A score 41.00 indicates inefficiency with the differ-
ence between the actual score and 1.00 measuring the amount all outputs
could be increased,holdinginputsconstant. In some instances,a hospital may
be unique to all others in the sample, in which case it would receive an
efficiency score of 1.00 because it lies on its own frontier. Therefore, we
reiteratethatefficiencyisa productofthedistribution ofhospitalsandthatthis
measure of efficiency in the engineering sense.
Many benefits of DEA have been cited in the literature. Coelli et al.
(2005) provide a comprehensive discussion, and we provide a brief summary
in Appendix A. Two benefits of DEA that are particularly germane to our
researchareasfollows.First,hospitals located intheinterior ofthefrontier are
strictly inefficient. This permits additional analyses exploring factors that sep-
arate best practice performers from less efficient producers. Second, we can
decompose the DEA total efficiency measure into its various sources of
inefficiency as shown in the following equation:
TotalEfficiencyðCRSÞ ¼ PureTechnicalEfficiencyðVRSÞ
? ScaleEfficiency ? Congestion
Total efficiency is measured under assumptions of constant returns to scale
outputs (SDO) (i.e., all outputs are considered desirable). Pure technical effi-
ciency, variable returns to scale (VRS), measures only the input–output cor-
respondence absent any scale or congestion effects. CRS/VRS measures scale
efficiency attributed to hospital size. Congestion is derived by assessing
productivity under assumptions of strong and weak disposability of outputs
(WDO) (i.e., some outputs may be undesirable). In the approach taken here,
we follow Balk’s (1998) assertion that no complete production study can
ignore the output of undesirables, particularly from a social point of view.
Earlier studies have used congestion analysis to account for pollution (Fa ¨re,
Grosskopf, and Lovell 1994), hospital uncompensated care (Valdmanis, Ku-
manarayake, and Lertiendumrong 2004; Ferrier, Rosko, and Valdmanis
2006), and hospital mortality rates (Clement et al. 2008).
As this derivation is described elsewhere (Valdmanis, Kumanarayake,
and Lertiendumrong 2004; Ferrier, Rosko, and Valdmanis 2006; Clement
et al. 2008), we only describe the essence of the model in brief here. A math-
ematical exposition of this approach is provided in Appendix A.
Hospital Quality, Efficiency, and Input Slack Differentials 1833
Total efficiency is a multiplicative total of all sources of inefficiency as
seen in equation (1). (Interested readers should see Fa ¨re, Grosskopf, and Lov-
ell  for more information regarding this decomposition and the relevant
economic proofs.) Here, we focus on quality congestion as it impacts the total
production of hospital care. While an input minimization approach has been
typically used in health care DEA studies, our quality-congestion analysis
requires an output orientation because of the possible association of a high
other words, we need to measure how patient safety may be compromised if
too many outputs are being produced, holding inputs fixed.
Under the SDO assumption, expansion of all outputs is desirable and
reducing one output leads to the possible increase of another output. In con-
trast, the assumption of WDO treats expansion of some outputs as undesir-
of inputs), represented by curves CD and EF, are shown in the figure. The
illustrates congestion——good output cannot be expanded without increasing
the‘‘bad’’output.Unlikethe typical outputsubstitution inproductivitystudies
(i.e., where one good is substituted or traded off for the production of another
good), the good and bad outputs move in the same direction as seen by the
Unadjusted/uncongested production frontier
Adjusted production frontier
Figure1: Comparison of Unadjusted and Adjusted Production Frontiers
1834HSR: Health Services Research 43:5, Part II (October 2008)
line. If congestion did not occur, the higher production frontier, CD, would
represent greater adjusted output (i.e., the downward adjustment would be 0).
account for poor quality. This is accomplished by dividing the outputs by the
quality-congestion measure, thereby discounting unadjusted output to form
quality-adjusted output. If high-output hospitals have no quality congestion,
we can assert that a volume–outcome relationship exists.
