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Hospital operations and length of stay performance
Article in International Journal of Operations & Production Management · August 2007
DOI: 10.1108/01443570710775847
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IJOPM
27,9
Hospital operations and length
of stay performance
1020
Lally School of Management, Rensselaer Polytechnic Institute,
Troy, New York, USA, and
Christopher McDermott
Gregory N. Stock
Department of Operations Management and Information Systems,
College of Business, Northern Illinois University, DeKalb, Illinois, USA
Abstract
Purpose – As hospital costs continue to rise, increasing attention is being paid to the way these
organizations are and should be managed. This attention typically comes in the form of focus on costs
of services, quality (often measured through mortality rates) and length of stay. Hospital management
has a broad array of choices at their disposal to address these challenges. As service operations,
hospitals present a significant opportunity to apply the many tools and techniques from the field of
operations strategy to this important industry. The objective of this paper is to use the operations
strategy framework to assess the relationship between a set of operational elements and hospital
performance in terms of average length of stay (ALOS), so that hospital managers improve the
effectiveness and efficiency of patient care of their hospitals.
Design/methodology/approach – Using the structural and infrastructural operations strategy
framework, this study examines the relationship between several strategic variables and hospital
performance. To analyze these relationships the paper employs data from the population of hospitals
in New York State. The performance measure is the ALOS for patients, adjusted for the mix and
severity of cases in each hospital.
Findings – The paper finds that a direct relationship exists between the dependent variable and
location, capacity, and teaching status, and failed to find a direct relationship for capital expenditures,
salary, and staffing levels. However, the paper did find significant interaction effects between capital
expenses and both salary and staffing levels.
Practical implications – There appear to be trade-offs between capital expenditures and workforce
decisions that have significant implications in light of current and expected hospital staffing
shortages. The findings indicate that reductions in staff may not be perfectly replaced by
corresponding increases in capital expenditures.
Originality/value – This paper further expands the body of research that addresses the important
challenges hospitals face from an operations management perspective.
Keywords Operations management, Health services, Service operations, Hospitals,
United States of America
Paper type Research paper
Introduction
The effective management of hospitals has become an important political and social
International Journal of Operations & issue over the last decade, and demographic trends in the USA indicate that the issues
Production Management
associated with better hospital management will only become increasingly important
Vol. 27 No. 9, 2007
pp. 1020-1042
q Emerald Group Publishing Limited
0144-3577
DOI 10.1108/01443570710775847
The authors would like to thank Treo Solutions for providing access to their database for this
research.
as the domestic population continues to age. As hospitals face an increasingly complex
list of challenges (e.g. aging population, cost pressures, and increasing concerns for
patient safety) there is much to be gained by applying the rich knowledge base from
the field of operations management to many of these problems. With decades of
research, often from the manufacturing sector, that explores such issues as quality
management, efficiency, and operations strategy, there is clearly a need to transfer
some of this important thinking into an industry that clearly has a need for
improvement of its operations. The objective of this paper is to use the operations
strategy framework to assess the relationship between a set of operational elements
and hospital performance in terms of average length of stay (ALOS) so that hospital
managers can improve the effectiveness and efficiency of patient care of their hospitals.
Within the operations management field, hospitals can be classified as “pure
services” (in Chase and Tansik’s (1983) customer contact model) or as “service shops”
(in Schmenner’s (1986) service process matrix). There has been a significant history of
“migration” of manufacturing-based tools and techniques into the healthcare setting.
Significant elements of the quality management body of knowledge that emerged in
the 1980s from manufacturing settings (e.g. total quality management) has moved to
and been embraced by hospitals as they tried to improve performance on that
dimension. It is now common to see control charts and process flow diagrams in many
hospitals (Carey, 2003; Carey and Lloyd, 2001).
As hospitals deal with the complex challenges discussed above, management is
faced with a broad array of choices regarding where and how to apply their resources
to respond to problems or to improve their competitive position. In this paper, we
continue with this tradition of “migration” by applying principles of the operations
strategy structural and infrastructural framework to address these issues (Butler and
Leong, 2000; Li et al., 2002). Specifically, we use this framework to examine the
relationship between a set of specific operational variables and hospital performance.
One performance goal that encompasses many of the traditional operations
performance dimensions (Skinner, 1969; Hayes and Wheelwright, 1984) is the ALOS,
which refers to the average length of time a patient spends in a hospital (Thomas et al.,
1997). Most hospitals have identified ALOS as a critical performance metric. Prior
research has shown that ALOS is related to cost, efficiency, quality of care, and speed
in service delivery (Ashby et al., 2000; Glick et al., 2003; Thomas et al., 1997; Burns et al.,
1994), so it can be viewed as an appropriate overall measure of performance. In this
context, lower values of ALOS generally are viewed as reflecting better operational
performance (Shi, 1996; Thomas et al., 1997; Langland-Orban et al., 1996). The relevant
strategic goal for a hospital would therefore be to reduce ALOS.
In this study, our objective is to better understand which operations areas are most
associated with better ALOS performance, as ALOS is a good proxy for many of the
performance issues of concern to hospital management. As we will outline below, there
is a significant body of important empirical research that has laid the groundwork for
examining these issues. The rest of the paper is structured as follows. In the next
section, we discuss existing literature relating to our study, conceptually link hospital
management and operations management/strategy, and present our hypotheses. We
next discuss our sample, data collection, variable definitions, and analysis methods.
We then present the results of our analysis. Finally, we discuss these findings and their
implications.
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Conceptual framework
Operations strategy
Operations strategy has been defined as “. . . the effective use of [operations] strengths
as a competitive weapon for the achievement of business and corporate goals”
(Swamidass and Newell, 1987). Operations strategy has two parts; the task itself, and
the pattern of choices made to accomplish that task effectively (Miller and Roth, 1994).
The task is the direction the unit must take in order to compete. For example, it may
not be clear whether an organization should aim to enter the market as a quality leader
or an innovation leader in its industry. The second element of operations strategy
literature relates to the pattern of choices the operating unit makes (Skinner, 1974;
Hayes and Wheelwright, 1984). Simply put, this element relates to the question of how
organizations organize the resources under their control to achieve their goals. One of
the key tenets of successful operations strategy is the effective linkage between these
two elements (Skinner, 1969, 1974; Anderson et al., 1989; Schroeder et al., 1986;
Swamidass, 1986). Within the area of operations, there is growing empirical evidence
that supports this intuitively appealing claim (Skinner, 1978; Buffa, 1984; Swamidass
and Newell, 1987; Lewis and Boyer, 2002).
