Please provide a 250-word response to the following discussion with at least 1 resource.
Healthcare AdministrationGlobalization
RESEARCH STUDY #2 – COMPENSATION OF CEO’S AT NON-PROFITS 7
Jennifer L. Naegele
Dr. Steven Szydlowski
HAD – 505
August 9, 2020
Writing Assignment – Research Study 2
Background and Study Need
Readmission rates in hospitals today are increasingly becoming a problem that needs to be understood and addressed. This phenomenon is costing the health care system 17.4 billion dollars annually, which should be used in other development agendas if readmission rates are lowered (Boulding,2011). Apart from increasing unprecedented cost to the department of health, understanding this problem helps in the evaluation of the quality of care given to patients by the health facilities. The government needs to aid and promote researchers who will eventually provide a solution to this problem, costing the taxpayer a lot of money yearly. Solving the underlying cause of hospital readmission will also improve the value of health maintenance services accessible to the sick, resulting in a healthy, productive population (Boulding,2011). The report aims to identify whether satisfaction arising from the interaction of patients with medical staff can influence the rate of readmission within a month. Also, the patient’s involvement in the discharge is a crucial variable that will be included to determine its effects in the readmission rates.
Statement of the Problem
To distinguish the connection among satisfaction of the patients during the inpatient care and the rate of readmission in the hospital in a period not exceeding thirty days.
Hypotheses/Research Questions
· Does higher satisfaction of patients within inpatient care lead to a decreased rate of hospital readmission in thirty days?
· Information cantered on patients is crucial in the management and assessment of hospital performance.
· Does good and accurate communication by hospital staffs increase patent’s satisfaction?
· A valid discharge plan of patients reduces the rate of readmission in the hospital within thirty days.
Research Method/Design
The research was centered on patients 18 years old and above in inpatient care and those who had received inpatient care. Information from these patients aided in determining the measure of the quality of services in respective medical facilities (Boulding,2011). The research used observational analysis to compare data from various hospitals on performance and the satisfaction of patients. Additionally, a measure of RSRR rate (Risk- standardized readmission rate) in a month for patients with pneumonia, heart failure, and acute myocardial infarction was conducted. This date is dated from July 2005 to June 2008.
The comparison statistics were sourced from the Department of Health and Human Services in the US. These statistics were released in June 2009 and contained information on RSRR among patients ailing from the three diseases mentioned above. The hospital’s comparison data was rich in care performance sourced from patients suffering from heart failure, pneumonia, and acute myocardial infarction (Boulding,2011). The three years of statistics were amalgamated to find a three-year average for the three ailments within each sickbay. The data also contained information on the readmission rate that evaluates the excellence of care offered by each hospital.
Secondly, the research sourced statistics from the HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Systems) study conducted between July 2007 and June 2008. Patients from 18 years old and having admitted for at least one night were questioned. The research was also cantered on patients admitted for surgical and medical care initiated between 2-42 days after discharge (Boulding,2011). Adjustments were made to account for factors that could jeopardize response from patients.
Population, Sample, and Participants
During the research process, 4469 medical facilities were identified and reported RSRR. The 6333 hospitals were recognized from American Hospital Association records. A total of 4488 medical facilities provided data on clinical performance procedures, while 3746 provided data based on HCAHPS studies. For a given disease (pneumonia, acute myocardial infarction, and heart failure), the researchers used medical facilities as the unit of investigation (Boulding,2011). Hospitals with comprehensive data of patient satisfaction, rate of patient’s readmission, measures of performance, and structural features from the American Hospital Association records. A sample that resulted from this process had 2562 medical facilities for pneumonia cases,1798 facilities for acute myocardial infarction, and the ones for heart failure were 2561 hospitals. A total of 430,982 patients with acute myocardial infarction provided information on clinical performance, giving an average of 240 patients per medical facility (Boulding,2011). There were 912,522 patients with pneumonia, presenting a proportion of 356 patients per hospital. Finally, patients suffering from heart failure were 1,029,578 giving an average of 402 patients per each medical facility used during the research.
Summary
The research was conducted to help in understanding the underlying cause leading to an increased rate of hospital readmission. Also, the study was to determine whether there is any correlation amid hospital readmission rate in a month and the satisfaction of patients with inpatients care (Boulding,2011). The Methods used in the study was observational analysis and multivariate regression analysis. This method was used in the study because there were more than one dependent and independent variables. With this method, the linear connection amongst patient gratification and frequency of readmission was established.
