Servces 
Department of Health and Human 

OFFICE OF

INSPECTOR GENERA

EFFECTS OF HOSPITAL MERGERS ON

COSTS, REVENUES, AND PATIENT

VOLUME

.. SUW/Ctn 
to\' 
';i. 
Richard P. Kusserow 
INSPECfOR GENERA 
'0 
JU 
clla  199  GENRA

OFFCE OF INSPECfOR 
The mision of the Office of Inspetor General (OIG), as mandated 
by Public Law 95-452, as
amended is to protect the integrty of the Department of Heath and Human Servce' (HHS)
programs as well as the heath and welfare of beneficiares selVed by thos programs. 
statutory miion is carred out through a nationwide netwrk of audits, investigations, and
inspections conducted by three OIG operating components: the Offce of Audit Servce, the 
Offce of Investigations, and the Offce of Evaluation and Inpetions. The OIG also inform 
the Secretary of HHS of program, and management problems, and recmmends cours to 
correct them. 
OFFCE OF AUDIT SERVICE 
The OIG's Offce of Audit Servce (OAS) provides all auditing servce for HHS, either by
conducting audits with its own audit resource or by overseeing audit work done by others.
Audits examine the performance of HHS programs and/or its 
grantee and contractors in 
carrng out their respective responsibilties and are intended to provide independent 
assesments of HHS programs and operations in order to reduce waste
, abuse, and 
mismanagement and to promote economy and effciency thrughout the Department. 
OFFCE OF INTIGATIONS 
The OIG's Offce of Investigations (01) conducts criminal, cil, and administrative 
investigations of allegations of wrongdoing in HHS programs or to HHS beneficiaries and of 
unjust enrichment by providers. The investigative efforts of 01 lead 
al  to criminal convictions 
administrative sanctions, or civil money penalties. The 01  oversee State Medicaid fraud 
control units which investigate and prosecute fraud and patient 
abuse in the Medicaid program. 
AN 
OFFICE OF EVALUATION  INSPECfONS 
The OIG's Offce of Evaluation and Inspetions (OEI) conducts short-term management and
program evaluations (called inspetions) that focus on isues of concern to the Department,
the Congres, 1l.!4 the public. The findings aQd recmmendations contained in these inspetion 
report generate rapid, accurate, and up-to-date information on the effciency, vulnerabilty, 
and effectivenes of departmental programs. 
This report was prepared by Thomas A Noplock, CPA, of the OEI Health Care Branch with 
Mos 
the asistance of W. Mark Krushat, Sc.  , and Linda M.  of the OEI Technical 
Support Staff.  Department of Health and Human Servces

OFFICE 
INSPECTOR GENERAL

EFFECTS OF HOSPITAL 
MERGERS ON

COSTS, REVENUES, AND PATIENT

VOLUME

.. SERV/Ctn
to\' 
Richard P. Kusserow 
INSPEcrOR GENERA 
"1 
;f~clla  9050 
OEI-12- ...................................... ............................................................ .
. . . . . . . . ..  ..  ..  ..  ..  ..  ..  ..  ...   ...   ...   ...   ...   ... .
.... ....... ....... ...... ....... ...... ....... ....... ......  ..  ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ..  ..  ..  ..  ..  ..  ..  ..  ..  . . . . .
TABLE OF CONTENTS

EXECUTIV SUMMY 
PURPOSE. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 
BACKGROUND. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 
METHODOLOGY 
FINDINGS 
APPENDIX A  . . . . . . . . . . . . . . A
APPENDIX B

