WORKING 

 PAPERS

A Retrospective Analysis of the Clinical Quality Effects of the Acquisition

 of Highland Park Hospital by Evanston Northwestern Healthcare

Patrick S. Romano

David J. Balan

WORKING PAPER NO. 307

November 2010

___________________________________________________________________________

FTC Bureau of Economics working papers are preliminary materials circulated to stimulate discussion

and critical comment. The analyses and conclusions set forth are those of the authors and do not

necessarily reflect the views of other members of the Bureau of Economics, other Commission staff, or

the Commission itself. Upon request, single copies of the paper will be provided. References in

publications to FTC Bureau of Economics working papers by FTC economists (other than

acknowledgment by a writer that he has access to such unpublished materials) should be cleared with the

author to protect the tentative character of these papers.

______________________________________________________________________________

BUREAU OF ECONOMICS

FEDERAL TRADE COMMISSION

WASHINGTON, DC 20580
1

A Retrospective Analysis of the Clinical Quality Effects 

of the Acquisition of Highland Park Hospital by Evanston 

Northwestern Healthcare *

Patrick S. Romano, MD MPH 

University of California, Davis 

Division of General Medicine 

4150 V Street 

PSSB Suite 2400 

Sacramento, CA 95817 

916-734-7237 

psromano@ucdavis.edu

David J. Balan, PhD 

Bureau of Economics 

Federal Trade Commission 

600 Pennsylvania Avenue NW 

Mail Drop NJ-4264 

Washington, DC 20580 

202-326-3214 

dbalan@ftc.gov 

November 2010 

Abstract: In 2004, the Federal Trade Commission brought legal action retrospectively challenging the 2000 acquisition of Highland Park Hospital by Evanston Northwestern Healthcare in 

Evanston, Illinois. A major issue in the case was whether the merger had resulted in improved 

clinical quality at Highland Park. In this paper, we report the findings of our analysis of that issue. We examined numerous quantitative measures of clinical quality and found little evidence 

that the merger improved quality. We also describe the conceptual framework in which we 

evaluated the case-specific evidence, as well as the applicability of that framework to the prospective analysis of unconsummated hospital mergers. 

JEL Codes: I10, L40 

Key Words: Hospital mergers, merger retrospectives, antitrust enforcement, health care quality. 

 

*

Romano served as the FTC’s testifying expert on the clinical quality effects of the Evanston Northwestern Healthcare/Highland Park Hospital merger. Balan is an FTC staff economist who worked with Romano on that case. We 

are grateful to Keith Brand, Joseph Farrell, Ted Frech, Daniel Hosken, David Schmidt, Steven Tenn, and Michael 

Vita for helpful comments; to Peter Newberry, who did excellent work as an FTC Research Analyst during the trial; 

and to Jordan Rhodes, who is currently an FTC Research Analyst and who provided excellent research assistance for 

this paper. The views expressed are those of the authors and do not necessarily represent the views of the Federal 

Trade Commission or of any individual Commissioner. 
2

I. Introduction:

 In 2004 the Federal Trade Commission brought legal action retrospectively challenging the 

2000 acquisition of Highland Park Hospital (HPH) by the Evanston Northwest Healthcare (ENH) 

hospital system, which prior to the merger had consisted of Evanston Hospital and Glenbrook 

Hospital. All three hospitals are in or near Evanston, Illinois. As discussed in Haas-Wilson & 

Garmon (2010), there is strong evidence that prices at the merging hospitals increased following 

the merger.1 Balan & Garmon (2008) discuss FTC Complaint Counsel’s interpretation of this 

price increase, and the alternative interpretation of Respondent’s Counsel. 

 Respondent’s Counsel claimed that the merger caused clinical quality at Highland Park Hospital to improve in several important ways.2

 The main purpose of this paper is to describe the 

analyses that we performed in response to those claims. Our study deals with clinical quality, as 

it has been defined by the Institute of Medicine and the World Health Organization, and not with 

hospital amenities. Some of our analyses relied on confidential data provided by the merging 

parties, and so cannot be reported here. However, the central component of our evaluation used 

publicly available quality measures and data, which can be reported (see Romano and Mutter 

(2004) for a detailed description of the variety of available measures of clinical quality). We find 

little evidence that the merger improved quality at Highland Park Hospital. 

 A secondary purpose of the paper is to lay out the conceptual framework that we believe 

should be applied in evaluating clinical quality claims in hospital merger cases, both retrospective and prospective. We discuss the basis for our prior belief that hospital mergers do not on average improve hospital quality, but are nevertheless likely enough to do so that substantial casespecific investigation is usually warranted. We also discuss the most likely sources of quality 

improvement, and ways to evaluate them. 
3

II. Data, Quality Measures, and Empirical Methodology:

A. Data. 

 Our data source is the Illinois Department of Public Health (IDPH) Universal Dataset. This 

data set contains all inpatient discharges from nonfederal acute care hospitals in Illinois from 

1998-2003. It contains information on the demographic characteristics of each patient, as well as 

ICD-9-CM (International Classification of Diseases, 9th Revision, Clinical Modification) diagnosis and procedure codes that describe the clinical condition of each patient and what procedures 

were performed. In preparation for trial, we also analyzed specialty-specific patient outcomes 

data from the National Registry of Myocardial Infarction, the Society for Thoracic Surgeons, and 

the National Perinatal Information Center, as well as patient satisfaction and experience data 

from the vendor Press Ganey. Hospitals voluntarily submit their data to these organizations and 

programs, and then receive benchmarking reports describing their performance in comparison 

with other facilities. The merging parties were required to provide these reports for review and 

analysis, and they were used in the first author’s testimony, but they belong to the merging parties and therefore are not available to report here. 

 

B. Quality Measures. 

 The primary quality measures analyzed in this paper come from Version 2.1 of the Inpatient 

Quality Indicators (IQIs) and the Patient Safety Indicators (PSIs) developed by the Agency for 

Healthcare Research and Quality (AHRQ). These indicators of health care quality make use of 

hospital inpatient administrative data such as the IDPH data, and focus principally on short-term 

patient outcomes. The IQIs reflect quality of care inside hospitals, including inpatient mortality 

for medical conditions and surgical procedures, and the PSIs focus on potentially avoidable 
4

complications and iatrogenic events. To implement these measures, we ran the data obtained 

from the IDPH through a commercial “grouper” software program that used each patient’s 

demographic information, diagnosis codes, and procedure codes to assign that patient to an “All 

Patient Refined Diagnosis Related Group” (APR-DRG) and to a Risk of Mortality (ROM) subclass. We then fed these APR-DRGs and ROMs, along with other elements from the IDPH dataset, into AHRQ’s publicly-available Quality Indicator software for SAS to generate risk-adjusted 

outcomes measures. IQI risk-adjustment incorporates age, gender, age-gender interactions, circumstances of admission (i.e., transfer from another hospital), and APR-DRGs with ROM subclasses. PSI risk-adjustment incorporates age, gender, age-gender interactions, circumstances of 

admission, base DRGs (i.e., aggregated across comorbidity/complication levels), and AHRQdefined comorbidities.3

 

 The other quality measures that we used were developed by the Joint Commission on Accreditation of Healthcare Organizations, now known as The Joint Commission (TJC). TJC is the 

largest accrediting organization for acute care hospitals in the US; its accreditation review process includes a broad array of Core Measures that hospitals are required to collect and report. TJC 

maintains measures of risk-adjusted mortality for heart attack patients, neonatal mortality, and 

obstetric trauma. We purchased these measures, which are now publicly available on Medicare’s 

HospitalCompare website but were not at the time, from a leading vendor (Iameter). 