In this paper, we will focus our discussion on the linkage of these elements – how
the configuration of a hospital’s operations is aligned with its performance in a given
strategic area. Consistency between operational choices and strategic goals has
been described as strategic fit (Jelinek and Burstein, 1982). This idea of strategic fit has
been shown to be a key determinant of operations performance (Skinner, 1974; Pesch
and Schroeder, 1996; Bozarth, 1993; Hill, 1994, Safizadeh et al., 1996). Skinner (1974)
articulates a framework for making such decisions. He describes the structural and
infrastructural operational decisions that affect firms’ ability to cohesively move in a
chosen strategic direction. Structural decisions are the “bricks and mortar” decisions of
an organization, and typically include decisions in the areas of facilities, location,
layout, equipment and technology, and capacity. Infrastructural decisions, on the other
hand, are the decisions relating to systems and people, including workforce skills and
training, planning and control systems, quality systems, and organizational structure
(Skinner, 1969; Hayes and Wheelwright, 1984). These decisions lead to hands-on,
action-oriented activities that dictate how a planned strategy is turned into an
effectively implemented one. The rise and success of Southwest Airline is a wonderful
(and commonly discussed) example of the importance of this link (Porter, 1996).
Operations strategy and healthcare
Within the service operations strategy literature, there is a small but growing body of
work that explores the links between operations strategy and healthcare performance.
As discussed above, with healthcare costs rising, the increasing importance of quality
in healthcare, and current demographic patterns, this link is now even more important.
Butler et al. (1996), as well as McLaughlin et al. (1991), provide compelling arguments
as to the importance of this link. Operations strategy has flourished as a field, yet there
is still much to be learned regarding how this knowledge base can be effectively
applied within the healthcare setting. Specifically, how transferable are these
manufacturing-derived principles to a setting where quality and costs take on a very
different meaning? For example, mortality rate is an important performance metric in
hospitals, but a manufacturing plant rarely must contend with matters of such gravity.
In this context, Butler et al. (1996, p. 142) define operations strategy for hospitals as:
. . . [t]he procurement and allocation of resources for the development of operations
capabilities . . . to support the hospital mission and business strategy, and to gain competitive
advantage in the marketplace.
Butler and Leong (2000) examine the relationship between operations strategy (cost,
quality, delivery, flexibility) and a multidimensional measure of performance (current
ratio, revenue-expenses, worker productivity, market share, revenue growth) in their
sample of hospitals. They find that a focus on costs and service delivery are most
closely related to superior performance, while quality plays the role of an “order
qualifier” (Hill, 1994).
Through structural (e.g. facilities, location, technology) and infrastructural (e.g.
workforce management, incentives, quality systems) investments (Skinner, 1969;
Hayes and Wheelwright, 1984) hospitals configure their operations to achieve their
strategic goals (Heineke, 1995; Butler et al., 1996; Goldstein et al., 2002; Li et al., 2002).
Our discussion below highlights some of the key findings to date with respect to this
framework within the healthcare environment.
Structural elements in healthcare operations. One key structural element is the level
of capital investment by a hospital for facilities and technology. Cleverley (1990), for
example, found that high-performing hospitals in his sample tended to have newer
facilities than low-performing hospitals. Similarly, Wang et al. (2001) explore the link
between investment in vertical integration and performance, finding that backward
vertical integration (e.g. working more closely with physician groups) is associated
with better hospital financial performance but had no affect on productivity, while
forward vertical integration (e.g. expanding into home care, rehabilitation centers)
helped productivity but actually damaged financial performance. An important
component of capital investment is the hospital’s investment in technology.
Investments in information technology (including both hardware and software) and
other types of equipment have been found to be positively associated with various
measures of hospital performance including quality (Li and Collier, 2000; Kumar and
Motwani, 1999; Li and Benton, 2003), efficiency (Watcharasriroj and Tang, 2004),
financial performance (Li and Collier, 2000), and costs (Kumar and Motwani, 1999).
Research examining the relative impact of structural elements such as location and
capacity within hospitals has also yielded insightful results into the
operations-performance link. Although these operational decisions have longer time
horizons and are more difficult to change, they are nonetheless strategic in nature and
need to be considered. Goldstein et al. (2002) found that geographic location is
important, with urban hospitals positively associated with performance (as measured
by occupancy rate). Similarly, Li et al. (2002) found that urban and rural hospitals
differed in their approaches to attaining continuous improvement.
There is significant evidence that there is a direct link between effective capacity
management in hospitals and performance (Smith-Daniels et al., 1988; Li and Benton,
2003). For example, one measure of capacity in a hospital is the number of beds (Li et al.,
2002), and bed size, as it is commonly referred to in the industry, has been found to be
related to performance as measured by length of stay (Shi, 1996; Younis, 2004) and
profitability (Kim et al., 2002; Younis and Forgione, 2005). A final structural element is
the status of a hospital as teaching or non-teaching. In addition to treating patients,
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teaching hospitals train physicians and conduct research, which means that their
teaching status probably affects their operations (Li et al., 2002). Teaching
status typically is generally linked to lower performance in terms of length of stay
(Srivastava and Homer, 2003; Younis, 2004). It should be mentioned that, as with
location, there may be little room to change this “variable” within a given hospital; it is
generally ingrained in the mission of the organization. However, as evidenced by the
extant research exploring this issue, it is still important to understand the extent to
which these elements affect performance, if only from an explanatory perspective.
In light of the above discussion, our analysis includes the following structural
variables: capacity, location, capital investment, and teaching status.
Infrastructural elements in healthcare operations. In contrast to structural elements
related to operations, infrastructural elements involve what may be called the
“people-oriented” side of operations. The typical infrastructural categories found in the
operations strategy literature can include organization, workforce management,
planning and control systems, and quality management systems (Boyer, 1998; Leong
et al., 1990; Swink and Way, 1995; Hayes and Wheelwright, 1984).
