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The purpose of this article is to shed light on hospital supply expenses, which form the second largest expense category after payroll and hold more promise for improving cost-efficiency compared to payroll. However, limited research has rigorously scrutinized this cost category, and it is rarely given specific consideration across cost-focused studies in health services publications. After reviewing previously cited estimates, we examine and independently validate supply expense data (collected by the American Hospital Association) for over 3,500 U.S. hospitals. We find supply expenses to make up 15% of total hospital expenses, on average, but as high as 30% or 40% in hospitals with a high case-mix index, such as surgery-intensive hospitals. Future research can use supply expense data to better understand hospital strategies that aim to manage costs, such as systemization, physician–hospital arrangements, and value-based purchasing.
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DOI: 10.1177/1077558717719928
Data and Trends
Hospital Supply Expenses:
An Important Ingredient in
Health Services Research
Yousef Abdulsalam1 and Eugene Schneller2
The purpose of this article is to shed light on hospital supply expenses, which form the
second largest expense category after payroll and hold more promise for improving
cost-efficiency compared to payroll. However, limited research has rigorously
scrutinized this cost category, and it is rarely given specific consideration across
cost-focused studies in health services publications. After reviewing previously cited
estimates, we examine and independently validate supply expense data (collected by
the American Hospital Association) for over 3,500 U.S. hospitals. We find supply
expenses to make up 15% of total hospital expenses, on average, but as high as 30%
or 40% in hospitals with a high case-mix index, such as surgery-intensive hospitals.
Future research can use supply expense data to better understand hospital strategies
that aim to manage costs, such as systemization, physician–hospital arrangements,
and value-based purchasing.
supply expense, supply chain management, hospital costs
Supply expenses form the second largest cost category in a hospital after labor
expenses and have been rising faster than labor expenses in the past several years
(Goodbaum, 2015). With regulatory and competitive pressures to reduce the cost of
This article, submitted to Medical Care Research and Review on February 5, 2017, was revised and
accepted for publication on June 20, 2017.
1Kuwait University, Shukwaikh, Kuwait
2Arizona State University, Tempe, AZ, USA
Corresponding Author:
Yousef Abdulsalam, College of Business, Kuwait University, P.O. Box 5486, Safat, 13005, Kuwait.
MCRXXX10.1177/1077558717719928Medical Care Research and ReviewAbdulsalam and Schneller
2 Medical Care Research and Review 00(0)
health care, many health systems are seeking cost-saving opportunities from their sup-
ply chain operations. Yet, almost half of the health care organizations in the United
States indicate that their “organization’s supply chain is at a low level of maturity”
(Nachtmann & Pohl, 2009, p. 4). Furthermore, a search in the health care services
journals provided very little empirical research that has specifically focused on this
important cost category.
Compared to other industries that find a strategic advantage through supply chain
integration, health care supply chains have been described as highly fragmented and
complex, with limited improvements in cost and quality over the years (Ballard, 2005;
Landry, Beaulieu, & Roy, 2016; McKone-Sweet, Hamilton, & Willis, 2005; Schneller
& Smeltzer, 2006). Hospital supply chains are inherently complex for multiple reasons.
First, health care systems interact intensely with numerous medical and nonmedical
stakeholders, including manufacturers, distributors, group purchasing organizations
(GPOs), and insurers (Begun, Zimmerman, & Dooley, 2003, p. 271). Health care sup-
ply chain activities are often outsourced to distributors or third-party logistics service
providers who coordinate many health care providers and suppliers, gaining negotia-
tion leverage and economies of scale. Consider that the average hospital may “own” up
to 35,000 stock-keeping units worth of products, but only 6,000 to 8,000 stock-keeping
units are available at the hospital with the rest being held by distributors or suppliers
(Darling & Wise, 2010). This creates challenges for hospital inventory management
and logistics. On the purchasing front, hospitals often outsource contract management
to GPOs, regional purchasing coalitions, or consolidated service centers.
The perceived cost-saving potential in health care through supply management has
motivated academics, industry practitioners, and management consultants to conduct
research in this area to identify specific improvement opportunities and factors (Dominy
& O’Daffer, 2011; McKone-Sweet et al., 2005; Nachtmann & Pohl, 2009). In this arti-
cle, we consider the term supply chain expense to encompass the cost of supplies in
addition to the labor and services related to procurement, logistics, and inventory man-
agement. However, this term is used flexibly with different inclusions and exclusions as
we show in the literature review. Consequently, it has been operationalized in different
ways, making any direct comparisons between studies difficult. Some studies estimate
that supply chain expenses account for approximately one-third of total expenses.
While this may be true in some cases, the accuracy of this estimate is dependent on
what cost items are included in this measure. For example, the estimates become sig-
nificantly different when labor cost related to supply chain operations is included.
Our study examines a measure of hospital supply expense: a more focused metric
that only considers the cost of tangible supplies, making it a subset of supply chain
expense. Supply expense includes only the tangible supplies, without including any of
the labor or services required to manage the supply chain. First, we review previous
studies that provide an estimate for average supply expense (or supply chain expense)
as a percentage of total expense. Second, we examine and independently validate the
American Hospital Association’s (AHA) hospital supply expense data. After the reli-
ability of the data is validated, the data are analyzed to provide insights about the sup-
ply expense of hospitals with different characteristics. Finally, the results of the
Abdulsalam and Schneller 3
analysis are discussed along with the potential for future research that can make use of
supply metrics.