High-volume hospitals have been shown to have lower mortality rates
than low-volume hospitals for certain technology-intensive, complicated pro-
cedures. A considerable body of literature supports the statistical association
hypothesized as an explanation for this relationship, although the reasons for
DEA has been criticized for its deterministic nature, which assumes no mea-
surement error (Newhouse 1994). But the stochastic nature of demand may
lead hospital decision makers to overestimate resource needs. Depending on
one’s perspective, this might overstate inefficiency. For example, assuming
Pauly 1986), unoccupied beds might represent reservation quality ( Joskow
1980) rather than slack resources. Conversely, operations that include poor
inefficient. The reality is probably a mix of the two situations, so the ineffi-
ciency estimates might be better viewed as an upper bound as there is no
compensation for periods of excessive utilization.
DEA has also been criticized for its inability to capture quality differ-
ences (Newhouse 1994). However, with our data we can capture more quality
differences than previous DEA studies, and this should partially address the
A recentDEA-based study by Clementet al.(2008)empirically assessed
how poor quality outcomes detract from overall hospital productivity. This
was accomplished by applying the WDO technology to a sample of hospitals
to measure the production of undesirable outputs, such as in-hospital mor-
tality. We follow the approach used in Clement et al. (2008) but expand upon
it by adjusting outputs (specifically the subset of the Agency for Healthcare
Research and Quality [AHRQ] Patient Safety Indicators [PSIs] that are nurse-
sensitive outcomes) to define undesirable outcomes rather than the AHRQ
Hospital Quality, Efficiency, and Input Slack Differentials1835
Inpatient Quality Indicators (IQIs) used by Clement et al. (2008). We also
expand on the work by Clement et al. (2008) by comparing the slack (i.e.,
excess inputs) values for inputs between the model where all outputs are
considered desirable and the slack values for inputs in the case when outputs
are adjusted to account for relatively poor quality. Below, we discuss the
insights we can gain by making these comparisons.
Slack is derived using the input-based Cooper, Seiford, and Zhu (2000)
approach.1Using the results derived from this second-step analysis, we mea-
sure the differences in the slack values for inputs obtained for the high-quality
we can analyze the over- or under-utilization of nurses, for example, by hos-
pital quality status. Ifthedifference between slacks is positive,it would suggest
that the hospital is employing excess input that leads to inefficiency. If the
difference is negative, it implies that inputs need to be increased to improve
quality of care. In fact, the difference indicates the amount of inputs that need
to be increased.
Unlike congestion, slack does not impede total production, but may
either represent a quality input or excessive inputs leading to inefficiency.
(It should be noted that while the congestion measure is multiplicative, the
slack measure is additive.)
to areas where inefficiency exists and adjust accordingly to optimize both
production and quality of care (Sherman 1984). Therefore, this analysis can
show managers how much their hospital needs to increase an input (to in-
crease quality) or decrease an input (to reduce inefficiency) as compared with
their hospital’s peers.
We also assess quality differentials and slack by organizational factors
(ownership, teaching status, resource expenditures, payer-mix, and system
membership) and market characteristics (health maintenance organization
[HMO] penetration and hospital competition). We conclude by analyzing
quality and slack in order to develop a direct link between production per-
formance and input slack.
Data come from the American Hospital Association (AHA) Annual Survey of
Hospitals, augmented by variables from the Medicare Hospital Cost Reports
(for number of patient days in nonacute care units), AHRQ (for measures of
1836HSR: Health Services Research 43:5, Part II (October 2008)
patient safety and hospital competition), and Solucient Inc (Evanston, IL) (for
data on county-level HMO enrollment and number of residents without
health insurance). Hospitals included in this study are those defined by the
variables from the application of the PSI module of the AHRQ Quality In-
dicator (QI ) software2to the Healthcare Cost and Utilization Project (HCUP)3
restricted to 34 states5supplying HCUP data. This yielded an analytical file of
1,377 urban hospitals in 2004. As in the case of any model, the selection of
inputs and outputs may affect the final results and/or ranking of hospitals in
terms of quality.6Being mindful of this concern, we follow the previous lit-
erature in determining inputs and outputs. Our inputs include bassinets, acute
beds (i.e., the number of licensed and staffed beds minus the number of beds
in nonacute units, such as long-term care), licensed and staffed ‘‘other’’ beds,
FTE (full-time equivalent) RNs, licensed practical nurses (LPNs), medical res-
idents, and other personnel. Outputs include Medicare Case Mix Index
(MCMI) adjusted admissions (MCMI ? admissions), total surgeries (inpa-
tient 1 outpatient surgeries), total outpatient visits (ER visits 1 outpatient
visits), total births, and total other patient days (i.e., patient days in nonacute
that typically use a mix of inpatient and outpatient care variables and specify
surgery separately from total admissions.8(See Hollingsworth  for a
Measures of undesirable events include the following risk-adjusted PSIs
that Savitz, Jones, and Bernard (2005) indicate are sensitive to nurse staffing:
failure to rescue (RPPS04), infection due to medical care (RPPS07), postop-
erative respiratory failure (RPPS11), and postoperative sepsis (RPPS13). Dec-
ubitus ulcer and postoperative pulmonary embolism (PE) or deep vein
thrombosis (DVT) are also nurse-sensitive measures of quality; however,
Houchens, Elixhauser, and Romano (2008) found that a high percentage of
these events are present on admission (POA). Therefore, they are not valid
measures of hospital quality, and we exclude them from our study.
