Statistical and Clinical Aspects of Hospital Outcomes Profiling
TL;DR: This work reviews the historical evolution of hospital profiling with special emphasis on outcomes; presents a detailed history of cardiac surgery report cards, the paradigm for modern provider profiling; discusses the potential unintended negative consequences of public report cards; and describes various statistical methodologies for quantifying the relative performance of cardiac Surgery programs.
Abstract: Hospital profiling involves a comparison of a health care provider's structure, processes of care, or outcomes to a standard, often in the form of a report card. Given the ubiquity of report cards and similar consumer ratings in contemporary American culture, it is notable that these are a relatively recent phenomenon in health care. Prior to the 1986 release of Medicare hospital outcome data, little such information was publicly available. We review the historical evolution of hospital profiling with special emphasis on outcomes; present a detailed history of cardiac surgery report cards, the paradigm for modern provider profiling; discuss the potential unintended negative consequences of public report cards; and describe various statistical methodologies for quantifying the relative performance of cardiac surgery programs. Outstanding statistical issues are also described.
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TL;DR: New STS risk models have been developed for CABG mortality and eight other endpoints and overall model performance is excellent, with acceptable calibration in the overall population and in numerous subgroups.
1,121 citations
TL;DR: The current and former chairs and vice-chairs of the FACC are: David Hillis, MD, FACC, Chair, Peter K. Smith,MD, F ACC, Vice Chair*, and Ola Selnes, PhD, PhD.
Abstract: L. David Hillis, MD, FACC, Chair†; Peter K. Smith, MD, FACC, Vice Chair*†; Jeffrey L. Anderson, MD, FACC, FAHA*‡; John A. Bittl, MD, FACC§; Charles R. Bridges, MD, SCD, FACC, FAHA*†; John G. Byrne, MD, FACC†; Joaquin E. Cigarroa, MD, FACC†; Verdi J. DiSesa, MD, FACC†; Loren F. Hiratzka, MD, FACC, FAHA†; Adolph M. Hutter, Jr, MD, MACC, FAHA†; Michael E. Jessen, MD, FACC*†; Ellen C. Keeley, MD, MS†; Stephen J. Lahey, MD†; Richard A. Lange, MD, FACC, FAHA†§; Martin J. London, MD ; Michael J. Mack, MD, FACC*¶; Manesh R. Patel, MD, FACC†; John D. Puskas, MD, FACC*†; Joseph F. Sabik, MD, FACC*#; Ola Selnes, PhD†; David M. Shahian, MD, FACC, FAHA**; Jeffrey C. Trost, MD, FACC*†; Michael D. Winniford, MD, FACC†
811 citations
University of Texas Health Science Center at San Antonio1, Duke University2, Primary Children's Hospital3, University of Pennsylvania4, Vanderbilt University5, Oregon Health & Science University6, Johns Hopkins University7, Harvard University8, University of Texas Southwestern Medical Center9, University of Virginia10, University of Connecticut11, University of California, San Francisco12, Baylor University13, Emory University14, Cleveland Clinic15, University of Mississippi16, Pennsylvania State University17
TL;DR: Alice K. Jacobs,MD, FACC, FAHA, Chair Jeffrey L. Anderson, MD, F ACC, FAH, Chair-Elect Nancy Albert, PhD, CCNS, CCRN,FAHA, chair-Elect.
766 citations
487 citations
TL;DR: New models have been developed focusing on specific surgical procedures, which provide opportunities to incorporate indication and other procedure-specific variables and outcomes to improve risk adjustment and comparative benchmark reports given to participating hospitals have been expanded considerably to allow more detailed evaluations of performance.