New Contributions
This article contributes to the health services research and management literature by con-
sidering the importance of assessing the current state of supply expenses as a percentage
of total hospital expense at U.S. hospitals. We encourage the use of a precisely defined
and reliable measure for hospital supply expenses. Previous studies that have measured
hospital supply expenses have done so using different definitions and inclusion/exclusion
criteria (as shown in Table 1). This has resulted in a large variance in claims about the
average supply expense as a percentage of total expense. We attempt to add precision
regarding the average and distribution of supply expenses in U.S. hospitals and highlight
differences across hospitals of different types, their geographic location, and their operat-
ing status. The goal is to get managers and researchers to consider this easily accessible
measure and use it (with consistency) to study hospital characteristics or design hospital
practices that correlate with higher levels supply chain performance. We demonstrate that
performance metrics derived from consistent measurements collected annually from over
3,500 hospitals by the AHA also serve as benchmarks for industry practitioners and trend
reports. Thoroughly understanding the value of the measure is critical to understanding
the possibilities and limits of research grounded in the measure.
Supply Expense Estimates in the Literature
Many studies have cited estimates of the average supply chain expense as a percentage
of total expense to emphasize the importance of supply chain management in health
care. In some cases, the estimate is based on primary data collected as part of the
research project, whereas others cite estimates from other studies. Table 1 provides
examples of supply chain expense (or supply expense) percentages provided by peer-
reviewed studies or white papers from a variety of academic journals or associations.
The table is not meant to be a comprehensive list of citations, but merely to demon-
strate the variations in definitions and estimates used to describe hospital supply
expenses. In the citations we found, estimates of supply expense per total expense
ranged from 17% to 45%. Importantly, it was unclear what items are included in “sup-
ply expenses” or “supply chain expenses” in most cases. In a few cases, labor cost
related to supply chain activities is explicitly mentioned in the definition (e.g.,
Kowalski, 2009), while in other estimates explicitly exclude it (e.g., Nyaga et al.’s,
2015, study of California hospitals).
We examine financial data reported in the AHA Annual Survey from the fiscal year
2013. The AHA Annual Survey Database is a reliable resource and used widely in
4 Medical Care Research and Review 00(0)
Table 1. Estimates of Supply Chain Expenses as a Percentage of Total Hospital Expenses.
Citation Reference to supply chain expenses Publication
Nyaga, Young, and
Zepeda (2015, p.
“With supply chain costs estimated to account for
more than 25% of hospitals’ operating budgets
(McKone-Sweet etal., 2005).”
Journal of Business
In the article, supply expense per total expense is estimated
to be 29%, on average, based on data from acute-care
hospitals in California (2005-2009).
Young, Nyaga, and
Zepeda (2015,
p. 2)
“It has been estimated that total supplies account
for approximately 30% of a hospital’s operating
budget and thus potentially represent an area for
substantial cost savings (Burns & Lee, 2008; Montgomery
& Schneller, 2007)”
Chen, Preston, and
Xia (2013, p. 391)
“Historically, expenses for hospital supplies and
materials have constituted up to 45% of a
hospital’s operating budget (Kowalski, 2009).”
Journal of Operations
McKone-Sweet etal.
(2005, p. 4)
“With the supply chain costing as much as 40 percent
of the typical hospital’s operating budget, the
strategic importance of hospital supply chain management
is evident.”
Journal of Supply
Chain Management
Nachtmann and Pohl
(2009, p. 5)
“The average health care provider organization in our
survey is spending more than $100 million each year on
supply chain functions, nearly one-third of their
annual operating budget.” (The authors conducted a
survey with 1,381 respondents.)
White paper: Center
for Innovation
in Healthcare
Kowalski (2009,
p. 90)
“Historically, total supply expenses (cost of supplies plus
all the labor costs related to operating the supply chain,
including all the supply inventories in the laboratory,
pharmacy, surgery, etc.) have consumed up to 45
percent of operating budget.”
Healthcare Financial
Montgomery and
Schneller (2007,
p. 308)
Supply costs now represent as much as 31 percent
of a hospital’s total cost per case (Schneller &
Smeltzer, 2006)”
The Milbank Quarterly
Landry and Beaulieu
(2013, p. 469)
“In fact, North American studies have found that more than
40% of a hospital’s expenses are related to supply
chain activities (AHRMM, 2010; Chow & Heaver, 1994;
Nachtmann & Pohl, 2009).”
Handbook of
Conway (2011, p. 2) 40-45%: Total hospital operating expenses
represented by supply chain.”
White paper: Global
Health Exchange
HFMA (2005, p. 7) [Data are presented as a bar graph] White paper:
“Supplies as a Percent of Operating Budget”
Small Hospitals (<$35M-125M): 13%
Mid-Sized Hospitals ($125-315): 15%
Large Hospitals (>$314M): 17%
Note. AHRMM = Association for Healthcare Resource & Materials Management; HFMA = Healthcare Financial
Management Association.
Abdulsalam and Schneller 5
health care management research. More than 1,000 data fields are collected from over
5,000 health care providers in the United States. In 2010, a line item was added in the
financial section of the survey for supply expense, which is the focal variable of this
study. We limited the results to hospitals in the 50 United States (and the District of
Columbia) and only hospitals that report both total expense and supply expense.
Finally, we eliminated hospitals with less than 100 admissions during the fiscal year,
to eliminate the impact of outliers, especially when measuring the ratio of total sup-
ply expense per patient admissions. This resulted in a sample size of 3,806 hospitals
out of a total of 6,230 records in the database. We checked for differences in hospital
size between hospitals that reported their supply expenses versus ones that did not.
The results of a two-sample t test showed that hospitals that reported their supply
expense tend to be larger (in terms of the number of beds) than hospitals that did not
report (t = −8.463, df = 5.747, p < .001). This is not surprising, as larger hospitals tend
to have more resources and information systems able to obtain financial measures
requested by the AHA with ease. The average Case-Mix Index (CMI) data were
obtained from the Center for Medicare & Medicaid Services. The average CMI of a
hospital is a measure of the clinical complexity and diversity of patients served.
Intensive care hospitals, such as Orthopedics, Heart, Cancer, and Surgical demon-
strate a relatively high CMI.
