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• W2766777799 abstract "Part 1 of this review summarizes the consequences of risk aversion and the observational studies and surveys relevant to this phenomenon, almost all of which are derived from cardiac surgery and interventional cardiology. In Part 2, we describe the root cause of risk aversion—the belief by providers that current risk adjustment is inadequate to account for the severity of their highest-risk patients, thereby prejudicing their publicly reported performance scores. Evidence supporting the robustness of current risk adjustment is presented, as well as nine potential strategies to further mitigate risk aversion: optimization of data source, risk models, and performance measures; exclusion of high-risk patients; exclusion of non–procedure-related end points; separate reporting of high-risk patients; reporting by condition or diagnosis rather than by procedures; reporting at the hospital or program level rather than the physician level; collaborative, cross-disciplinary decision making; active surveillance for risk aversion; and improved stakeholder education. Of these, the first is most desirable, widely applicable, and resistant to gaming. Part 1 of this review summarizes the consequences of risk aversion and the observational studies and surveys relevant to this phenomenon, almost all of which are derived from cardiac surgery and interventional cardiology. In Part 2, we describe the root cause of risk aversion—the belief by providers that current risk adjustment is inadequate to account for the severity of their highest-risk patients, thereby prejudicing their publicly reported performance scores. Evidence supporting the robustness of current risk adjustment is presented, as well as nine potential strategies to further mitigate risk aversion: optimization of data source, risk models, and performance measures; exclusion of high-risk patients; exclusion of non–procedure-related end points; separate reporting of high-risk patients; reporting by condition or diagnosis rather than by procedures; reporting at the hospital or program level rather than the physician level; collaborative, cross-disciplinary decision making; active surveillance for risk aversion; and improved stakeholder education. Of these, the first is most desirable, widely applicable, and resistant to gaming. As demonstrated in Part 1 of this review [1Shahian D.M. Jacobs J.P. Badhwar V. D’Agostino R.S. Bavaria J.E. Prager R.L. Risk aversion and public reporting. Part 1: observations from cardiac surgery and interventional cardiology.Ann Thorac Surg. 2017; 104: 2093-2101Abstract Full Text Full Text PDF PubMed Scopus (41) Google Scholar], risk aversion associated with public reporting undoubtedly exists, although its true extent remains uncertain. Empirical data and examples of this practice are derived mainly from interventional cardiology and cardiac surgery and are more consistent and convincing for the former. In Part 2 of this review, we examine the root cause of risk aversion—the belief by providers that current risk adjustment is inadequate to account for the severity of their most critically ill patients, whose anticipated worse outcomes might prejudice their report card ratings. We also examine a variety of strategies that have been proposed or implemented to mitigate risk aversion. Regardless of the actual extent of risk aversion related to public reporting, many cardiac surgeons and interventional cardiologists believe they are inadequately protected by current risk models when they accept high-risk patients. A detailed discussion of the theory and practice of statistical risk modeling for provider profiling is beyond the scope of this article, and relevant references and examples are available [2Iezzoni L.I. Risk Adjustment for Measuring Health Care Outcomes. Health Administration Press, Chicago2012Google Scholar, 3Krumholz H.M. Brindis R.G. Brush J.E. et al.Standards for statistical models used for public reporting of health outcomes: an American Heart Association Scientific Statement from the Quality of Care and Outcomes Research Interdisciplinary Writing Group: cosponsored by the Council on Epidemiology and Prevention and the Stroke Council. Endorsed by the American College of Cardiology Foundation.Circulation. 