Published online before print March 16, 2009, doi: 10.1161/CIRCULATIONAHA.108.764613
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(Circulation. 2009;119:1609-1615.)
© 2009 American Heart Association, Inc.
Health Services and Outcomes Research |
A Statewide Collaborative Initiative to Improve the Quality of Care for Patients With Acute Myocardial Infarction and Heart Failure
From the Virginia Chapter, American College of Cardiology, Charlottesville, Va (J.E.B., C.M.V.); Virginia Health Quality Center, Glen Allen (E.R., F.S., S.C., J.L., S.G.); Department of Biostatistics, Virginia Commonwealth University, Richmond (L.T.); Division of Cardiology, Northwestern University Medical School, Chicago, Ill (R.O.B.); and Sentara Healthcare, Norfolk, Va (J.B.).
Correspondence to John E. Brush, Jr, MD, Cardiology Consultants, Ltd, 844 Kempsville Rd, Norfolk, VA 23502. E-mail jebrush{at}earthlink.net
Received January 5, 2008; accepted January 21, 2009.
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Abstract |
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Background— To enhance quality improvement, we created a unique statewide collaboration among 3 organizations: the Virginia Health Quality Center (Virginia’s Medicare Quality Improvement Organization), the American College of Cardiology, and the American Heart Association. The goal was to improve discharge measures for acute myocardial infarction and heart failure.
Methods and Results— In 2004, 29 hospitals participated in the collaborative initiative. Using Medicare data submitted from 2004 through the second quarter of 2006, we analyzed adherence to individual discharge measures and all-or-none appropriate care measures for acute myocardial infarction, heart failure, and both. To control for differences in hospital characteristics, we were able to match 21 of the participating hospitals with 21 similar nonparticipating hospitals. In this paired analysis, the total appropriate care measure increased from 61% to 77% in participating hospitals compared with an increase from 51% to 60% in nonparticipating hospitals (P<0.0001). A generalized linear mixed model examining the full data set at the patient level failed to show a clear advantage among participating hospitals. Participating hospitals had higher baseline rates for most quality measures, suggesting a possible effect of a prior collaborative. Further analysis of only hospitals that participated in a prior collaborative showed that participants in the current collaborative initiative had higher rates of improvement for 7 of 10 quality measures and appropriate care measures for heart failure, acute myocardial infarction, or both (all P<0.05).
Conclusions— We report a unique collaboration of a Medicare Quality Improvement Organization and 2 national organizations to address quality of care for acute myocardial infarction and heart failure. A composite measure of quality (the total appropriate care measure) improved more in the participating hospitals during the timeframe of the intervention, although the greater improvement in this and other measures in the participating hospitals appeared to be dependent on participation in a prior collaborative initiative.
Key Words: heart failure • myocardial infarction • quality improvement • quality of health care
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Introduction |
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Observational studies have shown that the quality of medical care in the United States is not optimal.1–4 Quality of care is improving, but there are further opportunities to provide patients with the care that experts and professional organizations have determined to be standard care.5–7 Healthcare organizations continue to focus on closing this quality gap.
Clinical Perspective p 1615
Since 1992, the Centers for Medicare and Medicaid Services (CMS) (previously the Health Care Financing Administration) has contracted with Quality Improvement Organizations (QIOs) to address the quality of care for Medicare beneficiaries.8 Medicare QIOs are directed by CMS to work with healthcare providers to improve medical care in specified topic areas. Many QIOs use a collaborative model called the Breakthrough Series Collaborative promoted by the Institute for Healthcare Improvement.9–11 Using this model, the QIO recruits a group of hospitals to participate. These hospitals identify teams that attend learning sessions facilitated by the QIO. At these learning sessions and through other venues, the hospital teams learn and share strategies that will help them change processes for improved medical care. Hospitals are encouraged to initiate rapid-cycle small-scale changes and to use informal data collection to monitor and report their progress back to the QIO.