Measuring Supply Expense
In the AHA survey, the supply expense data item is defined as follows:
The net cost of all tangible items that are expensed including freight, standard distribution
cost, and sales and use tax minus rebates. This would exclude labor, labor-related
expenses, and services as well as some tangible items that are frequently provided as part
of labor costs. (Health Forum LLC, 2013, p. 22)
It is important to emphasize that this measure encompasses all supplies (i.e., physician
preference items, medical supplies, pharmaceuticals, nonclinical supplies, etc.)
Medical supplies make up about 60% of total supply expense, and they correlate with
nonmedical supplies by 0.80 (Nyaga et al., 2015).
Supply expense as a percentage of total hospital expense is the most common
ratio that is used for benchmarking and monitoring industry-wide trends. This is
illustrated in the references cited in Table 1. Another commonly measured supply
chain metric is supply expense per patient admission. The hospital’s CMI is some-
times used to adjust for the average severity of patients at the hospital in a given
year (Gapenski, 2011). This measure is commonly used to internally assess the
performance of supply chain management year-over-year (after adjusting for infla-
tion and other exogenous factors). A lower value indicates higher productivity,
either via lower purchasing prices or less supply consumption per patient (control-
ling for patient severity).
6 Medical Care Research and Review 00(0)
Data Validation
The AHA is widely accepted as a high quality and reliable data source for health care
management research and used in many peer-reviewed journal articles. Nonetheless,
supply expense is a relatively new data field in the AHA survey, raising concerns that
supply expense data may not be reflected with high accuracy for several reasons: (1)
the individual responding to the AHA survey may not have access to accurate and
complete supply information, (2) the AHA-provided definition may be interpreted dif-
ferently by the respondent than an executive-level supply chain manager, (3) supply
expense data are a metric that are hard to measure consistently, largely due to data
standardization issues and involvement of multiple third parties in the supply chain
function (Darling & Wise, 2010; Nachtmann & Pohl, 2009). To alleviate some of these
concerns, we sought to independently validate the supply expense data of hospitals.
We contacted the supply chain leaders (e.g., Vice President of Supply Chain or
equivalent) of three large health systems (covering 115 hospitals) and requested from
them their 2013 supply expense data for the individual hospitals they manage, based
on the definition provided by the AHA. Our informants provided data for 92 of these
hospitals. Changes in hospital ownership between 2013 and the time we requested this
data from our informants prevented them from accessing data for all 115 hospitals.
The correlation between the AHA’s supply expense data and our primary data was
0.985, providing a strong indication that the data provided by the AHA Annual Survey
are reliable. The few significant inconsistencies between AHA’s data and our primary
data were scrutinized further and found mainly to be a result of whether the expenses
of subsidiary clinics were rolled into the hospital’s financial statements or not.
The statistics regarding average hospital supply expense are presented in Table 2. The
average supply expense at U.S. hospitals is $3.76 million, with a median of $9.12 mil-
lion. The ratio of supply expense to total hospital expense was 15%, with the middle
50% of hospitals having a ratio between 9.24% and 18.94%. The difference between
the mean and median is indicative of a positive skew in the distribution of the sample.
On average, supply expense per patient admission was estimated to be $4,470. The
distribution of supply expense per patient admission demonstrated a noticeably long
right tail, indicating that some hospitals demonstrate much higher values for average
supply expense per admission. These were generally surgery-intensive hospitals,
where orthopedic, spine, cardiology, and neurosurgery frequently often use expensive
medical devices and instruments.
The frequency distributions of supply expense per total expense and supply expense
per admission are presented in Figures 1 and 2. Both distributions resemble a normal
distribution with a positive skew. Supply expense per admission demonstrates a thick
left tail, largely influenced by hospitals of low supply intensity, such as psychiatric and
rehabilitation hospitals. The right tail of that distribution is long, especially when con-
sidering that the mean approximately coincides with the 75th percentile of the range
(in a symmetric normal distribution the mean coincides with the 50th percentile).
Abdulsalam and Schneller 7
Table 3 presents supply expense statistics broken down by hospitals of different
specialties. First, it is important to note that the overall hospital statistics (Table 2) are
largely driven by the statistics of General Medical & Surgical (acute care) hospitals,
which account for about 80% of our sample’s observations. Of the entire population of
U.S. hospitals (including those that didn’t report supply expense), 75% are classified
as General Medical & Surgical. Also included in the table is the average CMI of the
hospitals in those specialties.1 Overall, a strong positive correlation is evident between
CMI and supply expense. We caution the interpretation of supply expense per
Table 2. Descriptive Statistics (N = 3,806).
Mean (SD) 25th Percentile 50th Percentile 75th Percentile
Supply expenses
(million $)
3.76 (63.27) 2.06 9.12 34.73
Total expenses
(million $)
169.41 (293.9) 21.26 63.25 198.08
Supply/total expense
15.02 (7.9) 9.24 13.98 18.94
Supply expense per
patient admission ($)
4,470 (6,171) 2,341 3,480 4,914
Supply expense
per CMI-adjusted
admission ($)
2,810 (3,425) 1,795 2,295 2,974
Note. CMI = Case-mix index.
Figure 1. Distribution of supply expense per total expense (N = 3,806).
8 Medical Care Research and Review 00(0)
Figure 2. Distribution of supply expense per patient admission (N = 3,806).
admission for hospital specialties where the sample size is small, as outliers heavily
influence the statistics.
Table 4 provides a breakdown of hospital supply expenses based on hospital operat-
ing status. The top portion of the table considers hospital membership in the Council
Table 3. Supply Expense Based on Hospital Specialty.