The study found that hospitals with a higher score in proper discharge planning and patient satisfaction had a lower readmission rate within 30 days (Boulding,2011). This was in samples from 1798 medical facilities for acute myocardial infarction to 2562 facilities with pneumonia cases. The communication between nurses and patients when offering inpatient services and during the discharge process was found to correlate strongly with patient satisfaction. The overall patient’s happiness was not enhanced by conditions in the medical facility but by service delivery and interaction with the medical practitioners.
Quite a number, several Medicare beneficiaries experienced unexpected readmission in the hospital within 30 days after discharge (Boulding,2011). To improve the delivery of services in hospitals, doctors and nurses should ensure that services are patient-centered. Patient-centered information is ideal for assessing great health results. Also, the use of data reported by patients should be used to accompaniment the commonly used clinical procedures when evaluating the quality of care given to patients in a particular medical facility. It is consistent with previous studies that virtuous communication is directly correlated with more patient’s satisfaction.
Additionally, overall patient satisfaction was the best forecast by the sick’s perception of the responsiveness and skills of medical practitioners. Also, it was noted that patients could distinguish between the medical and aesthetic aspects of Medicare. This means that patient’s contentment can’t be achieved by making them happy, for example, by decorating the facilities or giving them food (Boulding,2011). Happiness is achievable through improving quality relations and communication from medical personnel.
There is a statistical association between the readmission rate in hospitals and patient satisfaction during inpatient care. Patients with a higher satisfaction rate are discharged with accurate information on what to expect in the course home-based care. Such patients follow drug prescription according to it was communicated well by nurses and doctors how to take them. Finally, Medical practitioners should understand patient satisfaction is a significant characteristic of health care since it translates to quality and success.
References
Boulding, W., Glickman, S. W., Manary, M. P., Schulman, K. A., & Staelin, R. (2011). Relationship between patient satisfaction with inpatient care and hospital readmission within 30 days. The American journal of managed care, 17(1), 41.
Copyright 2014 American Medical Association. All rights reserved.
Compensation of Chief Executive Officers
at Nonprofit US Hospitals
Karen E. Joynt, MD, MPH ; Sidney T. Le, BA; E. John Orav, PhD; Ashish K. Jha, MD, MPH
H ospital chief executive officers (CEOs) play a criticalrole in shaping the performance of their organiza-tions through setting organizational priorities, allo-
cating resources, and hiring clinical leadership. Indeed, in a
recent large national survey1 of hospital board chairpersons,
respondents reported that CEOs were the single most influ-
ential individuals in shaping quality performance at their
institutions.
One way to potentially improve quality at an institution
is to tie the CEO’s compensation to the institution’s perfor-
mance. This has been broadly used in other industries, and data
suggest that metrics chosen for inclusion in CEO compensa-
tion packages can affect executives’ behavior.2,3 However, we
know little about how CEOs in the hospital industry are paid
and the specific factors that underlie their compensation, with
much of the data either decades old or focused on a limited
sample of institutions.4-8 These issues are particularly salient
among nonprofit institutions, in which the metric of organi-
zational success in many industries—the profitability of the or-
ganization—must be balanced against more mission-driven fac-
tors, such as the quality of care delivered and the degree of
community benefit provided. Yet, we are unaware of any em-
pirical data on the metrics by which CEOs of nonprofit hospi-
tals are paid or to what degree the hospital’s quality of care or
level of community benefit affects their compensation.
In mid-2012, national data on compensation of CEOs of
nonprofit entities became publicly available for the first time.
We used these newly available data to answer 3 questions. First,
IMPORTANCE Hospital chief executive officers (CEOs) can shape the priorities and
performance of their organizations. The degree to which their compensation is based on their
hospitals’ quality performance is not well known.
OBJECTIVE To characterize CEO compensation and examine its relation with quality metrics.
DESIGN, SETTING, AND PARTICIPANTS Retrospective observational study. Participants
included 1877 CEOs at 2681 private, nonprofit US hospitals.
MAIN OUTCOMES AND MEASURES We used linear regression to identify hospital structural
characteristics associated with CEO pay. We then determined the degree to which a hospital’s
performance on financial metrics, technologic metrics, quality metrics, and community
benefit in 2008 was associated with CEO pay in 2009.