Although many QIOs continue to use this model, the effectiveness of the QIOs and the collaborative model has been questioned.12–15 Critics have noted that QIOs vary in how effectively they engage participating hospitals.14 Furthermore, QIOs typically work with administrative staff and may have insufficient direct contact with front-line providers.14 Some investigators have concluded that engaging front-line practicing physicians through the use of physician champions might increase the effectiveness of collaborative initiatives.16,17 The recent Institute of Medicine report on QIO performance suggested that increasing the engagement of physicians and physician organizations might result in more effective improvement.15
Two organizations, the American College of Cardiology (ACC) and the American Heart Association, have developed collaborative initiatives to improve the quality of cardiac care with a particular focus on developing physician champions and with an emphasis on specific quality improvement tools. The ACC sponsored a pilot project in Michigan called Guidelines Applied in Practice to improve the care of patients with acute myocardial infarction.18,19 In this program, a tool called a discharge contract was developed to standardize discharge processes, and its use was promoted in the collaborating hospitals. The discharge contract, signed by the patient, the doctor, and the nurse, was a disease-specific checklist of important care processes. The AHA has created its own program called Get With the Guidelines.20,21 This nationwide program has encouraged the use of an on-line Patient Management Tool to improve adherence to standardized care.
In 2004, we created a unique quality improvement initiative in Virginia that brought together the ACC, the AHA, and the Virginia Medicare QIO, the Virginia Health Quality Center (VHQC). By combining the efforts of these 3 organizations, we hoped to create a successful statewide initiative that capitalized on the tools and methods of each organization. We anticipated that the involvement of these 2 prominent national organizations would provide increased hospital commitment and improved physician engagement. We also hoped to encourage the use of the ACC’s discharge contract, the AHA’s Patient Management Tool, and the VHQC’s collaborative methods of disseminating improvement strategies among the participating hospitals. Here, we describe this unique collaboration and analyze the effect of the collaborative initiative by comparing the rates of quality measures before and after the intervention in participating and nonparticipating hospitals.
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Methods |
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Description of the Initiative
The organizational structure for this quality improvement initiative was established through letters of intent signed by officials from the ACC, AHA, and VHQC in January 2004, and an oversight committee was created with representatives from the 3 organizations. The initiative became known locally as "GAP [Guidelines Applied in Practice] Virginia Using Get With the Guidelines." Participating hospitals were recruited by the VHQC by way of a series of written invitation letters. Consistent with the VHQC’s contract with the CMS, hospitals were recruited to address care pertaining to acute myocardial infarction (AMI), heart failure (HF), or both.
The project was promoted by the Virginia chapter of the ACC at its annual meeting in May 2004 through e-mail solicitation of its members and through other communication channels. The Virginia chapter of the ACC recruited physician champions at each of the participating hospitals. Borrowing from the prior ACC Guidelines Applied in Practice project in Michigan, participating hospitals were encouraged to use a disease-specific checklist called a discharge contract.18,22 The AHA promoted the project through its regional Get With the Guidelines staff. Participating hospitals were encouraged to participate in the Get With the Guidelines program and to use the Get With the Guidelines Patient Management Tool, an online data collection tool that provided on-screen reminders and real-time reports. Both the ACC and the AHA provided nationally recognized speakers for the VHQC’s learning sessions.
As in previous collaborative initiatives, the VHQC facilitated hospital interactions and sharing using the collaborative method modeled after the Institute for Healthcare Improvement’s Breakthrough Series.9–11 This model provided learning and sharing opportunities through monthly teleconferences and face-to-face learning sessions. Participants used an e-mail Listserv to ask questions and share tools. The first of 4 learning sessions was held June 7 to 8, 2004, with the final learning session at the VHQC’s yearly Outcomes Congress on May 16 to 17, 2005.
All Virginia hospitals received an invitation to participate. Of the 83 acute care hospitals in Virginia, 29 agreed to participate. Twenty of those hospitals had participated in previous heart care collaborative initiatives sponsored by the VHQC. Participating hospitals were invited to focus on AMI and/or HF measures as defined by the CMS and the Joint Commission on the Accreditation of Healthcare Organizations.23 Twenty-three of the 29 hospitals agreed to focus on AMI measures; 20 agreed to focus on HF measures; and 14 agreed to participate by focusing on both.
