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Hospital Prices Increase in California, Especially Among Hospitals in the Largest Multi-hospital Systems

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A surge in hospital consolidation is fueling formation of ever larger multi-hospital systems throughout the United States. This article examines hospital prices in California over time with a focus on hospitals in the largest multi-hospital systems. Our data show that hospital prices in California grew substantially (+76% per hospital admission) across all hospitals and all services between 2004 and 2013 and that prices at hospitals that are members of the largest, multi-hospital systems grew substantially more (113%) than prices paid to all other California hospitals (70%). Prices were similar in both groups at the start of the period (approximately $9200 per admission). By the end of the period, prices at hospitals in the largest systems exceeded prices at other California hospitals by almost $4000 per patient admission. Our study findings are potentially useful to policy makers across the country for several reasons. Our data measure actual prices for a large sample of hospitals over a long period of time in California. California experienced its wave of consolidation much earlier than the rest of the country and as such our findings may provide some insights into what may happen across the United States from hospital consolidation including growth of large, multi-hospital systems now forming in the rest of the rest of the country.
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Organization, Provision, and Financing
Volume 53: 1 –7
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Hospital Prices Increase in California,
Especially Among Hospitals in the Largest
Multi-hospital Systems
Glenn A. Melnick, PhD1 and Katya Fonkych, PhD1
Abstract
A surge in hospital consolidation is fueling formation of ever larger multi-hospital systems throughout the United States.
This article examines hospital prices in California over time with a focus on hospitals in the largest multi-hospital systems.
Our data show that hospital prices in California grew substantially (+76% per hospital admission) across all hospitals and all
services between 2004 and 2013 and that prices at hospitals that are members of the largest, multi-hospital systems grew
substantially more (113%) than prices paid to all other California hospitals (70%). Prices were similar in both groups at the
start of the period (approximately $9200 per admission). By the end of the period, prices at hospitals in the largest systems
exceeded prices at other California hospitals by almost $4000 per patient admission. Our study findings are potentially useful
to policy makers across the country for several reasons. Our data measure actual prices for a large sample of hospitals
over a long period of time in California. California experienced its wave of consolidation much earlier than the rest of the
country and as such our findings may provide some insights into what may happen across the United States from hospital
consolidation including growth of large, multi-hospital systems now forming in the rest of the rest of the country.
Keywords
hospitals, hospital prices, multi-hospital systems, consolidation, hospital spending, hospital market structure
Article
A surge in hospital consolidation is fueling the formation of
ever larger multi-hospital systems throughout the United
States.1 The New York Times reported, “Hospitals across the
nation are being swept up in the biggest wave of mergers since
the 1990s, a development that is creating giant hospital sys-
tems that could one day dominate American health care and
drive up costs.”2 The Affordable Care Act is cited as a driv-
ing force in the growth of larger multi-hospital enterprises.3-5
There are competing theories regarding motivations and likely
outcomes of this trend toward larger multi-hospital systems.6-8
One view is that hospitals join larger multi-hospital systems to
serve larger populations more efficiently and to focus on pop-
ulation health management to improve outcomes and reduce
costs. A competing view is that by consolidating into larger
multi-hospital systems, it becomes virtually impossible for
health plans to develop insurance products without including
at least some of the system’s member hospitals in their pre-
ferred contracted networks—so-called must-have hospitals.
When this occurs, the system gains leverage to negotiate con-
tracts with health plans on an “all-or-none” basis, requiring the
plan to include all system member hospitals in the plan’s pre-
ferred networks, regardless of their prices (or quality) relative
to other potential substitutes in the market.9,10 This could result
in higher prices to health plans and higher health insurance
premiums to consumers.
This paper examines hospital prices in California over time
(2004-2013) with a focus on hospitals in the largest multi-
hospital systems. Our data show that hospital prices in California
grew substantially (+76% per hospital admission) across all
hospitals and all services between 2004 and 2013 and that prices
at hospitals that are part of largest, multi-hospital systems grew
substantially more (+113%) than prices paid to all other
California hospitals (70%). Prices were similar in both groups at
the start of the period (approximately $9200 per admission). By
the end of the period, prices at hospitals in the largest systems
exceeded prices at other California hospitals by almost $4000
per patient admission.
Our study findings are potentially useful to policy makers
across the country for several reasons. First, we track actual
prices (as opposed to billed charges11 or aggregate prices
651555INQXXX10.1177/0046958016651555INQUIRY: The Journal of Health Care Organization, Provision, and FinancingMelnick and Fonkych
research-article2016
1University of Southern California, Los Angeles, USA
Received 9 March 2016; revised April 16 2016; revised manuscript
accepted 17 April 2016
Corresponding Author:
Glenn A. Melnick, Blue Cross of California Chair in Health Care Finance,
Director, Center for Health Financing, Policy and Management, Sol Price
School of Public Policy, University of Southern California, Los Angeles,
CA 90089-0626, USA.
