Curr Cardiol Rep (2015) 17: 1 DOI 10.1007/s11886-014-0551-y
ISCHEMIC HEART DISEASE (D MUKHERJEE, SECTION EDITOR)
Health Resource Variability in the Achievement of Optimal Performance and Clinical Outcome in Ischemic Heart Disease Partha Sardar & Amartya Kundu & Ramez Nairooz & Saurav Chatterjee & Gary S. Ledley & Wilbert S. Aronow
Published online: 24 January 2015 # Springer Science+Business Media New York 2015
Abstract A disparity between evidence and practice in the management of ischemic heart disease is frequently observed. Guideline adherence and clinical outcomes are influenced by system, provider, and patient factors. Recently, performance improvement measures for cardiovascular disease have gained a lot of popularity worldwide. These measures may facilitate the uptake of evidence-based recommendations and improve patient outcomes. While apparently valid as quality metrics, their impacts on clinical outcomes remain limited and are areas of further research. Several methods for optimizing This article is part of the Topical Collection on Ischemic Heart Disease P. Sardar (*) Department of Medicine, New York Medical College, Valhalla, NY, USA e-mail: [email protected] A. Kundu Department of Medicine, University of Massachusetts Medical School, Worcester, MA, USA e-mail: [email protected] R. Nairooz Division of Cardiology, University of Arkansas for Medical Sciences, Little Rock, AR, USA e-mail: [email protected] S. Chatterjee Division of Cardiology, St Luke’s-Roosevelt Hospital of the Mount Sinai Health System, New York, NY, USA e-mail: [email protected] G. S. Ledley Cardiology Division, Drexel University College of Medicine, Philadelphia, PA, USA e-mail: [email protected] W. S. Aronow Cardiology Division, New York Medical College, Valhalla, NY, USA e-mail: [email protected]
performance have been instituted and essentially involve three different approaches—improvement in the reporting of data on guideline adherence, providing infrastructure and tools, and providing incentives to improve guideline adherence. Public reporting of quality metrics and “pay-for-performance” are some novel performance improvement tools. The impact of these approaches on patient outcomes will be pivotal in improving cardiovascular outcomes in the future. Keywords Ischemic heart disease . Quality improvement . Health resource variability . Health outcome . Coronary artery disease . Acute myocardial infarction Abbreviations IHD Ischemic heart disease CAD Coronary artery disease AMI Acute myocardial infarction NSTE ACS Non-ST-segment elevation acute coronary syndromes
Introduction Although we have witnessed a meteoric expansion of scientific knowledge and an exponential increase in the number of clinical trials along with novel therapies and technological advancements in the management of cardiovascular disease over the past few decades, ischemic heart disease (IHD) still remains one of the leading causes of morbidity and mortality worldwide [1]. There are many therapeutic regimens which have proven mortality benefits in coronary artery disease (CAD). The American Heart Association (AHA) and the American College of Cardiology (ACC) carefully review the available therapeutic options as well as the latest evidence supporting their use, and incorporate this data into published
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guidelines [2, 3]. Despite widespread access to these guidelines, the Institute of Medicine, in the book Crossing the Quality Chasm: A New Health System for the 21st Century, indicated that apparently there is low compliance with evidence-based therapies on a global scale. There is also a significant disparity between guideline-recommended therapies and current clinical practice. Although different systems and tools have been developed by several organizations in the USA in order to facilitate the uptake of guideline based care, we have seen variable increases in optimal outcomes [4, 5]. A number of factors may account for this variability including available resources, workforce capacity, geographic location, and other local health service characteristics. Better understanding of the health service characteristics that may facilitate evidence translation is an important issue to consider in today’s clinical practice environment. It has also been shown that workforce is a major factor influencing adverse events and mortality. Nurse-to-patient ratios, hospitalization on a weekend versus a weekday, and access to invasive services are key factors that can influence outcomes [6–8, 9••, 10]. The past few decades have witnessed a rapid transformation in cardiovascular quality measures with initial endeavors soon transcending into national programs dedicated toward the public reporting of hospital performance on various markers of quality. Most recently, remuneration of hospitals and physicians have been linked to their performance on quality measures. With recent implementation of the Patient Protection and Affordable Care Act (PPACA), further changes in how we measure, report, and pay for quality health care will continue in the near future.
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provide hospitals with performance data and draw attention to areas for improvement [13]. In accordance with these federal endeavors, the 1980s and 1990s saw the growth of national registries to track, measure, and improve quality in cardiovascular care. The National Registry of Myocardial Infarction began to track and measure practice patterns and outcomes for AMI patients. In 1997, the American College of Cardiology developed the National Cardiovascular Data Registry (NCDR) to consolidate clinical data in cardiovascular care. Widespread underuse of thrombolytic therapy, aspirin, and beta-blockers especially in the elderly and patients with delayed AMI presentations was confirmed by early registry based studies [14]. In contrast to CMS and TJC programs, the registries were the first large-scale efforts that tracked patient outcomes in addition to process measures. The National Quality Forum (NQF) was formed in 1999 to set national standards of healthcare quality and was established as a result of public-private nonprofit partnership. In 2001, the public release of the Institute of Medicine’s landmark study “Crossing the Quality Chasm” further strengthened efforts at quality measurement for optimal performance. In response to the prevalent quality disparities described therein, TJC in 2002 introduced quality measures identical to CMS mandating over 3000 of its accredited hospitals to submit performance data on at least 2 of 4 conditionspecific measures, including AMI and heart failure [15, 16]. Latest evidence confirmed widespread variability in adherence to guideline-based process measures, suggesting that simply defining quality metrics did not necessarily translate into adoption by clinicians. Such frameworks however laid the foundation for understanding whether adherence to process-based care led to improved patient outcomes or not.