2006; 113: 456-462Crossref PubMed Scopus (293) Google Scholar, 4Ash A, Fienberg S, Louis T, et al; the COPSS-CMS White Paper Committee. Statistical issues in assessing hospital performance. Commissioned by the Committee of Presidents of Statistical Societies. Available at https://www.Cms.Gov/medicare/quality-initiatives-patient-assessment-instruments/hospitalqualityinits/downloads/statistical-issues-in-assessing-hospital-performance.Pdf. 2011. Accessed May 30, 2017.Google Scholar, 5Normand S.L. Shahian D.M. Statistical and clinical aspects of hospital outcomes profiling.Stat Sci. 2007; 22: 206-226Crossref Scopus (136) Google Scholar, 6Shahian D.M. O’Brien S.M. Filardo G. et al.The Society of Thoracic Surgeons 2008 cardiac surgery risk models: part 1—coronary artery bypass grafting surgery.Ann Thorac Surg. 2009; 88: S2-S22Abstract Full Text Full Text PDF PubMed Scopus (818) Google Scholar, 7O’Brien S.M. Shahian D.M. Filardo G. et al.The Society of Thoracic Surgeons 2008 cardiac surgery risk models: part 2—isolated valve surgery.Ann Thorac Surg. 2009; 88: S23-S42Abstract Full Text Full Text PDF PubMed Scopus (973) Google Scholar, 8Shahian D.M. O’Brien S.M. Filardo G. et al.The Society of Thoracic Surgeons 2008 cardiac surgery risk models: part 3—valve plus coronary artery bypass grafting surgery.Ann Thorac Surg. 2009; 88: S43-S62Abstract Full Text Full Text PDF PubMed Scopus (368) Google Scholar, 9Shahian D.M. He X. Jacobs J.P. et al.Issues in quality measurement: target population, risk adjustment, and ratings.Ann Thorac Surg. 2013; 96: 718-726Abstract Full Text Full Text PDF PubMed Scopus (24) Google Scholar]. No risk model is perfectly predictive for every patient and outcome, and investigators have described numerous theoretical and practical concerns that any public reporting program should consider [10Gupta A. Yeh R.W. Tamis-Holland J.E. et al.Implications of public reporting of risk-adjusted mortality following percutaneous coronary intervention: misperceptions and potential consequences for high-risk patients including nonsurgical patients.JACC Cardiovasc Intervent. 2016; 9: 2077-2085Crossref PubMed Scopus (19) Google Scholar, 11Young M.N. Yeh R.W. Public reporting and coronary revascularization: risk and benefit.Coron Artery Dis. 2014; 25: 619-626Crossref PubMed Scopus (4) Google Scholar, 12Rosenbaum L. Scoring no goal—further adventures in transparency.N Engl J Med. 2015; 373: 1385-1388Crossref PubMed Scopus (21) Google Scholar, 13Kirtane A.J. Nallamothu B.K. Moses J.W. The complicated calculus of publicly reporting mortality after percutaneous coronary intervention.JAMA Cardiol. 2016; 1: 637-638Crossref PubMed Scopus (3) Google Scholar, 14Wasfy J.H. Borden W.B. Secemsky E.A. McCabe J.M. Yeh R.W. Public reporting in cardiovascular medicine: accountability, unintended consequences, and promise for improvement.Circulation. 2015; 131: 1518-1527Crossref PubMed Scopus (47) Google Scholar, 15Cutlip D.E. Ho K.K. Kuntz R.E. Baim D.S. Risk assessment for percutaneous coronary intervention: our version of the weather report?.J Am Coll Cardiol. 2003; 42: 1896-1899Crossref PubMed Scopus (10) Google Scholar]. For example, not all conceivable risk factors, or combinations thereof, are captured even by the best databases, potentially introducing unmeasured confounding. Some risk factors are included in registries but may have excessive missing data that preclude their use or may be present so rarely that they cannot be modeled. Similarly, random sampling variation makes it difficult to model outcomes that occur infrequently, especially with small sample sizes (eg, individual physician reporting). Intentional upcoding of risk factors (a form of “gaming”) by registry participants or inadvertent miscoding caused by poorly specified variables may, over time, dilute the true effect of some risk model predictors by including patients whose actual clinical state does not meet the spirit of the variable. This phenomenon may also give the false impression that risk factor prevalence is increasing in the population, as observed by Green and Wintfeld [16Green J. Wintfeld N. Report cards on cardiac surgeons—assessing New York state’s approach.N Engl J Med. 1995; 332: 1229-1233Crossref PubMed Scopus (313) Google Scholar] in the early New York