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OCR for page 260
INTRODUCTION
The hospital industry is undergoing major
structural change. Shifts in Medicare and
Medicaid reimbursement policies, increasing
private concern about health care costs, tech-
nological change, and the growth of organized
delivery systems such as health maintenance
organizations (EIMOs) and preferred provider
organizations (PPOs) have radically altered the
environment facing hospitals. Hospitals have
responded in two primary ways: (1) by diver-
si*ing away from inpatient acute care services
to find new revenues Tom services such as
ambulatory care, home care, and skilled nurs-
ing care (vertical integration); and (2) by join-
ing existing multihospital systems or forming
new ones (horizontal integration) or starting
non-health-care businesses such as construc-
tion (diversification).
Both of these strategies aim to improve the
potential service benefits and economics of
hospital operations, the first primarily by rais-
ing service volumes by assuring continuity in
referrals, and the second primarily by achiev-
ing administrative economies of scale and bet-
ter access to capital. These Rends have resulted
in the large numbers of corporate reorgani-
zations of hospitals and the steady growth in
*Mr. Watt is a principal, Lewin and Associates,
Inc., San Francisco, California. Mr. Renn is a research
associate, lhe Johns Hopldns Center for Hospital Fi-
nance and Management, and the Department of Health
Policy and Management, The Johns Hopkins Univer-
sity, Baltimore, Maryland. Mr. Hahn is a research as-
sistant, Lewin and Associates, Inc. Mr. Derzon is vice
president, Lewis and Associates, Inc. Dr. Schra~nm is
director, The Johns Hopldus Center for Hospital Fi-
nance and Management, and associate professor, the
Department of Health Policy and Management, The
Johns Hopldns University, Baltimore, Maryland.
For-Profit Enterprise in Health Care. 1986.
National Academy Press, Washington, D.C.
The Effects of Ownership and Multihospital System
Membership on Hospital Functional Strategies and
Economic Performance
I. Michael Watt, Steven C. Renn, James S. Hahn, Robert A.
Derzon, and CarIJ. Schramm
numbers of hospitals in multihospital systems
that have been seen over recent years.
This paper focuses on the second of these
phenomena the behavior of multihospital
systems-and examines differences in the lo-
cation, strategies, and economic performance
of five classes of community hospitals: inves-
tor-owned multihospital system (IOMS) mem-
bers, not-for-profit multihospital system
(NFPMS) members, and investor-owned (IO)
freestanding, NFP freestanding, and govern-
ment hospitals. This study attempts to sepa-
rate differences associated with system
membership from those associated with own-
ership form and to develop answers to the fol-
lowing questions:
· Are there differences in demographics,
reimbursement systems, and regulatory struc-
tures between areas where multihospital sys-
tem members are located and those in which
freestanding hospitals are located? Are there
differences between the areas where IO and
NFP hospitals locate?
· Do multihospital systems produce econ-
omies in patient care or employ different h-
nancial strategies compared with freestanding
hospitals? Are there differences in the advan-
tages multihospital system membership gives
IO or NEP hospitals?
· Are the inter-hospital differences that may
be attributed to ownership-IO versus NFP-
greater than the differences that may be due
to organizational form chain-affiliated versus
freestanding?
~ What does Me historical record say about
the potential of each type of hospital for suc-
cess in a world in which purchasers of care are
increasingly price-conscious?
Issues of comparative quality of care and
access, while also important, were beyond the
scope of our analysis of economic performance.
260
OCR for page 261
OWNERSHIP AND ECONOMIC PERFORMANCE
METHODS
We analyzed a sample of hospitals that is
national in scope and includes about 10 per-
cent of all U. S. community hospitals, ranging
from urban academic health centers to small
rural facilities.
The Sample
The sample was chosen by a stratified ran-
dom sampling technique from the universe of
4,491 Medicare-certified, nonfederal, and
nonstate general acute care hospitals with
lengths of stay between 3 and 13 days in 1980.
Using the Guicle to Multihospital Systems of
the American Health Association (AMA) and
the Directory of the Federation of American
Hospitals (FAH), each of the hospitals was
classified based on ownership and system af-
filiation. We selected from this universe a sam-
ple of hospitals totaling 800 in aggregate. We
originally hypothesized that there would be
little difference (other than greater direct re-
ceipt of public funds) between voluntary hos-
pitals and those organized as county and hospital
district entities. However, in the models pre-
sented here, to control for differences such as
sunshine laws, limitations on outside contract-
ing, and restrictions on governing board com-
position that may remain even after adjusting
for public subsidies, we included a category
for government hospitals.
The original sample of 800 was reduced to
561 due to cost reports missing important
schedules or covering periods of less than 9 or
more than 15 months. The number of sample
hospitals in each of the five strata are as fol-
lows: IOMS members-122; NFPMS- 114;
IO freestanding hospitals 148; NFP free-
standing hospitals (voluntary or religious-
sponsored) 93; and government (county or
hospital district) hospitals-84.
We tested each of the samples against the
eligible universe from which it was drawn, us-
ing two-tailed l-tests of the difference between
means. We found the samples to represent
their classes well in hospital size, occupancy,
length of stay, and the ratio of outpatient to
total gross revenue, as shown in Table 1, with
two exceptions. First, the freestanding IO hos-
pitals in our sample are slightly smaller and
261
the sampled chain IOs slightly larger than their
classes overall. We do not believe these dif-
ferences have important consequences for the
results that follow because we explicitly con-
trol for number of beds in each equation. Sec-
ond, the inpatient proportion of total revenue
differs between the sampled freestanding NFPs
and their class. However, a variable to control
for this factor is also included in the equations.
There is little evidence of "response bias"-
differences between the chosen random sam-
ple and the final usable sample. The sample
has a good distribution of hospitals from the
major muldhospital systems. However, sev-
eral large public system teaching hospitals in
New York and California could not be included
in the sample due to our inability to disaggre-
gate some key financial data for the individual
hospitals. Kaiser hospitals were excluded for
slml. ar reasons.
Several measures of hospital costs and rev-
enues, utilization, and balance sheet strength
were taken Tom audited Medicare cost re-
ports. Data on the number of births, surgical
admissions, urban/rural location of the hospi-
tal, and contract management and member-
ship status in NFP systems in 1980 were taken
from the AMA's Annual Survey. Demographic
and physician supply information from the
county where each hospital is located was taken
from the U.S. Bureau of Health Professions'
Area Resource File. Figures for the percentage
of admissions covered by each type of third-
party payer were constructed from the U.S.
Office for Civil Rights' 1980 Survey of Hos-
pitals.2 Finally, the Health Care Financing
Administration (HCFA) supplied wage and case-
mix indices and lists of hospitals that were sole
community providers. This data base was
checked for consistency among sources and
within each hospital record.