History of Measurement of Optimal Performance The Joint Commission on Accreditation of Healthcare Organizations (formerly JCAHO, now The Joint Commission, TJC) in the 1950s came up with a set of “Minimum Standards” of quality which eventually went on to be incorporated into the process of hospital accreditation under the ORYX Initiative of the 1990s [11, 12] Under this scheme, accredited hospitals were to provide TJC with a subset of performance data on a regular basis, in order to identify areas in need of improvement. One of the first specialties in medicine where standardized quality measurement was attempted on a national scale was cardiovascular care. Under the Health Care Quality Improvement Initiative of 1992, the Health Care Financing Administration (HCFA, now the Center for Medicare and Medicaid Services, CMS), began measuring a series of disease-specific process-of-care measures for Medicare patients. The selected measures were based on evidence-based guidelines for prevention and treatment of multiple conditions, including acute myocardial infarction (AMI). In accordance with early TJC initiatives, the program intended to
Health Resource Variability and Adherence to Guidelines Bradley et al. [17] demonstrated that higher-volume centers adhere better to the AMI guidelines. Other studies showed that variables such as region (Northeast) and teaching status correlate with greater guideline adherence [8, 17]. This variation affirms the requirement for a multi-faceted team approach to the care of CAD patients. Another study by Pearson et al. [15] showed that 95 % of physicians questioned knew the National Cholesterol Education Program (NCEP) guidelines while only 18 % of high-risk patients were eventually treated according to these guidelines. Older age and female sex have long been shown to be associated with worse outcomes and decreased adherence with evidence-based therapies after acute myocardial infarction. Kumbhani et al. [18•] analyzed data from the American Heart Association’s Get with the GuidelinesCoronary Artery Disease registry and found that chronic renal insufficiency, chronic dialysis, and atrial fibrillation were associated with poorer adherence. Optimal adherence to current
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guideline recommendations requires a methodical program involving the entire medical team using tools such as care pathways and pre-printed order sets [8, 18•].
Methods to Ensure Optimal Performance Numerous methods for optimizing performance have been instituted and all of them essentially involve three different approaches. The first approach has been to increase the reporting of data on guideline adherence. Providing hospitals and physicians with the infrastructure and tools necessary to improve guideline adherence has been the second approach. This is exemplified by programs such as the ACC GAP program [19] and the AHA GWTG program [8], which were designed to improve guideline adherence through tools and system redesign strategies. The third and final approach has been to provide incentives to improve guideline adherence thereby linking a hospital’s potential financial success on improved guideline adherence and optimal performance.
Get With the Guidelines Program Get With the Guidelines (GWTG)-CAD program [8] is a hospital-based, quality improvement program developed by the AHA, to improve quality of care through increased adherence to guideline recommendations. The program, which started in 2000, included learning sessions, didactic sessions, best practice sharing, interactive workshops, postmeeting follow-up, and a Web-based patient management tool (Outcome, Cambridge, MA) which provides the opportunity for simultaneous data collection, ongoing real-time feedback of hospital data, and clinical decision support to enable rapid cycle improvement. The program also rewards hospitals using a performance recognition program, which serves as an effective incentive. The data which is collected includes patient demographics, medical history, symptoms on arrival, in-hospital treatment and events, discharge treatment and counseling, and patient disposition. Participating institutions were instructed to submit consecutive eligible patients to the GWTG database using case ascertainment techniques similar to the Joint Commission.
The GAP Projects To test the hypothesis that by incorporating the key priorities of national guidelines into AMI care itself leads to improved quality, the American College of Cardiology (ACC) initiated a pilot program in Michigan in 1999 in partnership with the Michigan quality improvement organization and the Greater
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Detroit Area Health Council [19, 20]. The GAP project, which targeted patients, physicians, and nurses, fostered systembased care from admission to discharge while incorporating evidence-based tools into clinical practice. A rapid-cycle quality improvement model was created, emphasizing a collaborative culture of learning, sharing, and problem solving among hospitals and by designing care processes to assure clinical tool use.
CRUS?ADE Initiative CRUSADE (Can Rapid Risk Stratification of Unstable Angina Patients Suppress Adverse Outcomes with Early Implementation of the American College of Cardiology/American Heart Association Guidelines) is a voluntary QI initiative of patients with non-ST-segment elevation acute coronary syndromes (NSTE ACS), which began on January 1, 2001. Clinical information pertaining to in-hospital care and outcomes of patients with NSTE ACS with high-risk clinical features are collected and submitted by specific CRUSADE centers [21]. A trained data collector at each hospital then abstracts data using standardized definitions. Examples of variables tabulated include demographic and clinical information, medical therapies and associated major contraindications, use and timing of cardiac procedures, laboratory results, hospital characteristics, in-hospital outcomes, and discharge therapies and interventions. Each CRUSADE hospital needs to designate a cardiology coadvocate (MD), an emergency medicine coadvocate (MD), and a QI coordinator, who identify the gaps and promote improvement in ACS care.