APPENDIX C . . . . . . . . . . .  EXECUTIVE  SUMMARY

PUROSE 
To determine the effects of hospital mergers on costs, revenues, and patient volume. 
BACKGROUN 
Dramatic changes have occurred in the health care industry in recent years, including 
growth in the number of hospital mergers. While some hospitals claim that merger is 
the solution to problems associated with patient volume and profitabilty, the U. 
Department of Justice and the Federal Trade Commission believe that some mergers 
can reduce healthy competition and are not necessarily the best way to increase 
effciency and decrease cost. 
Secretary Sullvan appointed a Task Force in November 1989 to examine the 
Department of Health and Human Servces (DHHS) policy on hospital mergers and 
he asked the Inspector General to analyze several issues surrounding mergers. 
MEODOLOY 
We randomly selected eleven hospital mergers from the American Hospital 
Association s list of 20 mergers occurrng in 1987. We also identified a control group 
of hospitals which were geographically similar to the eleven merged hospitals. We 
compared the two groups of hospitals using data from the Hospital Cost Report 
Information System (HCRIS) maintained by the Health Care Financing 
Admistration (HCFA). These data items represent various measures of costs 
patient revenues, and patient volume. 
FIINGS 
pa 
hoit  othe 
Mered  reded th co of doin b  but  were not 
dieren frm hopils whih ha not  in a meer 
di 
Bsapede o n our  it is  to predt what efec a meer wi have on a 
opetig characteti wi 
hopitaPs  any deee of accuacy.  INTRODUCTION 
PURE 
The purpose of this inspection is to determine the effects of hospital mergers on costs 
revenues, and patient volume. 
BACKGROUN 
Dramatic changes have occurred in the health care industry in recent years
, including
remarkable growth in the number of hospital mergers. Increasing market share 
patient volume, and profitabilty are some of the reasons 
given for hospital mergers.
A weak hospital that is losing money may fid it more acceptable to merge with a 
financially stronger hospital than to go out of business. The stronger hospital can 
increase its market share by selectively marketing the most 
successful servces of the 
weak hospital. While some hospitals claim that merger is the solution to their 
problems, the U.S. Department of Justice (DOJ) and the Federal Trade Commission 
believe that some mergers can reduce healthy competition and are not 
(FTC) 
necessarily the best way to increase effciency and decrease costs. 
The Secretary, Department of Health and Human Servces
, appointed a Task Force in
November 1989 to examie the Department's policy on hospital mergers. He asked 
the Inspector General to analyze several issues surrounding mergers. 
This inspection is a companion study to the Offce of Inspector General reports 
entitled "The Effects of Hospital Mergers on the Availabilty of Servces" (OEI-04-90
02400) and "The Effects of Hospital Mergers on the Availabilty of Servces: A Case 
Study of Eight Hospital Mergers" (OEI-04-91-oo5oo). 
MEODOLOY 
We randomly-selected eleven hospital mergers from the American Hospital 
Association s list of 20 mergers occurrg in 1987. We also identifed a control group 
of hospitals which were geographically similar to the eleven merged hospitals. We
compared the two groups of hospitals using data from the Hospital Cost Report 
Information System (HCRIS) maintained by the Health Care Financing 
Administration (HCFA). These data items represent various measures of costs 
patient revenues, and patient volume. 
Our purpose does not include a review of the criteria or formulas used 
by the
Department of Justice (DOJ) to approve or 
disapprove mergers. 
A full description of methodology and data analysis is included in appendix A.  , p-
FINDINGS