RESULTS The CEOs in our sample had a mean compensation of $595 781 (median, $404 938)
in 2009. In multivariate analyses, CEO pay was associated with the number of hospital beds
overseen ($550 for each additional bed; 95% CI, 429-671; P < .001), teaching status
($425 078 more at major teaching vs nonteaching hospitals; 95% CI, 315 238-534 918;
P < .001), and urban location. Hospitals with high levels of advanced technologic capabilities
compensated their CEOs $135 862 more (95% CI, 80 744-190 990; P < .001) than did
hospitals with low levels of technology. Hospitals with high performance on patient
satisfaction compensated their CEOs $51 706 more than did those with low performance on
patient satisfaction (95% CI, 15 166-88 247; P = .006). We found no association between
CEO pay and hospitals’ margins, liquidity, capitalization, occupancy rates, process quality
performance, mortality rates, readmission rates, or measures of community benefit.
CONCLUSIONS AND RELEVANCE Compensation of CEOs at nonprofit hospitals was highly
variable across the country. Compensation was associated with technology and patient
satisfaction but not with processes of care, patient outcomes, or community benefit.
JAMA Intern Med. 2014;174(1):61-67. doi:10.1001/jamainternmed.2013.11537
Published online October 14, 2013.
Invited Commentary page 68
Supplemental content at
jamainternalmedicine.com
Author Affiliations: Department of
Health Policy and Management,
Harvard School of Public Health,
Boston, Massachusetts (Joynt, Le,
Jha); Department of Biostatistics,
Harvard School of Public Health,
Boston, Massachusetts (Orav);
Cardiovascular Division, Department
of Medicine, Brigham and Women’s
Hospital, Boston, Massachusetts
(Joynt); Cardiology and Vascular
Medicine Section, Department of
Medicine, Veterans Affairs Boston
Healthcare System, Boston,
Massachusetts (Joynt); Division of
General Internal Medicine,
Department of Medicine, Brigham
and Women’s Hospital, Boston,
Massachusetts (Orav, Jha); General
Internal Medicine Section,
Department of Medicine, Veterans
Affairs Boston Healthcare System,
Boston, Massachusetts (Jha);
currently a medical student,
University of California, San Francisco
(Le).
Corresponding Author: Ashish K.
Jha, MD, MPH, Department of Health
Policy and Management, Office 405,
Harvard School of Public Health, 677
Huntington Ave, Boston, MA 02115
(ajha@hsph.harvard.edu).
Research
Original Investigation
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what is the range of compensation for CEOs of nonprofit hos-
pitals in the United States? Second, which structural and or-
ganizational factors of hospitals are associated with the com-
pensation of CEOs? Finally, to what extent is hospital CEO pay
associated with the institution’s financial performance, tech-
nologic capabilities, patient outcomes, or metrics of commu-
nity benefit provided?
Methods
Data Sources
We linked 7 data sources: (1) publicly available Form 990 tax
returns compiled by GuideStar (http://www.guidestar.org) for
US hospitals filing as nonprofit entities in 2009; (2) the Ameri-
can Hospital Association annual survey; (3) rural-urban com-
muting area codes9; (4) Hospital Compare data, which con-
tain process of care measures and the Hospital Consumer
Assessment of Healthcare Providers and Systems survey; (5)
the 2009 Medicare Provider Analysis and Review File; (6) Medi-
care cost reports; and (7) the Medicare impact file. This study
was approved by the Office of Human Research Administra-
tion at the Harvard School of Public Health.
Identifying Executives
From the 990 forms, we identified 1877 CEOs overseeing 2681
nonfederal, private, nonprofit acute-care hospitals in the
United States. These hospitals comprise more than 98% of pri-
vate nonprofit US hospitals.
Structural and Organizational Measures
We hypothesized that CEOs who oversaw more complex or-
ganizations (measured by number of beds, number of hospi-
tals overseen, membership in a system, or being a major teach-
ing hospital) would be compensated more highly than others.
We also examined other characteristics, including a hospi-
tal’s proportion of Medicare patients (a marker of payer mix)
and its disproportionate share hospital index (a measure of the
proportion of low-income patients), hypothesizing that the
CEOs of hospitals with a less favorable payer mix might re-
ceive lower compensation.
Financial Performance Measures
We examined 4 measures of financial performance: total mar-
gin (calculated from the Medicare Cost Reports as the ratio of
net income to net revenue plus other income), liquidity (the
ratio of current assets to total liabilities), capitalization (the ra-
tio of fund balances to total assets) all representing perfor-
mance in fiscal year 2008,10 and occupancy rate (from the
American Hospital Association survey representing calendar
year 2008). We examined hospital performance in 2008 be-
cause we assumed that the prior year’s performance would in-
fluence the subsequent year’s compensation.