Techniques
Financial data were standardized to a com-
mon, 12-month fiscal period ending Decem-
ber 31, 1980, using trend factors based on
monthly expense data from the AHA s Panel
Survey. Expense and statistical data from hos
OCR for page 262
262
TABLE 1 Sample Compare] to Universe, 1980
l
FOR-PROFIT ENTERPRISE IN HEALTH CARE
Beds per
Hospital
Occupancy Rate
Average
Length of Stay
Ratio of Inpatient
Gross Revenue to
Total Gross Revenue
N Mean N Mean N Mean N Mean
Chain-afEiliated investor-owned
Sample 122 167.48 122 0.62 122 6.17 122 0.91
Universe 310 146.19* 307 0.63 307 6.37 306 0.90
Chains liated not-for-profit
Sample 114 220.77 114 0.69 114 6.58 114 0.88
Universe 436 223.32 416 0.70 418 6.80 413 0.87
Freestanding investor-owned
Sample 148 94.01 148 0.63 148 6.80 142 0.91
Universe 213 111.63* 213 0.64 213 6.57 209 0.91
Freestanding not-for-profit
Sample 93 220.73 93 0.70 93 6.94 93 0.88
Universe 2,397 221.01 2,306 0.72 2,306 7.24 2,287 0.87
Government
Sample 84 103.56 84 0.60 84 5.96 82 0.87
Universe 1,264 108.41 1,244 0.61 1,247 6.16 1,221 0.87
NOTE: Levels of statistical significance are denoted by asterisks as follow: *probability <.10; **probability
<.05; ***probability <.01 that observed difference is due to chance.
SOURCE: Sample: Lewin and Associates and The Johns Hopkins University; Universe: American Hospital
Association Annual Survey of Hospitals.
pitals with reporting periods of other than 366
days were adjusted by the ratio of the days in
the period to 366. Edits were used to check
the consistency of the measures calculated, and
all outliers were verified.
We then analyzed this data set through or-
dinary least squares multiple regression on the
models discussed below. Multiple regression
was chosen to overcome two limits of the
matched-pairs methodology we have applied
in other studies. First, regression permits larger
sample sizes, because there is no need to match
hospitals explicitly in the respective samples
under study. This allows the study to be na-
tional in scope, rather than limited only to
states in which the hospital classes under study
are located, and increases one's confidence that
the measures calculated represent average re-
lationships nationwide. Second, because the
regression technique is not limited in its ap-
plication to two equal-sized samples, differ-
ences among IO and NAP system hospitals and
IO and NFP freestanding hospitals can be ex-
amined explicitly at the same time.
Dependent Variables
The 28 dependent variables included mea-
sures of case mix, operating revenues and costs,
capital structure, markups and profitability, and
activity and productivity. With few excep-
tions, these measures were taken from the
Medicare cost reports directly or calculated
from them, as we felt this was the most reliable
and consistent source. Table 2 shows the de-
pendent variables, their sources, and Weir mean
values for each of the hospital classes.
Severity and mix of cases are indicators of
patient selection strategies by the hospitals.
Those operate through decisions on siting, ser-
vice offenngs, and recruitment of different types
of physicians to the medical staff. We mea-
sured severity and mix of cases by the Medi-
care case-mix index, inpatient surgeries per
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OVKNERSHIP AND ECONOMIC PERFORMANCE
TABLE 2 Descriptive Statistics: Dependent Vanables
263
Standard
Variable Source: Definition NMeanDeviation
Case mix
Case-mix index (1980) HCFA, Federal Register 5610.98940.0822
Inpatient surgeries per 100
admissions AHA Survey 56037.348117.3455
Births per 100 admissions AHA Survey 5606.62766.6465
Outpatient revenue to total
gross revenue (ratio) Medicare Cost Report (MCR) 5590.10920.0659
Revenues and costs
Total patient care revenue MCR, adjusted for case mix,
per adjusted admission ($) wage rate, and outpatient
differences 4901,888.6383607.3580
Net patient care revenue per
adjusted admission ($)
Total operating expenses per
adjusted admission ($)
Total patient care expenses
per adjusted admission ($)
General and administrative
costs per adjusted
admission ($)
Home office costs per
adjusted admission ($)
Capital structure
Debt-to-asset ratio
Current ratio
Capital cost percent A
(excluding ROE, percent
of operating costs)
Capital cost percent B
(including BOE, percent
of operating costs)
Accounting average age of
plant
Total fixed assets per bed ($)
Markups and profitability
Gross patient care markup
ratio
Revenue deduction ratio
Nonoperating revenue to
total gross revenue
Total markup ratio
Return on total assets
Return on equity or fund
balance
MCR, as above
MCR, as above
MCR, as above
MCR, as above
MCR, as above
MCR: total liabilities/total assets
MCR: current assets/current
liabilities
MCR: depreciation, interest,
and capital leases/total
operating costs
MCR: depreciation, interest,
capital leases and Medicare
ROE/total operating costs
MCR: accumulated depreciation
plant/depreciation expense
MCR: fixed assets/beds
MCR: gross patient revenues/
operating expenses
MCR: (gross patient revenues
- net patient revenues)/gross
patient revenues
MCR: nonoperating revenue/
(operating revenue +
nonoperating revenue)
MCR: (gross patient revenue +
net nonpatient revenue)/total
operating expense
MCR: total net income/total
assets
MCR: total net income/equity
or fund balance
495 1,589.7865
543 1,598.0947
510 1,440.0125
510
495
548
540
550
455.5115
467.9824
399.4624
696.3724 230.4583
19.0471 40.1001
0.5315
2.7231
7.1173
7.8796
0.3193
2.2946
4.2766
4.5876
4424.97815.7084
49735,690.504225,489.4713
536
538
531
537
529
517
1.1935
0.1455
0.0301
1.2299
0.0573
0.1861
0.1654
0.0823
0.0324
0.1595
0.1067
0.3550
OCR for page 264
264
TABLE 2 Continued
FOR-PROFIT ENTERPRISE IN HEALTH CARE
Standard
Variable Source Definition N Mean Deviation
Activity and productivity ratios
Total asset turnover ratio MCR: gross revenue/assets 537 1.5215 0.9312
Current asset turnover ratio MCR: gross revenue/current
assets 533 4.2029 1.9565
Case flow MCR: admissions/beds 561 37.2416 11.0268
F1Es per adjusted daily MCR, supplemented by AHA:
census full-time equivalent
Occupancy rate
Medical education
personnel/adjusted average
daily census
MCR: inpatient days/total days
available
518 2.3923 0.8455
561 64.7465 17.0570
Interns and residents total MCR: interns and residents
expense per bed (S) expense/beds 561 278.1697 1,097.9548
100 admissions, births per 100 admissions, and
outpatient proportion of total gross revenue (a
proxy for outpatient volumes).