Prior research in healthcare operations has considered infrastructure elements such
as organization (Tucker and Edmondson, 2003; Edmondson, 2003; Goldstein and
Ward, 2004), workforce management (Li et al., 2002), demand management (a hospital
analogue to planning and control systems) (Li et al., 2002), and quality systems (Li et al.,
2002). In this paper, we focus on two dimensions of workforce management (salary and
staffing levels) in considering the impact of this key category of infrastructural activity
in the operations strategy of hospitals. Employee salary rates and headcount are
significant drivers of cost in this service industry. As such, they are scrutinized when
competitive cost pressures emerge. Workforce management generally includes
decisions in areas such as incentive systems, compensation levels, skill levels, training,
and design of work (Heineke, 1995; Flynn et al., 1999; Kathuria and Davis, 2001).
Efficiency wage theory suggests that higher wages will lead to better employee
performance. Higher relative wages will attract more highly qualified employees, will
cause employees to work more efficiently, and will result in a sense of obligation in
employees that will encourage them to work more effectively. Prior research has
largely supported these arguments: Brown et al. (2001, 2003) found in a survey of 394
hospitals that higher rates of pay led to better performance, both in terms of length of
stay and patient care outcomes.
Another workforce element that has a great deal of importance in a hospital is the
staffing levels of workers. The issue is particularly important in light of the pressure
on hospitals to cut costs by reducing staff levels, particularly those of nurses (Curtain,
2003). Prior research has consistently found that higher staffing levels are associated
with better performance, at least in the dimensions of length of stay and patient care
outcomes. For example, Brown et al. (2003) found that staffing level (as measured by
overall full-time-equivalent workers per patient) was associated with better length of
stay and survival rates for heart attack patients. Other research has focused on nurse
staff levels in particular. In these studies, higher nurse-to-patient ratios are related to
better performance as measured by length of stay (Flood and Diers, 1988; Curtain,
2003), patient mortality rates (Aiken et al., 2002a; Curtain, 2003), and nurse-reported
quality of care (Aiken et al., 2002b). Similarly, a review of research on physician
staffing patterns found that higher levels of physician staffing in intensive care units is
associated with lower length of stay and lower mortality rates (Pronovost et al., 2002).
Hospital performance
Hospital performance and its measurement provide unique challenges for researchers
examining the healthcare field. Operations management researchers find that rather
than defects per million, quality is often defined as patient mortality! A variety of
approaches have been used to assess performance in the healthcare management
literature. Healthcare costs have become an important issue, so the use of cost
improvement or cost containment as a performance measure has been very common in
healthcare research (Kumar and Motwani, 1999; Butler et al., 1996). Quality has also
been very widely used as a performance outcome in healthcare research (Butler et al.,
1996; Caron et al., 2004; Chesteen et al., 2005). Quality has been measured by many
different means, including self-reported quality measures (Butler and Leong, 2000),
medical errors (McFadden et al., 2004), and mortality rates (Caron et al., 2004; Madison,
2004; Gross et al., 2000; MacStravic, 1999; Landon et al., 1996).
Organizational performance in a hospital, particularly from a strategic perspective,
can also be viewed as a construct that combines multiple dimensions, such as clinical
outcomes, financial performance, productivity, and operational measures (Butler and
Leong, 2000). One measure that is found extensively in hospital management research is
the ALOS (Lagoe et al., 2005; Caron et al., 2004; Anderson et al., 2002; Raffiee and Wendel,
1991; Shi, 1996), which is the average number of days a patient stays in the hospital. The
ALOS reflects a number of different performance dimensions, including cost (Polverejan
et al., 2003; Ashby et al., 2000; Glick et al., 2003), quality (Thomas et al., 1997),
profitability (Langland-Orban et al., 1996; Sear, 1992), and efficiency (Burns et al., 1994).
In most of these studies, lower levels of ALOS indicate better performance (Shi,
1996; Thomas et al., 1997; Langland-Orban et al., 1996). Lower values of ALOS indicate
that a patient is treated and discharged more quickly, which would point to better
resource efficiency and lower costs (Thomas et al., 1997; Brown et al., 2003). The link
between quality of care and ALOS is less straightforward, however. One view would
suggest that lower ALOS would be related to lower quality, as lower ALOS would be a
sign that patients are prematurely discharged from the hospital before receiving
complete and competent treatment. An opposing view suggests that lower ALOS
reflects better quality of care. Patients who recover more quickly and have fewer
complications would probably be in the hospital for shorter periods of time, which
would indicate better quality of care. In fact, a comprehensive empirical investigation
of 13 different diseases showed that lower ALOS was associated with better quality
(Thomas et al., 1997). Because ALOS has been consistently associated with these
different dimensions of performance, ALOS provides a valid comprehensive measure
of hospital performance. In addition, in many ways ALOS can be thought of as
capturing similar elements of performance as are seen in just-in-time manufacturing –
quicker throughput reduces costs and improves quality.
An important issue in using ALOS as a performance measure is the consideration of
the mix of patient cases in a hospital in which ALOS is calculated. Hospitals that treat
more serious cases would be expected to have higher values of ALOS. Therefore, a
more appropriate measure for comparing the performance of hospitals would be the
case mix adjusted ALOS, where the ALOS is adjusted by an index reflecting the
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severity of the cases treated by the hospital (Thomas et al., 1997; Harman et al., 2004).
In this study, we employ a form of case mix indexed (CMI) ALOS as our measure of
operational performance. Additionally, we look at the percent difference (either
positive or negative) of this CMI ALOS variable from the expected CMI ALOS as the
performance metric. Our unit of analysis is the individual hospital. Hospitals that
have patients stay times that are significantly less than expected (given their condition)
are performing well, while those that take significantly longer than expected (again,
adjusting for condition) are not performing as well. We provide more details of how
this measure is calculated in the methods section below.
Hypotheses
As discussed above, the operations strategy literature frames resource allocation
decisions in terms of configuring the organization to achieve a set of performance
goals. Specifically, how does a manager make investments in the structure and
infrastructure of a hospital to achieve its strategic goals? In this section, we develop a
set of hypotheses that link structural and infrastructural decisions to strategic
performance outcomes.
Above we examined several types of structural decisions that are relevant to
operations strategy in hospitals. One such decision is the hospital’s level of capital
investment. The literature has shown that capital investments in technology and other
“bricks and mortar” operations structural elements can lead to improved cost and
quality outcomes (Cleverley, 1990; Li and Collier, 2000). The performance measure of
interest in this study is the ALOS, which captures elements of both cost and quality
performance. Lower values of ALOS indicate better performance. As such, our first
hypothesis follows:
H1. Higher levels of capital spending will improve ALOS performance.