Technologic Measures
For each hospital, we used a well-validated technologic
index11,12 that combines the presence of several advanced tech-
nologies into a single score (eg, positron-emission tomogra-
phy, magnetic resonance imaging, and the capability of per-
forming complex operations) (Supplement [eTable 1]). We
assigned higher weights to technologies that are relatively rare.
Quality-of-Care Measures
We selected a set of quality metrics that are endorsed by the
National Quality Forum13 and used by the Centers for Medi-
care and Medicaid Services for public reporting and have sub-
sequently formed the basis for new mandatory federal pay-
for-performance initiatives, such as value-based purchasing14
and the Hospital Readmission Reduction Program. Although
these metrics are not comprehensive, they represent the best
and most widely used measures of hospital quality. For each
hospital, we calculated a composite measure of performance
on processes of care for acute myocardial infarction, conges-
tive heart failure, and pneumonia for calendar year 2008
(Supplement [eTable 2]).15,16 To assess patient satisfaction, we
used Hospital Consumer Assessment of Healthcare Providers
and Systems data and focused on overall satisfaction (the pro-
portion rating the hospital a 9 or 10 on a 10-point scale). We
built patient-level hierarchical logistic regression models to cal-
culate 30-day risk-adjusted mortality and readmission rates for
patients admitted with acute myocardial infarction, conges-
tive heart failure, or pneumonia in calendar year 2008, ex-
cluding December because 30-day outcomes crossed into 2009
(Supplement [eTables 3 and 4]). We built composite mortality
and readmissions measures across the 3 conditions for each
hospital using indirect standardization.
Community Benefit Measures
We used hospital-reported 990 forms to determine charity care
and other community benefits provided in calendar year 2009,
which are self-reported as a proportion of total hospital ex-
penditures. Charity care represents uncompensated care, un-
reimbursed Medicaid care, and unreimbursed costs from other
means-tested government programs. Other community ben-
efits include community health services, health profession-
als’ education, subsidized health services, research, and con-
tributions to charitable organizations.17
Primary Outcome
Our primary outcome was CEO compensation in calendar year
2009. For each executive, we used the 990 forms to obtain re-
portable compensation from the primary organization (includ-
ing benefits and deferred compensation), reportable compen-
sation from related organizations (ie, a foundation associated
with a hospital), and estimated other compensation from the
primary and related organizations. We summed these ele-
ments to determine each CEO’s total compensation package.
To account for regional variations in the cost of care, we de-
flated each CEO’s compensation by the Medicare Wage In-
dex, used by Medicare to adjust prospective payments to hos-
pitals for hospital wage level differences between metropolitan
statistical areas.
Statistical Analysis
We began by plotting the distribution of CEO compensation.
We then calculated summary statistics for the characteristics
Research Original Investigation Compensation of CEOs at Nonprofit Hospitals
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of the hospitals that each CEO managed, breaking the sample
into the lowest decile, middle 8 deciles, and top decile of com-
pensation. In cases in which executives managed multiple hos-
pitals, we averaged hospital characteristics weighted by the
number of beds. Thus, analyses were carried out at the CEO
level, with each CEO assigned the aggregate characteristics of
the beds they managed. We examined the relationship be-
tween wage index–deflated CEO compensation and hospital
structural characteristics, using multivariable linear regres-
sion models with CEO compensation as the outcome and each
of the key hospital characteristics as predictors including num-
ber of beds overseen, number of hospitals overseen, teaching
status (major, minor, or nonteaching), census region, loca-
tion (urban vs rural), critical access status, proportion of Medi-
care patients, and disproportionate share hospital index.
Next, we examined whether a hospital’s performance on
measures of financial performance, technology index, qual-
ity and patient outcomes, or community benefit was associ-
ated with CEO compensation. We individually examined each
metric in models adjusted for the structural characteristics out-
lined above and then proceeded to our full model including
all of these variables. Because of interrelationships between
the quality measures,18,19 we tested for but found no evi-
dence of collinearity (Supplement [eTable 5]). We used the lin-
ear models to calculate adjusted compensation for low per-
formers (those at the 25th percentile) and high performers (75th
percentile) on each metric.
Because we were concerned that focusing on just 3 condi-
tions for our mortality outcomes may not adequately capture
the breadth of hospital care provided, we also created, as a sen-
sitivity analysis, a 19-condition composite of medical and sur-
gical mortality rates that comprise a large proportion of hospi-
tal care20 (Supplement [eTable 6]). We compared the relationship
between CEO pay and outcomes across these 19 conditions.