Category N
Average case
mix index
Average supply
expense per total
expense (SD)
Average supply
expense per
admission ($)
General medical and surgical 3,164 1.53 16.03% (7.2) 4,466
Psychiatric 186 0.99 6.28% (4.9) 3,081
Acute long-term care 167 1.19 9.87% (5.5) 4,719
Rehabilitation 145 1.31 5.96% (3.2) 1,240
Children’s general and surgery 47 12.15% (5.1) 6,793
Surgical 27 2.35 35.82% (11.7) 17,566
Children’s psychiatric 16 4.75% (72.5) 1,095
Orthopedic 11 2.33 34.00% (7.6) 10,511
Cancer 9 2.02 19.86% (10.2) 38,746
Heart 8 2.32 30.63% (5.5) 7,288
Obstetrics and gynecology 8 1.26 11.14% (4.8) 1,961
All other specialties 31
Overall average 1.55 15.02% (7.9) 4,470
Abdulsalam and Schneller 9
of Teaching Hospital of the Association of American Medical Colleges. Teaching hos-
pitals demonstrated higher relative supply expense. They also have a higher CMI on
average, which at least partially contributes to the higher supply expense. With respect
to hospital operating status (nonprofit, government, or for-profit), average supply
expense per total expense across the three categories appears to be relatively consis-
tent, especially when taking into consideration the standard deviations of these values
(in parentheses next to the mean values). Finally, hospitals classified as being in rural
locations (as defined by the U.S. Census Bureau) have a lower supply expense and a
lower average CMI.
Discussion and Conclusion
We investigated what previous researchers have observed regarding hospital supply
expense, then validated and analyzed hospital supply expense data to present an esti-
mate that is more precise and based on a much larger sample than previous attempts
we could find. Our resulting estimate of supply expense per total expense (Table 2)
was significantly different than what previous literature has cited (Table 1). Most
likely, the biggest source of divergence between estimates cited in the literature (which
generally ranged from 25% to 40%) and the estimate from the AHA Database (15%)
was the decision to include or exclude supply-related labor expenses. On one hand, the
human resources involved in procurement, logistics, storage, and materials handling
are important to consider. On the other, many hospitals outsource such functions to
GPOs and distributors. Including payroll costs makes it much more difficult to mea-
sure accurately and consistently. The inclusion of supply-related labor expenses leaves
much to the subjective judgment of the researchers regarding what to include or
exclude. For example, nurses frequently manage inventory, order supplies, and move
supply stocks around the hospital; so what portion of the nursing staff’s compensation
be factored into the supply chain cost? The measure that is captured by the AHA data-
base explicitly excludes all labor-related expenses. There is no denying that labor is an
important component in supply chain activities, but isolating labor-related expenses
Table 4. Supply Expense Based on Hospital Operating Status.
Category N
Average case
mix index
Mean supply
expense per total
expense (SD)
Average supply
expense per
admission ($)
Nonacademic 3,551 1.51 14.72% (7.85) 4,324
Academic 255 1.90 19.28% (7.32) 6,507
Nonprofit 2,321 1.55 15.64% (7.11) 4,506
For-profit 718 1.63 14.53% (10.39) 3,952
Government 767 1.44 13.62% (7.26) 4,847
Urban location 2,424 1.62 15.99% (8.36) 4,628
Rural location 1,382 1.32 13.33% (6.69) 4,191
10 Medical Care Research and Review 00(0)
provides a metric that can be more objectively measured and compared across hospi-
tals. Our data validation effort also confirmed that Supply Chain Executives at hospi-
tals can recreate this measure of supply expense when requested to do so with a high
degree of reliability.
As shown in Table 3, supply expenses per patient are highest in specialty hospitals
with a high CMI. However, there does not appear to be enough attention from clinicians
given to supply expenses in supply-intensive specialties (e.g., orthopedics and cardiol-
ogy). This sentiment is illustrated in a recent survey of 503 orthopedic surgeons reveal-
ing that a majority of them were unaware of the cost of the medical devices they
administered to their patients, even though they indicated that cost should be an impor-
tant device selection criterion (Okike et al., 2014). With regulation and pressure on hos-
pital leadership to reduce the cost of care, supply as an expense category is perhaps the
most actionable target for performance improvement, being the second largest after pay-
roll (Nachtmann & Pohl, 2009). Importantly, as reimbursement strategies such as bun-
dled payments are implemented in supply-intensive specialties, supply expenses become
a focal point for cost savings. Health services researchers will need to give greater atten-
tion to supply expenses as their utilization of medical supplies is recognized as a key
contribution to achieving financially sound outcomes along with high clinical quality.
Hospital executives already recognize the criticality of supply costs in hospital
operations and economic sustainability. In 2016, the Cleveland Clinic’s total
expenses increased by 19% from the previous year, being attributed to an increase in
the cost of pharmaceuticals and supplies, which were up 23% and 13%, respectively
(Ellison, 2017). Researchers in both Healthcare Management and Supply Chain
Management recognize the significant improvement opportunities that exist in
health care supply chains, as a way to curtail the rising health care costs (Landry &
Beaulieu, 2013; McKone-Sweet et al., 2005). Chief executive officers and chief
operating officers should consider both internal processes as well as interorganiza-
tional structures and partnerships that affect supply expenses at hospitals. First,
research shows that physicians have a significant hand in supply chain performance,
being the selectors and users of much of the hospital’s supplies (Burns, Housman,
Booth, & Koenig, 2009; Montgomery & Schneller, 2007; Robinson, 2008). From an
interorganizational perspective, the health care management literature presents
many motivations for hospital systemization (Bazzoli, Dynan, Burns, & Yap, 2004;
Burns et al., 2015). Studying the behavior of supply expenses at hospitals of similar
characteristics but different system structures may provide an indication of the effi-
cacy of systemization on supply management (Abdulsalam, Gopalakrishnan, Maltz,
& Schneller, 2015; Burns et al., 2015). Furthermore, using the supply expense met-
ric, one can begin to assess the efficacy of external supply management organiza-
tions such as GPOs, third-party logistics, and distributors (Burns & Lee, 2008;
Nollet & Beaulieu, 2005). Does affiliation with large/small GPOs or multiple GPOs
lead to better supply expense performance? Is there an optimal sourcing portfolio
split between GPO, distributor, or direct supplier contracts?