Another important structural variable is facility location. Prior research has found that
there are differences in ALOS among hospitals in different geographic regions
(Shi, 1996; Younis, 2004). Also, Li et al. (2002) found that urban and rural hospitals
differed with respect to continuous improvement approaches, which ultimately
is related to quality outcomes. Finally, Goldstein et al. (2002) found that urban hospitals
had better performance as measured by occupancy rate, which is likely to be related to
profitability. In our study, we consider facility location in terms of differences between
urban and non-urban hospitals. We hypothesize the following:
H2. ALOS performance will be better for hospitals in urban than for those in
non-urban areas.
Capacity in a hospital can be conceptualized in terms of the number of beds, which is
referred to as bed size (Li et al., 2002), and capacity is a critical structural element in
operations. Bed size has been found to have a significant but non-linear relationship to
hospital profitability (Kim et al., 2002). Specifically, related to our study, bed size has
been shown to be positively related to ALOS, meaning that smaller hospitals tend to
exhibit shorter ALOS (Shi, 1996; Younis, 2004). Therefore, the third hypothesis follows:
H3. Higher levels of bed size will be associated with worse ALOS performance.
The final structural element to be considered is the teaching status of the hospital.
Teaching and non-teaching hospitals have very different missions, which may lead to
very different strategies and outcomes (Li et al., 2002). Teaching hospitals train new
physicians and engage in research and tend to treat more difficult medical cases. Prior
research has found that teaching hospitals generally have higher values of ALOS than
non-teaching hospitals (Srivastava and Homer, 2003; Younis, 2004). Therefore, our
fourth hypothesis follows:
H4. Non-teaching hospitals will have better ALOS performance than teaching
hospitals.
Among the infrastructural elements that may be part of a hospital’s operations, we focus
specifically on those dealing with workforce management. Healthcare depends critically
on the employees providing this service. Therefore, strategic decisions related to
workforce compensation and staffing levels are particularly important to strategic
performance in a hospital. We first consider compensation systems. As we discussed
above, prior research has found that higher pay levels were associated with better patient
outcomes, including lower ALOS (Brown et al., 2003), so our fifth hypothesis follows:
H5. Higher salaries will improve ALOS performance.
In addition to workforce compensation, we also consider staffing levels. Higher nurse
to patient ratios have been found to be associated with better quality outcomes (Aiken
et al., 2002a; Curtain, 2003) and cost performance (Flood and Diers, 1988). In addition,
shorter ALOS has been associated with higher levels of staffing of both nurses and
physicians (Curtain, 2003; Pronovost et al., 2002). Finally, Brown et al. (2003) found that
higher overall staffing levels were associated with lower ALOS levels. Therefore, our
sixth hypothesis follows:
H6. Higher staffing levels will improve ALOS performance.
Note that H5 and H6 follow directly from the results of prior research. We include
these hypotheses as the infrastructural elements of the operations strategy framework
for completeness. However, one important distinction in our research is the inclusion of
capital spending in our analysis. Therefore, our analysis goes beyond simple
confirmation of prior research to provide a more robust test of these relationships.
Sample and measures
The data from this study are based on information submitted by the population of
New York State (NYS) Hospitals to the Statewide Planning and Research Cooperative
System (SPARCS) database. SPARCS is a database initially created in cooperation in
1979 between the healthcare industry and the government. Over time, this database
has evolved to a comprehensive system of mandatory healthcare inpatient and
outpatient data reported annually throughout the population of hospitals in the state.
The analysis presented here is based on 2002 data, the most recent available at the time
the study was performed. Elements of the database are publicly available, and other
subsets were acquired through the cooperation of Treo Solutions, a healthcare
consulting firm.
A total of 210 hospitals operate in NYS and qualify for inclusion in the study.
The data were accessed and examined for completeness. Of the hospitals, 20 were
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removed because of incomplete data, and two were removed because of errors in
entering data into the original database, leaving a working sample of 188 hospitals.
Several variables must be adjusted for risk and geographic-based wage differences.
Hospitals that take riskier or more involved cases would, on average, be expected to
have higher length of stay for their patients. Because of this, we use 3M’s All Patient
Refined Diagnostic Related Groups (APR-DRGs) to adjust for severity. APR-DRGs
group patients by the specific resources they consume, and also assign each case to a
severity of illness subclass (i.e. given a specific illness, how sick is the patient?) and a
risk of mortality subclass. Both subclasses are broken down using the following
format: 1 ¼ minor, 2 ¼ moderate, 3 ¼ major, 4 ¼ extreme. The assignment to
these subclasses is based on principal and secondary diagnoses, age, sex, and
interaction among diagnoses. By making this adjustment, our analysis captures the
relationship between expected length of stay and the case mix of a particular hospital.
For example, if a hospital gains a reputation for being particularly adept at performing
a particular procedure, it might, over time, begin to take a disproportionate number of
more difficult cases. As such, the average expected length of stay of their patients for
this procedure would most likely increase, since they were seeing more difficult cases
than the typical hospital. By breaking down illnesses into subclasses and adjusting
accordingly, our analysis accounts for this type of situation. Similarly, wages are
higher in metropolitan areas, and data were adjusted for this using a wage index from
the US Government Center for Medicare Services wage index.
In developing our analysis approach, we also include a control variable that
accounts for potential differences in ALOS associated with the insurance payer.
Insurance payers may be classified into several different categories, including health
maintenance organization (HMO), traditional commercial indemnity insurance,
Medicare, and Medicaid. Typically, those patients insured by HMOs typically have
shorter lengths of stay than those with other payers (Khaliq et al., 2003; Spencer et al.,
2004; Bradbury et al., 1991). Therefore, we defined a variable to control for this possible
confounding effect. This variable, HMO_PRCT, is defined as the percentage of cases
for which an HMO is the payer.
For our dependent variable, we use the percent difference ALOS (%DIFF_ALOS).