Performance Measures and Patient Outcomes Data showed that participation in GWTG-CAD was independently associated with improvements in guideline adherence. LaBresh et al. [22] demonstrated that the GWTG-CAD program was associated with increased adherence to smoking cessation counseling, lipid treatment, beta blocker, aspirin, and ACE inhibitor therapy as well as cardiac rehabilitation enrollment [8, 22]. The ACC-GAP program showed improvement in aspirin and ACE inhibitor use on discharge of the patient as well as in smoking cessation counseling. Embedding AMI guidelines into practice was associated with improved 30-day and 1-year mortality. This benefit was most marked when patients were cared for by the use of standardized, evidence-based clinical care tools [19, 20]. The use of QI tools provided by CRUSADE was associated with improved adherence from 2002 to 2004 and correlated with the biggest improvement in adherence scores compared to other factors. A hospital that moved from the minimum to the maximum factor score in use of CRUSADE QI tools
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demonstrated on average, a 6.7 % absolute increase in process adherence score for treating NSTE ACS. This is consistent with a reduction in mortality through incorporation of evidence-based clinical care tools in the GAP program. However, a larger hospital-level study of AMI care for Medicare patients showed that while receipt of beta-blockers and aspirin at time of discharge was associated with lower riskstandardized 30-day mortality rates, when taken together, performance on process measures explained only 6 % of hospital-level variation in risk standardized, 30-day mortality rates [21, 23].
Performance Improvement Through Public Reporting The principle behind endorsing public data reporting was twofold; firstly, public access to performance data would provide a powerful stimulus for clinicians and leaders to improve; and secondly, it would enable patients to make informed choices based on hospital and physician efficacy and performance. In 1989, New York (NY) State began reporting risk-adjusted mortality rates for coronary artery bypass grafting (CABG) surgery by hospital and surgeon. The Hospital Quality Alliance (HQA) was formed in the early 2000s as a collaborative venture between CMS, TJC, and several medical professional organizations and became the first large-scale national level enterprise aiming to report hospital quality data to the general public. In 2003, Congress passed the Medicare Modernization Act in an effort to support the HQA. This Act linked a hospital’s participation in public reporting of quality data to annual payment updates, effectively persuading hospitals to report data to CMS on evidencebased process measures for the management of AMI—the same set of metrics that had been collected by TJC and HCFA in the past [24, 25]. Initial studies examining the impact of public reporting on outcomes stemmed from state-level CABG reporting programs with early results suggesting that public reporting in NY led to decreases in CABG mortality over time. The first evaluations of the Hospital Compare national public reporting program were encouraging with studies showing that overall performance on process measures improved significantly over the first 2 years of public reporting and higher performance on these process measures was associated with lower riskadjusted mortality rates for AMI and heart failure [26, 27]. While public reporting may have its benefits, the major drawback is that it may encourage physicians to start avoiding high-risk patients in order to avoid poor outcomes. Racial and ethnic minorities are another group that may be perceived to be at higher risk of poor outcomes and may be at risk of decreased access to care under public reporting. A recent national study showed that the three states with mandatory public reporting of PCI outcomes (NY, PA, and
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MA) had significantly lower rates of use of this procedure for patients with an AMI. Therefore, it is still not certain whether the overall effect of public reporting is positive or negative. Comprehensive trials are required in the future in order to fully exclude the possible small-to-moderate benefits of public reporting [28, 29].
Performance Improvement Through Pay-for-Performance The latest quality improvement tool that has been implemented on a national scale for cardiovascular care is paying-forperformance (P4P). In a society obsessed with economic benefits, there was likely to be a strong motivation to improve on the part of both clinicians and hospital administrators when appropriate financial incentives were put forward. With analysis of cost-benefit ratios gaining widespread publicity and cost control becoming a priority for policy makers, P4P gained importance as a strategy for maximizing quality while prioritizing cost effectiveness [30, 31]. The success of pay-forperformance as a quality-improvement mechanism most likely lies in parameters such as amount of incentive offered, baseline performance levels, and a hospital’s intrinsic ability to improve and respond appropriately to such incentives. It is therefore likely that alternative designs to pay-forperformance programs, including larger incentives, targeting incentives at particularly high-impact measures, and considering both group and individual performance evaluation could lead to better outcomes.
Value-Based Purchasing The Value-Based Purchasing (VBP) program is a national P4P program that exemplifies an effort to essentially shift Medicare from a passive payer of services into active purchaser of quality health care. Starting with a 1 % “holdback” of Medicare payments, hospitals can earn bonuses ≤1 % based on a complex formula which rewards performance, improvement, and consistency on processes of care and patient experience with mortality rates and efficiency metrics planned to be incorporated in the near future. VBP penalties may be too punitive for hospitals that disproportionately care for poor patients, and it is projected that hospitals in disadvantaged areas would continue to have lower performance levels in comparison to hospitals in better-resourced areas, leading to significantly higher financial penalties. Nevertheless, these are based on hypothetical predictive models and only time will reveal the actual long-term benefits of VBP in improving quality [32, 33].