With these questions in mind, it is important to recognize the “Total Cost of
Ownership” (TCO) related to medical supplies are more complex than just the price
Abdulsalam and Schneller 11
tag of the supplies. James Robinson (2008) makes this point in his study of value-
based purchasing for medical devices, indicating a lack of information about medical
supply costs to make decisions based on the TCO mentality. Supply chain “TCO” is
influenced by a hospital’s utilization of supply chain intermediaries, especially GPOs
and distributors, in securing their materials. For example, hospitals contracting with a
GPO appear to avoid approximately 44% of the cost associated with purchasing per
contract (Schneller & Smeltzer, 2006, p. 218). Hospitals with a substantial reliance on
such organizations generally incur lower supply chain-related labor costs but pay ser-
vice charges and administrative fees to their intermediaries. With a reliable and consis-
tent measure of supply expense, research can isolate this portion from the total supply
chain costs to understand the “transaction costs” associated with supply chain activi-
ties and delivery of care.
Already, some empirical research has been conducted to uncover the best practices
in health care supply management (Bhakoo, Singh, & Sohal, 2012; Chen et al., 2013;
Young et al., 2015). However, such research has been largely limited to case studies or
confined to a limited sample size. Furthermore, the lack of consistency as to what
“supply expense” means limits the generalizability and replicability of results. By
bringing awareness to supply expense data that are clearly defined, reliable (based on
our independent investigation), and readily available in the AHA database, we hope
that more research explores the best supply chain practices at hospitals.
Without a doubt, there is a need for more fine-tuned data on supply expenses to
understand the efficacy of cost-saving strategies. Until this is achieved, the examina-
tion of the available supply expense data provides an opportunity to better understand
the medical supplies environment of health care delivery, and it may even be devel-
oped to understand the role of supplies in clinical quality and outcomes.
We thank the members of the Health Sector Supply Chain Research Consortium for providing
their feedback and support throughout this project. We also thank the Kuwait Foundation for the
Advancement of Science for the financial support that was provided.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship,
and/or publication of this article.
The author(s) disclosed receipt of the following financial support for the research, authorship,
and/or publication of this article: This work was supported by the Kuwait Foundation for the
Advancement of Science (KFAS) (Grant Number P115-67IM-01).
1. Not all hospitals from the AHA database had a corresponding CMI value from the Center
for Medicare & Medicaid Services database. The average CMI was calculated based on the
available data in each category.
12 Medical Care Research and Review 00(0)
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... Spending on medical supplies has historically been the largest non-labor cost category, representing up to 40% of a hospital's operating budget, and this continues to grow faster than labor costs ( [2], [13]). The U.S. Congressional Budget Office projected (prior to the pandemic) that 60% of hospitals could have negative profit margins by 2025 [12], in spite of the escalating hospital prices charged to Americans. ...
... And finally, there is no standardization across supply items. An average hospital in the U.S. maintains nearly 35,000 stock keeping units (SKUs), from inexpensive commodities (e.g., bandages) to critical, high-value, and life-saving surgical products (e.g., mitral stented valves) ( [15], [2]). Bon Secours Mercy Health, for example, used to have more than 150 different types of lotion throughout the system and still maintains nine different types today after standardization [37]. ...
Hospitals face significant pressure to reduce operational costs, particularly medical supply costs, which account for up to 40% of a hospital's expenses and continue to grow. In this paper, we present a new and practice-oriented approach to combatting high medical supply costs by exploiting cost-saving opportunities hidden in complex hospital procurement contracts. We first propose a four-dimensional structure to capture the complex and diverse requirements in multi-tier contracts. We then formulate a mixed-integer programming (MIP) model to optimize procurement decisions while taking into account a number of real-world complexities, including complicated tier pricing schemes, physician preference items (PPIs), and asymmetric product substitutability. A process is then presented to identify cost-critical PPIs by quantifying their financial impacts. We apply our approach to four regional and nationwide health systems and obtain the following findings: (a) the proposed model presents a new opportunity to reduce hospital procurement costs by up to 35%; (b) the financial impact of a PPI can be quantified; (c) not all PPIs are responsible for inflating costs, with the impact of an PPI ranging from none to 13% on the total 2 cost of a clinical product category; (d) the optimal number of suppliers varies, with some health systems benefiting from fewer suppliers while others better with a larger pool of suppliers; and (e) under the current contracting structure, reducing product variety does not necessarily decrease product acquisition costs, and the current contracts may not encourage product standardization for which hospitals have long been striving.
... The healthcare Supply Chain (SC) is a multifaceted and segmented process. SC management comprehends managing suppliers, procuring resources, and delivering goods and services to providers and patients [53,54]. Furthermore, the customer service level is of most significant importance, given its direct influence on the patients' health and safety [55,56]. ...
... The purchasing of medicine constitutes the highest cost in running a hospital [26,53], representing about 20% in Portugal of current spending with health [3]. As the cost of medicines continues to surge faster than other healthcare expenses, it is a constant target for cost containment initiatives [75]. ...
In recent decades, the purchasing function has occupied an essential role in managing companies in all industries. In healthcare, purchasing medicines is critical due to the amount of resources involved, the quantity and wide variety of items purchased, and the impact on the quality of health service. This research aims to propose a more efficient and effective practitioner-wise model for medicine purchasing strategic management in the healthcare industry. For such, this study combined action research with a case study to develop a Purchasing Portfolio Model based on the Kraljic Purchasing Matrix in the context of a central hospital. Analytic Hierarchy Process techniques were used for criteria prioritization and direct measurement to score each criterion. Multi-Dimensional Scaling was used to position the items in the quadrants of the Kraljic Purchasing Matrix. The Purchasing Portfolio Models developed allows the classification of different classes of medicine in terms of strategic impact and supply risk, enabling the development of differentiated purchasing strategies. The results confirmed the model's value in defining medicine purchasing strategies in a central hospital and provided guidelines for applying the model.