 All of the analyses described in this paper involve patient outcomes. This is an appropriate 

focus, as outcomes are of ultimate interest to patients, their families, and policy-makers. However, data limitations make it difficult to judge a hospital solely on its outcomes. This is partly 

because hospitals often have a relatively small number of patients of a given type, which makes 

outcomes a noisy measure of quality; and partly because there are many outcomes that cannot be 
5

measured at all with available data, such as post-hospitalization mortality, quality of life, and 

functional status. For this reason, hospital quality researchers also use “structural” quality measures, which focus on whether organizations have the human resources and technical infrastructure to provide high-quality care, and “process” measures, which focus on the specific diagnostic 

and therapeutic services that organizations provide. At trial, the first author discussed several of 

these measures, but we do not discuss them here as they mostly relied on proprietary data obtained from the merging parties. For this reason, the results reported below are confined to outcomes measures from AHRQ and TJC, which represented the core of our analysis. 

C. Empirical Methodology. 

 Our empirical methodology involves a series of difference-in-differences analyses of riskadjusted mortality and complication rates for a number of clinical conditions. We evaluate 

whether the changes in these rates at the merged hospitals were different than the average change 

at a set of control hospitals. Changes in the control group rates serve as a counterfactual proxy 

for what the changes would have been at the merging hospitals absent the merger. 

 The virtue of difference-in-differences analysis is that confounding factors that do not vary 

over time (i.e., hospital fixed effects) are “differenced out.” If the case mix of each hospital’s patients did not change from year to year, then any differences in patient severity of illness would 

also be differenced out, and there would be no problem using raw mortality and complication 

rates in the analysis. But patient mix can change over time, particularly following a merger that 

may alter referral practices in the community, leading to differential changes in hospitals’ case 

mixes. For this reason, we prefer to evaluate risk-adjusted mortality and complication rates, 

which are interpreted as the rate that a hospital would have had if its patients were of average se-
6

verity. Even with the risk-adjustment, we recognize that some confounding is likely to persist 

due to omitted clinical factors. 

 The disadvantage of using risk-adjusted rather than raw rates is that risk-adjusted rates depend to some extent on hospitals’ reporting practices. That is, when assigning diagnosis and procedure codes to patients, hospital coders rely upon physician documentation combined with their 

own professional experience and judgment.4 Changes in these coding practices over time may 

confound the difference-in-differences exercise. However, these coding practices seem less 

likely to change differentially over time (after a merger) than patient severity, which is why we 

emphasize risk-adjusted rates while also reporting raw rates. 

 Following Haas-Wilson & Garmon (this issue), we performed our analysis using four different control groups. The first group consisted of all non-federal general acute care hospitals in the 

Chicago Primary Metropolitan Statistical Area (PMSA). Using hospitals in the Chicago area as 

controls offers the advantage of adjusting for unmeasured cost or environmental factors that may 

affect changes in hospital quality at the local level (e.g., percentage of residents without health 

insurance, county/state support for uncompensated care, Medicaid payment rates, local quality 

improvement or public reporting programs, state regulations regarding physician or hospital licensure), but it may underestimate the impact of the merger because some control hospitals may 

be close enough to be affected the merger, and may respond by reducing quality. The other three 

control groups5

 consisted of subsets of the Chicago PMSA hospitals that arguably were particularly similar to the hospitals in the ENH system. Since the other three control groups generated 

very similar results, we only report results using the first group. 

 The merger occurred in early 2000. We omit 2000 as a transition year, as any mergerinduced changes would take some time to be implemented. We define the pre-merger period as 
7

1998-1999, and the post-merger period as 2001-2003. Our primary concern is with changes in 

clinical quality at HPH, as it is at HPH that Respondent’s Counsel claimed the merger improved 

quality. However, it is also possible that the merger could have had effects on clinical quality at 

Evanston Hospital and/or Glenbrook Hospital, as resources may have been diverted from Evanston or Glenbrook to Highland Park in such a way that Highland Park’s gain was Evanston’s or 

Glenbrook’s loss. This effect is most likely to be present for cardiac services because ENH 

started a new cardiac surgery and interventional cardiology program at HPH, and the resources 

for that program were drawn largely from Evanston and Glenbrook. To simplify the presentation, 

we report only analyses on HPH and Evanston Hospital, but these results are not materially affected by adding Glenbrook Hospital. 

 We define our difference-in-differences estimator β = Δpenh – Δpcontrol. Under the assumption 

of i.i.d. random sampling from a binomial distribution, the standard error of β is the denominator 

of the expression below. Therefore the following is (approximately) distributed standard normal 

under the null hypothesis that quality at ENH did not change relative to the control group: 

   

  11 1 1      

post pre post pre

enh enh control control

post post pre pre post post pre pre

enh enh enh enh control control control control

post pre post pre

enh enh control control

pp p p Z

p p p pp p p p

NNN N

  

    

, 

where p represents the probability of the adverse outcome (death or complication) and N represents the number of patients. 

 

III. Results:

 The analyses that we performed were chosen in response to specific quality claims made by 

Respondent’s Counsel. In this section, we discuss those claims, how we selected quality measures for testing them, and the results of the tests. As described above, we calculate pre- vs. post-
8

merger absolute (percentage point) differences at the treatment hospitals, and then calculate the 

difference between those differences and the absolute differences at the control hospitals. We report both risk-adjusted and raw rate results, which are similar in most cases. 

A. Cardiac Surgery and Interventional Cardiology.

 After the merger, ENH established cardiac surgery and interventional cardiology programs at 

HPH, so that coronary artery bypass graft (CABG) surgery and percutaneous coronary intervention (PCI) procedures began to be performed there. Since there was no such program at HPH before the merger, no pre- vs. post-merger comparison is possible. But it is possible that the establishment of the program at HPH had an adverse impact on Evanston Hospital, as resources may 

have been diverted to support the new program at HPH.6

 To test this hypothesis, we analyzed the 

AHRQ Inpatient Quality Indicators for CABG and PCI mortality at Evanston Hospital. Results 

are reported in Table 1. 