hopi  si  bus  pa
Mered  rceodemd pthn  cots of doinp la hobut  di not hinae ae reven 
pati volu in  to  whih  not 
a meer 
The results of our analysis show that, to the extent that the control group represents 
an appropriate comparison, hospitals that merged in 1987 differ only slightly from 
hospitals not participating in a merger. However, some differences do exist. 
Interpretation of the Data 
To compare the two groups of merged and non-merged hospitals we calculated the 
percent change in costs, revenues and patient volume after the mergers took place. 
Two statistical tests were used to determine if the merged group of 
hospitals differed 
signifcantly from the controls (that is, increased or decreased their costs, revenues and 
patient volume more than the non-merged hospitals). The Wilcoxon Rank Sum 
procedure tests to see if one group tends to have 
values lower or higher than the 
other. The Medians Test determines if one group has a significantly greater number 
of values above the median of both groups. 
Table 1 contains a listing of cost report items for merged and control hospitals. For 
each item tested, the p-values provide an indication of the probabilty that the two 
groups differ with respect to that variable, according to either the Wilcoxon Rank Sum 
procedure or the Medians Test. The lower this value, the more likely the observed 
diferences are not due to chance alone. The asterisks in Table 1 indicate those tests 
with p-values below the 0.05 cutoff value, the point traditionaly used to indicate 
statistica signifcance. However, because a large number of tests have been 
conducted, it is questionable that every difference with a p-value between 0.05 and 
01 is signcant. (When a high number of tests are performed, we increase the 
probabilty that at least some of the values wi difer purely by chance.) Even so 
values less than 0. potential trends. Also, included are the 
05 can be indicative  ot 
average percent changes for each group from the pre-merger period to the post-
merger period. 
Results 
As Table 1 indicated, most of the items that show a p-value of less than 0.05 on at 
least one of our tests are cost items. In addition, for most of the cost items, the 
merged hospitals showed either reductions in the values from pre- to post- merger
, or 
a smaller increase than the control hospitals. 
Two of the cost report items stand out: expenses associated with Medical and other 
Servces (Summary Sheet) and Total Fixed Assets. The results indicate that the 
merged hospitals reduced their costs in these categories 
while the control hospitals  experienced increases during the same time periods. Both of these differences were 
highly signficant for both tests. 
While the merged hospitals reduced costs (or limited increases in costs) in comparision 
to the control group of non-merged hospitals, few differences between the groups 
appeared with respect to revenues or patient volume. Only two variables in these 
categories showed any significant differences. The number of inpatient days for 
Medicare dropped in the merged hospitals while slightly increasing in the controls, and 
hospital ancilary charges for Medicare inpatient days increased at a lower rate in the 
merged hospitals than in the controls. 
di 
spe
Baed on OU  it is  to pred wha efect a meer wi hae on 
hoitl's opetig characteti wi an deee of accucy. 
The Coeffcient of Variation (COY) is a common statistical function that measures the 
variabilty in data. It also allows for comparisons of the variabilty across diferent 
data elements. The COV is the calculated standard deviation expressed as a percent 
of the mean. Table 1 includes the COV of the 39 data elements for the pre-merger 
post-merger differences. Overall, the average of these values is 
626 percent. This 
demonstrates extreme variabilty in the data presented. This extreme variabilty, which 
may be due to the small number of hospitals examined or inherent variabilty of the 
data, makes it diffcult for us to predict the outcomes mergers may have for specific 
hospitals on various aspects of costs, revenues, or patient volume.  Table 1

Non Parametric Test Results 
Pre-Post 
Average Percent  Difference 
p-Values  Change  Coffcient 
Cot Report Item  Wilcoon Median  Mergers Controls  of Variation 
Patient Volume:

Be 
Total  Available in the Hospital  293  677  11.5%  1.6%  172 %

Total Be Days Available in the Hospital  131  211  13.  1.5%  138 %

Medicare Inpatient Days-Hospital Total  076  037*  1453 %

Medicaid Inpatient Days-Hospital Total  1.00  677  49.  44.  220 %

Inpatient Days, All Patients-Hospital Total  131  211  -6.  279 %

Medicare Disharges-Hospital Total  211  1.3%  152 %

Medicaid Disharges-Facility Total  677  13.  16.  312 %

Total Disharges, All Patients-Hospital Total  101  211  15.5%  134 %

Revenues:

Total Ancillary Charges-All Cot Centers  189  211  13.  20.3%  148 %

Total Medicare Inpatient

Hospital Ancillary Charges  005*  7.5%  16.  331 % 
Total Outpatient Charges  131  211  25.  44.  99 % 
DRG Payments-Other Than Outlers  1.00  677  0.3%  565 % 
DRG Outlier Payments  237  211  132.  207.  147 % 
Total Patient Revenues  0.511  211  14.  183 % 
Net Income or (Lo)  677  20.  -6.  155 % 
Cot and Fixed Asts:

Deprecation-Buildings and Fixures  805  839  338 %

Deprecation-Movble Equipment  0.307  291  6.5%  21.6%  2021 %

Direc Salaries-

Al General Servce Cot Centers  0.358  211  -0.5%  1.0%  513 % 
All Hospital Inpatient Cot Centers  149  037*  -8.  3.3%  7389 % 
All Other Inpatient Cot Centers  049*  211  53.  80.  379 % 
Al Ancillary Servce Cot Centers  677  7.3%  11.9%  265 % 
AI Outpatient Servce Cot Centers  1.00  677  26.  31.6%  194 % 
Tota  149  211  1.%  358 % 
Other Dir. Cot-
Al General Servce Cot Centers  115  037*  2.4%  5.3%  778 % 
Al HQSpital Inpatient Cot Centers  0.793  677  78.  222%  570 % 
Al Other Inpatient Cot Centers  0.358  677  298.3%  141.9%  46 % 
Al Ancilary Servce Cot Centers  02*  037*  12.0%  367 % 
Al Outpatient Servce Cot Centers  101  211  26.3%  541 % 
Total  02*  037*  -0.3%  10.4%  910 % 
Total Capital-Related Cots  0.393  211  -0.4%  18.  36 % 
Total Facility Cots  049*  037*  1.3%  10.4%  340 % 
Total Inpatient Ancillary Cots  393  677  -4.  560 % 
Total Outpatient Cots  101  211  17.  29.  113 % 
Medicare Inpatient Ancilary Cots 
Before Limitation  0.399  677  -4.4%  847 % 
Medicare Malpractice Cots  470  677  1.6%  187.  188 % 
Total Medicare Inpatient Operating Cots 
Including Pas-Through Cots  076  037*  -4.  1605 % 
Reimbursble Bad Debts, Net of Receries  694  677  125.  69.  245 % 
Medical and Other Servce (Summary Sheet)  001*  00*  10.4%  29.  217 % 
Asts 
Total FIxed  010*  037*  11.6%  34 % 
Avera e Coffcient of Variation For Pre-Post Difference  626 %  APPENDIX A

MEODOLOY

Constrcton of Study Groups 
AH 
The  (American Hospital Association) identifed 20 mergers 
occurrng in 1987 
involvig at least two general, short term, acute care hospitals, coexisting either in the 
same county, if rural, or the same MSA (Metropolitan Statistical Area), if urban 
representing distinct physical plants that were unified under one management. The 
result of the merger could be the continuing operation of both facilties or only 
one. 
For this inspection, a merger also includes acquisitions of one hospital by another as 
long as the location requirements, as stated above, were met. Mergers that 
represented only administrative reorganiztions of a single physical plant were 
excluded. This definition conforms to that used in the previous OIG inspections. 
From this original group of 20, we randomly selected 11 mergers for study. Appendix 
C contains a list of the hospitals included in this report. 
To create a control group, we identified geographically similar hospitals in the areas 
identifed by the 11 mergers. Two factors were considered in the construction of the 
control groups: size of the hospital and geographic location. The size of 
the hospital 
was determined by the number of beds as reported in the PPS-5 cost report data (see 
below.) For the mergers, the bed size of the institution resulting from the merger was 
used. 
Geographic location was determed by identifg the State and County in which the 
merged hospitals were located. Al other hospitals located withi the same county and
of approxiately the same bed size were included in the control group. If there were 
too few hospitals, or no other hospitals located withi the county, the adjacent 
counties were included until at least two other hospital of approxiate size and 
proxiity were- found. Geographic location took precedence over bed size. That is 
control hospitals smaller that the merged hospital were included if counties considered 
too far from the index county were necessary to obtain control hospitals of 
approximately equal bed size. 
Analca Method