A hospital was considered to be a participating hospital if the VHQC received a signed statement of participation from the hospital chief executive officer and the names of key team members. All hospitals were asked to send at least 1 representative to each learning session and monthly teleconference. Hospitals also were asked to submit a monthly senior leader report describing actions implemented and monthly run charts showing the results of actions taken. Of the 23 hospitals participating in the AMI collaborative, 21 (91%) participated in more than half of the learning sessions, 13 (57%) participated in more than half of the monthly teleconferences, 20 (87%) submitted more than half of the monthly senior leader reports, and 18 (78%) submitted more than half of their monthly run charts. Of the 20 hospitals participating in the HF collaborative, 17 (85%) participated in more than half of the learning sessions, 14 (70%) participated in more than half of the monthly teleconferences, 14 (70%) submitted more than half of their senior leader reports, and 13 (65%) submitted more than half of their monthly run charts.
Data Analysis
We examined the effect of the initiative using data from abstracted charts submitted to the CMS data warehouse. Because the collaborative initiative was promoting tools pertaining to hospital discharge, we limited our analysis to discharge quality measures. For hospitals focusing on AMI, we analyzed 4 measures: aspirin at discharge, β-blocker use at discharge, angiotensin-converting enzyme inhibitor (ACEI) or angiotensin receptor blocker (ARB) use at discharge, and documentation of smoking cessation counseling. For those hospitals focusing on HF, we analyzed 3 measures: ACEI/ARB use at discharge, documentation of HF discharge instructions, and documentation of smoking cessation counseling. For individual measures, the numerator consisted of all eligible patients receiving the specified care, and the denominator consisted of all eligible patients.2
We analyzed appropriate care measures (ACM), looking at multiple quality measures at the individual patient level. For ACM, the numerator consisted of patients receiving all the care for which they were eligible. The denominator consisted of all eligible patients with the diagnosis of AMI, HF, or both in the case of the total ACM. For example, if a patient was admitted with HF and it was appropriate for that patient to receive discharge instructions and ACEI/ARB but not smoking cessation counseling, both appropriate measures (discharge instructions and ACEI/ARB) were required for the patient to be counted in the numerator.
In addition to computing the absolute change in the measure over time, we also calculated the reduction in failure rate (RFR), which measures how well a hospital reduced the gap in care. Using the RFR provided a way to measure improvement while correcting for differences in baseline rates. RFR was calculated using the following formula: RFR=(RM–BM)/(1–BM), where BM is baseline rate and RM is remeasurement rate.
To evaluate the quality of care over time and to assess the impact of the collaborative initiative, we calculated the change scores between a baseline period, consisting of the first 2 quarters in 2004, and a follow-up period, consisting of the last 2 quarters in 2005, for participating and nonparticipating hospitals for each of the quality measures. To evaluate persistence of changes after the collaborative initiative was concluded, we reported a later follow-up period consisting of the first 2 quarters in 2006.
In the primary analysis, to control for hospital characteristics that could potentially confound the analysis, we created a set of comparison participating and nonparticipating hospitals matched for urban/rural location, bed size, region, and profit status using propensity scores. The propensity scores were generated with a logistic regression model; then, hospitals in the 2 groups were matched by use of a caliper match algorithm.24,25 Because of the relatively small number of remaining nonparticipating hospitals from which to choose a match in this single-state analysis, only 21 of the participating hospitals could be paired with matched nonparticipating hospitals. In addition, as noted above, participating hospitals were invited to focus on AMI or HF measures, and only 14 of the 29 participating hospitals focused on both the AMI and HF measures. Therefore, in the paired analysis, small sample sizes limited our ability to evaluate individual quality measures for either AMI or HF. Analysis of the composite total ACM, however, could be performed in all 21 matched hospitals, enabling sufficient sample size for analysis. In these 21 matched pairs of hospitals, we used z tests to examine differences in the total ACM from baseline to follow-up for the 2 groups.
To evaluate the individual quality measures for AMI and HF, as well as ACM, while still controlling for hospital characteristics that could confound the analysis, we analyzed the full data set at the patient level using a generalized linear mixed model (SAS/STAT GLIMMIX Procedure, SAS Institute, Inc, Cary, NC). Hospital provider was considered to be a random effect, and all models included the following fixed effects: collaborative participant (yes or no), time, the interaction between time and collaborative participation, region, case mix, and hospital size. The parameter of interest was the interaction between collaborative participation and time. In this analysis, a statistically significant interaction effect was indicative of a difference in the slope of adherence over time between participants and nonparticipants.
Twenty of the 29 participating hospitals had participated in a prior VHQC collaborative initiative. To examine the potential effect of participation in the prior initiative, an additional analysis was performed to compare participating hospitals and nonparticipating hospitals that did or did not participate in the prior collaborative initiative. In all analyses, a value of P<0.05 was considered statistically significant.
