Snyder C, Anderson G. Do Quality Improvement Organizations Improve the Quality of Hospital Care for Medicare Beneficiaries?. JAMA. 2005;293(23):2900–2907. doi:10.1001/jama.293.23.2900
Author Affiliations: Johns Hopkins Bloomberg
School of Public Health, Baltimore, Md.
Context Quality improvement organizations (QIOs) are charged with improving
the quality of medical care for Medicare beneficiaries.
Objective To explore whether the quality of hospital care for Medicare beneficiaries
improves more in hospitals that voluntarily participate with Medicare’s
QIOs compared with nonparticipating hospitals.
Design, Setting, and Data Data from 4 QIOs charged with improving the quality of care in 5 states
(Maryland, Nevada, New York, Utah, and Washington) and the District of Columbia
were used. Hospitals participate with the QIOs on quality improvement on a
voluntary basis. A retrospective study was conducted comparing improvement
in the quality of care of patients in hospitals that actively participated
with the QIOs vs hospitals that did not. The medical records of approximately
750 Medicare beneficiaries per state in each of 5 clinical areas (atrial fibrillation,
acute myocardial infarction, heart failure, pneumonia, and stroke) were abstracted
at baseline (1998) and follow-up (2000-2001).
Main Outcome Measure Fifteen quality indicators associated with improved outcomes in the
prevention or treatment of the 5 clinical areas were used as quality of care
measures. These 15 indicators were specifically targeted by the QIOs for quality
improvement during the study period.
Results Hospitals that voluntarily participate with the QIOs are more likely
to be larger than nonparticipating hospitals (P<.05).
At baseline, there were statistically significant (P<.05)
differences between participating and nonparticipating hospitals on 5 of 15
quality indicators, with participating hospitals performing better on 3 of
5. There was no statistically significant difference in change from baseline
to follow-up between participating and nonparticipating hospitals on 14 of
15 quality indicators. The one exception was that participating hospitals
improved more on the pneumonia immunization indicator than nonparticipating
hospitals (P = .005).
Conclusion Hospitals that participate with the QIO program are not more likely
to show improvement on quality indicators than hospitals that do not participate.
Since its inception in 1965, the Medicare program has been concerned
that Medicare beneficiaries receive appropriate and efficiently provided medical
care. Initially, professional standards review organizations and, later, peer
review organizations focused on identifying quality problems in the treatment
of Medicare beneficiaries.1 Evaluations criticized
these organizations for overly emphasizing cost containment, creating adversarial
relationships with providers, and conducting quality reviews of unclear validity
and effectiveness.1- 4 A
1990 Institute of Medicine (IOM) report noted these and other problems with
the peer review organizations, including their unproven effect on improving
the quality of care for Medicare beneficiaries.5
In 1992, using the IOM report for guidance, the Medicare program shifted
its quality efforts to partnering with hospitals to improve the quality of
care overall in addition to the regulatory case reviews.6,7 Medicare
currently contracts with quality improvement organizations (QIOs) and allocates
approximately $200 million annually for quality improvement.8 Quality
improvement organizations work with hospitals on quality improvement in a
variety of ways, including providing educational materials, using data collection
and feedback to track performance on quality indicators, and assisting hospitals
in implementing systems changes (eg, standing orders, clinical pathways).
In dollar terms, the QIOs are the federal government’s largest initiative
for improving the quality of care.9
Despite this significant financial investment and the questionable impact
of previous quality assurance efforts, the QIOs’ effectiveness at improving
the quality of care has not been rigorously evaluated. Jencks et al8,10 have published 2 articles to track
improvements in the quality of care for Medicare beneficiaries using quality
process indicators adopted by Medicare and the QIOs. Their studies suggested
widespread improvement on the indicators; however, they provide little evidence
that the improvement can be specifically attributed to the QIOs because no
concurrent control group was included in the analyses. Many other quality
initiatives (eg, the National Committee for Quality Assurance, the Joint Commission
on Accreditation of Healthcare Organizations, and internal hospital quality
improvement efforts) were operating simultaneously. Analysis using only a
historical control makes it difficult to attribute improvements specifically
to the QIOs’ activities, particularly since the QIOs are focusing on
many of the same indicators as these other organizations.