Email: gmelnick@usc.edu
by guest on September 29, 2016inq.sagepub.comDownloaded from
2 INQUIRY
cited in other pricing studies) for a large sample of hospitals
over a long period of time (10 years). In addition, California
experienced its wave of consolidation much earlier than the
rest of the country and as such California’s experience with
large hospital systems may provide some insights into what
may happen across the United States from hospital consoli-
dation including growth of large, multi-hospital systems now
forming in the rest of the country.
Data and Methods
Hospital price and utilization data (2004-2013) were pro-
vided by Blue Shield of California, one of the largest com-
mercial health plans with coverage throughout the state of
California. Prices represent the amounts actually approved
for payment (as opposed to billed charges). Data on hospital
characteristics are from the California Office of Statewide
Health Planning and Development and the Centers for
Medicare and Medicaid Services (Diagnosis Related Groups
(DRG) weights, hospital wage index).
For each hospital, the average price (allowed payment)
per day and per admission is calculated for all services.
Hospital-level average prices are calculated for each hospital
for 2-year periods beginning in 2004 and across all hospitals
in the sample (n = 230 in 2012 and is relatively stable over
time). Prices are calculated separately for hospitals that are
members of the 2 largest, multi-hospital systems and com-
pared with all other hospitals. Data from California Office of
Statewide Health Planning Development (OSHPD) are used
to identify hospital members of the 2 largest multi-hospital
systems (Dignity Health, previously Catholic Healthcare
West, and Sutter Health). The number of hospitals in each of
these 2 systems has remained relatively constant throughout
the study period (Dignity Health = 32, Sutter Health = 25 in
2012 out of 320 hospitals statewide). The member hospitals
in these 2 systems are quite diverse: ranging in size from
under 50 beds to over 700 beds, urban and rural, trauma and
non-trauma status, and serving a varying range of commer-
cial and low income populations.
Regression Analysis
Hospital prices grew faster for hospitals in the 2 largest sys-
tems compared with all other hospitals. We constructed a
regression model to test for the possibility that greater price
increases observed in hospitals in the largest, multi-hospital
systems relative to all other hospitals are driven by the char-
acteristics of the hospitals in large systems separately from
their membership in a large hospital system. For example,
hospitals facing less competition may have higher price
increases even if they were not part of a large hospital sys-
tem. The regression model was applied to all hospitals to
control for membership in a large system and other factors
hypothesized to affect hospital prices separately from mem-
bership in a large hospital system including hospital owner-
ship and type (for-profit, district, teaching, rural, trauma),
total beds (log), payor mix (disproportionate share hospital,
percent total admissions commercial payors), percent total
admissions through emergency room (ER), Centers for
Medicare and Medicaid Services (CMS) wage index, and
local market competition (measured by a hospital specific
Herfindahl-Hirschman Index).12,13 Time dummy variables
are included to capture industry-wide effects of new technol-
ogy, quality, and other changes than may have occurred dur-
ing the study period affecting all hospitals. Inpatient prices
are measured as the allowed amount per admission divided
by the DRG weight. All measures are calculated at the hospi-
tal level and averaged over 2-year periods. The regression
analysis was conducted twice. Model 1 includes only time
trends and indicator variables interacted with time for hospi-
tals that are members of the largest systems. Model 2 includes
these same measures plus all the control variables. We com-
pare the estimated coefficients for indicator variables for
hospitals that are members of the largest systems (interacted
with time) between the 2 models to determine the extent to
which other factors explain and therefore reduce the substan-
tial difference in price trends between the 2 groups.
Results
Hospital prices per day and per admission (Figure 1) grew
substantially across all hospitals. Between 2004-2005 and
2012-2013, average per day prices across all hospitals, for all
services grew from $3277 to $5735 (75%) whereas average
per admission prices across all hospitals grew from $10 113
to $17 818 (76%). These price increases occurred during a
period that included the great recession, and, during which,
other economic indicators grew at moderate rates: California
household income grew by 23% and inflation (urban con-
sumer price index) grew by 24%. A review of detailed price
trend data for homogeneous service categories (not shown
here) such as maternity, surgery, medical, and so forth show
price increases were generally similar across all services.
Figures 2 and 3 show the results for regression models 1
and 2. Model 1 (includes only time trends and indicator vari-
ables over time for hospitals that are members of the largest
systems) results show a clear upward price trend over time
above for hospitals in the largest systems compared with all
other hospitals. Model 2 (includes the same measures as
model 1 plus the control variables) results confirm the
upward price trends for hospitals in large system hospitals
substantially exceeding all other hospitals.