The authors had full access to and take responsibility for the integrity of the data. All authors have read and agree to the manuscript as written.
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Results |
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The rates for quality measures and RFRs for participating and nonparticipating hospitals, unadjusted for hospital-level confounders, are shown in Table 1. The numbers of patients in the AMI and HF collaborative initiatives in the participating and nonparticipating hospitals during each quarter are shown in Table 2.
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In the unadjusted analysis of the raw data, the change in the provision of HF discharge instructions was greater in the participating hospitals compared with the nonparticipating hospitals (absolute improvement, 22% versus 15%). The RFR for the HF discharge instructions was 42% in the participating hospitals compared with 27% in nonparticipating hospitals. This improvement was sustained in the participating hospitals during the later follow-up period, whereas nonparticipating hospitals appeared to further improve in the later follow-up period. The change in the ACM for HF also was greater in the participating hospitals compared with the nonparticipating hospitals (absolute improvement, 21% versus 14%). The RFR for the HF ACM was 38% in the participating hospitals compared with 24% in the nonparticipating hospitals. Rates of ACEI/ARB use in HF, smoking cessation counseling in HF, and β-blocker use at discharge for AMI increased in both the participating hospitals and nonparticipating hospitals. Rates of aspirin use at discharge and smoking cessation counseling for AMI started out high and remained high in both groups. Rates of ACEI/ARB use in AMI, ACM for AMI, and the ACM for both AMI and HF started out higher in the participating hospitals compared with the nonparticipating hospitals. In the unadjusted analysis, the change in the ACM for AMI was actually greater in the nonparticipating hospitals compared with the participating hospitals (9% versus 5%), although the rate was higher in the participating hospitals at follow-up. The RFR for the AMI ACM was 45% in the nonparticipating hospitals compared with 36% in the participating hospitals.
Participating hospitals were more likely to be large, urban hospitals with a higher case-mix index compared with nonparticipating hospitals (Table 3). To control for these differences, we were able to match 21 of the participating hospitals with 21 similar nonparticipating hospitals. In this comparison, analyzing the composite ACM for AMI or HF enabled a sufficient sample size for statistical comparison. In this paired analysis, the ACM for AMI or HF (total ACM) increased from 61% to 77% in the participating hospitals compared with an increase from 51% to 60% in the nonparticipating hospitals (P<0.0001; Table 4). The RFR was 40% in the participating hospitals compared with 19% in the nonparticipating hospitals.
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Results from the generalized linear mixed model analysis using the full data set at the patient level are shown in Table 5. This analysis yielded mixed results. Two measures appeared to improve more in participating hospitals, 4 improved more in nonparticipating hospitals, and 4 showed no difference between the participating and nonparticipating hospitals.
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We repeated the analysis by comparing participants that also participated in a prior initiative with nonparticipants that also participated in a prior initiative, as shown in Table 6. This analysis showed that, compared with nonparticipants, participants in the current collaborative initiative had a higher rate of improvement for HF discharge instructions, ACEI/ARB use in HF, β-blocker use at discharge in AMI, smoking cessation counseling in AMI, the ACM for HF, the ACM for AMI, and the total ACM (all P<0.05). No differences were seen in the rates of improvement for HF smoking cessation counseling or aspirin or ACEI/ARB use at discharge in AMI. Likewise, we compared participants that did not participate in a prior initiative with nonparticipants that did not participate in a prior initiative, as shown in Table 7. Again, this analysis yielded mixed results. Among hospitals that did not participate in a prior collaborative initiative, current participants had a higher rate of improvement in ACEI/ARB use in HF and a lower rate of improvement in smoking cessation counseling in HF, the ACM for HF, and the total ACM. There were no differences in the rates of improvement in aspirin at discharge in AMI, ACEI/ARB in AMI, β-blocker use at discharge in AMI, smoking cessation counseling in AMI, or the ACM for AMI.
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Discussion |
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In the present collaborative initiative, participating hospitals were more likely to be large, urban hospitals with a higher case-mix index compared with nonparticipating hospitals (Table 3). These and other factors are known to affect adherence to quality measures.26,27 In a paired analysis controlling for these differences in hospital characteristics, we found that the participating hospitals showed significantly greater improvement in the ACM for AMI or HF (total ACM) during the timeframe of the intervention. Similarly, the RFR for the ACM for AMI or HF was significantly higher in the participating hospitals (Table 3).