Abstract: The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) collects detailed clinical data from participating hospitals using standardized data definitions, analyzes these data, and provides participating hospitals with reports that permit risk-adjusted comparisons with a surgical quality standard. Since its inception, the ACS NSQIP has worked to refine surgical outcomes measurements and enhance statistical methods to improve the reliability and validity of this hospital profiling. From an original focus on controlling for between-hospital differences in patient risk factors with logistic regression, ACS NSQIP has added a variable to better adjust for the complexity and risk profile of surgical procedures (procedure mix adjustment) and stabilized estimates derived from small samples by using a hierarchical model with shrinkage adjustment. New models have been developed focusing on specific surgical procedures (eg, "Procedure Targeted" models), which provide opportunities to incorporate indication and other procedure-specific variables and outcomes to improve risk adjustment. In addition, comparative benchmark reports given to participating hospitals have been expanded considerably to allow more detailed evaluations of performance. Finally, procedures have been developed to estimate surgical risk for individual patients. This article describes the development of, and justification for, these new statistical methods and reporting strategies in ACS NSQIP.
468 citations
References
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01 Jan 2000
TL;DR: Boken presenterer en helhetlig strategi for hvordan myndigheter, helsepersonell, industri og forbrukere kan redusere medisinske feil.
Abstract: Boken presenterer en helhetlig strategi for hvordan myndigheter, helsepersonell, industri og forbrukere kan redusere medisinske feil.
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TL;DR: Analyzing health care organizations as complex systems, Crossing the Quality Chasm also documents the causes of the quality gap, identifies current practices that impede quality care, and explores how systems approaches can be used to implement change.
Abstract: Crossing the Quality Chasm identifies and recommends improvements in six dimensions of health care in the U.S.: patient safety, care effectiveness, patient-centeredness, timeliness, care efficiency, and equity. Safety looks at reducing the likelihood that patients are harmed by medical errors. Effectiveness describes avoiding over and underuse of resources and services. Patient-centeredness relates both to customer service and to considering and accommodating individual patient needs when making care decisions. Timeliness emphasizes reducing wait times. Efficiency focuses on reducing waste and, as a result, total cost of care. Equity looks at closing racial and income gaps in health care.
15,046 citations
"Statistical and Clinical Aspects of..." refers background in this paper
...Since the publication of several Institute of Medicine reports on quality, transparency and patient safety (Institute of Medicine, 2000, 2001), attention has increasingly been focused on the objective measurement of health care outcomes....
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01 Mar 1981
7,518 citations
"Statistical and Clinical Aspects of..." refers background in this paper
...In the case of a collection of binary measures, the observed number of patients receiving needed therapy k may be thought of as arising from a Rasch model (Rasch, 1960), Yik | β, θi ∼ Bin(nik, pik), where logit(pik) = β0k − βθi(10) and θi i .i .d ....
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02 Nov 2002
TL;DR: In this article, a panel of experts documents this evidence and explores how persons of color experience the health care environment, examining how disparities in treatment may arise in health care systems and looking at aspects of the clinical encounter that may contribute to such disparities.
Abstract: Racial and ethnic disparities in health care are known to reflect access to care and other issues that arise from differing socioeconomic conditions. There is, however, increasing evidence that even after such differences are accounted for, race and ethnicity remain significant predictors of the quality of health care received. In Unequal Treatment, a panel of experts documents this evidence and explores how persons of color experience the health care environment. The book examines how disparities in treatment may arise in health care systems and looks at aspects of the clinical encounter that may contribute to such disparities. Patients’ and providers’ attitudes, expectations, and behavior are analyzed. How to intervene? Unequal Treatment offers recommendations for improvements in medical care financing, allocation of care, availability of language translation, community-based care, and other arenas. The committee highlights the potential of cross-cultural education to improve provider–patient communication and offers a detailed look at how to integrate cross-cultural learning within the health professions. The book concludes with recommendations for data collection and research initiatives. Unequal Treatment will be vitally important to health care policymakers, administrators, providers, educators, and students as well as advocates for people of color.
6,185 citations
01 Jan 2006
TL;DR: Regression models are frequently used to develop diagnostic, prognostic, and health resource utilization models in clinical, health services, outcomes, pharmacoeconomic, and epidemiologic research, and in a multitude of non-health-related areas.
Abstract: Regression models are frequently used to develop diagnostic, prognostic, and health resource utilization models in clinical, health services, outcomes, pharmacoeconomic, and epidemiologic research, and in a multitude of non-health-related areas. Regression models are also used to adjust for patient heterogeneity in randomized clinical trials, to obtain tests that are more powerful and valid than unadjusted treatment comparisons.
4,211 citations