Figure 4 graphs the trends in price per admission using the
results from Model 2 to compare hospitals in large systems
with all other hospitals, controlling for other factors that
might affect prices. Prices started (in 2004-2005) at about the
same level for both groups of hospitals, (approximately
$9200 per admission), and, though prices in both groups
grew over time, prices at hospitals in the largest, multi-
hospital systems grew much more rapidly than prices in all
other hospitals. The cumulative difference in the growth of
prices between the 2 groups is substantial—prices at
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Melnick and Fonkych 3
Figure 1. Payment per admission and per day, 2004-2013.
Source. BSCA hospital claims data.
Note. Nominal prices. BSCA = Blue Shield of California.
---------------------------------------------------------
Variable | Coefficient Std. Err. z P>|z|
-----------------+---------------------------------------
Period_2006/2007 | 1688.516 384.2472 4.39 0.000
Period 2008/2009 | 3978.953 383.562 10.37 0.000
Period 2010/2011 | 5650.605 382.4656 14.77 0.000
Period 2012/2013 | 6460.28 382.249 16.90 0.000
Large System X Period
LS x 2004/2005 | 830.3291 1176.443 0.71 0.480
LS x 2006/2007 | 819.3663 864.0999 0.95 0.343
LS x 2008/2009 | 3185.665 863.7954 3.69 0.000
LS x 2010/2011 | 4100.903 863.3091 4.75 0.000
LS x 2012/2013 | 4024.035 863.2131 4.66 0.000
Constant | 9182.188 519.1268 17.69 0.000
-----------------+---------------------------------------

Figure 2. Model 1: Estimated differences (nominal) in payment per admission between large system hospitals and all other hospitals, 2004-2013.
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4 INQUIRY
hospitals in the largest systems increased 113% compared
with 70% price growth in all other hospitals in California.
These trends created an ever widening and substantial price
differential over time—by 2012-2013 prices at hospitals in
the largest systems exceeded prices in other hospitals by
$3964 (25%), even after controlling for other factors.
Discussion
California has a long track record of hospital consolidation
into multi-hospital systems—almost half of all hospitals
have been in a multi-hospital system since 2004, with the 2
largest systems controlling almost 60 hospitals. Multi-
hospital systems form, ostensibly, to increase efficiency and
quality and to control cost and price increases. Yet, our data,
from a very large commercial payor, show that hospital
prices across all hospitals have increased substantially in
California during a period of low overall price inflation, low
economic growth, and declining demand for inpatient care
(commercial volume declined, −566 032 adjusted inpatient
days [−15%] between 2004 and 2012, OSHPD).
A potentially more troubling trend, however, is the sub-
stantially greater price increases observed in hospitals that are
members of California’s largest, multi-hospital
---------------------------------------------------------
Variable | Coefficient Std. Err. z P>|z|
-----------------+---------------------------------------
Period_2006/2007 | 1436.873 399.0125 3.60 0.000
Period 2008/2009 | 3535.01 456.1489 7.75 0.000
Period 2010/2011 | 5396.665 496.7213 10.86 0.000
Period 2012/2013 | 6191.478 536.177 11.55 0.000
Large System X Period
LS x 2004/2005 | 10.77541 1144.455 0.01 0.992
LS x 2006/2007 | 451.7509 874.653 0.52 0.606
LS x 2008/2009 | 2978.961 877.0601 3.40 0.001
LS x 2010/2011 | 3734.742 882.3095 4.23 0.000
LS x 2012/2013 | 3964.232 888.0799 4.46 0.000
Control Variables
For profit| -25.98621 868.1825 -0.03 0.976
District | -817.5756 1129.687 -0.72 0.469
Teaching | 2510.668 1300.707 1.93 0.054
Rural | 2014.653 1167.47 1.73 0.084
Trauma | 1219.672 775.4438 1.57 0.116
Beds (log)| 1656.405 428.8468 3.86 0.000
DSH | -37.69158 594.3525 -0.06 0.949
%Comm.Pay | 7335.849 2614.339 2.81 0.005
Wage Index| 10400.78 2082.27 4.99 0.000
HHI | 1100.928 1985.089 0.55 0.579
% Admit ER| -1098.983 1509.98 -0.73 0.467
Constant | -14089.52 3700.312 -3.81 0.000
Figure 3. Model 2: Estimated differences (adjusted) in payment per admission between large system hospitals and all other hospitals,
2004-2013.
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Melnick and Fonkych 5
systems—average prices grew 113% in hospitals in the 2
largest systems compared with 70% growth in all other hospi-
tals. It is important to note that this substantial price differen-
tial is not driven by other factors such as case mix, payor mix,
and changes in local wage costs and local market competi-
tion, or other hospital characteristics. We found that prices in
hospitals that are members of the largest multi-hospital sys-
tems are more than 20% higher by the end of the study period
when compared with other hospitals after controlling for a
wide range of factors.