Comparisons of the control hospitals to the merged hospitals were undertaken using 
data items in the Hospital Cost Report Information System (HCRIS) maintained by 
the Health Care Financing Administration (HCF A). Ths system maintains the cost
report filed by each hospital providing servces under Medicare. The data reported
on the cost reports is not just Medicare data but represents data for the entire 
operation of the hospital. This is important because the focus of this inspection is not 
limited to Medicare. The data is ordered into files representing reporting 
cycles based  ' .
upon fiscal years and are designated as PPS-1 through PPS-6. PPS-
l includes those 
cost reports with fiscal years beginnng after September 30, 
1983 but before October 1 
1984. PPS-2 includes those cost report with fiscal years beginning after September 
, 1984 but before October 1, 1985, and so on. No periods were chosen before PPS
1 in order to avoid mixng PPS cost report with non-PPS cost report. PPS-6 cost 
report were the most current available. The items on data elements were selected 
based on recommendation from the HCRIS staff to represent the 
economic changes 
to the entire hospital that would result from a hospital merger. The list of the items 
used is presented in Appendix B. For each hospital in both the merger and control 
group, each item from the cost reports for PPS-2 and PPS-3 were averaged to obtain 
an combined estimate. These were considered the pre-merger values.  The post-
merger values were obtained from the PPS-5 data for all hospitals. The pre-merger 
and post-merger values were again averaged across all of the control hospitals 
associated with each merged hospital. We thus obtained 22 pre-merger and 22 post-
merger values for each item in Appendix B; 11 from the merged hospitals and 11 from 
the average of the control hospitals. 
To account for the effects of inflation, the amounts reported in PPS-3 and PPS-5 were 
adjusted to reflect constant dollars using PPS-2 as the base year. The amounts in 
PPS-3 and PPS-5 were multiplied by 0.9614 and 0.8652, respectively. Percent changes 
were then calculated using these revised amounts. 
Constrcton of Control Group 
The number of hospitals used as controls in the index county, that is, the county where 
merger occurred, and in the adjacent counties are indicated on the maps. Also 
detag 
included is a table  the control group, by bed size, for each of the merger 
situations. In the cae of the merger that occurred in Arkansas, we were unable to 
identif appropriate control hospitals of simiar bed size in close proximity to the index 
county (Table AI). The following table summaries the distribution of merger and 
control hospils. by bed size. 
Number of Hospitals
Mergers 
Bed Size  Controls 
.c 100 Beds 
11 
100-299 Beds 
300+ Beds 
J.
Totals 
The eight control hospitals of less than 100 Beds include the six small hospitals 
imposed by the Arkansas case. A comparison of these two distributions indicates that 
this dictated difference is not statistically significant (Chi-square = 2. 138, degrees of 
freedom = 2). The average bed size of the merged hospitals is 259 beds, with a 95 
percent confidence interval of 188 beds to 330. That of the control hospitals is 227 
beds, with a 95 percent confidence interval of 
245 to 329 beds.  Selection of control hospitals in adjacent, or nearly adjacent, counties represent an 
attempt to control for demographic and economic conditions. For this analysis, it was
assumed that hospitals located in close proximity; at the county level, would be subject 
to similar pressures affecting changes between the pre- and post- merger time periods. 
These results would indicate that, on the whole, the control group is comparable to 
the merged hospitals, at least with respect to the variables included here. 
Table Al 
Bed Size Strata  Average Bed Size 
100 
299  300+  Controls  Mer ers 
ARKASAS 
74.  301 
IOWA 
135.  238 
KASAS 
219.  123 
MASACHUSETT 
444.  510 
MICHIGAN 
367.  230 
MISSOURI 
257.  246 
NEBRAKA 
188.  194 
NEW YORK 
292.  416 
PENNSYLVANIA 
332.  296 
TENNSEE 
197.  108 
WASHINGTON 
176.  187  :: 
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CD 
13    APPENDIX 