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The Hospital Readmissions Reduction Program
Conclusions
One indicator of deficient quality that results in increased expenditure and considerable financial loss is the rate of readmissions to a hospital. The Hospital Readmissions Reduction Program (HRRP) is a program which aims to reduce Medicare payments to hospitals with higher-than-expected readmission rates for AMI, heart failure, and pneumonia. This policy thereby lays greater emphasis on good discharge practices, encourages enhanced communication with outpatient providers, and reduces fragmentation of care. In the year leading up to the implementation of the HRRP, initial reports have suggested reductions in readmission rates nationally, which is a promising early sign for the likely success of this program. However, there are a few potential concerns as well. Previous studies have shown that only approximately 27 % of readmissions are “preventable” with, little relationship between typical measures of hospital quality and readmission rates. Furthermore, readmissions may be influenced by patient socioeconomic factors as well as by community resources which are not adjusted for in the CMS penalty scheme. Early research on the impact of the HRRP has so far shown that large hospitals, teaching hospitals, and safety-net hospitals are receiving the highest penalties at present. Whether or not this will have a major negative repercussion on these hospitals remains to be seen [34, 35, 36••].
Although their utility in improving patient outcomes remains unclear, performance improvement measures for cardiovascular disease have gained a lot of popularity worldwide. However, the performance measures need to be linked more closely to patient-relevant outcomes such as mortality, hospital readmission, or patient experience in order to exert maximal influence. Public reporting of quality metrics has not been shown to have positive impacts on outcomes although it may have value in improving transparency and promoting patient trust in the health care system. Finally, in spite of its somewhat limited success on a national level thus far, pay-forperformance appears to have significant face validity as a performance improvement approach. In the future, pay-forperformance initiatives may gain more success from approaches such as creating incentives that are large enough to influence provider behavior, focusing on high-impact metrics like mortality, and measuring performance in a minimally complex but clinically relevant manner. Thus, performance measurement in cardiovascular care remains a tool of paramount importance in today’s world and continues to be an area for future research, improvement, modification, and continued evaluation. Studying the effect of performance improvement measures on patient outcomes will be pivotal in our efforts of trying to improve cardiovascular outcomes in the years to come.
Limitations of Performance Measurements Compliance with Ethics Guidelines
While performance measures are apparently valid as quality metrics, their impact on outcomes remains limited possibly because some process-of-care measures are simply not meant to impact short-term mortality. For example, counseling patients on smoking cessation is unlikely to have an immediate impact on short-term mortality, despite being recognized as good clinical practice. Residual confounding by clinical complexity or socioeconomic factors may complicate risk-adjusted mortality measurements thereby limiting the ability to determine the true association between process-of-care and mortality. Despite their weak relationship with outcomes, performance measures continue to be effective quality metrics due to their innate face validity as well as their independent utility in ensuring the delivery of high-quality, guideline-based care. Future studies of quality metrics should include a comparison group, be sufficiently powered in sample size to overcome the issues of variability, and be able to account for secular trends. The absence of a strong, consistent association between process and outcome measures may be due to the fallacy of falsely generalizing hospital-level analyses to the patient level. In the future, studies should employ hierarchical analyses whenever possible to allow for adequate scrutiny of patient, hospital, and health system factors in achieving quality [37].
Conflict of Interest Partha Sardar, Amartya Kundu, Ramez Nairooz, Saurav Chatterjee, Gary S. Ledley, and Wilbert S. Aronow declare that they have no conflict of interest. Human and Animal Rights and Informed Consent This article does not contain any studies with human or animal subjects performed by any of the authors.
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1 Page 6 of 7 Disease: A Report of the American College of Cardiology/ American Heart Association Task Force on Practice Guidelines, and the American Association for Thoracic Surgery, Preventive Cardiovascular Nurses Association, Society for Cardiovascular Angiography and Interventions, and Society of Thoracic Surgeons. J Am Coll Cardiol. 2014;S0735-1097(14)04510-0. 3. Cannon CP, Brindis RG, Chaitman BR, et al. 2013 ACCF/AHA key data elements and definitions for measuring the clinical management and outcomes of patients with acute coronary syndromes and coronary artery disease: a report of the American College of Cardiology Foundation/American Heart Association Task Force on clinical data standards (writing committee to develop acute coronary syndromes and coronary artery disease clinical data standards). J Am Coll Cardiol. 2013;61:992–1025. 4. Frolkis JP, Zyzanski SJ, Schwartz JM, et al. Physician noncompliance with the 1993 National Cholesterol Education Program (NCEP-ATPII) Guidelines. Circulation. 1998;98:851–5. 5. Fox KA, Goodman SG, Avezum A, et al. Practice variation and missed opportunities for reperfusion in ST-segment-elevation myocardial infarction: findings from the Global Registry of Acute Coronary Events (GRACE). Lancet. 2002;359:373–7. 6. Bhatt DL, Roe MT, Peterson ED, et al. Utilisation of early invasive management strategies for high-risk patients with non-ST-segment elevation acute coronary syndromes: results from the CRUSADE Quality improvement Initiative. JAMA. 2004;292:2096–104. 7. Needleman J, Buerhaus P, Mattke S, et al. Nurse-staff levels and the quality of care in hospitals. N Engl J Med. 2002;346:1715–22. 8. Lewis WR, Peterson ED, Cannon CP, et al. An organized approach to improvement in guideline adherence for acute myocardial infarction: results with the Get With The Guidelines quality improvement program. Arch Intern Med. 2008;168(16):1813–9. 9.•• Astley CM, Macdougall CJ, Davidson PM, Chew DP. Lost in translation: health resource variability in the achievement of optimal performance and clinical outcome. Circ Cardiovasc Qual Outcomes. 2011;4:512–20. This study showed, facilitating uptake of evidence in clinical practice may need to consider quality improvement systems, tools and workforce to achieve optimal ACS outcomes. 