Operating revenues and costs per case were
examined as indicators of differences in hos-
pital pricing strategies, productive efficiency,
and the costs imposed by the home offices of
system members. Our measures for operating
revenues and costs were total patient care rev-
enue (gross charges), net patient care revenue,
total operating expense, total patient care ex-
pense, general and administrative costs, and
home office costs, all per admission and ad-
justed for outpatient volume, case mix, re-
porting period length and end, and wage rate
differences. We also measured total intern and
resident costs per bed.
Financial ratios addressing the capital struc-
tore of hospitals yield insight into the financial
strategies followed by hospitals in the different
classes as well as their differential access to
capital. These were measured by total fixed
assets per bed, debt-to-asset and current ra-
tios, capital costs as a percentage of operating
costs, and accounting age of plant (defined as
accumulated depreciation divided by depre-
ciation expense).
Markups and profitability measures reflect
other aspects of managerial strategy as well as
the relative success of the hospitals' policies.
The patient care markup ratio (gross patient
revenues divided by patient care expenses),
revenue deduction ratio (gross patient reve-
nues minus net patient revenues, divided by
gross patient revenues), ratio of nonoperating
revenue to total net revenue, total markup
ratio (total gross revenue divided by total op-
erating cost), return on total assets, and return
on equity (ROE) were used as measures of
markups and profitability.
Finally, measures of activity and productiv-
ity were included as dependent variables to
show differences that might exist in the effi-
ciency of use of assets across the hospital class-
es. These measures were total asset turnover,
current asset turnover, case flow (admissions
per bed), and full-time equivalent staff (FlEs)
per adjusted average census.
We controlled for case mix and wage dif-
ferences from the outset using the methods
employed by HCFA to standardize hospital
data for these factors under the Prospective
Payment System (PPS). Additionally, depen-
dent variables measuring revenues and costs
were adjusted for outpatient volume using the
technique of the AHA. Finally, as described
above, all financial data were standardized to
a 12-month reporting period ending Decem-
ber 31, 1980. Thus, all revenue and cost-de-
pendent variables were standardized for case-
mrx differences, wage rate differences, and dif-
ferences in outpatient volume before they en-
tered the regressions.
OCR for page 265
OWNERSHIP AND ECONOMIC PERFORMANCE
Independent Variables and Hypotheses
The independent variables of primary in-
terest were those representing ownership and
system membership, specified as a construct
of four dummy variables, with NFPF being
the omitted reference group; and length of
affiliation and contract management, which
were specified as a construct of three dummy
variables. Other independent variables con-
sisted of five tvDes that mav also affect hocnitn1
of,
economic performance:
1. Competition and regulation were mea-
sured by two proxies sole community pro-
vider status under Medicare (a measure of lack
of competition) and percentage of admissions
under state rate-setting programs.
2. Case mix and patient selection was mea-
sured by the Medicare case-mix index, sur-
geries per 100 admissions, births per 100
admissions, and percent charge-based payers.
We expected both higher case-mix indices and
percentages of surgical admissions to be as-
sociated with higher revenues and costs per
case.
3. Input costs and regional practice differ-
ences were examined through three measures,
the HCFA area wage index, a dummy equal
to 1 if the hospital was in a Standard Metro-
politan Statistical Area (SMSA), and a second
dummy equal to 1 if the hospital was in Census
Region nine the West Coast. This last vari-
able was specified following preliminary
regressions that found only this region to show
different hospital performance on many of our
measures. Hider wage indices and urban lo-
cation were expected to be associates} with
higher revenues and costs.
4. Productive capacity and utilization were
measured by the hospital's number of beds,
ratio of outpatient to total gross revenue, oc-
cupancy rate, and, in some models, length of
stay. Higher numbers of beds and lower oc-
cupancy rates were expected to be associated
with higher costs per stay. We entered the
linear form of the bed variable because pre-
liminary regressions showed it fit as well as
quadratic and other formulations.
5. Finally, medical education commitment
was measured by the medical resident and in-
tern expense per bed from the Medicare cost
265
report, and was expected to be associated with
higher costs.
The sources and means of the independent
variables are shown in Table 3.
We developed initial hypotheses about the
ejects of the independent variables on the de-
pendent variables and tested them through
the regressions. From our previous studies,2
we hypothesized that IO system hospitals would
tend to be located in the sunbelt states char-
acterized by faster population growth, higher
proportions of charge-based reimbursement,
endless regulation. They would showcase mixes
about equal in total (Medicare case-mix index)
and surgical proportions, and lower in obstet-
rical and outpatient proportions than NFP
freestanding ones.
We hypothesized that IOMS revenues and
administrative and home office costs per case
would be higher than either freestanding or
system NFPs, but their total operating and
patient care expenses per case would be equiv-
alent to the NFPs. We believed they would
show lower asset values per bed, higher debt-
to-asset ratios, higher capital costs as a per-
centage of operating costs and younger plants
(by accounting-based measures) than NFPs,
and that these strategies would result in higher
markups and profitability. Finally, we ex-
pected IO system hospitals to show faster asset
turnover on revenue-based measures, and
greater productivity measured by full-time
equivalent staff per occupied bed.
Our hypotheses regarding NFP system hos-
pitals were that they would exhibit similarities
to bow NEP freestanding hospitals (which most
of them once were) and IO system hospitals
(due to similar opportunities to achieve sys-
tem-derived economies and access to capital).
Therefore, for many relationships, our hy-
potheses were indeterminate. However, we
ejected their case mixes to be somewhat
higher overall and relatively richer in obstetri-
cal and outpatient services than IO system hos-
pitals. Like the IO systems, we expected that
their home office costs would increase admin-
istrative and general costs relative to NFP
freestanding facilities. Because one of the rea-
sons for forming a multihospital system is the
ability to pool resources to improve access to
capital, we expected to see relatively higher
OCR for page 266
266
TA;BLE 3 Descriptive Statistics: Independent Enables
FOR-PROFIT ENTERTR1SE IN HEALTH CARE
Standard
Variable Source N Mean Deviation
Ownership and affiliation
Investor-owned chain
Not-for-profit chain
Investor-owned
freestanding
Government
Not-for-profit
freestanding
(omitted reference
group)
Contract management and
length of affiliation
Contract managed in
1980
Chain affiliated in 1977
and 1980
Contract managed in 1977
and 1980
Competition and regulation
(D) ~ `` 561
( ) | American Hospital Association |
(D) (AMA) Guide, Federation of 561
(D) American Hospitals (FAH) 561
Directory, and Medicare
(D) ~Cost Reports (MCRs) 561
(D)
(D)
(D)
AHA Special Run
0.2175
0.2032
0.2638
0.1497
0.1658
0.4129
0.4027
0.4411
0.3571
0.3722
561 0.0963 0.2952
561 0.2175 0.4129
561 0.02S0 0.1561
_
Sole community provider (D) Health Care Financing
Administration (HCFA) 561 0.0303 0.1716
Percent of admissions Office for Civil Rights (OCR)
covered by rate setting survey and state rate
setting agencies 559 5.8147 21.0005
Case mix and patient selection
Case-mix index
Inpatient surgeries per 100
admissions
Births per 100 admissions
Percent of admissions
covered by charge-based
payers Association
HCFA, Federal Register
AHA Annual Survey
AHA Annual Survey
OCR survey and Blue Cross
561
560
560
0.9894
37.3481
6.6276
0.0822
17.3455
6.6465
539 41.3264 18.0965
Input cost and regional practice
differences
Wage index HCFA, Federal Register 5610.99270.1595
Located in Census
Region 9 (D) AHA 5610.15690.3640
Capacity and facility utilization
Number of beds MCR (available beds) 561158.1854173.2655
Occupancy rate (percent) MCR 56164.746517.0570
Outpatient revenue to total MCR (includes outpatient
gross revenue (ratio) ancillary and ER) 5590.10920.0659
Average length of stay MCR 5616.51471.6238
Medical education
Interns and residents: total
expense per bed ($) MCR 561278.16971,097.9548
NOTE: D = dummy variable: value equals 1 if hospital has attribute, 0 otherwise.