... Factors such as price increases and difficulties in purchasing critical supplies without a long term demand prediction require intelligent maintenance of stock levels so that there is no shortage of supplies in hospitals. It is worth noting that the supply chain costs account for the second-largest category of expenses in a hospital, which are only lower than labor costs (Abdulsalam & Schneller, 2019). ...
Many challenges lie ahead when dealing with COVID-19, not only related to the acceleration of the pandemic, but also to the prediction of personal protective equipment sets consumption to accommodate the explosive demand. Due to this situation of uncertainty, hospital administration encourages the excess stock of these materials, over-stocking products in some hospitals, and provoking shortages in others. The number of available personal protective equipment sets is one of the three main factors that limit the number of patients at a hospital, as well as the number of available beds and the number of professionals per shift. In this scenario, we developed an easy-to-use expert system to predict the demand for personal protective equipment sets in hospitals during the COVID-19 pandemic, which can be updated in real-time for short term planning. For this system, we propose a naive statistical modeling which combines historical data of the consumption of personal protective equipment sets by hospitals, current protocols for their uses and epidemiological data related to the disease, to build predictive models for the demand for personal protective equipment in Brazilian hospitals during the pandemic. We then embed this modeling in the free Safety-Stock system, which provides useful information for the hospital, especially the safety-stock level and the prediction of consumption/demand for each personal protective equipment set over time. Considering our predictions, a hospital may have its needs related to specific personal protective equipment sets estimated, taking into account its historical stock levels and possible scheduled purchases. The tool allows for adopting strategies to control and keep the stock at safety levels to the demand, mitigating the risk of stock-out. As a direct consequence, it also enables the interchange and cooperation between hospitals, aiming to maximize the availability of equipment during the pandemic.
... Our research suggests that such a proxy may merely scratch the surface regarding the presence in a high-integration system as incentivizing physicians to consider their product selections' cost and procurement implications. 4 As we have suggested, participating in gainsharing and other incentives may have a strong impact on physician utilization of costly products. ...
... Although peripheral to the healthcare activity, the weight of these logistics activities could take 30% to 45% of a health facility's total budget without the managers are fully aware of this (Abdulsalam and Schneller, 2019;Bourgeon et al., 2001;Landry and Beaulieu, 2018). ...
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This paper presents the development of a conceptual model to evaluate the impact of the improvement of the hospital supply chain on the cancer patient psychological state within the oncology department of the IBN ROCHD University Hospital (UH) in Casablanca. Through an empirical study using a survey submitted to a sample of 100 users of this department (patients, staff, etc.) and using latent variable structural modelling by the partial least squares (PLS) method via the XL-STAT software (version 2017), we evaluate the proposed model in terms of reliability and validity to conclude that the improvement of the hospital supply chain impacts positively the psychological state of cancer throughout their treatment trajectory in the oncology department of the IBN ROCH UH. The results obtained allow us to propose areas for improvement and action plans to be taken into account by managers at strategic and tactical levels in the studied department.
This manuscript addresses the problem of strategic procurement of high-cost medical devices at one of the largest nonprofit health-care organizations in the USA. A mixed-integer linear programming (MILP) model is used to reveal the effects of physicians’ allowing alternative devices to be used for their medical procedures, the impact of alternative incentive-pricing mechanisms negotiated with suppliers, and the potential impact of rigidly imposing supplier-scorecard standards on several dimensions – especially when such scorecards are commonly recognized as being subject to “common-variance” rater bias. Solutions are found to be more sensitive to general bias toward high or low ratings in producing scores for vendor performance than to correlations in scores due to common methods bias. Small changes in pricing parameters and scorecards can have outsized effects on “optimal” shares of business allocated to alternative suppliers. This may call for constraints to avoid disruptive shifts in business relationships when revising procurement strategies through time.
Background: Supply chain optimization is an effective method of generating front-end cost savings and increasing hospital profits. Through the negotiation and renegotiation of supply chain contracts, plastic surgeons can dramatically change the price at which they purchase surgical supplies and implants. This study characterizes the potential impact of supply chain optimization and puts forth a generalizable, systematic approach for successful sourcing. Methods: From October of 2017 to September of 2018, the authors examined all patients taken to the operating room for either a facial fracture or a hand fracture. Cost data were collected, Supply Chain Information Management numbers were used to determine whether each item used during the study period was under contract, and cost savings based on contract negotiation were calculated. Potential cost savings were calculated using the BillOnly calculator. Results: For the 77 facial trauma cases and 63 hand trauma cases performed, a total of 330 items (70 distinct items) were used, 47 percent of which were under contract (47 percent contract use), with an average negotiated discount of 49 percent. Based on BillOnly material cost estimates, the authors' institution would need to increase its contract use to 70 percent to achieve a net savings of 19 percent, and to 90 percent to achieve a net savings of 39 percent. The authors also estimated that if contract use increased to 90 percent, net savings would increase commensurately with increases in the average discount negotiated. Conclusion: Supply chain optimization offers plastic surgeons the potential to significantly decrease surgical costs while maintaining surgical quality.