10. Almanaseer Y, Mukherjee D, Kline-Rogers EM, et al. Implementation of the ACC/AHA guidelines for preoperative cardiac risk assessment in a general medicine preoperative clinic: improving efficiency and preserving outcomes. Cardiology. 2005;103:24–9. 11. Hanold LS, Koss RG, Loeb JM. The ORYX initiative: goals and potential application to physician quality improvement efforts. Tex Med. 2000;96:84–7. 12. The Joint Commission. 2010 ORYX performance measure reporting requirements for hospitals and guidelines for measure selections. Washington: The Joint Commission; 2009. 13. Jencks SF, Wilensky GR. The health care quality improvement initiative. A new approach to quality assurance in Medicare. JAMA. 1992;268:900–3. 14. Rogers WJ, Canto JG, Barron HV, et al. Treatment and outcome of myocardial infarction in hospitals with and without invasive capability. Investigators in the National Registry of Myocardial Infarction. J Am Coll Cardiol. 2000;35:371–9. 15. National Quality Forum. NQF-Endorsed_ Standards. Available at: http://www.qualityforum.org/Measures_List.aspx. Accessed August 01, 2014. 16. Institute of Medicine (U.S.); Committee on Quality of Health Care in America. Crossing the quality chasm: a new health system for the 21st century. Washington: National Academy Press; 2001. 17. Bradley EH, Herrin J, Mattera JA, et al. Hospital-level performance improvement: beta-blocker use after acute myocardial infarction. Med Care. 2004;42(6):591–9. 18.• Kumbhani DJ, Fonarow GC, Cannon CP, et al. Predictors of adherence to performance measures in patients with acute myocardial
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Curr Cardiol Rep (2015) 17: 1 36.•• Joynt KE, Jha AK. Characteristics of hospitals receiving penalties under the Hospital Readmissions Reduction Program. JAMA. 2013;309:342–3. This study found large hospitals, teaching hospitals, and safety-net hospitals are more likely to receive payment cuts under the Hospital Readmissions Reduction Program.
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ISCHEMIC HEART DISEASE (D MUKHERJEE, SECTION EDITOR)
Health Resource Variability in the Achievement of Optimal Performance and Clinical Outcome in Ischemic Heart Disease Partha Sardar & Amartya Kundu & Ramez Nairooz & Saurav Chatterjee & Gary S. Ledley & Wilbert S. Aronow
Published online: 24 January 2015 # Springer Science+Business Media New York 2015
Abstract A disparity between evidence and practice in the management of ischemic heart disease is frequently observed. Guideline adherence and clinical outcomes are influenced by system, provider, and patient factors. Recently, performance improvement measures for cardiovascular disease have gained a lot of popularity worldwide. These measures may facilitate the uptake of evidence-based recommendations and improve patient outcomes. While apparently valid as quality metrics, their impacts on clinical outcomes remain limited and are areas of further research. Several methods for optimizing This article is part of the Topical Collection on Ischemic Heart Disease P. Sardar (*) Department of Medicine, New York Medical College, Valhalla, NY, USA e-mail: [email protected] A. Kundu Department of Medicine, University of Massachusetts Medical School, Worcester, MA, USA e-mail: [email protected] R. Nairooz Division of Cardiology, University of Arkansas for Medical Sciences, Little Rock, AR, USA e-mail: [email protected] S. Chatterjee Division of Cardiology, St Luke’s-Roosevelt Hospital of the Mount Sinai Health System, New York, NY, USA e-mail: [email protected] G. S. Ledley Cardiology Division, Drexel University College of Medicine, Philadelphia, PA, USA e-mail: [email protected] W. S. Aronow Cardiology Division, New York Medical College, Valhalla, NY, USA e-mail: [email protected]
performance have been instituted and essentially involve three different approaches—improvement in the reporting of data on guideline adherence, providing infrastructure and tools, and providing incentives to improve guideline adherence. Public reporting of quality metrics and “pay-for-performance” are some novel performance improvement tools. The impact of these approaches on patient outcomes will be pivotal in improving cardiovascular outcomes in the future. Keywords Ischemic heart disease . Quality improvement . Health resource variability . Health outcome . Coronary artery disease . Acute myocardial infarction Abbreviations IHD Ischemic heart disease CAD Coronary artery disease AMI Acute myocardial infarction NSTE ACS Non-ST-segment elevation acute coronary syndromes
Introduction Although we have witnessed a meteoric expansion of scientific knowledge and an exponential increase in the number of clinical trials along with novel therapies and technological advancements in the management of cardiovascular disease over the past few decades, ischemic heart disease (IHD) still remains one of the leading causes of morbidity and mortality worldwide [1]. There are many therapeutic regimens which have proven mortality benefits in coronary artery disease (CAD). The American Heart Association (AHA) and the American College of Cardiology (ACC) carefully review the available therapeutic options as well as the latest evidence supporting their use, and incorporate this data into published
1 Page 2 of 7
guidelines [2, 3]. Despite widespread access to these guidelines, the Institute of Medicine, in the book Crossing the Quality Chasm: A New Health System for the 21st Century, indicated that apparently there is low compliance with evidence-based therapies on a global scale. There is also a significant disparity between guideline-recommended therapies and current clinical practice. Although different systems and tools have been developed by several organizations in the USA in order to facilitate the uptake of guideline based care, we have seen variable increases in optimal outcomes [4, 5]. A number of factors may account for this variability including available resources, workforce capacity, geographic location, and other local health service characteristics. Better understanding of the health service characteristics that may facilitate evidence translation is an important issue to consider in today’s clinical practice environment. It has also been shown that workforce is a major factor influencing adverse events and mortality. Nurse-to-patient ratios, hospitalization on a weekend versus a weekday, and access to invasive services are key factors that can influence outcomes [6–8, 9••, 10]. The past few decades have witnessed a rapid transformation in cardiovascular quality measures with initial endeavors soon transcending into national programs dedicated toward the public reporting of hospital performance on various markers of quality. Most recently, remuneration of hospitals and physicians have been linked to their performance on quality measures. With recent implementation of the Patient Protection and Affordable Care Act (PPACA), further changes in how we measure, report, and pay for quality health care will continue in the near future.