We conducted additional sensitivity analyses. Because CEOs’
compensation might be based on improvement over time rather
than performance in a single year, we conducted analyses using
the change in financial performance and quality performance
from 2006 to 2008 as our predictor; for patient satisfaction scores,
which became publicly available in 2007, we used change from
2007 to 2008. Finally, because the data on CEO pay were some-
what right-skewed and because the factors that affect CEO pay
at wealthy hospitals may be different from those that affect CEO
pay among low-paying hospitals, we repeated our analyses using
quantile regression at the 25th, 50th, and 75th percentiles.
We considered a 2-tailed value of P < .05 to be significant. Analyses were performed using commercial software (Stata, version 12.1; StataCorp).
Results
We identified 1877 executives responsible for 2681 hospitals.
The CEOs had an unadjusted mean compensation of $595 781
Figure. Distribution of Chief Executive Officer Pay
0
<50 000 1 000 000
1 500 000
2 000 000 ≥3 000 000
10.0
9.0
Ex
ec
ut
iv
es
, %
Compensation, $
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
500 000100 000 1 100 000
1 600 000
2 100 000
600 000200 000 1 200 000
1 700 000
2 200 000
700 000300 000 1 300 000
1 800 000
2 300 000
800 000400 000 1 400 000
1 900 000
2 400 000
900 000 2 500 000 2 700 000
2 800 000
2 900 000
2 600 000
Histogram of chief executive officer pay at US nonprofit hospitals in calendar year 2009.
Compensation of CEOs at Nonprofit Hospitals Original Investigation Research
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and a median compensation of $404 938 in 2009. Distribu-
tion of the compensation is shown in the Figure. The CEOs in
the lowest decile of compensation, with a median compensa-
tion of $117 933, were largely responsible for small, nonteach-
ing hospitals in rural areas, most frequently in the Midwest
(Table 1). The CEOs in the highest decile of compensation, with
a median compensation of $1 662 548, oversaw larger, urban
hospitals, which were more often major or minor teaching hos-
pitals. Overall median hospital margins were 3.5%, and me-
dian occupancy rate was 63.0% (Table 2). Hospitals’ median
performance on process measures was 93.4% and on patient
satisfaction, 65.0%. Median charity care and other commu-
nity benefit comprised 1.6% and 1.4% of total hospital expen-
ditures, respectively.
Hospital Characteristics and CEO Compensation
In multivariable analyses, compensation was higher for CEOs
managing more beds ($550 per additional bed; 95% CI, 429-
671; P < .001) (Table 3). Managing more hospitals was also as-
sociated with higher pay ($32 609 per additional hospital; 95%
CI, 5154-60 063; P = .02). The CEOs of major teaching hospi-
tals were paid $425 078 more (95% CI, 315 238-534 918; P < .001)
than were CEOs of nonteaching hospitals. The CEOs of hospi-
tals with a higher proportion of poor patients and Medicare pa-
tients were generally compensated less than were other CEOs
(Table 3).
Hospital Financial Performance and CEO Compensation
Hospital financial performance was not significantly associ-
ated with CEO compensation in our fully adjusted models
(Table 4). High performers on total margins, for example,
paid their CEOs similarly to poor performers (difference,
$7045; 95% CI, −16 463 to 30 553; P = .56), and the relation-
ships for liquidity, capitalization, and occupancy rate were
also nonsignificant.
Advanced Technologies and CEO Compensation
The presence of higher levels of advanced technology, as
measured by a technology index, was associated with sub-
stantially higher CEO compensation. High performers on
the technology index received $135 862 additional pay com-
pared w ith poor performers (95% CI, 80 744-190 980;
P < .001).
Hospital Quality Performance and CEO Compensation
We found no significant association between performance on
process quality, risk-adjusted mortality, or readmission rates
and CEO compensation (Table 4). High performers on mortal-
ity rates paid their CEOs $4667 less than poor performers (95%
CI, −27 457 to 18 123; P = .69). However, hospitals with higher
patient satisfaction scores compensated their CEOs more than
did hospitals with lower scores (difference, $51 706; 95% CI,
15 166 to 88 247; P = .006).
