Abstract P255: Should Models of Hospital Outcomes Include Race as a Covariate?

Author(s):  
Paul L Hebert ◽  
Joseph Ross ◽  
Nathan Goldstein ◽  
Elizabeth Howell

Objective: There remains controversy about whether models of hospital performance should account for patient race. We used simulated data to explore the effects on hospital rankings of including or excluding race as a covariate in risk-standardized hospital outcome models, using hospitalizations for heart failure as a case study. Methods We simulated three scenarios by which patient race might affect heart failure hospital outcome: a) a treatment bias simulation in which non-white patients were 20% less likely to receive optimal treatment regardless of hospital quality performance; b) an allocative bias simulation in which non-white patients systematically received care from lower performing hospitals that lower quality care to all patients uniformly; and c) a survival bias simulation in which nonwhite patients were 10% less likely to survive than white patients regardless of hospital quality performance. We evaluated the concordance in estimated hospital rank between models that did and did not include race for a simulation of 100,000 patients hospitalized at 1,000 hospitals. We also present the extent to which each model over- or under-predicted hospital quality for hospitals that treat a high percentage of nonwhite patients. Results When allocation or treatment bias scenarios were simulated, the model results were highly consistent (kappa>0.9) regardless of whether or not patient race was included in risk-standardization models; models were most disparate for the survival bias scenario (kappa =0.689). In both the allocative bias and the treatment bias scenarios, models that include race overestimated the quality of hospital care at hospitals that treat a higher percentage of nonwhite patients (beta =91.9 and 78.9, respectively; p<0.001) while models that excluded race did not (beta=31.5; p=0.184, and 2.5; p=0.916, respectively). In the survival disparity scenario, the model that included race performed well (beta=-36.7 p=0.15), whereas the model that excluded race significantly underestimated quality at highly nonwhite hospitals (beta= -326.6; p<0.001). Conclusion The impact of including race in risk standardization models of hospital performance depends on causal pathways by which race impacts clinical outcomes.

2012 ◽  
Vol 17 (6) ◽  
pp. 381-384 ◽  
Author(s):  
Kimberley A Kaseweter ◽  
Brian B Drwecki ◽  
Kenneth M Prkachin

BACKGROUND: Evidence of inadequate pain treatment as a result of patient race has been extensively documented, yet remains poorly understood. Previous research has indicated that nonwhite patients are significantly more likely to be undertreated for pain.OBJECTIVE: To determine whether previous findings of racial biases in pain treatment recommendations and empathy are generalizable to a sample of Canadian observers and, if so, to determine whether empathy biases mediate the pain treatment disparity.METHODS: Fifty Canadian undergraduate students (24 men and 26 women) watched videos of black and white patients exhibiting facial expressions of pain. Participants provided pain treatment decisions and reported their feelings of empathy for each patient.RESULTS: Participants demonstrated both a prowhite treatment bias and a prowhite empathy bias, reporting more empathy for white patients than black patients and prescribing more pain treatment for white patients than black patients. Empathy was found to mediate the effect of race on pain treatment.CONCLUSIONS: The results of the present study closely replicate those from a previous study of American observers, providing evidence that a prowhite bias is not a peculiar feature of the American population. These results also add support to the claim that empathy plays a crucial role in racial pain treatment disparity.


2009 ◽  
Vol 12 (3) ◽  
pp. A161
Author(s):  
A Bautista ◽  
BD Stein ◽  
TA Lee ◽  
DO Meltzer ◽  
R DiDomenico ◽  
...  

Author(s):  
Lauren Gilstrap ◽  
Jonathan S. Skinner ◽  
Barbara Gladders ◽  
A. James O’Malley, ◽  
Amber E. Barnato ◽  
...  

Background: To combat the high cost and increasing burden of quality reporting, the Medicare Payment Advisory (MedPAC) has recommended using claims data wherever possible to measure clinical quality. In this article, we use a cohort of Medicare beneficiaries with heart failure with reduced ejection fraction and existing quality metrics to explore the impact of changes in quality metric methodology on measured quality performance, the association with patient outcomes, and hospital rankings. Methods and Results: We used 100% Medicare Parts A and B and a random 40% sample of Part D from 2008 to 2015 to create (1) a cohort of 295 494 fee-for-service beneficiaries with ≥1 hospitalization for heart failure with reduced ejection fraction and (2) a cohort of 1079 hospitals with ≥11 heart failure with reduced ejection fraction admissions in 2014 and 2015. We used Part D data to calculate β-blocker use after discharge and β-blocker use over time. We then varied the quality metric methodologies to explore the impact on measured performance. We then used multivariable time-to-event analyses to explore the impact of metric methodology on the association between quality performance and patient outcomes and Kendall’s Tau to describe impact of quality metric methodology on hospital rankings. We found that quality metric methodology had a significant impact on measured quality performance. The association between quality performance and readmissions was sensitive to changes in methodology but the association with 1-year mortality was not. Changes in quality metric methodology also had a substantial impact on hospital quality rankings. Conclusions: This article highlights how small changes in quality metric methodology can have a significant impact on measured quality performance, the association between quality performance and utilization-based outcomes, and hospital rankings. These findings highlight the need for standardized quality metric methodologies, better case-mix adjustment and cast further doubt on the use of utilization-based outcomes as quality metrics in chronic diseases.


2010 ◽  
Author(s):  
J. A. Cully ◽  
L. L. Phillips ◽  
M. E. Kunik ◽  
M. A. Stanley ◽  
A. Deswal

2020 ◽  
Vol 28 ◽  
Author(s):  
Valeria Visco ◽  
Germano Junior Ferruzzi ◽  
Federico Nicastro ◽  
Nicola Virtuoso ◽  
Albino Carrizzo ◽  
...  

Background: In the real world, medical practice is changing hand in hand with the development of new Artificial Intelligence (AI) systems and problems from different areas have been successfully solved using AI algorithms. Specifically, the use of AI techniques in setting up or building precision medicine is significant in terms of the accuracy of disease discovery and tailored treatment. Moreover, with the use of technology, clinical personnel can deliver a very much efficient healthcare service. Objective: This article reviews AI state-of-the-art in cardiovascular disease management, focusing on diagnostic and therapeutic improvements. Methods: To that end, we conducted a detailed PubMed search on AI application from distinct areas of cardiology: heart failure, arterial hypertension, atrial fibrillation, syncope and cardiovascular rehabilitation. Particularly, to assess the impact of these technologies in clinical decision-making, this research considers technical and medical aspects. Results: On one hand, some devices in heart failure, atrial fibrillation and cardiac rehabilitation represent an inexpensive, not invasive or not very invasive approach to long-term surveillance and management in these areas. On the other hand, the availability of large datasets (big data) is a useful tool to predict the development and outcome of many cardiovascular diseases. In summary, with this new guided therapy, the physician can supply prompt, individualised, and tailored treatment and the patients feel safe as they are continuously monitored, with a significant psychological effect. Conclusion: Soon, tailored patient care via telemonitoring can improve the clinical practice because AI-based systems support cardiologists in daily medical activities, improving disease detection and treatment. However, the physician-patient relationship remains a pivotal step.


Cardiology ◽  
2000 ◽  
Vol 93 (1-2) ◽  
pp. 56-69 ◽  
Author(s):  
Carl V. Leier ◽  
Rene J. Alvarez ◽  
Philip F. Binkley

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