OCR for page 267
OWNERSHIP AND ECONOMIC PERFORMANCE
debt-to-asset ratios and newer physical plants
among system NFPs than freestanding NFPs.
Based on an earlier Lewin and Associates
study,3 we expected many similarities be-
tween IO freestanding hospitals and IO system
members. However, we expected higher pro-
portions of surgical admissions and a lower
overall case-mix index at freestanding IOs than
freestanding or system NFPs, lower overhead
costs due to the absence of home office costs,
and lower fixed assets per bed and debt-to-
asset ratios than system IOs.
We hypothesized that in many ways gov-
ernment hospitals would resemble freestand-
ing NFPs. However, they would have higher
outpatient volumes, older plants, lower ratios
of capital to operating costs, and higher FTEs
per occupied bed than NFPs (due to the public
service employer role of some of these facili-
ties). After adjusting to control for public sub-
sidies, we expected little difference in the
markups or proportion of nonoperating to total
revenue between government and NFP hos-
pitals.
Several studies have shown that it often takes
several years for the advantages of multihos-
pital system membership 'to be realized.
Therefore, we expected that hospitals affiliated
with systems for more than 3 years would show
better results than hospitals with shorter his-
tories. We hypothesized similar relationships
for contract-managed hospitals. Hospitals
managed for less Man 3 years would show poorer
financial performance (because correcting such
situations is'' a primary reason for becoming
managed), but performance would be better
in hospitals managed for a longer time.
Regression Models
The regression models were developed by
including, for each dependent variable,'the
independent variables which we hypothesized
would have an effect. To ease discussion of the
results and to provide a more complete expla-
nation of the effects and relationships between
variables, we included the same set of inde-
pendent variables in almost all the equations.
Two sets of regression equations were cal-
culated. First, freestanding NFP hospitals were
used as the reference group against which other
classes of hospitals' were compared. These re
267
suits are presented in the tables of regression
results (Tables 5-9), which show the freestand-
ing NFPs as the omitted reference group. Iben
the model was recalculated with the chain-
alliliated NFP hospitals as the omitted refer-
ence group in order to explicitly measure the
magnitude and statistical significance of the
differences between NFP and IO system hos-
pitals. This result is shown near the bottom of
He tables as "IONIS compared to NFPMS.',
The changed reference group does not affect
the coefficients or significance level values of
any of the independent variables except the
ownership construct, due to the mathematical
relationships involved.
Standard tests eliminated variables suffi-
ciently collinear to bias the results. However,
our research has four limitations. First, in some
cases the data we were able to obtain may not
represent completely the effects we were trying
to capture. For example, our payer-mix data
are based on anticipated source of payment,
not actual payer source.
Second, several factors that affect a hospi-
tal's performance are inadequately repre-
sented due to poor data with which to construct
measures. An example is that our measure of
the competitive environment is whether the
hospital was a Medicare-designated sole com-
munity provider a negative measure that does
not distinguish between cases in which a hos-
pital may be ringed by five larger ones and
cases in which the sample hospital is the dom-
inant one in a three-hospital town.4
Third, other factors that may be important
to strategies and performance, such as the way
in which strategies are formulated and com-
municated, the nature oftheincentives placed
on management, and management skill, could
not be included in the current study. Finally,
our mode! is cross-sectional and, thus, limits
the inferences that can be made about cau-
sality rather than association.
RESULTS
Our results include findings on SLY groups
of issues: siting and service area characteris-
tics, case mix, operating revenues and ex-
penses, markups and profitability, capital
structure, and productivity in the use of assets.
OCR for page 268
268
Siting and Service Area Characteristics
Hospitals of the five classes considered in
this study differ in their geographic distribu-
tion as a result of historical and economic fac-
tors. Table 4 shows the distribution of hospitals
across U.S. Census regions.
IO chain facilities are concentrated in the
South Atlantic region, including Florida (re-
gion 3), the East South Central region, in-
cluding Tennessee (region 5), the West South
Central region, including Texas (region 7), and
the Pacific region, including California (region
9) essentialRy the "sunbelt," characterized by
faster population growth than other areas of
the country. NFP systems are also somewhat
concentrated, but in different regions: the East
North Central region (Midwest region 4),
West North Central region (the Plains states
region 6), and the Mountain states (region 81.
They overlap the IO systems in the Pacific and
West South Central regions. Freestanding IO
hospitals are highly concentrated in the West
South Central and Pacific regions, but also re-
main on the Eastern Seaboard. NFP free-
standing hospitals are well distributed across
census regions. The government hospitals in
our sample tend to be the county and district-
sponsored facilities prominent in the South.
Since the geographic regions differ in demo-
graphics, income, and health care industry
regulation, as well as other factors, the broad
differences in the locations of hospitals of the
five classes influence our findings on five types
of siting characteristics.
Urban, Suburban, and Rural
Overall, about 60 percent of the hospitals
in our sample were located in counties or ag-
gregates of counties defined as SMSAs, com-
pared with about 51 percent of U. S. hospitals
overall. A higher proportion (74 percent) of
IOMS hospitals, and a significantly lower pro-
portion of government hospitals (27 percent),
were urban.5 Differences were sharper when
subregions of SMSAs were examined. About
30 percent of the hospitals in the sample over-
all were located in suburban areas (non-central
cities) of the SMSAs. However, about 40 per-
cent of the chain IOs and 42 percent of free-
standing IOs were located outside central city
FOR-PROFIT ENTERPRISE IN HEALTH CARE
city areas-significantly higher than the per-
centages of the freestanding NFP hospitals that
were located in suburban areas. NFPMS and
government hospitals were about as likely as
freestanding NFPs to be located in suburban
metropolitan areas, but less likely than IO chains
to be in the suburbs.