Table 1. Characteristics of Hospitals Associated With Each Chief Executive Officer
Characteristic
Decile of Compensation
Lowest 2-9 Highest
No. of officers 188 1502 187
No. of hospitals 189 1841 651
Total compensation, $
Median (IQR) 117 933 (89 221-136 390) 405 167 (264 196-635 226) 1 662 548 (1 358 702-2 327 567)
Mean (SD) 106 324 (38 985) 469 096 (254 133) 2 105 394 (1 201 963)
No. of beds
Median (IQR) 39 (25-73) 136 (61-256) 234 (80-451)
Mean (SD) 62 (76) 185 (169) 310 (290)
Region, %
Northeast 9.6 26.2 23.0
Midwest 50.5 34.0 32.1
South 23.4 26.4 31.5
West 16.5 13.4 13.4
Teaching, %
Major 0.0 6.8 33.8
Minor 12.8 25.2 33.6
Nonteaching 87.2 68.0 32.6
Rural-urban commuting area, %
Urban 12.8 53.9 88.7
Suburban 3.7 4.4 0.9
Large rural town 8.5 19.2 6.6
Small town/isolated rural 75.0 22.5 3.7
Medicare patients, % 53.2 (49.0-67.2) 48.6 (42.9-54.6) 41.8 (38.0-48.5)
Disproportionate share index 0.0 (0.0-0.0) 17.2 (3.6-26.3) 21.5 (12.2-30.3)
Critical access hospital 69.7 18.3 2.8
Abbreviation: IQR, interquartile range.
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Community Benefit and CEO Compensation
We found no association between charity care and CEO com-
pensation (−$52 for high performers vs poor performers; 95%
CI, −441 to 337; P = .79) or between community benefit and CEO
compensation ($0 additional compensation for high perform-
ers vs poor performers, 95% CI, −3 to 3; P = .88).
Sensitivity Analysis
When we substituted a 19-condition composite mortality
rate into the model, we found no association with CEO pay
(−$453 for high vs low performers; 95% CI, −35 992 to
35 086; P = .98) (Supplement [eTable 7]). Using changes in
financial performance and quality performance from 2006
to 2008 as predictors, technology was the only factor sig-
nificantly associated with CEO compensation (Supplement
[eTable 8]).
When we used quantile regression to examine the dis-
tribution of CEO salaries based on varying levels of financial
and quality performance, we found relatively similar results
in all but 2 instances. Whereas we found little relationship
between total margins and CEO pay in our overall analyses,
we found that the 25th percentile and median CEO salaries
were higher at high-margin hospitals compared with low-
margin hospitals (Supplement [eTables 9 and 10]). There
was no relationship between hospital total margin and CEO
pay at the upper end of the pay scale (Supplement [eTable
11]). Similarly, we found that the 25th percentile of CEO
compensation was somewhat higher among hospitals with
high performance on process quality measures compared
with hospitals with poor performance on the process quality
measures. The difference in median salaries was even
smaller and not statistically significant, and there was no
significant difference based on process quality scores at the
upper end of the CEO pay scale.
Discussion
We examined the compensation of CEOs of nonprofit US hos-
pitals and found that executives who oversaw larger teaching
hospitals were the most highly compensated. Furthermore,
even after accounting for these and other structural factors, a
higher level of advanced technologies was associated with sig-
nificantly higher compensation. Although we found no rela-
tionship with hospital performance on standard process or out-
come metrics, patient satisfaction had a modest but significant
relationship with CEO compensation. Finally, despite the fact
that we examined nonprofit institutions whose tax-exempt sta-
tus is based on their ability to demonstrate community ben-
efit, we found no relationship between the degree of that ben-
efit and CEO compensation.
Among the quality metrics we examined, only patient sat-
isfaction was consistently associated with CEO compensa-
tion. The factors that shape the compensation package of CEOs
likely reflect a combination of boards’ awareness of hospital
performance on key metrics and its assessment of the ability
of the CEO to influence those metrics. Boards may have an
easier time assessing patient satisfaction than other quality
metrics, such as risk-adjusted mortality rates, or may see pa-
tient satisfaction as a key measure of organizational perfor-
mance and marketability. Of course, it is possible that boards
reward CEOs on other factors that we could not measure, such
as staff satisfaction.
Table 2. Hospital Performance
Characteristic % Median (IQR)
Hospital financial outcomes
Total margins 3.5 (0.1-7.5)
Liquidity 200.1 (135.3-298.4)
Capitalization 51.6 (33.5-66.4)
Occupancy rate 63.0 (50.3-72.8)
Presence of advanced technologies
Technology index 0.406 (0.246-0.609)
Hospital quality
Process measures, 2008 93.4 (89.7-95.5)
Patient satisfaction, 2008 65.0 (60.0-70.0)
Risk-adjusted mortality rates, 2008 12.2 (10.3-14.6)
Risk-adjusted readmission rates, 2008 21.2 (18.8-24.3)
Community benefit
Charity care provided,
% of total expenditures
1.6 (0.7-2.8)
Other community benefit,
% of total expenditures
1.4 (0.5-3.3)
Abbreviation: IQR, interquartile range.