This study addresses this methodological weakness by using a concurrent
control group. Because hospitals participate with the QIOs on quality improvement
on a voluntary basis, it is possible to compare the improvement in hospitals
that voluntarily partnered with the QIOs with the improvement in hospitals
that did not voluntarily partner with the QIOs.
Our study had 2 objectives: (1) to explore characteristics of hospitals
that voluntarily participate with the QIOs vs those that do not and (2) to
determine whether hospitals voluntarily participating with the QIOs improve
the quality of care for Medicare beneficiaries more than nonparticipating
This was a retrospective comparative study that assessed performance
of hospitals on 15 quality of care indicators using data from a cross-sectional
sample of Medicare beneficiary medical records abstracted in 1998 (baseline)
and a separate sample of medical records abstracted in 2000-2001 (follow-up).8,10 These 15 indicators were the focus
of the QIOs’ improvement efforts in the inpatient setting during the
study period. The improvement in the performance on these 15 quality indicators
by hospitals that actively participated with the QIOs was compared with the
improvement by hospitals that did not participate with the QIOs.
Medical records were assigned to the “participating” and
“nonparticipating” groups based on information obtained from the
QIOs. Using records kept by the QIOs and reported to Medicare during the study
period regarding the QIOs’ collaborations with individual hospitals,
the QIOs classified the hospitals using 4 nonmutually exclusive categories:
(1) hospital did not participate with the QIO at all; (2) hospital expressed
an interest in participating with the QIO but did not track performance using
data or implement systems changes; (3) hospital used data collected by itself
or by the QIO for quality performance tracking as a result of working with
the QIO; and (4) hospital implemented systems changes as a result of working
with the QIO.
While there is no single definition of “active” participation,
based on consultations with internal experts at the QIOs, a hospital was considered
as “actively” participating with the QIOs in the primary analysis
if it either (1) used data collected by itself or by the QIO for quality performance
tracking as a result of working with the QIO or (2) implemented systems changes
(eg, standing orders, critical pathways, chart reminders, and the like) as
a result of working with the QIO. A hospital was classified as (1) “participating”
if it performed either function at any point during the study period and (2)
“not actively participating” if it performed neither of these
activities. To test the sensitivity of the study findings to alternative definitions
of “active” participation, 4 different definitions of hospital
participation were analyzed: (1) participation defined as hospitals that only
used data to track quality performance; (2) participation defined as hospitals
that only implemented systems changes; (3) participation defined as hospitals
that both used data to track quality performance and implemented systems changes;
and (4) nonparticipation defined as hospitals that did not participate with
the QIO at all (ie, excludes hospitals that expressed an interest in participating
from the control group).
Hospital participation was classified for each of the 5 clinical areas
separately because a hospital may choose to work with the QIO on improving
care in one clinical area but not another. Because the atrial fibrillation
and stroke indicators were combined together in previous analyses,8,10 hospital participation for these 2
clinical areas was assigned jointly. Data on hospital bed size using 3 categories
(1-100, 101-250, ≥251 beds) and profit status using 2 categories (not-for-profit/government,
for-profit) were also provided by the QIOs.
Quality of care was assessed using 15 dichotomous process indicators
of quality care associated with improved outcomes for treatment and prevention
in 5 clinical areas prevalent among Medicare beneficiaries: atrial fibrillation,
acute myocardial infarction, heart failure, pneumonia, and stroke (Table 1). Two other inpatient indicators (time
to thrombolytic therapy and time to angioplasty in acute myocardial infarction)
are not included in this analysis or in the analyses by Jencks et al8,10 because they did not have sufficient
sample sizes due to the limited number of eligible cases. Also, the heart
failure indicator is a combination of 2 separate indicators (evaluation of
left ventricular ejection fraction [LVEF]; angiotensin-converting enzyme inhibitor
prescribed at discharge for patients with LVEF <40%). This combined indicator
has also been used in other analyses.11 (For
more information on the development and testing of these indicators, see Jencks
All QIOs were invited to participate in this study. Four QIOs with responsibility
for 5 states (Maryland, New York, Nevada, Utah, and Washington) and the District
of Columbia were both willing to participate in the study and able to provide
the data required to conduct the analyses. The QIOs provided exactly the same
data used by Jencks et al.8,10 Specifically,
approximately 700 to 800 medical records per state per clinical area were
abstracted at baseline and follow-up as part of the Jencks et al studies8,10 to provide state-level measures of
performance on the quality indicators. The medical records selected for abstraction
were systematically sampled from a random starting point after sorting by
age, race, sex, and hospital.8 The baseline
sample of records was collected in 1998 (prior to the start of the QIOs’
contract cycle that began in 1999), and the follow-up sample of records was
collected in 2000-2001 (toward the end of the QIOs’ contract cycle that
ended in 2002) for the studies by Jencks et al.8,10 These
medical records were abstracted by 2 Medicare contractors.