 For CABG, the control hospitals experienced a 0.80% absolute reduction in risk-adjusted 

mortality, and EH experienced a 0.56% increase, for a difference-in-differences of 1.37%. For 

PCI, the decrease at the control hospitals was 0.11% and the increase at EH was 0.54%, for a difference-in-differences of 0.65%. Both of these estimates are in the direction of a quality reduction, although neither is statistically significant. In both cases, the effects are smaller (but in the 

same direction) for the raw rates than for the risk-adjusted rates. 

B. Advantages of Teaching Hospitals.

 There is evidence that teaching hospitals on average outperform non-teaching hospitals in 

caring for patients with a variety of conditions, including acute myocardial infarction (AMI, or 
9

heart attack), inpatient congestive heart failure (CHF), pneumonia, and stroke.7

 Respondent’s 

Counsel claimed that the merger allowed HPH to realize the advantages of a teaching hospital.8

Based on publicly available data, the first author argued that neither EH nor HPH met the definition of a teaching hospital, as defined in most prior studies (i.e., membership in the Council of 

Teaching Hospitals or at least 0.10-0.27 residents per bed). Nevertheless, we tested the claim by 

analyzing the AHRQ Inpatient Quality Indicators for those four conditions as well as the corresponding TJC indicator for AMI. Results are reported in Table 2. 

 According to the IQI measure, risk-adjusted AMI mortality at the control hospitals decreased 

by 1.88%, and increased at HPH by 0.34%, for a difference-in-differences of 2.22%. This finding 

suggests a decrease in quality at HPH, but is not statistically significant. However, for this measure there is an unusually large divergence between the risk-adjusted result and the raw difference-in-differences of -3.21% (also not statistically significant). Both the risk-adjusted and raw 

rates show a large and statistically significant decrease in quality at EH, difference-in-differences 

of 4.46% and 3.33%, respectively. 

 According to the TJC measure, risk-adjusted AMI mortality decreased by 1.52% at the control hospitals (compared to 1.88% according to the IQI measure), and decreased by 5.01% at 

HPH (compared to an increase of 0.34% according to the IQI measure), for a non-significant difference-in-differences of -3.48%. Similarly, the difference-in-differences at Evanston is -0.59%, 

as compared to an increase of 4.46% for the IQI measure. These differences may be partially explained by differences in risk-adjustment, and also by the exclusion of patients who were transferred in from other hospitals (as well as out-transfers) from the TJC measure; by contrast, the 

AHRQ measure only excludes out-transfers, because it is not known whether they survived the 

acute hospital stay. However, the discrepancy is large enough to cause us to suspect that there 
10

may have been a coding error in the commercial software that we used to group the APR-DRGs, 

in the AHRQ IQI software, or in the software used by Iameter to calculate the TJC measures. 

 CHF mortality improved non-significantly at both HPH (risk-adjusted difference-indifferences of -1.60%) and EH (-0.19%) after the merger. Risk-adjusted and raw pneumonia 

mortality and stroke mortality deteriorated non-significantly at HPH (risk-adjusted difference-indifferences of 0.30% for pneumonia and 2.42% for stroke). There was a large and statistically 

significant deterioration in risk adjusted pneumonia (3.14%) and stroke (4.94%) mortality at 

Evanston Hospital. 

 

C. Nursing-Sensitive Indicators.

 Another claim made by Respondent’s Counsel was that the merger improved nursing care at 

HPH.9

 We evaluate this claim by examining Patient Safety Indicators that are known to be sensitive to the quality of nursing care, and that reflect concepts endorsed by the National Quality Forum (NQF) as voluntary consensus standards for nursing-sensitive care.10 These include Decubitis Ulcers (pressure sores), Failure to Rescue (death among surgical patients with potentially 

serious but treatable in-hospital complications), Selected Infections Due to Medical Care, and 

Post-Operative Hip Fracture. Results are reported in Table 3. 

 HPH experienced slight post-merger deterioration for Post-Operative Hip Fracture (difference-in-differences in risk-adjusted rates of 0.10%), and improvements in the other three indicators (a statistically significant -0.76% for Decubitis Ulcer and non-significant -2.74% for Failure 

to Rescue and -0.01% for Selected Infections Due to Medical Care). However, for Decubitis Ulcer and Failure to Rescue, the raw differences-in-differences are much smaller in magnitude and 
11

in the case of Failure to Rescue it is of the opposite sign. The results for EH are mixed, with several nursing-sensitive PSIs showing statistically significant improvements. 

D. Obstetrics. 

 Another quality claim made by the Respondent’s Counsel was that the merger had improved 

obstetric care at HPH.11 We evaluate this claim by examining the three PSIs that relate to obstetrics: Birth Trauma, Obstetric Trauma (Vaginal with Instrument), and Obstetric Trauma (Vaginal 

without Instrument). We also examine all three of the TJC obstetrics measures: Obstetric 

Trauma,12 Neonatal Mortality, and Vaginal Birth after Caesarian (VBAC). Note that VBAC is 

not considered to be a quality measure in the traditional sense, but it reflects whether a hospital is 

capable of providing patient-centered care to women who have had prior cesarean deliveries. Results are reported in Table 4. 

 For the risk-adjusted obstetric PSI measures, results at HPH were unfavorable, with statistically significant deteriorations of 0.33% for Birth Trauma and 1.28% for Obstetric Trauma 

(Vaginal without Instrument), and a non-significant deterioration of 3.76% for Obstetric Trauma 

(Vaginal with Instrument). EH deteriorated on all three obstetric indicators, and the deterioration 

was statistically significant for Birth Trauma and Obstetric Trauma, (Vaginal with Instrument). 

 For the risk-adjusted TJC measures, HPH had a non-significant difference-in-differences of 

0.12% for Neonatal Mortality, and a statistically significant -1.14% difference-in-differences for 

Obstetric Trauma. EH had statistically significant difference-in-differences of 0.32% for Neonatal Mortality and -1.08% for Obstetric Trauma. 
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IV. Interpretation of Results:

 As can be seen in the tables, the standard errors on the Highland Park Hospital differencesin-differences estimates are generally quite large, so in many instances where the point estimate 

indicates a relative deterioration, substantial improvement cannot be ruled out. However, although we did not attempt a formal statistical test of whether quality improved “overall” across 

all the quality measures, our results taken together suggest that an overall improvement at HPH 

is unlikely, and that a large overall improvement is very unlikely.13 This conclusion is reinforced 

by the fact that, as discussed below, the relevant literature does not support a prior belief that 

hospital mergers are likely to improve quality. 

 Our results must be interpreted with caution. The statistical significance of some findings 

may be overstated because we did not account for heterogeneity among control hospitals. We 

also cannot exclude the possibility of endogeneity; a decision to merge may reflect hospital managers’ inside knowledge of emerging trends in quality, such that the experience of control hospitals may not represent what would have happened at the merging hospitals absent the merger. 