Table 3. Hospital Characteristics
and Chief Executive Officer Compensation
Characteristic
Additional Compensationa
(95% CI)
P
Value
Mean wage-adjusted
compensation, $
595 781 (not applicable)
No. of beds, $ 550 (429 to 671) <.001
No. of hospitals, $ 32 609 (5154 to 60 063) .02
Region, $
Northeast Reference
Midwest 29 206 (−36 476 to 94 888) .38
South 77 449 (9070 to 145 829) .03
West 32 533 (−51 917 to 116 983) .45
Teaching, $
Not teaching Reference
Major 425 078 (315 238 to 534 918) <.001
Minor 151 352 (87 899 to 214 805) <.001
Rural-urban commuting area, $
Urban Reference
Suburban −137 690 (−272 195 to −3184) .045
Large rural town −124 491 (−198 703 to −50 279) .001
Small town/isolated rural −195 553 (−284 305 to −106 801) <.001
Critical access hospital, $ −1156 (−136 390 to 134 078) .99
Disproportionate share index, $ −38 938 (−59 621 to −18 254) .000
Percent Medicare patients, $ −21 112 (−43 137 to 913) .060
a For continuous predictors, additional compensation represents the change in
compensation associated with a 10% change in the independent variable.
Model is adjusted for all listed covariates.
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There was no consistent association between CEO pay and
more traditional measures of quality, such as adherence to pro-
cess measures or patient outcomes. In one set of sensitivity
analyses, we found that the low end (25th percentile) of CEO
pay at hospitals with better adherence to process measures was
somewhat higher than at hospitals with worse adherence to
process measures, although the difference was small. This
could represent a chance finding, or it might suggest that at
hospitals with lower-paid CEOs, there appears to be a little more
attention given to process quality metrics.
Given that hospital boards have a fiduciary responsibility
to represent the welfare of the community, they could make
the link between their CEO’s pay and hospital quality perfor-
mance more explicit. It may be that they have not done so be-
cause they may believe that these quality metrics are not ad-
equate measures of CEO performance. Whether linking CEO
compensation to quality metrics would lead to better care is
unknown; an alternative possibility is that linking CEO com-
pensation explicitly to quality metrics could have unin-
tended consequences, such as reducing hospitals’ incentive to
provide to medically or socially complex populations.
We were surprised to find only weak nonsignificant rela-
tionships between CEO pay and financial performance. How-
ever, indirect measures of financial performance, including
payer mix and advanced technology, were associated with CEO
compensation. Furthermore, we found that at the lower half
of the pay scale, there appeared to be a modest relationship
between hospital total margins and CEO pay. It may be that total
margins play a role in CEO salary up to a point, beyond which,
as compensation rises, it has little bearing on measures of qual-
ity or financial performance. The fact that hospitals with higher
levels of technology paid their CEOs more has several poten-
tial explanations. First, the advanced technologies may iden-
tify hospitals that provide more “complex” care and there-
fore need to pay more to attract a leader who can manage a
complex organization. Alternatively, boards may value their
hospitals being seen as technologic leaders in the commu-
nity. Finally, high levels of technology may simply be a reflec-
tion of financial health: hospitals with the resources and ac-
cess to capital to purchase advanced technologies may be
rewarding their CEOs for this success.
Although there has been prior work21 examining the
relationship between CEO pay and firm performance
broadly, there have been few recent data on the compensa-
tion of hospital CEOs. Four large studies using data from the
1990s found that CEO compensation was linked to hospital
financial performance,4 profit status,5 size, and location6
and only somewhat to quality.22 Two recent studies of non-
profit CEO compensation, one in Connecticut7 and one in
New Hampshire,8 also found no relationship between CEO
p ay a n d q u a l it y o r c o m m u n it y b e n e f it , a l t h o u g h t h e
samples were small (29 and 23 hospitals, respectively). We
are unaware of any recent data on national patterns of hos-
pital CEO compensation.