ITS 
INCLUDED IN ANALYSIS

Report Items and HCRIS Data Element Numbers 
Be 
Total  Available in the Hospital  F45 
Be 
Total  Days Available in the Hospital  F52 
Medicare Inpatient Days-Hospital Total  F59 
Medicaid Inpatient Days-Hospital Total  F65 
Inpatient Days, All Patients-Hospital Total  F72 
Medicare Discharges-Hospital Total  F82 
Medicaid Discharges-Facilty Total  F85 
Total Discharges, All Patients-Hospital Total 
Depreciation-Buildings and Fixures 
Depreciation-Movable Equipment  F9l 
Direct Salaries-All General Servce Cost Centers 
Direct Salaries-All Hospital Inpatient Cost Centers  F94 
Direct Salaries-Al Other Inpatient Cost Centers  F95 
Direct Salaries-All Ancilary Servce Cost Centers 
Direct Salaries-Al Outpatient Servce Cost Centers  F97 
Direct Salaries-Total  F10l 
Other Dir. Cost-All General Servce Cost Centers  F102 
Other Dir. Cost-All Hospital Inpatient Cost Centers  Fl04 
Other Dir. Cost-All Other Inpatient Cost Centers  F105 
Other Dir. Cost-All Ancilary Servce Cost Centers  Fl06 
Other Dir. Cost-All Outpatient Servce Cost Centers  F107 
Other Dir. Cost-Total  Flll 
Total Capital-Related Costs  F22l 
Total Facilty Cots  F23 
Total Ancilary Charges-All Cost Centers  F290 
Total Medicare Inpatient Hospital Ancilary Charges  F3l7 
Total Inpatient Ancilary Costs  F344 
Total Outpatient Charges  F44 
F44 
Total Outpatient Costs 
Medicare Inpatient Ancilary Costs, Before Limitation  F456 
Medicare Malpractice Costs  F457 
Total Medicare Inpatient Operating Costs 
Including Pass-Though Costs  F458 
DRG Payments-Other Than Outliers  F470 
DRG Outlier Payments  F471 
Reimbursable Bad Debts, Net of Recveries  F487 
Medical and Other Servce (Summary Sheet)  F492 
Total Fixed Assets  F509 
Total Patient Revenues  F525 
Net Income or Loss  F533  APPENDIX 
STY 
MERGER INCLUDED IN TIS 
Men!er  Former Hospital  New Hospital 
El Dorado, Arkansas  Union Medical Center 
048 
Provider Number: 
Warner Brown Hospital  South Arkansas Med. Sytem 
Provider Number:  Provider Number: 
Ottumwa, Iowa  Ottumwa Rgnl Health Center

Provider Number: 1609

St. Joseph Health & Rehab
 Ottumwa Rgnl Health Center

160

Provider Number:  Provider Number: 1609

Newton, Kansas  Axell Chritian Hospital

1700

Provider Number: 
Bethel Deaconess Hospital
 Newton Medical Center

Provider Number: 170103
 Provider Number: 170103

New Bedford  St. Luke s Hospital 
Massachusetts  Provider Number: 22001

Parkwood Hospital
 St. Luke s Hospital

Provider Number: 220121
 Provider Number: 220021

Detroit, Michigan  Samaritan Health Center

Provider Number: 23147

Mount Carmel Mercy Hospital
 Mercy Hospitals & Health

2339

Provider Number:  Provider Number: 230147

Kansas City, Missouri  Trinity Lutheran Hospital

Provider Number: 2631

St. Marys Hospital
 Trinity Lutheran Hospital

Provider Number: 26118
 Provider Number: 2631

Grand Island, Nebraska  Grand Island Memorial Hosp 
287

Provider Number: 
St. Franci Med Center  St. Francis Med Center 
Provider Number: 2823
 Provider Number: 2823

Staten Island  CHS - SI 
New York
 Provider Number: 330160

Richmond Hospital  CHS - SI Division 
Provider Number: 330076
 Provider Number: 330160

Allentown  Allentown Hospital 
Pennsylvania  Provider Number: 390133

Lehigh Valley Hospital  The Allentown Hosp-Lehigh

Provider Number: 390261
 Provider Number: 390133

C - 1
 Men!:er  Former Hospital  New Hospital 
Winchester, Tennessee  Methodist Hosp. of Mid TN 
Provider Number: 4458 
Sewanee, Tennesee  Emerald-Hodgson Hospital  Methodist Hosp. of Mid TN 
445 
Provider Number:  Provider Number: 4458 
Longvew, Washington  St. John s Hospital 
Provider Number: 5001 
Monticello Med Cent  St. John s Med Cent 
Provider Number: 50070  Provider Number: 5001 
C - 2