Wage Index and Sole Community
Provider Status
As a consequence of their differing patterns
of urban-rural location, the wage indices de-
veloped by BCFA were slightly higher for the
investor-owned hospitals (both those in mul-
tihospital systems and freestanding ones) and
lower for the government hospitals in our sam-
ple than for the NFPs.
No IO hospitals in our sample had been
given Medicare sole community provider sta-
tus, a designation indicating that a hospital
lacks local competition from other hospitals.
Hospitals must apply for this designation, and
the benefit of doing so in 1980 was to exempt
the hospital from Section 223 controls on rou-
tine costs under Medicare. Therefore, fewer
sole community providers among the IOs could
indicate either fewer rural hospitals among the
IOs (supported by our findings above) or fewer
hospitals needing relief from the cost limits.
Of the 17 sole community providers in our
sample, 6 were government hospitals, 3 were
freestanding NFPs, and 8 were members of
NFP-systems.
Area Demographics
On average, about 14 percent of the pop-
ulation in the counties in which the sample
hospitals were located were below the poverty
level in 1980. The figure was highest for gov-
ernment hospitals (about 16 percent). IO hos-
pitals, both Eeestanding and chain-a~liated,
also had slightly higher rates of poverty in their
home counties than did the average freestand-
ing or multihospital system NFP hospital in
our sample, but when the broad census re-
gions of the hospitals were controlled for (e.g.,
New England and Pacific), none of these dif-
ferences was significant. The hospitals' home
counties showed essentially no difference in
the percentage of their populations over 65
OCR for page 269
269
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OCR for page 270
270
years of age. While the home counties of the
IO hospitals showed higher percentages of
nonwhite population (about 20 percent versus
16 percent in the sample overall), these dif-
ferences were primarily related to the differ-
ences in the census regions in which the
hospitals were located.
Area Health Care Capacity and
Utilization
The home counties of NFP chain hospitals
showed higher ratios of beds per 1,000 persons
than the sample as a whole (about 5.3 com-
pared with 4.8 overall). The counties in which
the government hospitals in our sample were
located showed lower rates of hospitalization
as measured by admissions per 1,000 persons
(156 versus 175 overall) and surgical operations
per 1,000 persons (65 versus 80 overall) com-
pared to freestanding NFPs.
These differences are measured after con-
trolling for regional differences in capacity and
use, which other studies have found to be large
and significant. When these regional differ-
ences in practice were not controlled for, the
sampled NFP chain hospitals, located primar-
ily in the Midwest and Rocky Mountain states,
show higher rates of admission per 1,000 per-
sons than the other classes of hospitals in the
sample.
Third-Party Coverage and Rate Setting
We found essentially no difference in the
percentage of admissions covered by Medicare
and Medicaid across the hospitals. However,
the shares of the insurance market held by
Blue Cross and the bases for Blue Cross pay-
ment differ across geographic regions and show
in lower proportions of Blue Cross admissions
at the IO and government hospitals than at the
NFPs of both classes. IO chain hospitals had
a significantly higher proportion (35 percent
versus 31 percent overall) of their admissions
covered by insurance. Coupled with the fact
that Blue Cross more often paid charges in
their service areas, this led the IO system sam-
ple to have higher proportions of charge-based
payers (48 percent versus 41 percent overall)
than any other group. Virtually none of the IO
FOR-PROFIT ENTERPRISE IN HEALTH CARE
chain hospitals and few freestanding IOs were
located in rate-setting states in 1980.
Discussion
We believe these findings, taken together,
suggest several important differences in the
siting strategies of IO and NFP hospitals. First,
IO hospitals in 1980 preferred to locate in areas
with rising populations-the sunbelt and the
suburban areas, rather than the Midwest, cen-
tral cities, and rural areas which were losing
population. As a consequence of this ~er-
ence, IO hospitals had higher wage indices
than the average NFP hospital in our sample
or the government hospitals, which were pri-
marily rural. Also, the lower proportion of ru-
ral hospitals among the IOMS systems in 1980
suggests they may have favored areas in which
they could be the seconcl provider in town,
where they could differentiate the services they
offered from those of the existing provider and
become the hospital of choice for scheduled
medical-surgical patients.6
Second, at least at the county level, the ser-
vice areas of the IO hospitals do not show ev-
idence of attempts to "cream-skim." IO hospitals
did not avoid counties with relatively higher
rates of poverty, proportions of nonwhite pop-
ulation, or high proportions of elderly. Below
the county level, however, we have no data,
except for whether the hospital is located in
the suburban area or the central city of an
SMSA. Their more frequent location in sub-
urban areas may afford the IO hospitals im-
med~iate service areas relatively richer in charge-
paying patients.
A third difference concerns per capita hos-
pital capacity. Even after controlling for re-
gional differences, NFPMS members are
located in areas of higher hospital capacity
(measured in beds per 1,000), which is one
measure of the extent of hospital competition
in an area. IOMS multihospital system mem-
bers, on the other hand, have not located in
areas of high bed-to-population ratios. NFP
system hospitals may believe that they will be
the survivors in competition in overbedded
areas, or may have joined the systems to im-
prove their chances.
Fourth, the lower use of health services in
the home counties of the government hospitals
OCR for page 279
279
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OCR for page 280
280
deductions, and required lower markup ratios
(measured as gross revenues to costs) to achieve
higher profitability.
Hospitals in areas with higher wage indices
took higher total markups of revenues over
costs. Pacific Coast hospitals earned less of their
total revenue from nonoperating sources than
hospitals in the remainder of the county.
Capacity and utilization variables were sta-
tistically significant, but explained only small
proportions of the differences in markups and
profitability across the classes of hospitals. In-
creased occupancy allowed hospitals to earn
higher markups on patient services and in to-
tal, and was therefore associated with in-
creased revenue deductions and higher return
on assets and return on equity. Increased out-
patient proportions of total revenue were as-
sociated with lower patient service markups
and deductions, but with higher proportions
of the hospital's revenue coming from non-
operating sources. Longer average lengths of
stay were associated with lower return on as-
sets (1.2 percent decrease for each additional
day).
Finally, higher medical education costs per
bed were associated with slightly lower patient
and total markups in the hospitals.
Discussion
These results support our earlier findings
on revenues and costs: the IO hospitals in mul-
tihospital chains took higher markups and
earned higher returns than did NFP hospitals
of either type. Freestanding IO facilities were
somewhat more moderate in their pricing
practices, but both classes of IO hospitals earned
higher returns on equity than Me NFP hos-
pitals. NFP system hospitals took relatively
lower markups than freestanding NFPs, and
earned only modest returns.