experience. Finally, in risk model development there is often a tension between models with many predictor variables, which can better accommodate patients with important but uncommon risk factors, and so-called parsimonious models, which are less time consuming and labor intensive to use but may not contain infrequently occurring risk factors. Well-constructed parsimonious models may have overall performance nearly identical to that of full models with more predictors. However, these may underestimate the risk of the few patients who have rare but particularly high-risk characteristics that are not included in the models. Only a few such patients could theoretically affect the risk-adjusted outcomes of a specific provider, especially during short sampling periods or with small volumes. Given all of these potential issues, what is known about the performance of risk models used in cardiac operations and percutaneous coronary interventions (PCI)? Virtually all published risk models for these procedures have acceptable statistical calibration and discrimination, the most basic tests of risk model performance. A number of studies have also specifically examined the real world protection afforded by risk adjustment in both cardiac surgery and PCI. In early reports of the New York Cardiac Surgery Reporting System (CSRS), Hannan and colleagues [17Hannan E.L. Kilburn Jr., H. Racz M. Shields E. Chassin M.R. Improving the outcomes of coronary artery bypass surgery in New York state.JAMA. 1994; 271: 761-766Crossref PubMed Scopus (666) Google Scholar] and Chassin and colleagues [18Chassin M.R. Hannan E.L. DeBuono B.A. Benefits and hazards of reporting medical outcomes publicly.N Engl J Med. 1996; 334: 394-398Crossref PubMed Scopus (307) Google Scholar] demonstrated that New York coronary artery bypass grafting (CABG) risk models performed well at the patient level across all strata of expected risk. There was slight overprediction of risk for the most severely ill patients, suggesting more than adequate protection for programs accepting such patients. Importantly, both studies documented a significant negative correlation between expected mortality and risk-adjusted mortality (RAM) in each of the first 4 years of the New York CSRS; that is, programs that cared for the highest-risk patients often had the lowest RAM, and vice versa. This may reflect the excellent protection afforded by these risk models or the superior performance of programs that are willing to accept high-risk patients (a desirable matching of risk and capability), or both. Hannan and colleagues [19Hannan E.L. Siu A.L. Kumar D. Racz M. Pryor D.B. Chassin M.R. Assessment of coronary artery bypass graft surgery performance in New York. Is there a bias against taking high-risk patients?.Med Care. 1997; 35: 49-56Crossref PubMed Scopus (57) Google Scholar] further studied the adequacy of risk adjustment using 1990 to 1992 New York CABG data from 31 hospitals and 87 surgeons. This analysis was performed for all 44,918 patients as well as a high-risk (≥7.5% predicted mortality risk) subset of 3,281 patients (7.3% of the total). Observed mortality for the high-risk group was 15.88% vs 1.96% for the remaining 41,637 low-risk patients, but the RAM was actually lower for high-risk patients (2.94% vs 3.02%). No hospital had significantly different RAM for low-risk patients alone vs all patients (ie, with high-risk patients included), and half of the hospitals had lower RAM for all of their CABG patients than for only their lower-risk patients. At the hospital level, RAM for all CABG patients correlated strongly with RAM for low- and high-risk patients separately. Even if a higher threshold were used to classify high-risk patients (ie, three times average, or 9%), the RAM for high-risk patients in 18 of 31 hospitals was less than the state average of 2.99%. All of these findings affirm the adequacy of risk adjustment and do not support the belief that avoiding high-risk patients would improve risk-adjusted outcomes. Contemporary investigations have verified the protection afforded by modern risk models developed from large clinical registries. Englum and colleagues [20Englum B.R. Saha-Chaudhuri P. Shahian D.M. et al.The impact of high-risk cases on hospitals’ risk-adjusted coronary artery bypass grafting mortality