Department of Health and Human 
 Servces 

OFFICE OF


INSPECTOR GENERA


EFFECTS OF HOSPITAL MERGERS ON


COSTS, REVENUES, AND PATIENT


VOLUME


.. SUW/Ctn 

';i. to\' 

Richard P. Kusserow 

INSPECfOR GENERA '0 

clla JU 199 
OFFCE OF INSPECfOR GENRA


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 to criminal convictions 

administrative sanctions, or civil money penalties. The 01 al oversee State Medicaid fraud 

control units which investigate and prosecute fraud and patient abuse in the Medicaid program. 

OFFICE OF EVALUATION AN 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 

the asistance of W. Mark Krushat, Sc. , and Linda M. Mos 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 OEI-12-9050 
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. . . . . . . . . . . . . . . . . . . . . . . . .

TABLE OF CONTENTS


EXECUTIV SUMMY 

PURPOSE. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 

BACKGROUND. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 

METHODOLOGY 

FINDINGS 

APPENDIX A . . . . . . . . . . . . . . AAPPENDIX 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 

Mered hoit reded th co of doin b but othe were not 

dieren frm hopils whih ha not pa in a meer 

Baed on our it is di to predt what efec a meer wi have on a 

spe hopitaPs opetig characteti wi 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 (FTC) believe that some mergers can reduce healthy competition and are not 

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


Mered hopi reded th cots of doin bus but di not ineae reven 

pati volu 

a meer 

in compn to si pla ho whih ha not pa

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 0.05 can be indicative ot potential trends. Also, included are the 

average percent changes for each group from the pre-merger period to the postmerger 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. 

Baed on OU it is di to pred wha efect a meer wi hae on spe 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:


Total Be 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. CotAl 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 % Total FIxed Asts 010* 037* 11.6% 34 % 

Avera e Coffcient of Variation For Pre-Post Difference 626 % 
APPENDIX A


MEODOLOY


Constrcton of Study Groups 

The AH (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 PPS1 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 postmerger 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 postmerger 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 

included is a table detag 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. 

Bed Size 

Number of Hospitals

Mergers Controls 

.c 100 Beds 

100-299 Beds 

300+ Beds 

Totals 11 J.

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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APPENDIX 


ITS INCLUDED IN ANALYSIS


Report Items and HCRIS Data Element Numbers 

Total Be Available in the Hospital F45 

Total Be 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 

Total Outpatient Costs F44 

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 

MERGER INCLUDED IN TIS STY 

Men!er 

El Dorado, Arkansas 

Ottumwa, Iowa 

Newton, Kansas 

New Bedford 

Massachusetts 

Detroit, Michigan 

Kansas City, Missouri 

Grand Island, Nebraska 

Staten Island 

New York


Allentown 

Pennsylvania 

Former Hospital 

Union Medical Center 

Provider Number: 048 

Warner Brown Hospital 

Provider Number: 

Ottumwa Rgnl Health Center


Provider Number: 1609


St. Joseph Health & Rehab


Provider Number: 160


Axell Chritian Hospital


Provider Number: 1700


Bethel Deaconess Hospital


Provider Number: 170103


St. Luke s Hospital 

Provider Number: 22001


Parkwood Hospital


Provider Number: 220121


Samaritan Health Center


Provider Number: 23147


Mount Carmel Mercy Hospital


Provider Number: 2339


Trinity Lutheran Hospital


Provider Number: 2631


St. Marys Hospital


Provider Number: 26118


Grand Island Memorial Hosp 

Provider Number: 287


St. Franci Med Center 

Provider Number: 2823


CHS - SI 

Provider Number: 330160


Richmond Hospital 

Provider Number: 330076


Allentown Hospital 

Provider Number: 390133


Lehigh Valley Hospital 

Provider Number: 390261


C - 1


New Hospital 

South Arkansas Med. Sytem 

Provider Number: 

Ottumwa Rgnl Health Center


Provider Number: 1609


Newton Medical Center


Provider Number: 170103


St. Luke s Hospital


Provider Number: 220021


Mercy Hospitals & Health


Provider Number: 230147


Trinity Lutheran Hospital


Provider Number: 2631


St. Francis Med Center 

Provider Number: 2823


CHS - SI Division 

Provider Number: 330160


The Allentown Hosp-Lehigh


Provider Number: 390133

Men!:er Former Hospital 

Winchester, Tennessee Methodist Hosp. of Mid TN 

Provider Number: 4458 

Sewanee, Tennesee Emerald-Hodgson Hospital 

Provider Number: 445 

Longvew, Washington St. John s Hospital 

Provider Number: 5001 

Monticello Med Cent 

Provider Number: 50070 

New Hospital 

Methodist Hosp. of Mid TN 

Provider Number: 4458 

St. John s Med Cent 

Provider Number: 5001 

C - 2