As discussed above, this variable is adjusted for case mix using the APR-DRG
grouping mechanism. We use percent difference, as opposed to “raw” reduction in
ALOS to adjust for the case mix of particular hospitals. For example, if hospital X sees,
on average, more complex cases than most other hospitals, there might be more
potential for length of stay reduction than hospital Y that typically performs only
routine procedures. By improving their operations, hospital X might be able to reduce
average stay by half a day, whereas this would be unrealistic in hospital Y. By using
the percent difference reduction in the risk adjusted variables, we can better compare
across hospitals, regardless of case mix. The formula for this is thus ((Observed
ALOS 2 expected ALOS)/expected ALOS).
In order to calculate several of the independent variables in our analysis, it is necessary
to calculate the intermediate adjusted patient days variable. As the name implies, this
variable is an indicator of how much activity occurs in a given hospital. Hospitals with
more (or sicker) patients would be expected, in turn, to consume more resources.
While using a measure of hospital inpatient days certainly captures much of this, there is
an increasing trend to make more and more procedures become outpatient activities.
To deal with this phenomenon, the adjusted patient days variable incorporates outpatient
data by creating an index that is the ratio of gross patient revenue (which includes both
inpatient and outpatient services) divided by inpatient revenue. Using this index, a
hospital’s outpatient activities are included in the calculation. This index is multiplied by
the non-nursery hospital days (i.e. days associated with child delivery are not included) to
result in our adjusted patient days variable.
The calculation of the variables associated with the structural decisions (H1-H4) is
now discussed. For H1, we assess the level of capital investment in a hospital. This
variable is CAPITAL, which is the capital related costs per adjusted patient day,
adjusted for wage index. The capital spending variable is normalized by adjusted
patient days and wage index for several reasons. We are investigating the effects of
capital spending on patient care, so capital spending should be adjusted in a way that
is directly related to the patients treated. In addition, as we noted above, the adjusted
patient days intermediate variable takes into account the provision of outpatient
services as well as inpatient services. Normalizing by some other measure of size, such
as the number of beds, would not account for the volume of patient care or the
provision of outpatient services. Also, although capital spending may be associated
with tangible assets, there is often a labor component to capital spending – for
example, construction costs may depend on labor rates. Therefore, it makes sense to
include a partial adjustment for differences in wage rates for different areas of NYS.
This variable includes all hospital capital investments in both clinical and non-clinical
technologies (in a given facility, including both hardware and software) in a given
facility’s investments. This variable also includes capital spending on non-technology
investments such as buildings and other facilities. Although the capital spending
variable is limited in that it does not distinguish between different types of capital
spending, it provides an overall measure of the hospital’s structural decisions with
respect to facilities and technology. Preliminary analysis of this variable indicated that
its distribution was skewed. Therefore, the CAPITAL variable employed in our
analysis has been transformed using a natural logarithm to reduce the effects of
skewness or other non-normal properties (Kleinbaum et al., 1988).
H2 examines the relationship between geographic location and %DIFF_ALOS. For
this analysis, we created a variable that indicates when a hospital is located in NYC,
where NYC ¼ 1 if the hospital is located in NYC and 0 otherwise. There are other
urban areas in NYS, so we created a second dummy variable to indicate when a
hospital is located in one of these urban areas. After NYC, the next five largest cities
are Buffalo (population 292,648), Rochester (219,773), Yonkers (196,086), Syracuse
(147,306), and Albany (95,658) (US Census Bureau, 2005). This second variable,
URBAN, is equal to 1 if the hospital is located in one of these five cities and 0 otherwise.
Note that these variables only show whether the hospitals are located in different
locations within NYS. An explicit analysis of the specific factors that may be
associated with differences between these cities is beyond the scope of this study but
would be an interesting direction for future research.
H3 examines the effect of bed size on %DIFF_ALOS. The SPARCS data base
reports only the range of the number of beds in the hospital (less than 100 beds, 101-300
beds, and greater than 300 beds). Therefore, for this variable, BED_SIZE, hospitals
have been classified into three ordered groups. BEDSIZE has a value of 1 for hospitals
with less than 100 beds, 2 for hospitals with between 101 and 300 beds, and 3 for
Operations and
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performance
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hospitals with more than 300 beds. H4 is examined by use of a dummy variable
associated with teaching status of the hospital, where TEACH ¼ 1 if a hospital is a
teaching hospital and 0 otherwise.
Regarding our infrastructural variables, H5 examines the relationship between
higher salaries and %DIFF_ALOS reduction. We assess this by examining the salary
per full time equivalent worker, again adjusted using the wage index. This variable,
SALARY, is a strong individual indicator of the general salary structure of a given
hospital.
Finally, H6 examines the relationship between staffing levels and %DIFF_ALOS.
The staffing level variable we employed was FTE, which is the number of full time
equivalent employees per adjusted occupied bed. The number of adjusted occupied
beds is calculated by dividing the adjusted patient days variable by 365.
Table I summarizes the variables used in the analysis, and Table II provides
descriptive statistics and correlations for these variables.
Results
Evaluation of hypotheses
Hierarchical regression analysis was used to test the hypotheses. In hierarchical
regression, theoretically grouped sets of variables are entered into the regression model.
For each regression, the R 2-value of the model with the added variables is compared to
the R 2 of the previous model. If the R 2 increase is statistically significant, then there is
evidence that this set of variables explains variation in the dependent variable. In this
Variable name
Description
HMOPRCT
Percentage of cases for which HMO insurance is the
payer
Geographic location
1 if located in NYC
0 if located elsewhere in NYS
Geographic location
1 if located in Buffalo, Rochester, Yonkers,
Syracuse, or Albany
0 if located elsewhere in NYS
Teaching status
1 if a teaching hospital
0 if a non-teaching hospital
Number of beds
0 if 1-100 beds
1 if 101-300 beds
2 if greater than 300 beds
Log10(wage index adjusted capital spending per
adjusted patient day)
Wage index adjusted salary per full-time-equivalent
employee
Full-time equivalent employee per adjusted occupied
hospital bed
Percentage difference between ALOS and expected
LOS
NYC
URBAN
TEACH
BED_SIZE
CAPITAL
SALARY
FTE
Table I.
Variables
%DIFFALOS
analysis, we use standardized variables to ensure that differences in scale do not affect
the results. The results of the analysis are shown in Table III. We first enter the control
variable, HMO_PRCT, which is the percentage of cases for which the payer is an HMO.
In this model, HMO_PRCT is not statistically significant. In the second model, the group
of structural variables enters. These variables include TEACH, NYC, BED_SIZE, and
CAPITAL. Of this group, all variables except URBAN and CAPITAL are significant.