Using data from each record, algorithms were used by Jencks et al8,10 to determine whether patients were
eligible for a given quality indicator based on guidelines and contraindications
and whether eligible patients received the care outlined by the quality indicator. Table 1 reports both the total number of records
abstracted for each clinical area and the number of patients eligible for
each of the 15 quality indicators. The data set includes only Medicare beneficiaries
enrolled in the traditional fee-for-service plans (approximately 85% of beneficiaries
during this time period) because the quality of Medicare managed care is tracked
Data on patient age (continuous), sex, and race (white vs nonwhite)
were provided by the QIOs. These variables were included because of their
association with the quality of care delivered. The race variable was derived
from Social Security Administration files for which persons self-identify
their race using predefined categories. For the purposes of this study, race
was collapsed into 2 categories to maintain patient confidentiality. Overall,
82% of the sample was classified as white and 18% was classified as nonwhite.
The analysis of the first question, to determine whether hospital characteristics
(bed size, profit status) are associated with participation with the QIOs,
used χ2 tests. This analysis was performed at the hospital
level for each clinical area separately, with each unique hospital identifier
from baseline representing a single observation. The bed size and profit status
of participating hospitals were compared with nonparticipating hospitals.
Data from all 5 states and the District of Columbia were combined, and then
analyses were conducted on a state-by-state basis. Also, the baseline and
follow-up performance on the 15 quality indicators of participating and nonparticipating
hospitals in the data set combining all states and the District of Columbia
were compared using χ2 tests in unadjusted analyses and using
logistic regression adjusting for both hospital and patient characteristics.
The analysis of the second question, whether hospitals participating
with the QIOs improved the quality of care more than hospitals not participating
with the QIOs, used logistic regression models. The analyses were conducted
aggregating all medical records from participating hospitals into one group
and all medical records from nonparticipating hospitals into the comparison
group. Sample sizes were insufficient for reliable estimation of individual
hospital performance. Separate models were constructed for each of the 15
dichotomous quality indicators using patient-level data. The outcome of performance
of the quality indicator among eligible patients (yes/no) was modeled as a
function of participation with the QIOs (yes/no), period (baseline/follow-up),
and an interaction between participation and period. The coefficient for the
interaction term in this model indicates whether participating hospitals improved
more or less than nonparticipating hospitals from baseline to follow-up. These
models were analyzed adjusting for patient age, sex, and race, and hospital
bed size and profit status.
Generalized estimating equations12 were
also tested because logistic regression assumes independence of observations,
but these data sets could include more than 1 patient per hospital, leading
to potential clustering. However, the generalized estimating equation analyses
produced similar results and are, therefore, not reported.
The initial analysis was conducted combining the data from all 5 states
and the District of Columbia; the same analyses were also conducted on a state-by-state
basis. State-level analyses were considered exploratory due to small sample
Because this study used separate cross-sectional samples of medical
records for baseline and follow-up, there may be some differences in which
hospitals were sampled at the 2 time points (ie, a hospital may appear in
the baseline sample but not the follow-up sample and vice versa). A sensitivity
analysis was conducted using only hospitals included in both the baseline
and follow-up data sets. Analysis of this subset reached similar study conclusions,
probably because 98% of patient records were sampled from hospitals included
in both the baseline and follow-up data sets. As a result, only the full data
set results are presented here.
All tests were conducted at the P<.05 level
of significance. This is a liberal definition of significance for this study
because using a .05 significance level would be expected to produce a statistically
significant result in approximately 1 of 20 tests by chance alone, and 15
indicators are being tested in this study. SAS software, version 9.00 (SAS
Institute Inc, Cary, NC) was used for all analyses.
The initial data collection did not require informed consent because
the data were collected by the Medicare program for administrative purposes.8 Prior to these data being shared with the study authors,
all identifying information was removed. The Committee on Human Research of
the Johns Hopkins Bloomberg School of Public Health reviewed this study and
determined that it qualified as exempt because the data sets used included
no identifiable information.