All hospitals, regardless of ownership, must
earn operating surpluses in order to remain
attractive to lenders of debt capital and to be
able to replace their plant and equipment. All
five classes of hospitals in our sample, on av-
erage, were able to do so in 1980. However,
the higher surpluses of the IO hospitals, cou-
pled with their ability to issue stock, give them
a flexibility in developing capital that is not
matched by the ability of the NFP hospitals
FOR-PROFIT ENTERPRISE IN HEALTH CARE
to attract and accept donations or government
grants.
Capital Structure
Table 8 shows the results of equations ex-
amining capital structure variables. Chain-af-
filiated IOs had higher debt-to-asset ratios than
NFP system members (about 0.66 versus 0.461;
higher capital costs as a percentage of total
operating costs, whether excluding or includ-
ing Medicare's return-on-equity payment;
younger plants, as measured by the ratio of
accumulated depreciation to depreciation ex-
pense (2.22 versus 6.50 years); and lowerfixed-
asset values per bed ($6,533 less). While we
found no statistically significant differences be-
tween the NFP hospitals in our sample in sys-
tems and those that were freestanding, the
NFP system sample showed slightly lower debt-
to-asset ratios, current ratios, and capital costs
as percentages of total operating costs; older
plants; and lower fixed-asset values per bed
than freestanding NFPs.
Freestanding IOs had higher debt-to-asset
ratios (0.15 higher), younger plants (1.95 years
younger, and considerably lower fixed-asset
values per bed ($22,386 less) than freestanding
NFPs. On the fixed-asset value measure, the
freestanding IO figure is about half the NFP
freestanding one. Government hospitals had
lower debt-to-asset ratios (0.13 lowers, lower
capital costs as a percentage of operating costs,
and lower fixed-asset values per bed ($9,559
less) than freestanding NFPs.
Hospitals contract-managed for less than 3
years had higher debt-to-asset ratios (0.19
higher) than freestanding NFPs that were not
contract-managed. Our results also showed
contract-managed hospitals as having less de-
preciated plants (about 2 years lower in ac-
counting age of plant than freestanding NFPs).
Variables measuring the length of affiliation
with a chain or under contract management
had no significant effects on capital structure.
Our variables reflecting competition and
regulation in the hospitals' environments
yielded only two strong relationships. First,
sole community providers appear to not have
capital structures significantly different from
those of the average hospital in our sample.
Second, hospitals operating under rate-setting
OCR for page 281
281
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OWNERSHIP AND ECONOMIC PERFORMANCE
programs had higher debt-to-asset ratios and
lower current ratios than those in less regu-
lated states. Me higher debt-to-asset ratio may
indicate both that hospitals in these states re-
quired more borrowing and that these states
assisted hospitals to borrow by issuing "com-
fort orders" explaining that debt service was
an allowable expense under the rate-setting
program.
Few other independent variables were
strongly associated with differences in capital
structure. Higher case-mix indices were as-
sociated with slightly higher capital cost as a
percentage of operating costs and higher fixed
assets per bed, confinning the need for so-
phisticated and costly equipment to render
more intensive care. Higher numbers of sur-
gical procedures per 100 admissions were re-
lated to higher debt-to-asset ratios, lower
current ratios, and higher capital cost as a per-
centage of operating costs. However, we found
no significant relationship between higher sur-
geries and higher fixed assets per bed. Higher
percentages of births among a hospital's case
load were associated with lower capital cost as
a percentage of operating costs, but had no
strong effects on any of the other measures of
capital structure. Higher wage indices, often
indicating urban areas, were linked to higher
debt-to-asset and lower current ratios.
Capacity and facility utilization had statis-
tically significant but small effects on capital
structure ratios. Higher occupancy rates re-
sulted in lower capital cost as a percentage of
total operating costs, showing the effect of
spreading the fixed capital costs over greater
volumes. Higher occupancy also was present
in younger physical plants and in hospitals with
higher fixed assets per bed. This may indicate
that new physical plants tend to be built in
areas of need where the capacity will be uti-
lized by growing populations. An alternative
hypothesis, that patients are attracted to new
buildings, is less in accord with our experi-
ence. Higher proportions of total revenue from
outpatient services, including ancillaries, were
associated with lower capital cost as a per-
centage of operating costs. Finally, hospitals
with longer stays apparently also had some-
what older physical plants.
283
Discussion
Despite their better access to capital, IO
multihospital systems appear to be more ju-
dicious in its use, as shown by their lower
fixed-asset values per bed. The significantly
lower fixed-asset figures for freestar~ding IO
hospitals are consistent with their lower case-
mix indices and smaller size, and indicate hos-
pitals which probably provide fewer sophisti-
cated services.
Consistent with national studies, IO hos-
pitals of both classes had higher capital costs
as a percentage of their total operating costs
than other hospitals. This is due to their higher
leverage, and also, in part, due to the younger
age of their physical plants on accounting-based
measures. However, since "accounting age" is
affected by sales of hospitals (revaluation of the
assets, which Medicare until recently allowed,
"reset the accounting clock"), it is not clear
whether other hospitals in fact will require
replacement or major capital projects earlier
than IO hospitals. Clearly, in the short run,
IO hospitals would be less advantaged by a
payment system that would include capital as
a fixed-percentage add-on to the DRG rates.
IO hospitals might be disadvantaged over the
long term as well, unless their higher capital
costs in fact show the financing of more "future
costs," rather than being accounting artifacts
of more recent purchase of plant assets of the
same physical age as those of the NFPs.
Activity and Productivity
Table 9 presents the estimated coefficients
for the activity and productivity regressions.
The total asset turnover ratio measures the
revenue dollars a hospital generates for each
dollar invested in fixed assets, and the current
asset turnover ratio measures revenue per dol-
lar of current assets. Case flow is measured as
admissions per bed, and is, thus, directly re-
lated to occupancy and inversely related to
length of stay.
Chain-affiliated IO hospitals showed better
use of assets on those measures based on dol-
lars total and current asset turnover ratios
than NFP system hospitals (ratios 0.39 and
2.32 higher), due to their higher charges and
lower asset values. However, they showed
OCR for page 284
284
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285
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OCR for page 286
286
weaker performance on case flow (2.4 fewer
admissions per staffed bed per year), a pro-
ductivity measure based on use of beds in ser-
vice, due to their significantly lower occupancy.
However, despite their lower occupancy, IO
chain hospitals employed 0.28 fewer FlEs per
adjusted average daily census than NFP sys-
tem hospitals. NFP system members showed
slightly better total asset turnover numbers
than freestanding NFPs, but showed no other
significant differences from NFPs on these
measures of activity and productivity. Gov-
ernment hospitals were also similar to free-
standing NFPs on these measures. Unlike the
chain-affiliated IOs, however, freestanding IO
hospitals did not use fewer FlEs per average
daily census than NFP hospitals.