Hospitals are under tremendous pressure to improve multiple performance dimensions that may be at odds, including cost containment, clinical quality, and patient satisfaction. The need to improve performance on multiple fronts is compounded by sources of uncertainty that include upstream diversity of supply chain (SC) partners, internal diversity of clinical specialties provided, and downstream diversity of patient conditions. Information processing theory (IPT) suggests two strategies for dealing with uncertainty: reduce the amount of information elevated to higher levels for processing and accommodate the amount of information by increasing information processing at other levels. We apply IPT to investigate (i) the relationship between the two information processing strategies in a hospital's SC and its cost containment, clinical quality, and patient satisfaction performance, and (ii) how these relationships may be moderated by diversity in the hospital's SC partners, clinical specialties, and patients, the common sources of uncertainty in hospital SCs. These questions were examined using regression analysis of primary data from acute care hospitals matched with secondary data from the Centers for Medicare and Medicaid Services, American Hospital Association and the Agency for Healthcare Research and Quality measuring hospital operating characteristics and performance. The analysis yielded several important findings: (i) while a slack resources strategy (uncertainty reduction) is effective for improving patient satisfaction performance, it is ineffective for reducing cost, (ii) a lateral relations strategy (uncertainty accommodation) is effective in improving all three performance dimensions, and (iii) these relationships are moderated differentially by the three diversity dimensions only for lateral relations. These findings offer important managerial insights about hospitals’ efforts to improve potentially conflicting performance outcomes and suggest interesting opportunities for future research applying IPT to this important topic.
Objective To determine whether the variation in prices paid for cardiac medical devices was associated with management practices in cardiac units. Study Setting Cardiac units in US hospitals. Study Design We regressed unit prices on management practice scores and other hospital characteristics, with and without controls for device fixed effects, for the 11 top-spending cardiac device categories. Data Collection A trusted third party that had entered into a confidentiality agreement combined deidentified medical device price data for N=213 US hospitals from ECRI's Supply Guide benchmarking service, with survey responses regarding management practices in those hospitals' cardiac units; the resulting merged data were made available to researchers for analysis with hospital identifiers removed. N=1,980 hospitals with interventional cardiac catheterization laboratories and at least 25 annual acute myocardial infarction discharges in 2010 were eligible for inclusion; N=648 responded to the management practices survey; N=213 subscribed to Supply Guide and purchased at least one of 11 top cardiac medical device categories. Principal Findings Cardiac units with better management practices paid lower prices for cardiac devices (percent decrease in price for one standard deviation increase in management score = 1.33%, 95% confidence interval 0.99-1.67). This was comparable in magnitude to the price decrease associated with a one standard deviation increase in patient volume. Conclusions Better management practices were associated with lower device prices. This relationship is robust, but modest in magnitude. This modest magnitude is similar, though, to other events expected to lower input prices, such as transparency in the form of benchmarking information and hospital mergers. This article is protected by copyright. All rights reserved.
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Health care provision in the US can be characterized by its ongoing consolidation of systems and facilities in its efforts to achieve clinical and business integration. An important goal in health care systems is integration to reduce fragmentation and gain value. Functional integration has a long history of association with clinical performance. Supply chain can dramatically support the goals of cost avoidance, improved clinical outcomes, and buffer the system from external dependencies. This paper examines the efforts by the Department of Defense to consolidate the medical supply chain and explores lessons for and from the civilian health sector supply chain.
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Hospital supply chains can be conceptualized as complex systems with a large number of players and a high degree of interrelatedness among them, creating an environment that is difficult to optimize and manage. In recent years, a consolidated service center (CSC) strategy (sometimes also referred to as “self-distribution” strategy), has emerged in some healthcare systems, showing a strong potential for reducing the complexity in a hospital's supply chain to achieve high levels of performance and innovation. We examine three CSCs using a qualitative case method to understand the unique characteristics of this supply chain strategy, and how CSCs move hospital supply chains toward a less complex state. We find that CSCs demonstrate characteristics that distinguish them from other supply chain strategies. These characteristics enable the CSC to orchestrate supply base rationalization and disintermediation initiatives in the hospital's supply chain to effectively reduce the number of components and interrelatedness in this complex system.
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Improving hospital supply chain performance has become increasingly important as healthcare organizations strive to improve operational efficiency and to reduce cost. In this study, we propose a research model based on a relational view, delineating the factors that influence hospital supply chain performance: trust, knowledge exchange, IT integration between the hospital and its suppliers, and hospital–supplier integration. Testing results of the research model based on data from a sample of 117 supply chain executives from U.S. hospitals show positive direct effects: (1) from trust and from IT integration to knowledge exchange respectively; (2) from knowledge exchange and from IT integration to hospital–supplier integration respectively; and (3) from hospital–supplier integration to hospital supply chain performance. The results also show the following indirect effects: (1) the influences of knowledge exchange and IT integration on hospital supply chain performance are partially and fully mediated by hospital–supplier integration, respectively and (2) the influences of trust and IT integration on hospital–supplier integration are fully and partially mediated by knowledge exchange, respectively. In addition, the results show the following moderating effects: (1) hospital system membership moderates the relationships between IT integration and knowledge exchange and between trust and knowledge exchange; (2) hospital environmental uncertainty moderates the relationship between trust and knowledge exchange; and (3) trust moderates the relationship between knowledge exchange and hospital–supplier integration. Implications of the study findings and directions for future research are discussed.
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Purpose – The article deals with issues such as the size of a purchasing group, the types of benefits aimed for, and the real beneficiaries of purchasing groups. Design/methodology/approach – The observations are based on the literature, as well as on interviews, mostly with Canadian and US health‐care managers. Findings – Although often associated with the public sector, purchasing groups are also an alternative considered more and more by managers of the private sector. A purchasing group increases volume consolidation, making it possible to have only one negotiation, in order to increase the purchasing group members' power vis‐à‐vis that of its suppliers. However, a purchasing group also constitutes an additional link in the supply chain and its objectives could go contrary to those of some of its members. This is why organisations considering joining a purchasing group should analyse this option strategically, in order to assess correctly the potential long‐term benefits. Originality/value – This article suggests key questions and an analytical framework to help managers assess the potential benefits and drawbacks of joining a purchase group.