In a secondary analysis, we examine the relationship between DEA-
based inefficiency estimates and various correlates of inefficiency. We include
an array of internal factors including ownership, which reflects the role of
property rights. Teaching status is regularly included as an organizational
feature that may affect a hospital’s productive performance. System mem-
resource use and are better able to exploit bulk purchasing (and other types of
Hospital Quality, Efficiency, and Input Slack Differentials1837
discounts) than independent institutions (Lindrooth, Bazzoli, and Clement
Higher resource use in hospitals has also been associated with higher
quality. Therefore, we include the number of high-technology services
offered,9cost per case-mix-adjusted admission (MCMI ? admissions) and
outpatient volume, the amount of capital expended per bed (depreciation 1
interest expense), and the ratio of FTE personnel to both adjusted admissions
We also analyze a variety of variables related to patient and payer mix,
including the following percentages: births to total admissions, emergency
room (ER) visits to total outpatient visits, outpatient surgeries to total outpa-
as average length of stay.
Historically, hospitals often competed on a nonprice basis, which
resulted in the duplication of services. More recent literature on market com-
petition generally finds more efficient, less costly hospitals in more compet-
itive markets. Therefore, we include the county-level Herfindahl–Hirschman
index (HHI) to reflect the amount of competition faced by the hospitals in our
sample.10We also use variables compiled by Solucient Inc. to reflect HMO
penetration and the percentage of the population without health insurance
in the county where the hospital is located as measures of financial pressure.
(We used 2002 data for the Solucient variables, as it was the most recent data
available to us.)
Descriptive statistics of the inputs, outputs, and environmental factors
used in our analysis are presented in Table 1.
We begin our results section with a presentation of the output-based measures
for the unadjusted sample.11In Table 2, we present the results for the output-
based efficiency measures, as well as statistical analysis of these efficiency
measures by organizational factors.
On average, under CRS technology, outputs could be increased by
25.9 percent [(1.35?1)/1.35)] without increasing inputs in our sample hos-
amount of total production lost due to congestion is 2.9 percent. The average
scale inefficiency, 19.4 percent, dominates pure technical efficiency, implying
that hospitals may be either too large or too small.
1838HSR: Health Services Research 43:5, Part II (October 2008)
Table1: Descriptive Statistics——Inputs and Outputs (N51,377)
Data envelopment analysis (DEA) inputs
Full-time equivalent registered nurses (FTE RNs)
FTE licensed practical nurses (LPNs)
FTE other personnel
Other patients days
Failure to rescue (RPPS04)
Infection due to medical care (RPPS07)
Postoperative respiratory failure (RPPS11)
Postoperative sepsis (RPPS13)
Environmental and organizational factors
Number high-tech services
Emergency room (ER) visits (%)
Outpatient surgeries (%)
Average length of stay
Percent of population in county without health insurancen
Percent of population in county covered by health
maintenance organization (HMO)n
Herfindahl–Hirschman index (HHI)n
Member of a system
nThese are market-level variables.
COTH, Council of Teaching Hospitals; FP, for-profit hospitals; NFP, not for profit hospitals.
Hospital Quality, Efficiency, and Input Slack Differentials1839
Given the wide variation among the efficiency scores, we are interested
among the sample hospitals. We present efficiency scores by organizational
factors in Table 3.
measures of inefficiency (i.e., overall, technical, scale, and quality congestion).