 

V. Conceptual Framework:

A. Priors. 

 Since the ENH/HPH case was retrospective, our primary evidence came from our differencein-differences analyses, which we used to directly measure the effect of the merger on clinical 

quality. However, the strength of the direct evidence required to reach a conclusion regarding the 

effect of a particular merger depends on one’s prior beliefs regarding hospital mergers in general. 

These priors matter less when the direct evidence is strong, but absent completely persuasive direct evidence they still affect the posterior estimate of the merger effect. For this reason, we 
13

briefly review the relevant research that informs our prior belief that on average hospital mergers 

do not substantially improve clinical quality. 

 On balance, the empirical evidence does not support a strong prior that hospital mergers 

improve quality. Here we discuss a few papers from the relevant literature; see Vogt & Town 

(2006) for a thorough review. Mutter, Romano, and Wong (this issue), examined the impact of 

42 consolidations involving 136 hospitals in 16 states in 1999-2000 on 25 measures of quality 

using difference-in-differences models. Hospitals were categorized as acquiring institutions, 

target institutions, or participants in a “merger of equals.” Acquiring hospitals experienced 

significantly improved quality in terms of abdominal aortic aneurysm mortality, iatrogenic 

pneumothorax, and postoperative hemorrhage or hematoma, but the quality impacts for target 

hospitals and “mergers of equals” were mixed. 

Ho and Hamilton (2000) used difference-in-differences methods to test the impact of hospital 

consolidations in California between 1992 and 1995. Hospital consolidations had no impact on 

inpatient AMI or stroke mortality, although parameter estimates were imprecise. All three forms 

of consolidation (i.e., 21 mergers of independent hospitals, 54 independent hospitals acquired by 

a system, 65 acquisitions of one system by another) were associated with increased 90-day readmission rates after AMI. Only the purchase of a system hospital by another system led to earlier discharge of healthy newborns. Cuellar and Gertler (2005) used 1995-2000 patient discharge 

data from Arizona, Florida, Massachusetts, and Wisconsin to estimate pre-post differences for 

facilities that reported joining a system during the study period. They found no significant 

changes in any of three composite measures of quality (i.e., mortality for 13 conditions and procedures, utilization of 3 potentially overused procedures, and 20 potential complications of inpatient care) among hospitals that joined systems, except that consolidating hospitals reduced the 
14

rate of potentially overused procedures by 1.2% among managed care patients. Finally, Gowrisankaran & Town (2003) found that competition improved quality for HMO patients, and reduced quality for Medicare patients, with the net effect being close to zero. 

 In addition to these empirical results, we can also take some guidance from economic theory. 

As discussed in Gaynor (2006), the effect of reduced competition on quality (holding the cost of 

producing quality constant) is theoretically ambiguous when profit-maximizing firms choose 

both price and quality.14 But when prices are fixed rather than chosen by the firm, optimal 

quality unambiguously decreases following a competition-reducing merger; absent the ability to 

raise price, the only way for the firm to benefit from reduced competition is by cutting quality 

and thereby reducing costs. After the merger, the gain from a small reduction in quality (lower 

costs on retained sales) is the same as it was before, but the loss (reduced sales) is smaller 

because the residual demand has become less elastic. Therefore, the pre-merger level of quality 

can no longer be optimal and the post-merger level of quality must be lower. This result argues 

strongly against a U.S. hospital merger increasing optimal quality absent a change in cost, 

because prices are fixed under fee-for-service Medicare and Medicaid, and because these two 

programs (including both fee-for-service and negotiated payments) together pay for about 55% 

of hospital services in the U.S.15

 Since hospital mergers are unlikely to improve quality absent a cost change, the proper focus 

of case-specific quality analysis is the effect of the merger on the cost of producing quality. This 

means that the quality analysis can be separated from the question of how the merger affects 

demand, and hence from the analysis of the price effects (which is convenient, as combining the 

analyses would be complicated). As long as the firm’s optimal quality holding the cost of 

producing quality constant is non-increasing following the merger, a finding that the merger will 
15

not reduce those costs is sufficient (but not necessary) to show that quality will not increase as 

the result of the merger. 

B. Sources of Quality Improvement. 

 Though the literature does not provide support for strong priors that a given merger is likely 

to improve hospital quality, it is still possible that it will do so by reducing the cost of producing 

quality. And there is likely a minority of mergers that can improve quality very substantially. 

This means that case-specific quality analysis is properly a major focus of hospital merger 

investigations. And as discussed above, there are well-developed methods for measuring quality, 

which makes such an analysis feasible. 

 Even in a retrospective case where direct evidence is available, the posterior estimate of the 

merger effect is influenced by evidence of the sort that would be used in a prospective merger 

case (including the question of “merger specificity”16). So there is value in identifying potential 

sources of reduced cost of providing quality and evaluating them directly. We introduce three 

such sources here, and defer further discussion to Section VI below. These are: (i) pre-merger 

clinical superiority of one hospital over another; (ii) economies of scale; and (iii) differences in 

resources available for investment. 

 

 i. Clinical Superiority. 

One way that a merger can reduce the cost of producing quality is if one or more of the merging hospitals is operating far below its cost/quality frontier (see Pauly, 2004), and the other(s) 

can move it closer. That is, a merger can improve quality at one hospital if the others have superior practices or institutions that can be readily imported. If the pre-merger management of a 
16

hospital is sufficiently ineffective, the acquiring system can achieve large gains by substituting 

better management. See Section VI below for a discussion of how claims of clinical superiority 

can be evaluated. If a merger is found likely to improve clinical quality by means of exporting 

superior practices, this benefit would likely require geographic proximity and therefore be 

merger-specific, because the process of improving the inferior hospital likely requires the physical presence of personnel from the superior one. 

 ii. Economies of Scale. 

 Another way that a merger can improve clinical quality is through economies of scale in the 

provision of quality (which is distinct from economies of scale in producing output). There are 

some quality-improving pieces of equipment with high fixed costs and low marginal costs that 

are not worthwhile for an independent hospital or a small hospital system, but are worthwhile for 

a sufficiently large system.17 A merger may put the merged entity above this threshold, resulting 

in additional investment in quality, or the larger of the merging entities may (with little incremental cost) be able to extend to the smaller entity the benefits of investments that have already 

been made. Such scale economies can be a source of improved clinical quality, but may yield 

only marginal benefits because investments with large benefits are likely to be made even by 

smaller hospitals (given that standalone hospitals face the same regulations and market expectations regarding quality as multi-hospital systems). Pure system size can have a large effect on 

quality only if the economies of scale are correspondingly large and if the interventions that provide large economies of scale are highly clinically important. There is limited evidence of such 

substantial economies of scale for electronic health record systems, which are costly but can 
17

yield large quality benefits due to increased data portability across sites of care and decreased incidence of medication-related errors.18