rankings.Ann Thorac Surg. 2015; 99: 856-862Abstract Full Text Full Text PDF PubMed Scopus (16) Google Scholar] studied 494,955 patients who underwent isolated CABG between 2008 and 2010 and who were included in The Society of Thoracic Surgeons (STS) Adult Cardiac Surgery Database (ACSD). The 1,002 sites that cared for these patients were divided into quintiles by their average expected risk of operative mortality, ranging from 1.46% in quintile 1 to 2.87% in quintile 5. The overall calibration of the STS risk model in this cohort was excellent, although there was slight overprediction of death among the highest-risk 1% of patients (expected risk >20%). Observed-to-expected (O/E) mortality ratios were not significantly different than unity for any but the highest-risk quintile, in which O/E was 0.80 (95% confidence interval [CI], 0.77 to 0.84), indicating better-than-expected performance. Similar findings were observed when all of a hospital’s highest-risk patients during a 3-year period were analyzed as if they had occurred in 1 “nightmare year” scenario. These analyses demonstrated that the STS CABG risk model provides adequate risk adjustment even for hospitals that care for the highest-acuity patients. They challenge the widely held notion that avoiding high-risk patients will improve a provider’s risk-adjusted outcomes. Indeed, the STS CABG risk model (like many other risk models) offers some degree of “overprotection” for surgeons and hospitals caring for the highest-risk patients. Sherwood and colleagues [21Sherwood M.W. Brennan J.M. Ho K.K. et al.The impact of extreme-risk cases on hospitals’ risk-adjusted percutaneous coronary intervention mortality ratings.JACC Cardiovasc Interv. 2015; 8(1 Pt A): 10-16Crossref Scopus (25) Google Scholar] performed a similar investigation for PCI, analyzing 624,286 patients from 1,168 sites that contributed data to the American College of Cardiology National Cardiovascular Data Registry (NCDR) CathPCI data set in 2010. Using the NCDR PCI mortality risk model, they found good calibration over the wide range of predicted and observed mortality rates. Hospitals were grouped into quintiles by overall hospital expected mortality rates, and O/E ratios were estimated for each quintile. Sensitivity analyses ranked hospitals by quintiles by their percentage of shock, cardiac arrest, or other extremely high-risk (≥10% mortality) patients. Overall, O/E ratios for most risk quintiles were close to 1, except for the hospitals in the highest-risk quintile, for whom performance was better than expected (0.91; 95% CI, 0.87 to 0.96); results were similar for the sensitivity analyses. These hospitals also had somewhat lower RAM than the lowest-risk hospitals. When each site’s highest-risk patients from 2009 to 2011 were combined into a single, simulated, exceptionally high-risk year, O/E ratios all remained approximately 1, there was no increased identification of outlier hospitals, and there was generally good agreement of O/E ratios between the extreme high-risk year and average-year values. Thus, the authors argued that current PCI risk models are adequate to cover the risk of very high-acuity patients and will not unfairly penalize providers who care for them [22Sherwood M.W. Peterson E.D. Risk adjusted mortality ratings and public reporting for high-risk PCI.JACC Cardiovasc Interv. 2015; 8: 1134-1135Crossref PubMed Scopus (2) Google Scholar]. Notwithstanding these reassuring findings, persistent provider anxiety and the resulting potential for risk aversion are a continuing concern, and numerous mitigation strategies have been suggested in response [10Gupta A. Yeh R.W. Tamis-Holland J.E. et al.Implications of public reporting of risk-adjusted mortality following percutaneous coronary intervention: misperceptions and potential consequences for high-risk patients including nonsurgical patients.JACC Cardiovasc Intervent. 2016; 9: 2077-2085Crossref PubMed Scopus (19) Google Scholar, 11Young M.N. Yeh R.W. Public reporting and coronary revascularization: risk and benefit.Coron Artery Dis. 2014; 25: 619-626Crossref PubMed Scopus (4) Google Scholar, 13Kirtane A.J. Nallamothu B.K. Moses J.W. The complicated calculus of publicly reporting mortality after percutaneous coronary intervention.JAMA Cardiol. 