Hospitals chain-affiliated for 3 or more years
showed higher turnover of current assets (ratio
0.65 higher), better case flows (2.4 more ad-
missions per bed per year), and higher occu-
pancy than hospitals that had joined
multihospital systems more recently, but
showed no better productivity in the use of
personnel. Hospitals newly contract managed
had fewer admissions per bed and lower oc-
cupancy than other hospitals in the sample.
Case flow was faster and occupancy higher
in hospitals in rate-setting states (several states
encouraged more admissions per bed through
low-occupancy penalties in their reimburse-
ment systems), but activity and productivity
apparently were little affected otherwise by
our competition and regulation variables.
Hospitals with higher case-mix indices ap-
parently had somewhat lower total asset turn-
over, primarily due to their higher asset values.
They also showed higher occupancy, graduate
medical education (GME) costs, and ratios of
personnel to daily census than did hospitals
with less intense caseloads. Higher numbers
of surgeries and births per 100 a~nissions were
associated with better case flow and somewhat
higher FTEs per adjusted average daily cen-
sus. Higher proportions of charge-based pay-
ers also were associated with better case flow,
perhaps through shorter stays.
Higher area wage indices, perhaps due to
their relation to urban areas, were associated
with slightly higher asset turnover ratios arid
higher GME costs, but not with better per-
sonnel productivity. Pacific Coast hospitals
FOR-PROFIT ENTERPRISE IN HEALTH CARE
showed lower current asset turnover, occu-
pancy, and GME costs, but better personnel
productivity than hospitals elsewhere.
Larger hospitals had lower current asset
turnover, higher occupancy, and slower case
Dow (0.009 fewer admissions per bed per year
for each additional bed) than the smaller hos-
pitals in our sample, but were not different in
personnel productivity. They also had higher
GME costs. Occupancy, oddly, appears to be
directly related to the number of FTEs per
adjusted average daily census (0.025 increase
per each 1 percent increase In occupancy). More
expected results are that the proportion of a
hospital's revenue from outpatient services also
increases FTEs per adjusted daily census (0.086
additional FTE for each additional percent of
outpatient revenue), as does GME. Longer
lengths of stay are also associated with higher
occupancy.
Discussion .
IO hospitals of both classes, due to their
higher markups and lower asset values per bed,
generate more revenue per dollar of total or
current assets than do NFP hospitals. They
also operate with fewer staffper occupied bed.
While IO hospitals did not translate their higher
productivity into lower costs per case in 1980,
they achieved better productivity despite op-
erating at much lower occupancy rates than
NFP hospitals. Thus, IO hospitals, particularly
those in systems, stand to benefit more Man
NFP hospitals from improvements in facility
utilization, which will allow them to spread
Heir higher administrative costs and lower Heir
overall costs per case. While their higher
charges would disadvantage them in contract-
ing with organizations like PPOs and HMOs
which can ship blocks of patients, their cost
functions might encourage the IO system fa-
cilities to offer the deeper discounts necessary
to secure greater patient volumes.
CONCLUSIONS
This study of a national sample of commu-
nity hospitals has shown a number of differ-
ences in economic performance across hospitals
of different ownership and affiliation classes
from which differences in strategies may be
OCR for page 287
OWNERSHIP AND ECONOMIC PERFORMANCE
inferred. On 24 of our 28 measures of case
mix, revenues and operating costs, markups
and profitability, capital structure, and activity
and productivity, statistic tests showed that
hospital ownership and system affiliation were
significant factors in explaining the differences
across hospitals. However, on most measures
except revenues per case, home office costs,
and debt-to-asset ratios, other factors such as
hospital capacity were also important explan-
atory factors.
Ownership and Chain Affiliation
The clear pattern that emerges from this
study is that in 1980 there were much greater
similarities among hospitals of the same own-
ership type (IO or NFP) than among hospitals
of the same organizational type (multihospital
system or freestanding). IO hospitals, whether
freestanding or members of systems, were
similar on the majority of measures in the study.
The NFP members of multihospital systems
in our study differed from freestanding NFPs
only on the measures of home office costs,
markup ratios, and asset turnover. Clearly, in
1980 the strategies and performance of IO and
NFP multihospital system hospitals had not
converged as some observers believe they have
today.
Functional Strategies
The study suggests several conclusions about
the functional strategies of IO and NFP hos-
pitals. First, patient selection by the IO hos-
pitals appears largely a function of siting and
service-offering decisions. In 1980, as in 1984,
less than 30 IO system hospitals, and none in
our sample, were Medicare sole community
providers, indicating that their service areas
lacked competition. IO hospitals are primarily
(74 percent) suburban or urban, and located
in areas of relatively more rapid population
growth and moderate bed-to-population ratios
than characterize the home counties of NFP
hospitals. They appear to have preferred to be
Me second hospital in town, which allows them
to differentiate their service offerings from those
of other providers. This type of service deci-
sion was shown in 1980 in their less frequent
offering of obstetrical services, ones which ap
287
pear to be unprofitable among the hospitals in
our sample. IO multihospital systems in 1980
also preferred to locate in areas where Blue
Cross paid charges; this resulted in higher per-
centages of their patients being covered by
charge-based insurance than the patients of
the other classes of hospitals in our sample.
NFP multihospital systems, on the other
hand, appear to have hospitals more evenly
distributed across locations ranging from cen-
tral cities to rural communities. Although sev-
eral NFP system hospitals in our sample are
sole community providers, on average, the NFP
multihospital system facilities are located in
areas with more competition, as indicated by
bed-to-population ratios, than any other class
of hospital.
Second, in 1980 pricing was the key to the
IO hospitals' higher profitability. Services in
IO systems in 1980 cost the public more than
in other classes of hospitals, even when ad-
justed for differences in case mix, wage rates,
and outpatient volumes, and controlling for a
wide range of other factors affecting costs. The
NFP system members in our sample, on the
other hand, earned somewhat lower returns
than other classes of hospitals by marking up
their patient care services less in relation to
their costs.
Third, the financial strategies of IO hospi-
tals of both types enabled them to earn higher
returns for their investors and gave the IO
hospitals an advantage in access to capital. These
strategies combined higher patient service
prices with financing a higher proportion of
their assets through debt than the NFPs.
Finally, the costs of medical education in
1980 were virtually all borne by NFP hospi-
tals. The direct expenses associated with train-
ing interns and residents were dramatically
higher in hospitals in NFP multihospital sys-
tems than in IO ones. While not quantified in
this study, it is intuitive that the indirect costs
of teaching were also higher in NFP multi-
hospital systems.