The substantial difference in prices between hospitals in
the largest multi-hospital systems and all other hospitals is
consistent with a model that suggests that hospitals in large
multi-hospital systems, by tying their hospitals together
using “all-or-none” contracting, are able to achieve market
power over prices beyond any local market advantages. A
further potential danger is that with large size comes the
potential to expand and protect market power. Large hospital
systems that conduct “all-or-none” contracting have report-
edly added other anti-competitive language to their contracts
to protect and expand their market power including clauses
that prohibit health plans or employers from developing
“tiered” benefit packages that would allow them to accept
the “all-or-none” demands to include all system hospitals in
contracted networks but at the same time develop new prod-
ucts to stimulate competition through differential cost shar-
ing across member hospitals.13-17 Another example is
so-called gag-clauses which prohibit health plans from
Figure 4. Payment per admission: Hospitals in largest multi-hospital systems versus all other hospitals (controlling for other factors),
2004-2013.
Source. BSCA hospital claims data.
Note. Payment amounts are adjusted for differences in between groups within each year based on regression coefficients in Figures 2 and 3. BSCA = Blue
Shield of California.
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6 INQUIRY
sharing detailed hospital specific utilization and pricing data
with large employers which might be used to develop benefit
packages that provide incentives for employees to use lower
priced (and/or higher quality) hospitals.18,19
Conclusion
Our high-quality pricing data paint a potentially troubling
picture both for California and the rest of the country.
Hospital prices increased substantially during a period of
slow economic growth and may have been driven in part by
increased market power by large, multi-hospital systems
(and possibly other smaller systems) practicing “all-or-none”
contracting. If this interpretation is correct, there are several
important lessons for policy makers across the country as
they face decisions regarding consolidation. First, our regres-
sion findings suggest that the market power effects of large
hospital systems do not necessarily require consolidation
between local competitors. Indeed, many of the hospitals in
California’s largest systems do not have substantial overlap-
ping markets with other system member hospitals. This sug-
gests that hospitals in large hospital systems, by tying their
hospitals together, are able to achieve market power over
prices beyond any local market advantages.
It is important to note that we have not controlled explic-
itly for differences between large system hospitals and other
hospitals with regard to quality and technology differences
and other factors such as financial status of hospitals or that
hospitals that joined the largest systems may be different in
some other unmeasured way. While model 2 does not
include explicit measures of hospital quality due to the
absence of quality data for earlier time periods, quality data
are available covering years at the end of the study period
and these data show minimal effects on price differences
between the 2 groups of hospitals. IN addition, our analyses
only cover systems within a single state and not multi-state
systems. Further research is needed to address these issues
and to more precisely control for other potential price related
factors.
However, policy makers at both the federal and state levels
might consider the potential lessons from California as we
await further research as they develop policies to shape a more
cost-effective health care system in an era of consolidation.
Specifically, policy makers could consider limiting “all-or-
none” contracting by multi-hospital systems and prohibiting
other anti-competitive contract language that flows from mar-
ket power achieved by large multi-hospital systems.20 Such
pro-competitive regulation would allow for hospital systems
to integrate to improve efficiencies without the deleterious
side effects of increased market power which can result in
reduced price competition and higher costs to consumers.
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.
Funding
The author(s) disclosed receipt of the following financial support
for the research, authorship, and/or publication of this article: This
study was supported by USC Center for Health Financing, Policy,
and Management.
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... 10 The size and scope of the largest health systems may have implications for antitrust enforcement. 11 Provider consolidation into integrated systems may lead to highly concentrated markets along both horizontal and vertical dimensions. 12 Future research should examine the drivers of consolidation and variation in performance by ownership type; 13,14 geographic variation in the extent of health system penetration across local health care markets; and the ramifications of increased consolidation on cost, access, and quality of care. ...
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Has the nature of hospital competition changed from a medical arms race in which hospitals compete for patients by offering their doctors high quality services to a price war for the patients of payors? This paper uses time-series cross-sectional methods on California hospital discharge data from 1986-1994 to show the association of hospital prices with measures of market concentration changed steadily over this period, with prices now higher in less competitive areas, even for non-profit hospitals. Regression results are used to simulate the price impact of hypothetical hospital mergers.
Wave of consolidation engulfing health care systems http://www.jsonline.com/busi- ness/wave-of-consolidation-engulfing-health-care-systems- b99474527z1-298731631.html
  • G Boulton
Boulton G. Wave of consolidation engulfing health care systems. Journal Sentinel. http://www.jsonline.com/busi- ness/wave-of-consolidation-engulfing-health-care-systems- b99474527z1-298731631.html. Published April 5, 2015. Accessed May 10, 2016.