The positive sign of the coefficient estimate of NYC indicates that the percentage
difference in expected ALOS is higher for hospitals in NYC. Similarly, the positive sign
for the coefficient estimate of BED_SIZE shows that larger hospitals experience a higher
percentage difference from expected ALOS. In contrast, the negative sign for the
estimate for the coefficient for TEACH indicates that teaching hospitals have a lower
percentage difference ALOS than non-teaching hospitals. In addition, the increase in R 2
from 0.000 to 0.203 was significant at the 0.001 level. In the third model, the
infrastructural variables SALARY and FTE are entered. The R 2-value does not increase
significantly, and neither variable is statistically significant.
We now turn to an evaluation of the hypotheses. H1, which argues that greater
capital investment will be associated with lower ALOS, was not supported.
1. HMOPRCT
2. BED_SIZE
3. CAPITAL
4. SALARY
5. FTE
6. %DIFF_ALOS
Mean
SD
1
2
3
4
5
6
14.645
2.091
4.514
48,276
5.616
2 0.041
11.478
0.701
0.558
6,654
1.966
0.173
1
0.320 * *
0.217 * *
0.112
20.009
20.006
1
0.311 * *
0.264 * *
0.107
0.313 * *
1
0.166 *
0.660 * *
0.069
1
20.108
20.033
1
0.040
1
Notes: n ¼ 188, * p , 0.05; * *p , 0.01
Operations and
length of stay
performance
1031
Table II.
Correlations and
descriptive statistics
Model
HMOPRCT
TEACH
NYC
URBAN
BED_SIZE
CAPITAL
SALARY
FTE
SALARY £ CAPITAL
FTE £ CAPITAL
Overall F
Overall R 2
F for change
R 2 change
1
2
3
4
20.005
20.017
20.128 * * *
0.145†
20.009
0.168†
20.014
20.126 * * *
0.149†
20.006
0.179†
20.026
20.027
20.011
0.023
0.000
0.023
0.000
9.202†
0.202
11.495†
0.202
5.917†
0.209
0.555
0.007
2 0.007
2 0.135 * * *
0.157†
0.003
0.168†
2 0.033
2 0.038
0.049
0.053 *
2 0.066 * *
5.626†
0.241
3.738 * *
0.032
Notes: n ¼ 188; *p , 0.10; * *p , 0.05; * * *p , 0.01; †p , 0.001
Table III.
Regression results.
Dependent variable:
percentage of difference
in expected ALOS
IJOPM
27,9
1032
The variable used for such investments, CAPITAL, was not significant. H2 was
supported, as the variable NYC was statistically significant. Hospitals in NYC show a
positive percentage difference in ALOS compared to other regions in NYS. However,
hospitals in other urban areas are not significantly different, so only the hospitals in
the largest city in the state are different in ALOS performance. H3 hypothesized that
larger hospitals, in terms of the number of beds, would have a higher ALOS than
smaller hospitals. The BED_SIZE variable was significant, so H3 is also supported by
this model. H4 suggests that teaching hospitals will experience higher ALOS than
non-teaching hospitals. However, this hypothesis was not supported. In fact, our
results contradict H4, as the coefficient for TEACH was negative and significant. H5
argues that higher salaries would result in lower ALOS. In the third model, SALARY is
not significant, so it appears that H5 is not supported. The final hypothesis, H6, argues
that higher staffing levels will result in lower ALOS. The results also failed to support
H6, as FTE was not significant.
Additional analysis
The results of the analysis supported two of the hypotheses and contradicted one of the
hypotheses. For the remaining three hypotheses, the variables of interest were not
significant. In particular, neither of the infrastructural variables showed any
relationship to percentage difference from expected ALOS, and capital investment did
not exhibit a relationship to percentage difference from expected ALOS. Although we
did not hypothesize interactions between these variables, in an attempt to explain these
non-significant results we tested an additional regression model that included two-way
interactions between each of the infrastructural variables and the capital investment
variable. Our rationale for examining the interactions between these variables follows
from the idea that productivity is generally determined by a combination of more than
one factor, in this case labor and capital (Chase et al., 2001). Capital or labor alone may
not be sufficient to improve ALOS performance. However, it is quite conceivable that
improvements are achieved through their combination. We therefore also examine the
extent to which the effect of capital investment on ALOS depends on both the quality
of the labor (as measured by salary) and on the quantity of labor (as measured by the
number of full-time equivalent workers per patient day). Therefore, supplemental H7
and H8 follow:
H7. The application of increased capital expenses combined with higher staffing
levels will improve ALOS performance.
H8. The application of increased capital expenses combined with higher salary
levels will improve ALOS performance.
The results of this regression model, including the supplemental hypotheses, are
shown as Model 4 of Table III. The coefficient estimates of the other three structural
variables remain almost unchanged, and all three are still significant. The main effects
of CAPITAL, SALARY, and FTE remain non-significant. However, the interactions
FTE £ CAPITAL and SALARY £ CAPITAL are both significant, although with
different signs. The FTE £ CAPITAL interaction has a negative sign, and the
SALARY £ CAPITAL interaction has a positive sign. We discuss possible
interpretations for these results in the next section.
Discussion
Our findings provide insights into where hospitals in our sample invested in structural
and infrastructural operations to find improvements in ALOS performance, and,
equally important, where they did not realize the expected benefits. Our analysis
reveals that several of the hypothesized operations strategy relationships were not
supported in our data set. Surprisingly, the level of capital investment (H1) was not
associated with length of stay reductions. More spending on this aggregated “bricks
and mortar” metric was not significantly associated with reduction in percent
reduction in ALOS. The interpretation of this finding is challenging, as one would
expect hospitals with newer facilities and equipment to exhibit some gain in ALOS
performance. For example, one might expect that the infusion of newer capital
equipment would offer new capabilities that in turn might enable hospitals to operate
in a more efficient or effective manner, as measured by a reduction in ALOS. While the
straightforward interpretation would lead to the conclusion that no relationship exists,
we explore an alternative explanation in supplemental H7 and H8 that rests on a more
complex notion of the effects of capital investment. In particular, the effects of capital
investment must be considered in combination with the human resources employed by
the hospital to provide care to patients. We explore this interpretation below when we
discuss the results of the interactions between the capital investment and workforce
variables.