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provide hospitals with performance data and draw attention to areas for improvement [13]. In accordance with these federal endeavors, the 1980s and 1990s saw the growth of national registries to track, measure, and improve quality in cardiovascular care. The National Registry of Myocardial Infarction began to track and measure practice patterns and outcomes for AMI patients. In 1997, the American College of Cardiology developed the National Cardiovascular Data Registry (NCDR) to consolidate clinical data in cardiovascular care. Widespread underuse of thrombolytic therapy, aspirin, and beta-blockers especially in the elderly and patients with delayed AMI presentations was confirmed by early registry based studies [14]. In contrast to CMS and TJC programs, the registries were the first large-scale efforts that tracked patient outcomes in addition to process measures. The National Quality Forum (NQF) was formed in 1999 to set national standards of healthcare quality and was established as a result of public-private nonprofit partnership. In 2001, the public release of the Institute of Medicine’s landmark study “Crossing the Quality Chasm” further strengthened efforts at quality measurement for optimal performance. In response to the prevalent quality disparities described therein, TJC in 2002 introduced quality measures identical to CMS mandating over 3000 of its accredited hospitals to submit performance data on at least 2 of 4 conditionspecific measures, including AMI and heart failure [15, 16]. Latest evidence confirmed widespread variability in adherence to guideline-based process measures, suggesting that simply defining quality metrics did not necessarily translate into adoption by clinicians. Such frameworks however laid the foundation for understanding whether adherence to process-based care led to improved patient outcomes or not.
History of Measurement of Optimal Performance The Joint Commission on Accreditation of Healthcare Organizations (formerly JCAHO, now The Joint Commission, TJC) in the 1950s came up with a set of “Minimum Standards” of quality which eventually went on to be incorporated into the process of hospital accreditation under the ORYX Initiative of the 1990s [11, 12] Under this scheme, accredited hospitals were to provide TJC with a subset of performance data on a regular basis, in order to identify areas in need of improvement. One of the first specialties in medicine where standardized quality measurement was attempted on a national scale was cardiovascular care. Under the Health Care Quality Improvement Initiative of 1992, the Health Care Financing Administration (HCFA, now the Center for Medicare and Medicaid Services, CMS), began measuring a series of disease-specific process-of-care measures for Medicare patients. The selected measures were based on evidence-based guidelines for prevention and treatment of multiple conditions, including acute myocardial infarction (AMI). In accordance with early TJC initiatives, the program intended to
Health Resource Variability and Adherence to Guidelines Bradley et al. [17] demonstrated that higher-volume centers adhere better to the AMI guidelines. Other studies showed that variables such as region (Northeast) and teaching status correlate with greater guideline adherence [8, 17]. This variation affirms the requirement for a multi-faceted team approach to the care of CAD patients. Another study by Pearson et al. [15] showed that 95 % of physicians questioned knew the National Cholesterol Education Program (NCEP) guidelines while only 18 % of high-risk patients were eventually treated according to these guidelines. Older age and female sex have long been shown to be associated with worse outcomes and decreased adherence with evidence-based therapies after acute myocardial infarction. Kumbhani et al. [18•] analyzed data from the American Heart Association’s Get with the GuidelinesCoronary Artery Disease registry and found that chronic renal insufficiency, chronic dialysis, and atrial fibrillation were associated with poorer adherence. Optimal adherence to current
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guideline recommendations requires a methodical program involving the entire medical team using tools such as care pathways and pre-printed order sets [8, 18•].
Methods to Ensure Optimal Performance Numerous methods for optimizing performance have been instituted and all of them essentially involve three different approaches. The first approach has been to increase the reporting of data on guideline adherence. Providing hospitals and physicians with the infrastructure and tools necessary to improve guideline adherence has been the second approach. This is exemplified by programs such as the ACC GAP program [19] and the AHA GWTG program [8], which were designed to improve guideline adherence through tools and system redesign strategies. The third and final approach has been to provide incentives to improve guideline adherence thereby linking a hospital’s potential financial success on improved guideline adherence and optimal performance.