2016; 1: 637-638Crossref PubMed Scopus (3) Google Scholar, 14Wasfy J.H. Borden W.B. Secemsky E.A. McCabe J.M. Yeh R.W. Public reporting in cardiovascular medicine: accountability, unintended consequences, and promise for improvement.Circulation. 2015; 131: 1518-1527Crossref PubMed Scopus (47) Google Scholar, 23Hannan E.L. Cozzens K. King 3rd, S.B. Walford G. Shah N.R. The New York state cardiac registries: history, contributions, limitations, and lessons for future efforts to assess and publicly report healthcare outcomes.J Am Coll Cardiol. 2012; 59: 2309-2316Crossref PubMed Scopus (126) Google Scholar, 24Peberdy M.A. Donnino M.W. Callaway C.W. et al.Impact of percutaneous coronary intervention performance reporting on cardiac resuscitation centers: a scientific statement from the American Heart Association.Circulation. 2013; 128: 762-773Crossref PubMed Scopus (82) Google Scholar, 25Bavaria J. Presidential address: quality and innovation in cardiothoracic surgery: colliding imperatives? Presented at The Society of Thoracic Surgeons 53rd Annual Meeting, Houston, Texas, January 21–25, 2017.Google Scholar]. They all share the common goal of enhancing provider trust in the accuracy and fairness of the performance measures and processes used in public reporting, thereby addressing the major driver of risk-averse behavior. Because providers often mistrust the ability of risk models and quality metrics to accurately characterize their performance [26Sherman K.L. Gordon E.J. Mahvi D.M. et al.Surgeons’ perceptions of public reporting of hospital and individual surgeon quality.Med Care. 2013; 51: 1069-1075Crossref PubMed Scopus (33) Google Scholar, 27Schneider E.C. Epstein A.M. Influence of cardiac-surgery performance reports on referral practices and access to care—a survey of cardiovascular specialists.N Engl J Med. 1996; 335: 251-256Crossref PubMed Scopus (363) Google Scholar, 28Jarral O.A. Baig K. Pettengell C. et al.National survey of UK consultant surgeons’ opinions on surgeon-specific mortality data in cardiothoracic surgery.Circ Cardiovasc Qual Outcomes. 2016; 9: 414-423Crossref PubMed Scopus (23) Google Scholar, 29Narins C.R. Dozier A.M. Ling F.S. Zareba W. The influence of public reporting of outcome data on medical decision making by physicians.Arch Intern Med. 2005; 165: 83-87Crossref PubMed Scopus (168) Google Scholar, 30Burack J.H. Impellizzeri P. Homel P. Cunningham Jr., J.N. Public reporting of surgical mortality: a survey of New York state cardiothoracic surgeons.Ann Thorac Surg. 1999; 68: 1195-1200Abstract Full Text Full Text PDF PubMed Scopus (174) Google Scholar], the ideal defense against risk aversion is to optimize all steps of the quality measurement process. The development of credible performance measures begins with use of the best data, which we believe are prospectively collected, standardized, clinical registry data [23Hannan E.L. Cozzens K. King 3rd, S.B. Walford G. Shah N.R. The New York state cardiac registries: history, contributions, limitations, and lessons for future efforts to assess and publicly report healthcare outcomes.J Am Coll Cardiol. 2012; 59: 2309-2316Crossref PubMed Scopus (126) Google Scholar, 31Shahian D. Silverstein T. Lovett A. Wolf R. Normand S.L. Comparison of clinical and administrative data sources for hospital coronary artery bypass graft surgery report cards.Circulation. 2007; 115: 1518-1527Crossref PubMed Scopus (158) Google Scholar, 32Mack M.J. Herbert M. Prince S. Dewey T.M. Magee M.J. Edgerton J.R. Does reporting of coronary artery bypass grafting from administrative databases accurately reflect actual clinical outcomes?.J Thorac Cardiovasc Surg. 2005; 129: 1309-1317Abstract Full Text Full Text PDF PubMed Scopus (85) Google Scholar], as exemplified by the STS National Database and the American College of Cardiology NCDR. Although registry data are readily available in cardiology and cardiac surgery, other specialties lag behind, and the establishment of such registries across all of health care should be an urgent priority. These structured clinical data are much more useful and accurate for performance measurement than the largely unstructured information collected in electronic health records; similarly, administrative claims data were designed primarily for billing and not performance measurement [31Shahian D. Silverstein T. Lovett A. Wolf R. Normand S.L. Comparison of clinical and administrative data sources for hospital coronary artery bypass graft surgery report cards.Circulation. 