Analysis using a general linear mixed model to analyze the entire data set showed mixed results, with no clear advantage for the participating hospitals. Because participating hospitals had higher baseline rates for most quality measures, suggesting a possible effect of a prior collaborative initiative, we performed an additional analysis of only hospitals that participated in a prior collaborative initiative. This analysis of prior participants showed that participants in the current collaborative initiative had a greater rate of improvement in 7 of 10 measures, including the ACM for AMI, HF, and both, compared with nonparticipants in the current collaborative initiative. Hospitals that did not participate in a prior collaborative initiative, however, did not appear to benefit from the current collaborative initiative. This analysis suggests that the higher rate of improvement in participating hospitals in the paired analysis was dependent on participation in prior collaborative initiatives.
There are several potential explanations for these findings. That improvement was dependent on prior participation may suggest a volunteer bias; ie, hospitals that volunteer to participate in an initiative are a self-selected group of hospitals with a greater baseline focus and ability to improve on quality measures. It is possible that the greater improvement in the participating hospitals was due only to volunteer bias. It is also possible that participation in the prior collaborative initiative enabled the participants in the current initiative to more fully take advantage of the opportunities to improve offered in the current collaborative initiative. Perhaps the intervention provided in the current initiative was not sufficiently intense or prolonged to have an effect without prior participation. Finally, it is possible that the prior collaborative initiative had a lasting effect that carried over to the timeframe of the current analysis and that lasting effect of the prior collaborative was the main reason that the participating hospitals appeared to improve in the current initiative.
In the unadjusted analysis, participating hospitals showed greater improvement in the rate of adherence to discharge instructions for HF and the ACM for HF, whereas improvement in the ACM for AMI was actually greater at the nonparticipating hospitals. For this measure, the nonparticipating hospitals started at a lower rate, which may have offered more opportunity for improvement, although the RFR was also higher in the nonparticipating hospitals and the RFR should have adjusted for baseline differences. It is possible that the nonparticipating hospitals were susceptible to spillover effects of the initiative or more susceptible to other environmental influences and secular trends during the timeframe of the study.
The care generally improved during the study timeframe in both participating and nonparticipating hospitals. There are several potential explanations for this finding. The CMS directs Medicare QIOs to work with all hospitals at the statewide level, regardless of whether a hospital agrees to actively participate in a collaborative initiative. All hospitals in the state received e-mail communication, and the VHQC responded to all requests related to topic areas regardless of whether the hospital was a participating hospital. Furthermore, the Virginia chapter of the ACC and the AHA Get With the Guidelines staff mounted significant educational and promotional efforts directed to all hospitals in the state during the time period of the initiative. All of these influences could have created a spillover effect that may have contributed to the improvement in the nonparticipating hospitals. In addition, there were certainly influences other than our collaborative initiative during this time. There was substantial attention in the lay press regarding quality of care during this timeframe. In addition, public reporting of hospital performance became a factor during this time. The CMS began working with the Hospital Quality Alliance in 2002 to post hospital quality measures on its Web site, and the Medicare Modernization Act of 2003 provided financial incentives for hospitals to publicly report their quality measures. Other financial incentives may have contributed to the improvement; a prominent payer in the state began a program of hospital-based pay-for-performance during this time. It is impossible to control for these environmental factors and to determine with certainty the specific causes of the observed statewide improvement in both the participating and nonparticipating hospitals.
It is difficult to directly assess the impact of the collaboration with the ACC and AHA in this initiative. Logistical constraints prevented a survey of participating hospitals on the degree of uptake of the tools and the relative contribution of these organizations. It is possible that the discharge contract borrowed from the previous ACC Guidelines Applied in Practice project and the Patient Management Tool provided by the AHA Get With The Guidelines program contributed to the favorable improvement in discharge measures. It is also possible that the efforts by the Virginia ACC chapter and the AHA regional staff to engage physician champions resulted in improved engagement of practicing physicians at the participating hospitals. Physician champions have been shown to have a favorable effect on quality improvement.17,28,29 It is possible that the combination of factors—promotion of tools by the national organizations, recruitment of physician champions, and collaborative encouragement—all contributed to the favorable improvement in discharge measures.