 Another potential source of scale economies is surgical procedures that exhibit a volumeoutcome relationship in which more repetition of the procedure generates better clinical outcomes for both individual surgeons and hospitals. This effect appears to be strong for high-risk, 

technically complex procedures, such as resection of esophageal cancer, pancreatic cancer, and 

aortic aneurysms, and inconsistent for lower-risk, more straightforward procedures such as “isolated” coronary bypass surgery and percutaneous coronary interventions.19 “Learning curves” 

have been demonstrated for technology-dependent laparoscopic procedures, whereby operator 

outcomes improve with accumulated experience over time.20 By consolidating a procedure at 

fewer hospitals or by sending experienced personnel from one hospital to another, a system can 

theoretically extend the benefits of scale enjoyed by a high-volume acquiring hospital to the acquired hospital.21

 Other interventions that have been shown to have substantial effects on quality can be readily 

implemented by hospitals of any size and are thus unlikely to be related to scale.22 Also, clinical 

benefits of increased scale are likely to be merger-specific only if they involve consolidation of 

services or sharing of personnel, as these mechanisms require geographic proximity. Some interventions, such as electronic health record systems, do not require such proximity, as the same 

benefits could be achieved through a merger with a geographically distant partner, and thus cannot be considered merger-specific.23
18

 iii. Financial Resources. 

 Another possible means by which a merger can improve clinical quality is via qualityimproving investments that one party to the transaction (usually the acquired hospital) was previously unable to make due to lack of financial resources. The standard theory of corporate finance suggests that firms will make those investments, and only those investments, for which the 

present value of the net benefits, discounted at the appropriate rate, exceeds the investment cost, 

regardless of the ownership of the firm. This conclusion might fail to hold if for some reason the 

acquiring system has a lower cost of capital than the acquired hospital. In that case, the acquired 

hospital would have made those investments that were worthwhile given its original cost of capital, and the acquiring system would make additional investments that would not have been 

worthwhile at that cost of capital, but are worthwhile given its new, lower cost. But these incremental investments are expected to be the marginal (i.e., least valuable) investments. 

 Any clinical quality benefit resulting from increased financial resources will not be mergerspecific if there is an alternative acquirer that does not represent a competitive concern, is willing 

to pay a price that the acquired hospital would accept if the merger under investigation were 

blocked, and is willing to make similar investments. These conditions will be met if the investments are worthwhile on their own merit, but not if the willingness to make the investments, or 

even the willingness to undertake the merger, is dependent on the profits resulting from an anticompetitive price increase. The marginal value of incremental investments, combined with the 

likelihood that their benefits will not be merger-specific, suggest that enhanced financial resources are an unlikely source of significant merger benefits. 
19

C. Health Effects of Higher Prices for Health Insurance. 

 Even if our analysis had found a merger-specific quality increase at the merging hospitals, 

the indirect effect of a price-increasing merger on health must still be considered. Higher hospital 

prices cause health insurance premiums to increase, which causes some people to lose or forego 

insurance. Town et al. (2006) estimated that in 2003 there were 695,000 fewer insured people in 

the U.S. than there would have been had there been no hospital merger activity in the 1990s. 

There is a substantial literature showing that lack of insurance harms health, and may be responsible for 18,000-22,000 premature deaths each year in the US,24 although this estimate has recently been challenged by Kronick (2010). This harm would not be realized at the merging hospitals, as the people who lose their insurance would not necessarily have used the merging hospitals (or any hospital). The magnitude of this effect is difficult to quantify, as it would require estimating the insurance premium increase resulting from the hospital price increase, the number of 

people who would lose their insurance as a result of that premium increase, and the health harm 

accruing to the people who lost their insurance. But the effect is present, and it means that any 

measured health benefit at the merging hospitals represents an upper bound on the total beneficial effect of the merger on health. Because of the absence of demonstrable quality improvement 

in the ENH/HPH case, it was not necessary for us to address this question. 

VI. Applicability to Prospective Merger Analysis:

This paper has focused on retrospective evaluation of the Evanston Northwestern Healthcare/Highland Park Hospital case. But the great majority of merger cases are prospective in nature, where the objective is to predict the effects of the merger, rather than to measure them after 

the fact. A recent example is the proposed acquisition of Prince William Hospital in Manassas, 
20

Virginia by the Inova Health System. It is therefore worth discussing what kind of analysis of 

clinical quality is possible in such cases. 

In section V above, we argue that hospital mergers are unlikely to improve quality absent a 

reduction in the cost of producing quality. Quality analysis in a prospective case should therefore 

focus on ways in which the merger is likely to reduce those costs, whether by means of a superior hospital exporting its knowledge and practices to an inferior one, through economies of 

scale, or (less likely) as a result of differences in financial resources. 

Quality claims regarding clinical superiority and economies of scale can sometimes be investigated directly. The likelihood of an improvement as a result of clinical superiority is greater if 

there are specific quality-improving measures that have been adopted by the acquiring system 

and are likely to be exported following the merger. Similarly, improvements due to economies of 

scale are unlikely if a hospital targeted for acquisition does not actually perform the most volume-sensitive procedures, or if it has already reached a plateau on the learning curve for technology-dependent procedures. 

A less direct but still valuable way to evaluate the likely quality effects of a merger is to 

compare their pre-merger quality levels. Large and consistent differences in these levels constitute evidence that the target hospital is under-performing and/or that the acquiring hospital is enjoying significant economies of scale relative to the acquiring system, providing potential opportunities for post-acquisition improvement.25 These inter-hospital differences can be substantial. 

For example, Keroack et al. (2007) showed substantial variation in quality among academic 

health systems of similar size, which was linked to variation in management and organizational 

culture, and Jha et al. (2003) showed how the Veterans Health Administration used superior 

management to improve and reduce intra-system variation in clinical quality. Comparison of pre-
21

merger quality trends may also be of some value, but can be misleading because trends may not 

persist, particularly if one hospital started with a much lower level of clinical performance. 

Large pre-merger differences in quality levels are neither necessary nor sufficient for a 

merger to result in a quality increase. It is possible that a superior acquiring hospital will fail to 

improve an inferior one, and it is also possible that one hospital can improve another even if it is 

not superior. Nevertheless, pre-merger quality differences suggest that one hospital has something of value to impart to the other, absent a specific indication that it does not, for example if 

an acquiring hospital system had been superior to, but nevertheless failed to improve, a previously acquired hospital. 

It is worth noting that in a retrospective difference-in-differences analysis, any variation 

across hospitals in coding practices or in patient severity that is not fully captured by riskadjustment will not confound the analysis as long as these differences do not vary over time. In a 

prospective analysis, such differences in coding practices or patient severity will confound the 

analysis. For example, a hospital with particularly aggressive coding practices will have patients 

that appear sicker, and so its performance will appear better even when it really is not. So an important part of any prospective analysis is to gather as much evidence as possible regarding between-hospital variation in baseline severity of illness and coding practices. 