Across the 5 clinical areas, between 56% and 69% of hospitals participated
with the QIOs using the primary definition of active participation. There
were statistically significant differences in the characteristics of hospitals
that participated with the QIOs vs those that did not (Table 2). Across all clinical areas, nonparticipating hospitals
were more likely to be smaller and for-profit. The findings related to differences
in bed size persisted in state-by-state analyses, but the findings related
to differences in profit status varied from state to state.
There were also statistically significant differences on the baseline
performance of 5 of 15 quality indicators, with the participating hospitals
performing better at baseline on 3 of 5 indicators. At follow-up, there were
statistically significant differences on 4 of 15 quality indicators, with
participating hospitals performing better at follow-up on 4 of 4 indicators.
In unadjusted analyses, participating hospitals improved on 13 of 15 indicators
and nonparticipating hospitals improved on 12 of 15 indicators (Table 3).
Participating hospitals showed statistically significantly greater improvement
than nonparticipating hospitals controlling for hospital and patient characteristics
on only 1 of 15 indicators (Table 4).
The one indicator showing greater improvement in participating hospitals was
patient screened for or given pneumococcal vaccine (P = .005).
For the remaining nonstatistically significant 14 indicators, the participating
hospitals improved more on 8 indicators and the nonparticipating hospitals
improved more on 6 indicators. There was no trend regarding which indicators
the participating hospitals improved more on based on either clinical area
or baseline performance.
The sensitivity analyses that used alternative definitions of hospital
participation had little impact on the findings. There were never more than
2 indicators statistically significantly different between groups. Some of
these statistically significant differences showed greater improvement among
participating hospitals and some showed greater improvement among nonparticipating
The same trend was found in the exploratory analyses conducted at the
state level. In each of the 5 states and the District of Columbia, there were
never any more than 2 statistically significant differences by participation
and there was no consistent trend of one group improving more than the other.
Participating hospitals improved more than nonparticipating hospitals on between
5 and 8 indicators but improved less on between 6 and 9 indicators (some indicators
were not analyzable due to small sample sizes). The one exception is the state
of Washington, where participating hospitals improved more than nonparticipating
hospitals on 11 of 13 indicators, although none were statistically significant.
Whether the differences here represent a true effect of the specific QIO should
be further explored. This is not possible with the existing data.
The IOM is currently evaluating the QIO program, including operations,
program evaluations, and whether other entities could perform the QIOs’
functions.13 The findings from this study do
not support the hypothesis that the QIO program improves the quality of care
for Medicare beneficiaries in the inpatient setting.
The QIO program was initiated because evaluations of previous Medicare
quality efforts did not find clear evidence of their effectiveness.1- 4,14,15 Evaluations
of the QIO program suggest that it leads to improvement in the quality of
care, but these evaluations have significant methodological flaws. One study
found that providing participating hospitals with performance data was associated
with improved care processes.16 However, this
study focused on only a small percentage (4%) of quality improvement projects
and had no control group. The Cooperative Cardiovascular Project assessed
improvements in care for acute myocardial infarction following implementation
of a demonstration project in 4 pilot states.17,18 Significant
improvements were found in the pilot states, but evidence from the study suggests
the nation as a whole also improved during this time. The one national study
examined change across all states and all clinical areas and found improvement
over time.8,10 It is important
to note that this statewide improvement over time was what the QIOs were contracted
to accomplish. However, because this national study used only the historical
controls to calculate improvement, it is difficult—if not impossible—to
determine whether the improvement found was directly related to the QIOs.
The current study was undertaken to incorporate a control group but
has some of its own limitations. This study focuses on inpatient care only
and does not evaluate the QIOs’ efforts in the outpatient setting. Also,
the baseline and follow-up medical records were not necessarily sampled from
the same hospitals. However, 98% of patient records sampled were from hospitals
included in both the baseline and follow-up data sets, and analysis of the
subset of overlapping hospitals did not alter the study’s overall conclusions.
Medicare was unable to provide the national-level data, so individual
QIOs had to be recruited for participation. Only 4 QIOs representing 5 states
and the District of Columbia participated in this study. While these QIOs
are from different regions of the country and the 4 QIOs represent a range
of average performance on the quality measures based on the analyses by Jencks
et al,10 care should be taken in generalizing
these findings across all QIOs, especially given the high proportion of positive
but statistically insignificant results from Washington State.