Not-for-profit (NFP) hospitals were the next most inefficient category,
although we found little difference between public and NFP hospitals.
For-profit (FP) hospitals performed best, on average.
Turning next toteaching status,compared tohospitalswitheitherminor
teaching or no teaching program, COTH hospitals were significantly less
efficient on the overall and scale measures, but did outperform the other two
types of hospitals in both pure technical inefficiency and quality congestion.
The latter result is consistent with the observation that better quality of care is
provided in major teaching hospitals (Taylor, Whellan, and Sloan 1999).
Interorganizational arrangements may also affect efficiency. System-
member hospitals outperformed nonsystem hospitals on all four measures of
inefficiency. Therefore, this organizational factor appears to have a positive
effect on efficiency.
Building on the quality framework in this paper, we also assess the slack
values from each of the two production frontiers——one based on all outputs
(which we call the unadjusted frontier) and the other based on the adjusted
frontier where we discount outputs by quality congestion to account for poor
outcomes. There exist few significant results, but none that are consistent.
For descriptive statistics for variables with statistically significant slack by
organizational factors and environmental effects, see Tables A1 and A2 in
We next assess how environmental factors and slack values are more
directly associated given the quality provided by the hospitals in our sample.
Envelopment Analysis [DEA]) (N51,371)
Descriptive Statistics——Efficiency Scores (Output-Based Data
Constant returns to scale (CRS) efficiency
Variable returns to scale (VRS) efficiency
1840HSR: Health Services Research 43:5, Part II (October 2008)
to their quality congestion scores. Those without congestion (i.e., quality con-
gestion scores of 1.0) are classified as high quality (n5901) while the cut-off
score (based on the median value for congested hospitals) for medium-quality
(n5233) and low-quality (n5237) hospitals was 1.04. These findings are
given in Table 4.
In general, our results show that increasing total expenditures and the
availability of high-technology services were related to high quality at
statistically significant levels (po.05). Associated with this finding is our
result that teaching hospitals in our sample experienced no congestion. In
addition, we also find that high-quality hospitals had higher mean overall
efficiency than the lower-quality hospitals. The main differences among
the three levels of hospital quality in terms of patient mix, were
Medicare percent and HMO enrollment, and definite trends emerged
regarding resources expended per patient. Hospitals providing higher
quality were also in more competitive markets, indicated by the lower aver-
age HHI score.
Turning next to slack values and quality, we find that patterns are most
similar between high-quality and medium-quality hospitals. Specifically, effi-
Analysis [DEA]) and Statistically Significant Wilcoxon Tests by Ownership
Status, Teaching Status, and System Membership (N51,371)
Mean Efficiency Score Values (Output-Based Data Envelopment
CharacteristicCRS EfficiencyVRS Efficiency Scale EfficiencyCongestionw
wEven though these means appear very similar, the Wilcoxon test is a rank score, so that more
highly congested hospitals are either public or NFP relative to FP hospitals.
CRS, constant returns to scale; VRS, variable returns to scale.
Hospital Quality, Efficiency, and Input Slack Differentials1841
ciency could be improved if high- and medium-quality hospitals used fewer
other personnel, and bassinets. High-quality hospitals needed to cut fewer
other personnel than medium-quality hospitals. The only variable for which
the sign of the slack variable diverged was LPNs——high-quality hospitals had
too few, while medium-quality hospitals had too many. Low-quality hospitals
should increase inputs in the LPN and other personnel labor categories and
decrease inputs in each of the capital categories.