VII. Conclusions:

 There is considerable evidence that hospital mergers can cause substantial price increases.26

These price increases can in principle be counterbalanced by pro-competitive effects, the most 

important of which is improved clinical quality. In this paper, we describe our analysis of the 

clinical quality effects of the Evanston Northwestern Healthcare/Highland Park Hospital merger. 
22

Specifically, we use a straightforward difference-in-differences methodology to determine 

whether the merger resulted in improved performance on a variety of clinical outcomes measures 

(risk-adjusted inpatient death and complications). We find little evidence that the merger caused 

quality to improve at Highland Park. 

 On the basis of these findings, the Administrative Law Judge found “no evidence of improvement in overall quality of care relative to other hospitals.”27 We believe that our basic 

framework for analyzing the clinical quality effect of mergers will be applicable to future cases, 

including prospective ones. There are plausible mechanisms through which a merger can cause a 

substantial quality improvement, which means that case-specific quality analysis is important. 

While we take no position on how price and quality should be traded off against each other when 

they are in conflict, our methodological approach will characterize the magnitude of any quality 

effect, which can then be weighed against the predicted (or observed) price effect in the manner 

deemed appropriate by the decision maker. 

 

1

 Dr. Haas-Wilson estimated that ENH’s inpatient price increased 11.1 to 17.9 percentage points more than the price 

at various control groups after the merger. See In re Evanston Northwestern Healthcare Corp., Dkt. No. 9315, slip 

op. at 35 (Aug. 6, 2007) (opinion of the Commission) available at 

http://www.ftc.gov/os/adjpro/d9315/070806opinion.pdf. Respondent’s expert Dr. Jonathan Baker estimated that 

ENH’s inpatient price increased 9 to 10 percentage points more than at his control group after the merger. Id at 38. 2 Pretrial Brief of Respondent at 31, In re Evanston Northwestern Healthcare Corp., Dkt. No. 9315 (Oct. 20, 2005) 

(initial decision), available at http://www.ftc.gov/os/adjpro/d9315/050127resppretrialbrief.pdf; Post-Trial Brief of 

Respondent at 74, In re Evanston Northwestern Healthcare Corp., Dkt. No. 9315 (Oct. 20, 2005) (initial decision) 

available at http://www.ftc.gov/os/adjpro/d9315/050527respposttrialbrief.pdf; and Respondents’ Corrected Appeal 

Brief at 68, In re Evanston Northwestern Healthcare Corp., Dkt. No. 9315 (Aug. 6, 2007) (opinion of the Commission), available at http://www.ftc.gov/os/adjpro/d9315/060112enhappealbriefcorrected.pdf. 3

 Additional information regarding risk-adjustment is available at the AHRQ Quality Indicators website, 

http://qualityindicators.ahrq.gov . 4

 See Lorence et al. (2003) and Santos et al. (2008). 5

 The other three control groups were all non-federal general acute care hospitals in the Chicago PMSA that: (i) were 

not involved in a merger between 1996 and 2002; (ii) had residency programs at the time of the merger; and (iii) had 

more than 0.25 residents and interns per staffed bed between 1998 and 2002. 

6

 It is also possible that the existence of the programs at HPH improved access to those services, and thereby improved cardiac health in the broader community. We investigated this question and found no such evidence, and so 

we do not report those results here. 

7

 See Ayanian and Weissman (2002) for an excellent summary of this literature, including description of various 

definitions of teaching hospitals that have been used in 20 published studies. 8

 Post-Trial Brief of Respondent, supra at 93; and Respondent’s Corrected Appeal Brief, supra at 4. 
23

 9

 Post-Trial Brief of Respondent, supra at 83; and Respondent’s Corrected Appeal Brief, supra at 12. 10 See http://www.qualityforum.org/Projects/n-r/NursingSensitive_Care_Initial_Measures/Nursing_Sensitive_Care__Initial_Measures.aspx 11 Post-Trial Brief of Respondent, supra at 75; and Respondent’s Corrected Appeal Brief, supra at 12. 12 With respect to obstetric trauma, the TJC indicator includes both types of vaginal deliveries (with and without instrumentation) whereas the AHRQ indicator stratifies them to create two separate indicators. In addition, in the version of the AHRQ Quality Indicators software that we used in this analysis (Version 2.1), the numerator definition 

was somewhat different than that in the TJC indicator, capturing high vaginal and cervical trauma but excluding 

third degree perineal lacerations. 

13 Several results suggest deterioration at Evanston Hospital as well, with the notable exception of some nursingsensitive indicators. The larger sample sizes at Evanston Hospital mean that the tests have more power, and so more 

results achieve statistical significance. However, it is not clear that this deterioration was a result of the merger with 

HPH. It is possible that the merger harmed EH through diversion of resources or lack of focus, but it is also possible 

that the deterioration had some other cause. 

14 Many hospitals, including ENH, are not-for-profit (NFP). There is limited evidence that NFP hospitals tend to 

have somewhat higher clinical quality (see Devereaux et al., 2002; Eggleston et al., 2008; Picone et al., 2002; Shen, 

2002; and Farsi, 2004), but we are aware of no direct evidence on differences between NFP and FP hospitals in their 

quality response to mergers. It is possible that NFP hospitals reinvest a larger fraction of the gains from competitionreducing mergers, although we are aware of no direct evidence on this question either. The quality effect of any such 

additional spending will depend on the pre-merger condition of the acquired hospital and on the specific investments 

chosen by managers of the merged entity. If the merging hospitals had adequate resources already, then the additional expenditures will likely only generate small incremental benefits (see Section VBiii). 15 According to the Healthcare Cost and Utilization Project website, in 2008 Medicare and Medicaid together accounted for about 56% of hospital discharges (http://hcupnet.ahrq.gov). According to the American Hospital Association, in 2008 government payments represented about 55% of the U.S. total. 

(http://www.aha.org/aha/trendwatch/chartbook/2010/chart4-5.pdf) 

16 For an improvement in clinical quality to justify an otherwise anti-competitive merger, the benefit due to 

improved quality must outweigh (on balance, according to the decision-maker) the harm due to higher prices. But to 

be cognizable, a quality improvement must also be “merger specific,” meaning that it would only be realized 

through that particular merger, and not by any other means, such as an alternative merger that does not pose 

competitive concerns. Since quality analysis is only relevant for mergers that are expected to cause a price increase 

(otherwise the quality issue will never be reached), and since price-increasing mergers usually involve hospitals that 

are in geographic proximity, past or future quality improvements are unlikely to qualify as merger-specific unless 

they require a geographically proximate merger partner. Since there was little meaningful evidence of quality 

improvement in the ENH/HPH case, merger specificity was not a major issue. 17 Note that the relevant issue is the size of the system, not the size of the individual hospital. Mergers typically do 

not affect the size of the individual hospitals, so hospital-level economies of scale are not relevant. 18 The role of multihospital system membership in electronic medical record adoption was explored by Li et al. 