Get With the Guidelines Program Get With the Guidelines (GWTG)-CAD program [8] is a hospital-based, quality improvement program developed by the AHA, to improve quality of care through increased adherence to guideline recommendations. The program, which started in 2000, included learning sessions, didactic sessions, best practice sharing, interactive workshops, postmeeting follow-up, and a Web-based patient management tool (Outcome, Cambridge, MA) which provides the opportunity for simultaneous data collection, ongoing real-time feedback of hospital data, and clinical decision support to enable rapid cycle improvement. The program also rewards hospitals using a performance recognition program, which serves as an effective incentive. The data which is collected includes patient demographics, medical history, symptoms on arrival, in-hospital treatment and events, discharge treatment and counseling, and patient disposition. Participating institutions were instructed to submit consecutive eligible patients to the GWTG database using case ascertainment techniques similar to the Joint Commission.
The GAP Projects To test the hypothesis that by incorporating the key priorities of national guidelines into AMI care itself leads to improved quality, the American College of Cardiology (ACC) initiated a pilot program in Michigan in 1999 in partnership with the Michigan quality improvement organization and the Greater
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Detroit Area Health Council [19, 20]. The GAP project, which targeted patients, physicians, and nurses, fostered systembased care from admission to discharge while incorporating evidence-based tools into clinical practice. A rapid-cycle quality improvement model was created, emphasizing a collaborative culture of learning, sharing, and problem solving among hospitals and by designing care processes to assure clinical tool use.
CRUS?ADE Initiative CRUSADE (Can Rapid Risk Stratification of Unstable Angina Patients Suppress Adverse Outcomes with Early Implementation of the American College of Cardiology/American Heart Association Guidelines) is a voluntary QI initiative of patients with non-ST-segment elevation acute coronary syndromes (NSTE ACS), which began on January 1, 2001. Clinical information pertaining to in-hospital care and outcomes of patients with NSTE ACS with high-risk clinical features are collected and submitted by specific CRUSADE centers [21]. A trained data collector at each hospital then abstracts data using standardized definitions. Examples of variables tabulated include demographic and clinical information, medical therapies and associated major contraindications, use and timing of cardiac procedures, laboratory results, hospital characteristics, in-hospital outcomes, and discharge therapies and interventions. Each CRUSADE hospital needs to designate a cardiology coadvocate (MD), an emergency medicine coadvocate (MD), and a QI coordinator, who identify the gaps and promote improvement in ACS care.
Performance Measures and Patient Outcomes Data showed that participation in GWTG-CAD was independently associated with improvements in guideline adherence. LaBresh et al. [22] demonstrated that the GWTG-CAD program was associated with increased adherence to smoking cessation counseling, lipid treatment, beta blocker, aspirin, and ACE inhibitor therapy as well as cardiac rehabilitation enrollment [8, 22]. The ACC-GAP program showed improvement in aspirin and ACE inhibitor use on discharge of the patient as well as in smoking cessation counseling. Embedding AMI guidelines into practice was associated with improved 30-day and 1-year mortality. This benefit was most marked when patients were cared for by the use of standardized, evidence-based clinical care tools [19, 20]. The use of QI tools provided by CRUSADE was associated with improved adherence from 2002 to 2004 and correlated with the biggest improvement in adherence scores compared to other factors. A hospital that moved from the minimum to the maximum factor score in use of CRUSADE QI tools
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demonstrated on average, a 6.7 % absolute increase in process adherence score for treating NSTE ACS. This is consistent with a reduction in mortality through incorporation of evidence-based clinical care tools in the GAP program. However, a larger hospital-level study of AMI care for Medicare patients showed that while receipt of beta-blockers and aspirin at time of discharge was associated with lower riskstandardized 30-day mortality rates, when taken together, performance on process measures explained only 6 % of hospital-level variation in risk standardized, 30-day mortality rates [21, 23].
Performance Improvement Through Public Reporting The principle behind endorsing public data reporting was twofold; firstly, public access to performance data would provide a powerful stimulus for clinicians and leaders to improve; and secondly, it would enable patients to make informed choices based on hospital and physician efficacy and performance. In 1989, New York (NY) State began reporting risk-adjusted mortality rates for coronary artery bypass grafting (CABG) surgery by hospital and surgeon. The Hospital Quality Alliance (HQA) was formed in the early 2000s as a collaborative venture between CMS, TJC, and several medical professional organizations and became the first large-scale national level enterprise aiming to report hospital quality data to the general public. In 2003, Congress passed the Medicare Modernization Act in an effort to support the HQA. This Act linked a hospital’s participation in public reporting of quality data to annual payment updates, effectively persuading hospitals to report data to CMS on evidencebased process measures for the management of AMI—the same set of metrics that had been collected by TJC and HCFA in the past [24, 25]. Initial studies examining the impact of public reporting on outcomes stemmed from state-level CABG reporting programs with early results suggesting that public reporting in NY led to decreases in CABG mortality over time. The first evaluations of the Hospital Compare national public reporting program were encouraging with studies showing that overall performance on process measures improved significantly over the first 2 years of public reporting and higher performance on these process measures was associated with lower riskadjusted mortality rates for AMI and heart failure [26, 27]. While public reporting may have its benefits, the major drawback is that it may encourage physicians to start avoiding high-risk patients in order to avoid poor outcomes. Racial and ethnic minorities are another group that may be perceived to be at higher risk of poor outcomes and may be at risk of decreased access to care under public reporting. A recent national study showed that the three states with mandatory public reporting of PCI outcomes (NY, PA, and
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MA) had significantly lower rates of use of this procedure for patients with an AMI. Therefore, it is still not certain whether the overall effect of public reporting is positive or negative. Comprehensive trials are required in the future in order to fully exclude the possible small-to-moderate benefits of public reporting [28, 29].