ity: CONGo1.04; Low Quality: CONG41.04)wEnvironmental Factors and
Quality-Based Results (High Quality: CONG51; Medium Qual-
Overall data envelopment analysis (DEA)
Organizational and environmental factors
Average length of stayn
Full-time equivalent (FTE)/bednn
Health maintenance organization
Herfindahl–Hirschman index (HHI)n
FTE registered nurses (RNs)
FTE licensed practical nurses (LPNs)nnnn
FTE other personnelnn
0.30 0.36 0.43
wA congestion score of 1 indicates no congestion (i.e., bads crowding out goods). The cut-off
between medium and low levels of CONG is 1.04, which is the number of hospitals between the
1842 HSR: Health Services Research 43:5, Part II (October 2008)
of our analysis. First, DEA assumes that there is no measurement error. While
we see little potential for a systematic bias, it is quite likely that measurement
error exists. Therefore, it is better to focus on broad trends (e.g., in what areas
do low-quality hospitals tend to have slack or insufficient inputs?) than
on specific point estimates. Further, like most studies of efficiency or costs,
there is always the potential for specification error (i.e., omission of important
We found that certain hospital characteristics are associated with ineffi-
ciency and quality congestion. The overall inefficiency score was 1.35 (i.e.,
outputs could be increased by 26 percent). Of this, about 3 percent of the total
inefficiency can be attributed to quality congestion. Using this measure, we
adjust hospital outputs in order to determine if increases in any of the inputs
could increase quality or whether these increases just added to the overall
Consistent with property rights theory, FP hospitals exhibited the least
amount of inefficiency relative to either public or NFP hospitals. We also
found that most hospitals in our sample were operating at diseconomies of
scale, which suggests that slack, especially on beds, may be a factor leading
to inefficiency. The finding that public hospitals tend to have too many
acute care beds——was supported by the relatively high-scale inefficiency
(operating at diseconomies of scale). The analysis of slack on inputs by
ownership demonstrated that public hospitals tended to have too many other
Teaching hospitals were relatively more efficient in terms of pure effi-
ciency and were not congested; the main source of their inefficiency was
diseconomies of scale. Members of systems performed better relative to their
counterparts, suggesting this type of organization led to better performance.
resource use in terms of total expenditures and high-technology equipment
was associated with higher quality. Higher quality hospitals were also oper-
ating in markets with a higher percentage of HMO enrollees and more
hospital competition on average. Interestingly, these hospitals also had rel-
atively less labor per bed than hospitals with medium quality.
Organizationally, high-quality hospitals tended to have too many labor
inputs, leading to inefficiency. Low-quality hospitals, however, hired too few
labor inputs in all categories, especially FTE other personnel. An interesting
Hospital Quality, Efficiency, and Input Slack Differentials1843
finding from our analysis is that there appears to be a serious need for more
LPNs in hospitals producing many poor outcomes.
One of the main benefits of using the DEA approach that we have
adopted here is that hospitals are compared to their peers. This approach
yields a more realistic picture for policy makers and hospital managers than
setting up a theoretical engineering standard that hospitals may or may not
be able to achieve. As we are able to identify sources of inefficiency as they
relate to quality enhancement, decision makers have the ability to alter input
mixes in order to realize savings from both reducing inefficiency and poor
quality. The policy implications include more evidence that costs and quality
quality tended to have higher overall efficiency than the other hospitals in our
study. Thus, our results indicate that both these objectives can be met with the
more efficient use of resources at hand.
Joint Acknowledgement/Disclosure Statement: Dr. Ryan Mutter is an employee of
AHRQ, which provides the HCUP data, as well as the Quality Indicator (QI )
software, which are used in this paper.
The authors gratefully acknowledge the data organizations in partici-
patingstatesthatcontributeddata toHCUPandthat weusedinthisstudy:the
Planning and Development; CHIME Inc. (Connecticut); Florida Agency for
Health Care Administration; Georgia Hospital Association; Hawaii Health
Information Corporation; Illinois Health Care Cost Containment Council;
Indiana Hospital and Health Association; Kansas Hospital Association; Ken-
Review Commission; Massachusetts Division of Health Care Finance and
Policy; Michigan Health and Hospital Association; Minnesota Hospital As-
sociation; Missouri Hospital Industry Data Institute; Nebraska Hospital
Association; University of Nevada, Las Vegas; New Hampshire Department
of Health and Human Services; New Jersey Department of Health and Senior
Services; New York State Department of Health; North Carolina Department
of Health and Human Services; Ohio Hospital Association; Oregon Associ-
ation of Hospitals and Health Systems; Rhode Island Department of Health;
South Carolina State Budget and Control Board; South Dakota Association of
Healthcare Organizations; Tennessee Hospital Association; Texas Depart-
1844 HSR: Health Services Research 43:5, Part II (October 2008)