(2008). The quality benefit of an electronic medical record with computerized physician order entry and decision 

support features was first demonstrated by Bates et al. (1998). 19 See Halm et al. (2002), Killeen et al. (2005), and the report of the ECRI Institute (2010). 20 See Moore & Bennett (1995) and Watson et al. (1996). 21 It is also possible that the acquiring system may find it impossible to transfer its scale economies to an acquired 

hospital. For example, if local factors preclude shutting down a low-volume but technically complex service, or if 

transferring personnel across facilities is not feasible, then quality may not improve post-merger at the acquired hospital. Therefore, it cannot be assumed that any merger will lead to economies of scale related to the quality of technically complex procedures. 

22 See Provonost et al. (2006), Provonost et al. (2010), Haynes et al. (2009), and Weiser et al. (2010). 23 It is conceivable that an electronic health record system involving geographically proximate hospitals may confer 

greater quality benefits than an equivalent system involving non-proximate hospitals, by allowing patients to obtain 

coordinated care from multiple local facilities, but this hypothesis has never been empirically tested. 

24 The estimate of 18,000 comes from the Institute of Medicine (2002); the higher estimate comes from Dorn (2008). 25 In contrast, there is no direct link between pre-merger price level differences and the price effects of mergers, so a 

comparison of pre-merger price levels would not be informative. 
24

 26 See Vogt & Town (2006), Vita & Sacher (2001), Haas-Wilson & Garmon (this issue), Thompson (this issue), and 

Tenn (this issue). 

27 See In re Evanston Northwestern Healthcare Corp., Dkt. No. 9315, slip op. at 173 (Oct. 20, 2005) (initial decision), available at http://www.ftc.gov/os/adjpro/d9315/051020initialdecision.pdf. 
25

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28

4.15% 4.00% N/A 1.55% N/A 2.67% 

(0.25%) (0.22%) N/A (0.89%) N/A (0.62%) (

3.90% 3.10% N/A 1.83% N/A 4.04% 

(0.24%) (0.19%) N/A (0.96%) N/A (0.76%) (

1.79% 1.54% N/A 2.15% N/A 0.76% 

(0.13%) (0.10%) N/A (0.59%) N/A (0.31%) (

1.34% 1.23% N/A 1.63% N/A 0.57% 

(0.12%) (0.09%) N/A (0.52%) N/A (0.27%) (

Notes:a. The pre-merger period was 1998-1999, and the post-merger period was 2001-2003. The merger took place in early 2000, so 2000 is excluded as a transition year.b. See text for a description of the control group.c. Standard errors in parentheses.d. One, two and three stars represent statistical significance at the 10%, 5%, and 1% levels, respectively.e. "AHRQ" denotes a quality measure employed by the Agency for Healthcare Research and Quality.f. Highland Park Hospital did not perform coronary artery bypass grafts or percutaneous coronary interventions prior to the merger.

9.44% 7.42% 12.99% 7.76% -3.21% 6.96% 

(0.36%) (0.26%) (2.53%) (1.05%) (2.77%) (0.87%) (

10.09% 8.21% 9.31% 9.65% 2.22% 5.68% 

(0.37%) (0.27%) (2.18%) (1.16%) (2.52%) (0.79%) (

9.86% 7.57% 12.79% 8.77% -1.73% 6.85% 

(0.40%) (0.28%) (2.58%) (1.21%) (2.89%) (0.90%) (

10.54% 9.02% 16.52% 11.51% -3.48% 15.26% 1

(0.41%) (0.30%) (2.87%) (1.37%) (3.22%) (1.28%) (

4.13% 3.79% 5.03% 2.56% -2.12% 4.62% 

(0.18%) (0.13%) (1.12%) (0.70%) (1.34%) (0.66%) (

4.35% 3.33% 5.02% 2.39% -1.60% 4.65% 

(0.19%) (0.13%) (1.12%) (0.68%) (1.33%) (0.66%) (

9.24% 8.18% 9.33% 9.19% 0.93% 7.58% 

(0.25%) (0.20%) (1.21%) (0.97%) (1.58%) (0.73%) (

8.83% 6.72% 10.16% 8.34% 0.30% 8.67% 

(0.24%) (0.19%) (1.26%) (0.93%) (1.59%) (0.78%) (

10.66% 10.83% 10.63% 12.14% 1.34% 7.00% 1

(0.39%) (0.31%) (2.14%) (1.95%) (2.94%) (1.09%) (

10.53% 9.42% 10.33% 11.65% 2.42% 7.14% 1

(0.39%) (0.29%) (2.12%) (1.92%) (2.90%) (1.11%) (

Notes:a. The pre-merger period was 1998-1999, and the post-merger period was 2001-2003. The merger took place in early 2000, so 2000 is excluded as a transition year.b. See text for a description of the control group.c. Standard errors in parentheses.d. One, two and three stars represent statistical significance at the 10%, 5%, and 1% levels, respectively.e. "AHRQ" denotes a quality measure employed by the Agency for Healthcare Research and Quality. "TJC" denotes a measure employed by The Joint Commission.

Control Hospitals Highland Park Hospital 

Pre-merger

rate (%)

Post-merger

rate (%)

Sample Size 

(pre/post)

Pre-merger

rate (%)

Post-merger

rate (%)

Difference-inDifferences 

Coronary Artery Bypass Graft (raw) AHRQ 6,379 0 

Sample Size 

(pre/post)

Pre-merger

rate (%)

Pos

r

Table 1: Cardiac Services

Coronary Artery Bypass Graft (risk adjusted) AHRQ 7,945 194 Percutaneous Coronary Intervention (raw) AHRQ 9,715 0 Percutaneous Coronary Intervention (risk adjusted) AHRQ 16,840 605 

Control Hospitals Highland Park Hospital 

Pre-merger

rate (%)

Post-merger

rate (%)

Sample Size 

(pre/post)

Pre-merger

rate (%)

Post-merger

rate (%)

Difference-inDifferences 

Acute Myocardial Infarction (raw) AHRQ 6,726 177 

Sample Size 

(pre/post)

Pre-merger

rate (%)

Pos

r

Table 2: Advantages of Teachng Hospitals

Acute Myocardial Infarction (risk-adjusted) AHRQ 10,415 644 Acute Myocardial Infarction (raw) TJC 5,644 168 Acute Myocardial Infarction (risk-adjusted) TJC 8,916 545 Congestive Heart failure mortality (raw) AHRQ 11,828 378 Congestive Heart failure mortality (risk-adjusted) AHRQ 20,043 508 Pneumonia mortality (raw) AHRQ 13,827 579 Pneumonia mortality (risk-adjusted) AHRQ 17,997 881 Stroke mortality (raw) AHRQ 6,284 207 Stroke mortality (risk-adjusted) AHRQ 10,033 280 
29

2.02% 2.31% 1.63% 1.59% -0.33% 1.89% 1.59% 

(0.04%) (0.03%) (0.21%) (0.17%) (0.27%) (0.13%) (0.11%) 

2.22% 2.39% 1.89% 1.31% -0.76%*** 1.83% 1.45% 

(0.04%) (0.03%) (0.23%) (0.15%) (0.28%) (0.13%) (0.10%) 