Performance Improvement Through Pay-for-Performance The latest quality improvement tool that has been implemented on a national scale for cardiovascular care is paying-forperformance (P4P). In a society obsessed with economic benefits, there was likely to be a strong motivation to improve on the part of both clinicians and hospital administrators when appropriate financial incentives were put forward. With analysis of cost-benefit ratios gaining widespread publicity and cost control becoming a priority for policy makers, P4P gained importance as a strategy for maximizing quality while prioritizing cost effectiveness [30, 31]. The success of pay-forperformance as a quality-improvement mechanism most likely lies in parameters such as amount of incentive offered, baseline performance levels, and a hospital’s intrinsic ability to improve and respond appropriately to such incentives. It is therefore likely that alternative designs to pay-forperformance programs, including larger incentives, targeting incentives at particularly high-impact measures, and considering both group and individual performance evaluation could lead to better outcomes.
Value-Based Purchasing The Value-Based Purchasing (VBP) program is a national P4P program that exemplifies an effort to essentially shift Medicare from a passive payer of services into active purchaser of quality health care. Starting with a 1 % “holdback” of Medicare payments, hospitals can earn bonuses ≤1 % based on a complex formula which rewards performance, improvement, and consistency on processes of care and patient experience with mortality rates and efficiency metrics planned to be incorporated in the near future. VBP penalties may be too punitive for hospitals that disproportionately care for poor patients, and it is projected that hospitals in disadvantaged areas would continue to have lower performance levels in comparison to hospitals in better-resourced areas, leading to significantly higher financial penalties. Nevertheless, these are based on hypothetical predictive models and only time will reveal the actual long-term benefits of VBP in improving quality [32, 33].
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The Hospital Readmissions Reduction Program
Conclusions
One indicator of deficient quality that results in increased expenditure and considerable financial loss is the rate of readmissions to a hospital. The Hospital Readmissions Reduction Program (HRRP) is a program which aims to reduce Medicare payments to hospitals with higher-than-expected readmission rates for AMI, heart failure, and pneumonia. This policy thereby lays greater emphasis on good discharge practices, encourages enhanced communication with outpatient providers, and reduces fragmentation of care. In the year leading up to the implementation of the HRRP, initial reports have suggested reductions in readmission rates nationally, which is a promising early sign for the likely success of this program. However, there are a few potential concerns as well. Previous studies have shown that only approximately 27 % of readmissions are “preventable” with, little relationship between typical measures of hospital quality and readmission rates. Furthermore, readmissions may be influenced by patient socioeconomic factors as well as by community resources which are not adjusted for in the CMS penalty scheme. Early research on the impact of the HRRP has so far shown that large hospitals, teaching hospitals, and safety-net hospitals are receiving the highest penalties at present. Whether or not this will have a major negative repercussion on these hospitals remains to be seen [34, 35, 36••].
Although their utility in improving patient outcomes remains unclear, performance improvement measures for cardiovascular disease have gained a lot of popularity worldwide. However, the performance measures need to be linked more closely to patient-relevant outcomes such as mortality, hospital readmission, or patient experience in order to exert maximal influence. Public reporting of quality metrics has not been shown to have positive impacts on outcomes although it may have value in improving transparency and promoting patient trust in the health care system. Finally, in spite of its somewhat limited success on a national level thus far, pay-forperformance appears to have significant face validity as a performance improvement approach. In the future, pay-forperformance initiatives may gain more success from approaches such as creating incentives that are large enough to influence provider behavior, focusing on high-impact metrics like mortality, and measuring performance in a minimally complex but clinically relevant manner. Thus, performance measurement in cardiovascular care remains a tool of paramount importance in today’s world and continues to be an area for future research, improvement, modification, and continued evaluation. Studying the effect of performance improvement measures on patient outcomes will be pivotal in our efforts of trying to improve cardiovascular outcomes in the years to come.
Limitations of Performance Measurements Compliance with Ethics Guidelines
While performance measures are apparently valid as quality metrics, their impact on outcomes remains limited possibly because some process-of-care measures are simply not meant to impact short-term mortality. For example, counseling patients on smoking cessation is unlikely to have an immediate impact on short-term mortality, despite being recognized as good clinical practice. Residual confounding by clinical complexity or socioeconomic factors may complicate risk-adjusted mortality measurements thereby limiting the ability to determine the true association between process-of-care and mortality. Despite their weak relationship with outcomes, performance measures continue to be effective quality metrics due to their innate face validity as well as their independent utility in ensuring the delivery of high-quality, guideline-based care. Future studies of quality metrics should include a comparison group, be sufficiently powered in sample size to overcome the issues of variability, and be able to account for secular trends. The absence of a strong, consistent association between process and outcome measures may be due to the fallacy of falsely generalizing hospital-level analyses to the patient level. In the future, studies should employ hierarchical analyses whenever possible to allow for adequate scrutiny of patient, hospital, and health system factors in achieving quality [37].
Conflict of Interest Partha Sardar, Amartya Kundu, Ramez Nairooz, Saurav Chatterjee, Gary S. Ledley, and Wilbert S. Aronow declare that they have no conflict of interest. Human and Animal Rights and Informed Consent This article does not contain any studies with human or animal subjects performed by any of the authors.
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