Our analysis reveals that there is a relationship between ALOS reduction and two
key operations management dimensions: location (H2) and capacity (H3). When
controlled for such factors as case mix and labor rates, there was a systematic
difference in performance depending on the hospital’s location (NYC or the rest of
NYS). Hospitals in NYC showed a statistically significant increase in percent difference
ALOS when compared with the rest of the state. In other words, patients in NYC
hospitals stayed longer than their counterparts in other regions of the state. However,
hospitals in other urban locations in NYS (the cities of Buffalo, Rochester, Yonkers,
Syracuse, and Albany) were not significantly different from other non-NYC locations.
Again, our data set controls for severity of illness, so this is an interesting finding, and
one that clearly begs for further research. In addition, patients in larger hospitals also
stayed longer than those in smaller hospitals. This second finding is interesting, in that
it appears to undermine the notion of scale economies in implementing ALOS reduction
techniques in hospitals. Larger hospitals appear to take longer to treat patients than
smaller hospitals, even when adjusted for the severity of cases they treat. One possible
explanation for this finding is that the bureaucracy associated with larger hospitals
results in greater trouble in delivering service quickly. In the operations strategy
literature, this may be analogous to the idea of smaller, more focused facilities
outperforming their larger counterparts (Skinner, 1974).
Teaching hospitals (H4) were shown to be associated with better ALOS
performance. This result is surprising, as it contradicts the consensus found in prior
research. It appears that the added mission of training and education of new physicians
does not impose a penalty on the length of time taken to treat a patient, and in
fact results in an improvement in ALOS. One possible explanation of our apparent
contradiction may be that our measure of length of stay explicitly takes into account
the severity of the cases treated in the hospital. It is not clear that prior research has
always taken this factor into account. Another explanation for this surprising finding
Operations and
length of stay
performance
1033
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1034
might be that faculty and staff at teaching hospitals, as educators and researchers, are
more conversant in, and comfortable with, adopting newer techniques in operations
improvement practices. This in turn might impact their ability to move patients
through the hospital more quickly. For example, Massachusetts General Hospital, a
teaching institution, was a pioneer in the development of care paths, which is a method
used to standardize pre- and post-operative care for various surgical procedures. It is
interesting to note that ALOS for coronary artery bypass surgery patients dropped
after the implementation of care paths at this hospital (Wheelwright and Weber, 1995).
In addition to the structural operations hypotheses discussed above, our analysis
also examined two infrastructural variables. H5 examined the extent to which higher
salaries would affect percent ALOS reduction. In our sample, paying a premium for
labor (again, adjusted with geographic region wage index) was not significantly related
to percent difference ALOS. Paying higher wages for staff appeared to have no effect
on ALOS performance in our dataset. This non-significant result might indicate that
ALOS performance is more a function of management focus and attention, as opposed
to being a function of paying more for employees. Looking at our other infrastructural
variable, we found that increases in the staffing level (as measured by the number of
full time equivalents per adjusted patient day) was also not associated with a
statistically significant difference in ALOS. Contrary to what we predicted in H6,
having more employees per patient did not, in and of itself, significantly help reduce
the length of stay.
As noted above, the non-significant findings for both the infrastructural variables
as well as the capital investment variable were unexpected. To better understand this
result, and to ensure that we were not missing important relationships that may be
hidden in interaction effects, we also investigated possible interactions between these
two infrastructural variables and capital investment in supplemental H7 and H8.
The interaction between staffing level per patient and capital investment was
significant and negative. When the staffing level is high, higher levels of capital
investment are associated with better ALOS performance (i.e. ALOS is lower than
expected). When staffing levels are low, however, increased capital investment was
associated with worse ALOS performance (i.e. ALOS is higher than expected). One
possible explanation for this interaction effect is that investments in facilities or new
technology may require corresponding increases in staffing to gain the productivity
benefits that are expected with such investments. Figure 1 shows this relationship. In
Figure 1, the relationship between percent difference from expected ALOS and capital
spending per patient is shown for high and low levels of employees per patient day[1].
Figure 1 clearly shows that for low levels of staffing per patient day (FTE), increases in
capital spending per patient (CAPITAL) are associated with higher than expected
ALOS. Conversely, for higher levels of FTE, increases in CAPITAL are associated with
lower than expected ALOS.
The significant interaction between salary and capital investment was another
interesting result. A significant interaction implies that the relationship between
capital investment and ALOS performance depends on the level of salary. Figure 2
shows these relationships[2]. At lower salary levels, higher levels of capital investment
result in lower than expected ALOS (i.e. ALOS performance is better), and lower levels
of capital investment are associated with greater than expected ALOS. However, for
higher salary levels, we do not see this relationship between capital investment and
Operations and
length of stay
performance
0.2
% Diff ALOS
0.15
FTE Low
0.1
FTE High
0.05
1035
0
– 0.05
– 0.1
–1.5
–1
– 0.5
0
0.5
1
1.5
Capital Investment
Figure 1.
Interaction between
staffing level and capital
spending
0.15
0.1
salary low
% Diff ALOS
salary high
0.05
0
– 0.05
– 0.1
–1.5
– 0.5
0.5
Capital Investment
1.5
ALOS performance. Here, higher levels of capital investment are associated with
higher than expected ALOS (i.e. ALOS performance is worse). One possible
explanation may be that more highly paid employees, who might be more highly
skilled, are able to be more productive with lower levels of capital investment. In
contrast, lower paid employees, who may not be as highly skilled, might require more
resources, reflected here by capital investment, to be productive. In other words, the
impact of higher levels of capital investment appears to be greater for lower paid
workers than for higher paid workers. This result is consistent with the notion of job
deskilling, wherein organizations utilize technology to replace worker skills. A related
implication also might be that a hospital choosing to employ a lower paid (and likely
lower skilled) workforce would most likely see worse than expected ALOS
performance unless capital spending is increased. Although we have provided a
reasonable explanation for this result, we leave a definitive answer as a topic for future
research.
Figure 2.