Defining hospital participation was one of the greatest challenges in
this study; however, sensitivity analyses that varied the definition of participation
did not significantly affect the findings. Also, the classification of hospital
participation by the QIOs is subject to bias. While records were kept by the
QIOs and reported to Medicare during the study period to track hospital participation,
the validity of these data is difficult to evaluate retrospectively. Further,
at that time, QIOs had the incentive to attribute hospital activities as being
related to the QIO. This potential misclassification could lead to biased
estimation of the QIOs’ impact. Spillover effects of QIO interventions
to nonparticipating hospitals and the conduct of some QIO interventions statewide
could also contribute to biased estimates.
This study has a relatively short period between baseline (1998) and
follow-up (2000-2001) for demonstrating change. The analysis by Jencks et
al10 showed that change did occur over this
time period, but some hospitals classified as participating with the QIOs
may not have fully implemented their QIO-related quality improvement interventions
at the time of follow-up data collection. Because the QIO contract period
did not end until 2002, the follow-up data may not reflect the full extent
of the QIO interventions.
While these data are several years old, they are from the most recently
completed QIO contract cycle. The seventh contract cycle will end in 2005,
and evaluations of the QIOs’ impact in the nursing home and outpatient
settings using prospectively identified participants may be available.
Because hospitals participate with the QIOs on a voluntary basis and
because QIOs specifically target certain hospitals for intervention, there
may be important differences between participating and nonparticipating hospitals
other than their participation that could affect their ability to improve
care, thus leading to selection bias. Whether the differences in hospital
characteristics and baseline performance found here reflect other important
differences in participating vs nonparticipating hospitals that may have affected
their ability to improve the quality of care requires further research. Hospital
bed size and profit status, 2 important factors associated with the quality
of care,19,20 were controlled
for. Because hospitals had to be nonidentifiable in the data set, additional
information on hospital characteristics could not be obtained.
While this study does not definitively answer the question of whether
the QIOs improve the quality of care for Medicare beneficiaries, the findings
suggest that the improvement demonstrated over time by Jencks et al8,10 using these inpatient quality indicators
cannot be attributed to the QIOs. Additional efforts to assess and improve
the QIOs’ effectiveness may be needed. The current IOM assessment of
the QIO program provides an opportunity to evaluate the program further and
recommend program modifications as needed.13
Corresponding Author: Claire Snyder, PhD,
Johns Hopkins Bloomberg School of Public Health, 624 N Broadway, Sixth Floor,
Baltimore, MD 21205-1901 (firstname.lastname@example.org).
Author Contributions: Dr Snyder had full access
to all of 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: Snyder, Anderson.
Acquisition of data: Snyder, Anderson.
Analysis and interpretation of data: Snyder,
Drafting of the manuscript: Snyder.
Critical revision of the manuscript for important
intellectual content: Anderson.
Statistical analysis: Snyder.
Obtained funding: Snyder.
Study supervision: Anderson.
Financial Disclosures: None reported.
Funding/Support: This project was supported
by grants 1 R36 HS014509 and T32 HS 00029 from the Agency for Healthcare Research
Role of the Sponsor: The conclusions presented
are solely those of the authors and do not represent those of Delmarva Foundation,
Qualis Health, IPRO, HealthInsight, or the Centers for Medicare and Medicaid
Services. However, these groups were provided 30 days to review the manuscript
prior to its submission. The funding organization had no participation in
the study other than supporting Dr Snyder’s doctoral education and dissertation
Acknowledgment: The authors acknowledge the
assistance of Delmarva Foundation for Medical Care, Inc, Qualis Health, IPRO,
HealthInsight, and the Centers for Medicare and Medicaid Services in providing
data that made this research possible. Special thanks to Matthew E. Fitzgerald,
DrPH, and Mariana Albert Lesher, MS, from Delmarva Foundation, Sharon Eloranta,
MD, and Greg Baumgardner, MS, from Qualis Health, Anthony Shih, MD, MPH, and
Ti-Kuang Lee, ScM, from IPRO, and Michael Silver, MPH, and Emily Sim, MStat,
from HealthInsight. Finally, many thanks to dissertation committee members
Leon Gordis, MD, DrPH, Giovanni Parmigiani, PhD, and Donald Steinwachs, PhD.