Multihospital System Economies
This study does not show evidence of net
economies achieved through multihospital
system operation in either the IO or the NFP
sector. The total patient care costs and total
OCR for page 288
288
FOR-PROFIT ENTERPRISE IN HEALTH CARE
operating expenses per adjusted admission of tats in less challenging environments. This
the muitihosoital system hospitals were not strategy is open to all systems, but not to free-
standing hospitals. Second, the greater capital
available in systems makes them more able to
offer their own insurance products. Finally,
the IO chains cost and profit performance has
been achieved in hospitals with generally low
occupancy. Winning or offering contracts to
improve occupancy might result in greater
economies of scale in the IO hospitals, allow-
ing them to achieve their historical profit mar-
gins despite the necessarily lower net prices
of services. IO hospitals might use these fac-
tors to erode the NFP hospitals initially greater
attractiveness to payers, but they will need to
run faster and farther than the NFPs to do so.
Second, faced with potentially shrinking
margins, IO systems may redeploy capital to
develop other sources of revenues. Humana,
for example, was initially a nursing home com-
pany, but left that business to concentrate on
hospitals. In recent years, it has sold more
hospitals than it has acquired, and concen-
trated on freestanding centers and insurance
products. If changing market economics force
hospitals to become price-takers and allow in-
surers to be price-setters, IO systems may in-
creasingly redefine themselves as insurers to
capture the higher margins possible Tom other
levels of the health care industry. NFP hos-
pitals, whether freestanding or in systems, may
be slower to change their historical missions.
Third, their higher capital costs as a per-
centage of operating costs would disadvantage
IO hospitals in the short run if a method were
enacted to pay for capital as a fixed percentage
increment to operating costs. This disadvan-
tage would persist in the long run as well to
the extent that the IO hospitals higher current
capital costs are an artifact of the revaluation
of order assess on purchase, rasher then related
to physically newer buildings.
Fourth, the current magnitude of the in-
direct teaching cost adjustment under Medi-
care gives teaching hospitals an edge over
nonteaching ones that may become even more
important as the payment formula becomes
based more on national averages. Along with
their desire to integrate more vertically, this
may be part of the reason that IO chain hos-
pitals have sought to acquire several major
teaching hospitals recently. However, the fu
significantly different from those of freestand-
ing NFPs. This may be due in part to the
incentives of He Medicare reimbursement
system in place in 1980, under which hospitals
were paid on the basis of costs rather than
revenues. However, it is also important to note
that these operating cost and patient care cost
results were obtained by system hospitals de-
spite their home office costs, a category of costs
not borne by freestanding facilities. The total
general and administrative costs per admission
(including home office costs) of the IO chains
were only slightly higher than those of free-
standing NFPs, and those of multihospital sys-
tem NFPs were lower than those offreestanding
hospitals. This suggests that the home office
operations of both types of systems, in the
main, substituted for costs borne by hospitals
locally and may, in fact, have resulted in some
offsetting direct cost savings as well.
Potential for Future Success
The environment that hospitals of all classes
face in 1984 is considerably different from the
world of 1980. HMOs, PPOs, employers, and
insurers have begun to shop for hospital ser-
vices. Medicare has set prospective prices, and
other public payers have experimented with
contracting with selected hospitals. New types
of providers such as surgicenters have also
emerged that chip away at the edges of hos-
pitals traditional markets. This snapshot of the
behavior and performance of hospitals under
cost reimbursement, while not conclusive, may
allow some inferences about the potential suc-
cesses of the classes of hospitals in a more
price-competitive environment.
First, the considerably higher prices per ease
among the IO system facilities, wider geo-
graphic dispersion (rather than regional con-
centration), and their less frequent offering of
outpatient and obstetric services may put them
at a disadvantage to NFP hospitals In winning
contracts to provide services for organized lo-
cal groups of patients. However, this expec-
tation could be tempered by three factors. First,
through multiunit operation, systems can sub-
sidize lower prices and profits in areas where
competition is keen, with profits from hospi
OCR for page 289
OWNERSHIP AND ECONOMIC PERFORMANCE
ture of this adjustment factor is in doubt. Cur-
rent proposals suggest halving the indirect al-
lowance factor and freezing allowable salary
and benefit costs per intern and resident at
some historical level. The advantage of more
favorable reimbursement to teaching hospitals
may be quickly eroded.
Finally, for multihospital systems of both
classes, the challenge of the new environment
will be to use their greater capital resources
effectively and achieve the scale economies
that theory suggests should be possible through
system operation. Neither the IO nor the NFP
multihospital systems demonstrated solid ev-
idence of such economies in 1980. Basic issues
such as how to motivate physicians and staff,
reduce the costs of service, increase patient
volumes in maturing markets, anticipate the
behavior of third-party payers, and develop
new revenue sources will be a large part of
the new equation. The question of relative fu-
ture success thus depends significantly on the
managerial creativity and leadership each sec-
tor of the. industry brings to solving problems.
On these factors of production, no sector of
the industry has a monopoly.
ACKNOWLEDGMENTS
The authors gratefully acknowledge the
technical assistance of George D. Pillari.
This research was supported in part by As-
sociated Hospital Systems. The views ex-
pressed are solely those of the authors.
289
NOTES
Using a matrix for each state, these data were used
to construct the "percent charge-based" and "percent
of admissions subject to rate-setting" variables de-
scribed below.
2 Watt, T. M., R. A. Derzon, S. C. Renn, and C. J.
Schramm, T. S. Hahn, and G. D. Pillari (1986) The
comparative economic performance of investor-owned
and not-for-profit hospitals, New England Journal of
Medicine 314:89-96; Lewin and Associates (1981) Stud-
ies in the Comparative Performance of Investor-Owned
and Not-for-Profit Hospitals, Vol. 1: Industry Analysis
(Washington, D.C.: Lewin and Associates); and Lewin,
L., R. Derzon, and R. Margulies (1981) Investor-owneds
and nonprofits diner in economic performance, Hos-
pitals 55:52-58.
3Lewin and Associates (1976) Investor-Owned Hos-
pit~s: An Examination of Performance (Chicago: Heals
Services Foundation).
4This measure is also more restrictive than those
based solely on distance between hospitals, e.g., no
other hospitals in a 15-mile radius. Using the Medicare
designation, none of the IO hospitals in our sample are
sole community providers. However, using other def-
initions, HCA, among other systems, may have a large
number of sole community provider hospitals.
sIn this Chapter, "significant" indicates p <.05. Here,
significant differences are measured against not-for-profit
freestanding hospitals, which constitute the majority of
the hospitals in the universe Mom which our samples
were drawn.
6This inference is supported by our earlier case study
research. See Lewin and Associates, Inc. (1981) Studies
in the Comparative Performance of Investor-Owned
and Not-For-Proft Hospitals. Volume III: Two Case
Studies of Competition Between Hospitals (Washing-
ton, D.C.: Lewin and Associates).
Representative terms from entire chapter:
economic performance