Interaction between salary
and capital spending
IJOPM
27,9
1036
Conclusion
With increasing attention being focused on escalating healthcare costs, there is
considerable energy being put forth to improve hospital operations. One focus of these
energies has been on attempts to reduce the ALOS of their patients, as this measure has
been found to be related to both the cost and quality of care. However, a clear picture has
yet to emerge that shows where to focus their attention and resources. There are many
key areas in which the organization can invest, each with seemingly logical arguments
for their validity. The aim of this paper is to further expand the body of research that
addresses the important challenges hospitals face from an operations management
perspective. In particular, this research examines the link between a set of key
operations-related elements and the percent reduction of expected ALOS using objective
data from hospitals in NYS. Using elements from the structural/infrastructural
operations strategy framework, we set out to find the correlates to this important
hospital performance metric. By examining how the configuration of a hospital’s
strategic operations decisions are aligned with their performance on this given strategic
area, it is our aim not only to advance the literature stream in this area, but also to
provide guidance to healthcare professionals facing these very real decisions every day.
In the operations strategy literature, structural elements include capacity,
technology, and facility location. For a hospital’s operations, we considered the size
or capacity as measured by the number of beds, the location of the hospital (urban and
non-urban areas), the educational mission of the hospital (teaching or non-teaching),
and the level of capital investment in facilities and technology. Our measure of length
of stay explicitly took into account the severity and mix of cases treated by the hospital
by calculating a value of expected stay based on the severity and mix of cases. The
ALOS measure used in our analysis was then calculated as the percentage deviation
from that expected ALOS. We found that teaching hospitals tended to achieve a lower
than expected ALOS, while larger hospitals and hospitals located in NYC tended to
exhibit higher than expected ALOS. The better performance for teaching hospitals
may reflect an emphasis on learning and improvement found in an organization with a
mission based on education and research, and is interesting because it contradicts the
findings of prior research. One explanation might be that our study explicitly takes
into account ALOS performance relative to the severity of the cases treated by the
hospital, which most prior research considering teaching status does not.
Our findings related to capital investments and labor raise some interesting
questions. While these variables did not affect ALOS directly (as shown by the lack of
support for H1, H5, and H6), we found significant interaction effects between these
variables and the dependent variable. Our observations relating to H7 (capital
spending and staffing levels) point to some potentially troubling implications, where it
appears that additional capital investment in situations of lower staffing levels actually
reduced, rather than helped ALOS performance. As hospitals increasingly face nursing
shortages, this result could be bad news, as it implies that this nursing deficit cannot be
“made up,” at least in terms of ALOS performance, through increased investment in
facilities and equipment. That said, our observations relating to H8 (capital spending
and higher salary levels) may offer some potentially good news. Salary, which could be
viewed as representative of the skill level of labor (Brown et al., 2003) does interact with
capital investments in such a way that ALOS performance is greatly enhanced by
capital investment at lower salary levels. The reverse is true at high-salary levels,
where capital investment actually appears to damage ALOS performance. While it is
beyond the scope of this paper to provide a definitive explanation for this result, it may
be the case that these ALOS improvements, made through combining capital
investment for lower paid staff, may be greater than the losses incurred through the
combination of capital investment where there are work shortages.
What are the implications of these findings? We have established that larger
hospitals and urban hospitals showed lower ALOS performance, while teaching
hospitals showed better ALOS performance. An awareness of these tendencies might
encourage a manager in larger, urban, non-teaching hospitals to focus attention on
activities that may reduce the length of stay. Our observations relating to capital
spending and labor also imply that managers cannot necessarily assume that workers
can uniformly be replaced with capital investments in this environment.
There are a number of implications for research as well. For example, although we
know that there are relationships between length of stay and hospital size, location,
and teaching status, we do not know the specific mechanisms leading to these
relationships. Future research may therefore investigate hospital organizations in more
depth to understand the specific activities found in larger hospitals that are likely to
cause longer length of stay, or why teaching hospitals might exhibit better length of
stay. Research of this type might employ large-scale surveys or more in-depth case
studies to explore these relationships further. One question that emerges through the
results relating to capital investment relates to how hospitals are actually targeting
their new equipment purchases. Capital purchases aimed at increased diagnostic
accuracy are effective, but if improvement in ALOS is truly a goal of the organization,
it is unclear as to the extent to which this is being reflected in the organization’s capital
expenditures. Given the current state of rising healthcare costs and nursing shortages
in many locations, one might see this as a significant area for future investment.
Moreover, the application of new innovative technology in healthcare has been viewed
by many as a means to address the issues on the horizon. Because of the longitudinal
nature of the data, future research can identify interesting trends as they emerge over
the next few years. For example, since the hospital sector has seen a spate of
consolidation through mergers and acquisitions, it may be interesting to see if this has
had an effect on performance. While our findings relating to capital purchases give
insight, future research is needed that uses a finer lens through which to identify
specific types of technology and their relative impact on performance.
While this study is notable for its use of a large, risk-controlled data set, it is not
without limitations. As noted above, our capital expenses variable is broadly
measured, and may not fully address the effects of different types of investment.
Future research is warranted that can separate out the nuances of this variable. In
addition, any variable that measures capital investments may inherently suffer from a
lag effect – the effects of investments made today may not be seen for months or years.
Our data set includes both current investments as well as those that are still in the
process of being paid. Ideally, future research can address this issue in a longitudinal
manner by mapping investments and performance over time. Finally, limiting the
sample to hospitals in NYS may bring its own biases.
As we have discussed, the issues facing the healthcare industry will become
increasingly important over the coming years. The field of operations strategy has
much to offer the healthcare industry as they face the challenges of rising costs and an
Operations and
length of stay
performance
1037
IJOPM
27,9
aging population. It is our hope that our present research helps to further our
understanding of the operations related issues that are being faced, and that our
findings help to begin to provide some insight into the impact that the decision
variables studied have on ALOS performance. We also hope and encourage other
researchers in the operations field to pour their talents into this issue, for it is clearly
one that will be with us for some time to come.
1038
Notes
1. For this example, we have arbitrarily chosen high and low levels of these variables to be þ1
and 21. Because the variables are standardized, these values correspond to one standard
deviation above and below the mean, respectively. In addition, because we are illustrating
the interaction between FTE and CAPITAL, we have included only the coefficients for the
variables CAPITAL and FTE from Model 4 in Table III for simplicity.
2. Figure 2 was constructed in the same manner as Figure 1, with high and low levels of FTE
and CAPITAL chosen to be þ 1 and 21, respectively.
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Further reading
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Corresponding author
Christopher McDermott can be contacted at: mcderc@rpi.edu
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