2007; 115: 1518-1527Crossref PubMed Scopus (158) Google Scholar, 32Mack M.J. Herbert M. Prince S. Dewey T.M. Magee M.J. Edgerton J.R. Does reporting of coronary artery bypass grafting from administrative databases accurately reflect actual clinical outcomes?.J Thorac Cardiovasc Surg. 2005; 129: 1309-1317Abstract Full Text Full Text PDF PubMed Scopus (85) Google Scholar]. Even when derived from clinical registries, the accuracy of data used for public reporting must be verified. In their study of the early New York CABG report card experience, Green and Wintfeld [16Green J. Wintfeld N. Report cards on cardiac surgeons—assessing New York state’s approach.N Engl J Med. 1995; 332: 1229-1233Crossref PubMed Scopus (313) Google Scholar] reported that between 1989 and 1991, the coded prevalence of numerous risk factors increased dramatically, including renal failure, 0.4% to 2.8%; congestive heart failure, 1.7% to 7.6%; chronic obstructive pulmonary disease, 6.9% to 17.4%; unstable angina, 14.9% to 21.8%; and low ejection fraction, 18.9% to 22.2%. They hypothesize that this might have resulted from upcoding of risk factors by some programs to inflate their expected mortality rates, thereby reducing their mortality O/E ratios and RAM without actually improving outcomes. Chassin and colleagues [18Chassin M.R. Hannan E.L. DeBuono B.A. Benefits and hazards of reporting medical outcomes publicly.N Engl J Med. 1996; 334: 394-398Crossref PubMed Scopus (307) Google Scholar] asserted that these findings were largely the result of risk factor definition changes during the early years of public reporting, as well as some undercoding in 1989, which was identified and corrected; risk factor prevalence stabilized thereafter. However, periodic data quality issues did persist and were the impetus for ongoing audit. Hannan and colleagues [23Hannan E.L. Cozzens K. King 3rd, S.B. Walford G. Shah N.R. The New York state cardiac registries: history, contributions, limitations, and lessons for future efforts to assess and publicly report healthcare outcomes.J Am Coll Cardiol. 2012; 59: 2309-2316Crossref PubMed Scopus (126) Google Scholar] encountered instances not only of inflated risk factor prevalence but also of omitted cases in which the patients had died. Thus, multiple levels of data audit [18Chassin M.R. Hannan E.L. DeBuono B.A. Benefits and hazards of reporting medical outcomes publicly.N Engl J Med. 1996; 334: 394-398Crossref PubMed Scopus (307) Google Scholar, 23Hannan E.L. Cozzens K. King 3rd, S.B. Walford G. Shah N.R. The New York state cardiac registries: history, contributions, limitations, and lessons for future efforts to assess and publicly report healthcare outcomes.J Am Coll Cardiol. 2012; 59: 2309-2316Crossref PubMed Scopus (126) Google Scholar] and adjudication [33Barringhaus K.G. Zelevinsky K. Lovett A. Normand S.L. Ho K.K. Impact of independent data adjudication on hospital-specific estimates of risk-adjusted mortality following percutaneous coronary interventions in Massachusetts.Circ Cardiovasc Qual Outcomes. 2011; 4: 92-98Crossref PubMed Scopus (24) Google Scholar] are essential. Data that are out of range, unusually high-risk factor prevalence (eg, upcoding), critical outcomes, and case completeness, should all be reviewed. Submitted case lists should be compared with hospital operating room logs to ensure that patients with poor outcomes were not omitted. STS National Database studies comparing cases submitted from STS sites against procedures billed to Centers for Medicare and Medicaid Services suggest high completeness rates (98% in 2012) [34Jacobs J.P. Shahian D.M. He X. et al.Penetration, completeness, and representativeness of The Society of Thoracic Surgeons Adult Cardiac Surgery Database.Ann Thorac Surg. 2016; 101: 33-41Abstract Full Text Full Text PDF PubMed Scopus (65) Google Scholar]. This data quality oversight may be accomplished through a combination of random, nationally supervised audits, such as the annual STS audit of 10% of participants, which consistently demonstrates 96% to 97% data accuracy; and state or regional adjudication conducted by physicians and data