The VHQC has a history of collaborative working relationships with providers in its state. In addition, our collaborative initiative likely signaled to both hospital administrators and physicians that the Medicare QIO was eagerly working with 2 prominent national organizations, and this working relationship may have positively affected the provider’s perceptions of the VHQC’s activities. The ACC and AHA provided nationally known speakers for the learning sessions and demonstrated a very active role in the collaborative initiative.
The degree of commitment by hospitals in a collaborative initiative is voluntary and not mandated. Although the participation in the collaborative initiative activities was variable among hospitals, >85% of hospitals participated in at least half of the learning sessions. All 3 organizations worked together to create learning sessions that were of value to the participants. The intent was to work together to provide a program that resulted in deeper hospital commitment to quality improvement.
Our study has several recognized limitations. It is very difficult to prove causality in trying to analyze the effects of a quality improvement initiative. Analyzing complex interventions in a real-world setting creates methodological challenges.30,31 Plus, improvement in quality can be influenced by a variety of environmental factors other than the initiative. Furthermore, nonparticipating hospitals are not isolated and could be subject to a spillover effect from the initiative, further contaminating the analysis. It is impossible for an analysis such as this to separate out the environmental factors and spillover effects that can affect the quality of care at participating and nonparticipating hospitals. Many baseline adherence rates were higher at the participating hospitals, and these high starting points make it difficult to show significant improvement. Hospitals were not allocated to the participating hospital group randomly, and there was undoubtedly some degree of volunteer bias that influenced hospitals’ willingness and ability to improve. It is difficult, if not impossible, to objectively gauge the intensity of involvement of the participating hospitals and physician champions, and we were unable to retrospectively measure the uptake of the tools. The collaborative initiative would have been strengthened by a plan to prospectively survey the participating hospitals, which would have provided important insights into the degree of involvement and tool uptake, and the survey itself may have induced more intense involvement in participating hospitals. Logistical constraints also limited our ability to address other interesting questions related to long-term patient outcomes and the costs and potential opportunity costs of our collaborative initiative.
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Conclusions |
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We describe a statewide collaborative initiative that was organized through the cooperation of the VHQC, a Medicare QIO, and 2 national professional organizations, the ACC and the AHA. A composite measure of quality (the total ACM) improved more in the participating hospitals during the timeframe of the intervention, although the greater improvement in this and other measures in the participating hospitals appeared to be dependent on participation in a prior collaborative initiative.
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Acknowledgments |
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Sources of Funding
The analyses on which this article is based were performed under contract 500–02–VA03, entitled "Utilization and Quality Control Peer Review Organization for the State (Commonwealth) of Virginia," sponsored by the CMS, Department of Health and Human Services. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the US government.
This article is a direct result of the Health Care Quality Improvement Program initiated by the Centers for Medicare & Medicaid Services, which has encouraged identification of quality improvement projects derived from analysis of patterns of care, and therefore required no special funding on the part of this contractor. Feedback to the author concerning the issues presented is welcomed. VHQC/1c/7-10-2007/615.
Disclosures
None.
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References |
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CLINICAL PERSPECTIVE
Various organizations continue to strive to improve the quality of medical care in the United States. In Virginia in 2004, 3 organizations, the Virginia Health Quality Center (Virginia’s Medicare Quality Improvement Organization), the American College of Cardiology, and the American Heart Association, combined their efforts into a single collaborative initiative with the goal of improving discharge measures for acute myocardial infarction and heart failure. Twenty-nine hospitals participated in the collaborative initiative. Analysis of Medicare data before and after the initiative showed evidence of greater improvement in the participating hospitals. A paired analysis showed that the participating hospitals had greater improvement in a composite total appropriate care measure, which is an all-or-none measure of quality for both heart failure and acute myocardial infarction patients. A generalized linear mixed model examined the full data set while controlling for hospital-level characteristics. This analysis showed that 7 of 10 individual quality measures improved more in the participating hospitals; however, the improvement was dependent on participation in a prior collaborative initiative. Thus, this collaborative initiative led by 3 organizations appeared to have a favorable impact on quality of care. The experience that we describe and our analysis may provide important insights into how quality improvement efforts can be enhanced.
Circulation 2009 119: 1553-1555.
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