There are limitations to our study. To assess CEO com-
pensation, we relied on data that, although audited, have
not been extensively validated. Although the 990 forms are
intended to include all compensation, even that which is
indirect or in-kind, this type of compensation may be under-
reported. Hospitals may not consistently use the same defi-
nitions of uncompensated care or may inflate costs attrib-
uted to charity by using list prices for this care.23 The quality
measures we assessed were those that are publicly available
and that are components of federal pay-for-performance
programs but may have their own limitations. For example,
patient satisfaction metrics represent a combination of hos-
pital performance and patient expectations. Furthermore,
our selected quality metrics, although likely reflective of
overall quality at each hospital, are not exhaustive; it is pos-
sible that hospitals track internal quality metrics, such as
infection rates, and that these may be more closely corre-
lated with financial remuneration. Our metrics of financial
performance may not wholly reflect the financial health of
the institution, although we aimed to include a set of met-
Table 4. Hospital Financial Outcomes, Technology, Quality, and Community Benefit
and Chief Executive Officer Compensationa
Characteristic
Low
Performersb
High
Performersb Difference (95% CI)
P
Value
Hospital financial outcomes, $
Total margins 592 244 599 289 7045 (−16 463 to 30 553) .56
Liquidity 595 903 595 889 −14 (−89 to 60) .71
Capitalization 596 561 594 901 −1660 (−7764 to 4445) .59
Occupancy rate 583 950 609 384 25 435 (−11 128 to 61 997) .17
Presence of advanced technologies, $
Technology index 527 938 663 800 135 862 (80 744 to 190 980) <.001
Hospital quality, $
HQA process of care, 2008 593 610 601 353 7744 (−17 312 to 32 799) .54
Patient satisfaction, 2008 574 076 625 782 51 706 (15 166 to 88 247) .006
Risk-adjusted mortality, 2008 597 485 592 817 −4667 (−27 457 to 18 123) .69
Risk-adjusted readmission, 2008 593 406 599 734 6328 (−17 480 to 30 135) .60
Community benefit
Charity care provided 595 983 595 930 −52 (−441 to 337) .79
Other community benefit 595 723 595 723 0 (−3 to 3) .88
Abbreviation: HQA, Health Quality
Alliance.
a Model is adjusted for all hospital
characteristics shown in Table 3,
including hospital size, number of
hospitals, region, ownership,
teaching status, rurality, Critical
Access Hospital status,
Disproportionate Share Index, and
proportion Medicare and
additionally adjusted for all listed
covariates.
b Low performers are those at the
25th percentile on each metric; high
performers are those at the 75th
percentile of performance on each
metric. R2 for the model is 0.41.
Research Original Investigation Compensation of CEOs at Nonprofit Hospitals
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Copyright 2014 American Medical Association. All rights reserved.
rics that as a group provide an adequate assessment of finan-
cial performance. There may be personal characteristics of
the CEO that are associated with compensation, including
experience or duration of time in the organization; we did
not examine these possibilities. There could be unmeasured
confounders that explain the associations we found. Finally,
our data examine relationships in 2008 and 2009, and these
could change as health care payment models increasingly
reward quality rather than simply quantity of care. Future
research should track these associations over time.
Conclusions
Executive compensation metrics are a powerful reflection of
the priorities of an institution and likely have the ability to
shape the focus of the CEO. We found that CEO compensa-
tion at nonprofit US hospitals varies widely and is associated
with greater use of technology and higher patient satisfac-
tion but not with the quality of care delivered, patient out-
comes, or community benefit.
ARTICLE INFORMATION
Accepted for Publication: May 25, 2013.
Published Online: October 14, 2013.
doi:10.1001/jamainternmed.2013.11537.
Author Contributions: Dr Jha had full access to all
the data in the study and takes responsibility for the
integrity of the data and the accuracy of the data
analysis.
Study concept and design: Joynt, Le, Jha.
Acquisition of data: Le, Jha.
Analysis and interpretation of data: Joynt, Le, Orav.
Drafting of the manuscript: Joynt, Le, Jha.
Critical revision of the manuscript for important
intellectual content: Joynt, Orav, Jha.
Statistical analysis: Le, Orav.
Obtained funding: Jha.
Administrative, technical, and material support: Jha.
Study supervision: Jha.
Conflict of Interest Disclosures: None reported.
Funding/Support: Dr Joynt was supported by
grant 1K23HL109177-01 from the National Heart,
Lung, and Blood Institute and by the Lerner
Research Award from Brigham and Women’s
Hospital, Division of Cardiovascular Medicine.
Additional support was provided from internal
department funds from the Harvard School of
Public Health.
Role of the Sponsor: The funders had no role in the
design and conduct of the study; collection,
management, analysis, and interpretation of the
data; preparation, review, or approval of the
manuscript; and decision to submit the manuscript
for publication.
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Compensation of CEOs at Nonprofit Hospitals Original Investigation Research
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