managers, such as the Massachusetts Data Analysis Center adjudication process for CABG and PCI report cards [35Mass Data Analysis Center, Department of Health Care Policy, Harvard Medical School. Adult percutaneous coronary intervention reports. Available at http://www.massdac.org/reports/cardiac-study-annual/. Accessed May 12, 2017.Google Scholar, 36Mass Data Analysis Center, Department of Health Care Policy, Harvard Medical School. Adult coronary artery bypass graft surgery annual reports. Available at http://www.massdac.org/reports/cardiac-study-annual/. Accessed May 12, 2017.Google Scholar]. The latter approach is particularly well suited to review all patient records coded for uncommon risk variables with high impact (eg, emergency status or cardiogenic shock), as well as all excluded patients, to ensure that relevant specifications are met [33Barringhaus K.G. Zelevinsky K. Lovett A. Normand S.L. Ho K.K. Impact of independent data adjudication on hospital-specific estimates of risk-adjusted mortality following percutaneous coronary interventions in Massachusetts.Circ Cardiovasc Qual Outcomes. 2011; 4: 92-98Crossref PubMed Scopus (24) Google Scholar, 35Mass Data Analysis Center, Department of Health Care Policy, Harvard Medical School. Adult percutaneous coronary intervention reports. Available at http://www.massdac.org/reports/cardiac-study-annual/. Accessed May 12, 2017.Google Scholar]. Ideally, clinical registry data used for public reporting should be verified against governmental sources [23Hannan E.L. Cozzens K. King 3rd, S.B. Walford G. Shah N.R. The New York state cardiac registries: history, contributions, limitations, and lessons for future efforts to assess and publicly report healthcare outcomes.J Am Coll Cardiol. 2012; 59: 2309-2316Crossref PubMed Scopus (126) Google Scholar], such as the Statewide Planning and Research Cooperative System registry in New York [23Hannan E.L. Cozzens K. King 3rd, S.B. Walford G. Shah N.R. The New York state cardiac registries: history, contributions, limitations, and lessons for future efforts to assess and publicly report healthcare outcomes.J Am Coll Cardiol. 2012; 59: 2309-2316Crossref PubMed Scopus (126) Google Scholar], the Social Security Death Master File (currently unavailable) [37Jacobs J.P. O’Brien S.M. Shahian D.M. et al.Successful linking of The Society of Thoracic Surgeons database to Social Security data to examine the accuracy of Society of Thoracic Surgeons mortality data.J Thorac Cardiovasc Surg. 2013; 145: 976-983Abstract Full Text Full Text PDF PubMed Scopus (27) Google Scholar, 38Jacobs J.P. Edwards F.H. Shahian D.M. et al.Successful linking of The Society of Thoracic Surgeons database to Social Security data to examine survival after cardiac operations.Ann Thorac Surg. 2011; 92: 32-37Abstract Full Text Full Text PDF PubMed Scopus (75) Google Scholar], Centers for Medicare and Medicaid Services Medicare Provider Analysis and Review files (primarily for patients aged >65 years) [34Jacobs J.P. Shahian D.M. He X. et al.Penetration, completeness, and representativeness of The Society of Thoracic Surgeons Adult Cardiac Surgery Database.Ann Thorac Surg. 2016; 101: 33-41Abstract Full Text Full Text PDF PubMed Scopus (65) Google Scholar, 39Jacobs J.P. Edwards F.H. Shahian D.M. et al.Successful linking of The Society of Thoracic Surgeons Adult Cardiac Surgery Database to Centers for Medicare and Medicaid Services Medicare data.Ann Thorac Surg. 2010; 90: 1150-1156Abstract Full Text Full Text PDF PubMed Scopus (1) Google Scholar], and the National Death Index [40Morales D.L. McClellan A.J. Jacobs J.P. Empowering a database with national long-term data about mortality: the use of national death registries.Cardiol Young. 2008; 18: 188-195Crossref PubMed Scopus (47) Google Scholar]. To reassure providers that their outcomes are being fairly adjusted for inherent patient severity, risk models should be available for all performance measures. These should be developed through collaborations of clinicians and statisticians, using state of the art modeling techniques. All available, clin" @default.
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• W2766777799 title "Risk Aversion and Public Reporting. Part 2: Mitigation Strategies" @default.
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