Efficiency in supply chain operations is increasingly a key strategic goal as organizations focus on containing cost. This is especially the case for hospitals where supply chain costs account for a substantial portion of operating budgets. In this study, we examine the impact of incentive mechanisms for internal stakeholders and negotiating leverage with external partners on supply chain efficiency in the hospital industry. We premise the study on agency, stewardship, and power theories. We use data from over 200 California hospitals in a five-year period (pooled data) to estimate regression models using generalized estimating equation. We find that physician employment and contract management enhance supply chain efficiency. Surprisingly, we did not find that better supply chain efficiency was associated with either hospital affiliation with systems or high-volume purchases through large national group purchasing organizations. The results have important theoretical and managerial implications for supply chain management.
As hospital employment of physicians becomes increasingly common in the United States, much speculation exists as to whether this type of arrangement will promote hospital operating efficiency in such areas as supply chain management. Little empirical research has been conducted to address this question. The aim of this study was to provide an exploratory assessment of whether hospital employment of physicians is associated with better supply chain performance. Drawing from both agency and stewardship theories, we examined whether hospitals with a higher proportion of employed medical staff members have relatively better supply chain performance based on two performance measures, supply chain expenses and inventory costs. We conducted the study using a pooled, cross-sectional sample of hospitals located in California between 2007 and 2009. Key data sources were hospital annual financial reports from California's Office of Statewide Health Policy and Development and the American Hospital Association annual survey of hospitals. To examine the relationship between physician employment and supply chain performance, we specified physician employment as the proportion of total employed medical staff members as well as the proportion of employed medical staff members within key physician subgroups. We analyzed the data using generalized estimating equations. Study results generally supported our hypothesis that hospital employment of physicians is associated with better supply chain performance. Although the results of our study should be viewed as preliminary, the trend in the United States toward hospital employment of physicians may be a positive development for improved hospital operating efficiency. Hospital managers should also be attentive to training and educational resources that medical staff members may need to strengthen their role in supply chain activities.
The practice of patient care is supported by a range of healthcare supply chain management activities, also referred to by many as healthcare logistics. Improving the efficiency of these activities can provide opportunities for healthcare institutions and health systems to increase the quality of care and reduce costs. Hospitals represent a key link in the supply chain and face their own particular challenges due the complexity of their internal supply chain. The distribution of medical supplies to nursing units represents a vital component of the internal hospital supply chain. Indeed, all doctors, nurses, and clinical support staff deal with and depend on supplies in one way or another and are thus affected by their accessibility and availability on a daily basis. The methods most often used to distribute supplies to nursing units range from requisition-based systems that depend on clinical staff involvement to methods where the hospital’s central stores manage inventory and replenishment. In the latter category, common storage and distribution method options include par level systems and automated cabinets, among others, as well as the two-bin/kanban replenishment method, which has been identified as a best practice. For its part, RFID technology has further enhanced this leading practice and introduced the possibility of proactively managing supplies by triggering replenishment rounds based on a range of user-defined criteria. Beyond its expanded benefits, this innovation opens the door to a large number of research avenues in the areas of capacitated vehicle routing problems, inventory optimization, and simulation.
Hospital system formation has recently accelerated. Executives emphasize scale economies that lower operating costs, a claim unsupported in academic research. Do systems achieve lower costs than freestanding facilities, and, if so, which system types? We test hypotheses about the relationship of cost with membership in systems, larger systems, and centralized and local hub-and-spoke systems. We also test whether these relationships have changed over time. Examining 4,000 U.S. hospitals during 1998 to 2010, we find no evidence that system members exhibit lower costs. However, members of smaller systems are lower cost than larger systems, and hospitals in centralized systems are lower cost than everyone else. There is no evidence that the system's spatial configuration is associated with cost, although national system hospitals exhibit higher costs. Finally, these results hold over time. We conclude that while systems in general may not be the solution to lower costs, some types of systems are. © The Author(s) 2015.
Orthopedic procedures represent a large expense to the Medicare program, and costs of implantable medical devices account for a large proportion of those procedures' costs. Physicians have been encouraged to consider cost in the selection of devices, but several factors make acquiring cost information difficult. To assess physicians' levels of knowledge about costs, we asked orthopedic attending physicians and residents at seven academic medical centers to estimate the costs of thirteen commonly used orthopedic devices between December 2012 and March 2013. The actual cost of each device was determined at each institution; estimates within 20 percent of the actual cost were considered correct. Among the 503 physicians who completed our survey, attending physicians correctly estimated the cost of the device 21 percent of the time, and residents did so 17 percent of the time. Thirty-six percent of physicians and 75 percent of residents rated their knowledge of device costs "below average" or "poor." However, more than 80 percent of all respondents indicated that cost should be "moderately," "very," or "extremely" important in the device selection process. Surgeons need increased access to information on the relative prices of devices and should be incentivized to participate in cost containment efforts.
Purpose The purpose of this paper is to develop an understanding of the nature of collaborative arrangements that partners in Australian hospital supply chains use to manage inventories. Design/methodology/approach A case study involving a supply chain network of ten healthcare organisations (three pharmaceutical manufacturers, two wholesalers/distributors and five public hospitals) was studied. Data included 40 semi‐structured interviews, site visits and examination of documents. Findings This study highlights the existence of a variety of collaborative arrangements amongst supply chain partners such as the “Ward Box” system (a variant of the vender managed inventory system) between wholesalers/distributors and hospitals. The materials management departments were more willing than their pharmacy counterparts to participate in a variety of partial and complete outsourcing arrangements with wholesalers/distributors and other hospitals. Several contingent factors were identified that influenced development of collaborative arrangements. Research limitations/implications This study is limited to the Australian healthcare sector. To improve generalisability, this study could be replicated in other industry sectors and countries. Practical implications Application of collaborative arrangements between manufacturers and wholesalers/distributors would improve inventory management practices across the supply chains. Also, learning from materials management departments could be transferable to pharmacy departments. Originality/value Several contingent variables for the implementation of collaborative inventory management arrangements between healthcare supply chain partners have been identified. Methodologically, data across three echelons in the supply chains (manufacturers, wholesalers/distributors and hospitals) were collected and analysed.