12.21% 11.03% 9.82% 9.23% 0.59% 11.40% 7.53% 

(0.27%) (0.19%) (1.99%) (1.28%) (2.39%) (1.22%) (0.72%) 

11.41% 9.81% 9.79% 5.45% -2.74% 8.25% 5.19% 

(0.26%) (0.18%) (1.99%) (1.01%) (2.25%) (1.05%) (0.60%) 

0.26% 0.29% 0.08% 0.11% 0.00% 0.12% 0.10% 

(0.01%) (0.01%) (0.03%) (0.02%) (0.04%) (0.02%) (0.01%) 

0.25% 0.21% 0.13% 0.08% -0.01% 0.15% 0.06% 

(0.01%) (0.01%) (0.03%) (0.02%) (0.04%) (0.02%) (0.01%) 

0.11% 0.07% 0.11% 0.19% 0.12% 0.03% 0.04% 

(0.01%) (0.01%) (0.08%) (0.07%) (0.11%) (0.02%) (0.02%) 

0.12% 0.01% 0.10% 0.10% 0.10% 0.03% 0.02% 

(0.01%) (0.00%) (0.07%) (0.05%) (0.09%) (0.02%) (0.01%) 

Notes:a. The pre-merger period was 1998-1999, and the post-merger period was 2001-2003. The merger took place in early 2000, so 2000 is excluded as a transition year.b. See text for a description of the control group.c. Standard errors in parentheses.d. One, two and three stars represent statistical significance at the 10%, 5%, and 1% levels, respectively.e. "AHRQ" denotes a quality measure employed by the Agency for Healthcare Research and Quality.

0.55% 0.40% 0.22% 0.40% 0.33%** 0.26% 0.85% 

(0.03%) (0.02%) (0.08%) (0.10%) (0.13%) (0.06%) (0.09%) 

0.54% 0.40% 0.22% 0.40% 0.33%** 0.26% 0.85% 

(0.03%) (0.02%) (0.08%) (0.10%) (0.13%) (0.06%) (0.09%) 

23.05% 24.01% 27.29% 31.99% 3.74% 27.52% 35.05% 

(0.58%) (0.53%) (2.11%) (2.42%) (3.30%) (1.83%) (1.98%) 

22.80% 23.76% 26.75% 31.48% 3.76% 27.12% 34.72% 

(0.57%) (0.53%) (2.09%) (2.41%) (3.29%) (1.82%) (1.97%) 

9.00% 9.92% 7.26% 9.55% 1.38%* 10.00% 11.43% 

(0.13%) (0.12%) (0.51%) (0.50%) (0.74%) (0.40%) (0.37%) 

9.17% 10.16% 7.75% 10.02% 1.28%* 10.51% 11.98% 

(0.14%) (0.12%) (0.53%) (0.51%) (0.76%) (0.41%) (0.37%) 

5.49% 6.08% 7.55% 5.83% -2.32%*** 6.21% 5.10% 

(0.11%) (0.09%) (0.49%) (0.38%) (0.63%) (0.31%) (0.24%) 

4.64% 5.17% 5.97% 5.35% -1.14%** 4.90% 4.34% 

(0.11%) (0.08%) (0.44%) (0.36%) (0.58%) (0.27%) (0.23%) 

37.71% 27.16% 42.01% 19.19% -12.27%*** 43.77% 29.86% 

(0.61%) (0.42%) (2.43%) (1.72%) (3.07%) (1.70%) (1.29%) 

21.05% 21.29% 22.47% 22.95% 0.24% 21.41% 21.86% 

(0.51%) (0.38%) (2.06%) (1.84%) (2.83%) (1.41%) (1.17%) 

1.08% 0.80% 0.00% 0.12% 0.39%*** 0.47% 0.51% 

(0.04%) (0.03%) (0.00%) (0.05%) (0.07%) (0.07%) (0.07%) 

1.09% 0.85% 0.28% 0.16% 0.12% 0.74% 0.82% 

(0.04%) (0.03%) (0.09%) (0.06%) (0.12%) (0.09%) (0.08%) 

Notes:a. The pre-merger period was 1998-1999, and the post-merger period was 2001-2003. The merger took place in early 2000, so 2000 is excluded as a transition year.b. See text for a description of the control group.c. Standard errors in parentheses.d. One, two and three stars represent statistical significance at the 10%, 5%, and 1% levels, respectively.e. "AHRQ" denotes a quality measure employed by the Agency for Healthcare Research and Quality. "TJC" denotes a measure employed by The Joint Commission.

Control Hospitals Highland Park Hospital Evans

Pre-merger

rate (%)

Post-merger

rate (%)

Sample Size 

(pre/post)

Pre-merger

rate (%)

Post-merger

rate (%)

Difference-inDifferences 

Decubitis Ulcers (raw) AHRQ 130,755 3,622 

Sample Size 

(pre/post)

Pre-merger

rate (%)

Post-merg

rate (%)

Table 3: Nursing-Sensitive Indicators

Decubitis Ulcers (risk adjusted) AHRQ 200,313 5,677 Failure to Rescue (raw) AHRQ 14,533 224 Failure to Rescue (risk adjusted) AHRQ 27,705 509 Selected Infections Due to Medical Care (raw) AHRQ 356,759 12,523 Selected Infections Due to Medical Care (risk adjusted) AHRQ 578,193 20,077 Post-Operative Hip Fracture (raw) AHRQ 75,823 1,818 Post-Operative Hip Fracture (risk adjusted) AHRQ 127,334 3,698 

Control Hospitals Highland Park Hospital Evans

Pre-merger

rate (%)

Post-merger

rate (%)

Sample Size 

(pre/post)

Pre-merger

rate (%)

Post-merger

rate (%)

Difference-inDifferences 

Birth Trauma to Newborn Infants (raw) AHRQ 58,695 3,559 

Sample Size 

(pre/post)

Pre-merger

rate (%)

Post-merg

rate (%)

Table 4: Obstetrics

Birth Trauma to Newborn Infants (risk adjusted) AHRQ 94,276 3,982 Obstetric Trauma (Vaginal with Instrument) (raw) AHRQ 5,336 447 Obstetric Trauma (Vaginal with Instrument) (risk adjusted) AHRQ 6,473 372 Obstetric Trauma (Vaginal without Instrument) (raw) AHRQ 44,965 2,547 Obstetric Trauma (Vaginal without Instrument) (risk adjusted) AHRQ 62,909 3,517 Obstetric Trauma (raw) TJC 39,632 2,963 Obstetric Trauma (risk adjusted) TJC 71,437 3,885 Vaginal Birth after Caesarian (raw) TJC 6,287 411 Vaginal Birth after Caesarian (risk-adjusted) TJC 11,427 522 Neonatal Mortality (raw) TJC 53,739 3,798 Neonatal Mortality (risk-adjusted) TJC 108,588 4,211