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Journal ArticleDOI

Interpreting Hospital Mortality Data: How Can We Proceed?

TLDR
This report document and justify some of the design decisions made to assess the impact of the diagnosis related group (DRG)-based prospective payment system (PPS) on the quality of care for hospitalized Medicare patients.
Abstract
DATA about whether patients live or die following short-term—care hospitalization can serve at least two purposes: (1) to determine if hospital performance has improved or deteriorated over time and (2) to determine if performance differs across hospitals at one given time. Hospital mortality figures have been used in this manner primarily because such data are readily available, easy to understand, and potentially important to the public. However, if mortality data are to be used for these purposes, they must reflect actual differences in the quality of care, not other factors, such as patient sickness at admission. We have been studying hospital mortality and other outcomes as part of a clinically detailed national study to assess the impact of the diagnosis related group (DRG)-based prospective payment system (PPS) on the quality of care for hospitalized Medicare patients. In this report we document and justify some of the design decisions we made

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Citations
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Journal ArticleDOI

League Tables and Their Limitations: Statistical Issues in Comparisons of Institutional Performance

TL;DR: The paper emphasizes that statistical methods exist which can contribute to an understanding of the extent and possible reasons for differences between institutions and urges caution by discussing the limitations of such methods.
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Practical considerations on the use of the Charlson comorbidity index with administrative data bases.

TL;DR: It is concluded that the Charlson index may be an efficient approach to risk adjustment from administrative data bases, although it should be tested on other conditions.
Journal ArticleDOI

Risk adjustment in outcome assessment: the Charlson comorbidity index.

TL;DR: The Charlson Index is a useful approach to risk adjustment in outcomes research from administrative databases using the MED-ECHO database ( Quebec) to measure the burden of comorbid diseases.
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Hospital use and mortality among Medicare beneficiaries in Boston and New Haven.

TL;DR: It is concluded that the lower rate of hospital use by Medicare enrollees in New Haven was not associated with a higher overall mortality rate, and population-based and hospital-based statistics are needed to evaluate differences in hospital mortality rates for high-variation medical conditions.
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Chronic comorbidity and outcomes of hospital care: length of stay, mortality, and readmission at 30 and 365 days.

TL;DR: The highest comorbidity measured by the CHI was associated with greater LOS and in-hospital mortality and increased readmission at 30 and 365 days, and the rate of readmissions at 1 year dropped, however, in the group with the greatest comorbridity.
References
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Journal ArticleDOI

A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation☆

TL;DR: The method of classifying comorbidity provides a simple, readily applicable and valid method of estimating risk of death fromComorbid disease for use in longitudinal studies and further work in larger populations is still required to refine the approach.
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APACHE II: a severity of disease classification system.

TL;DR: The form and validation results of APACHE II, a severity of disease classification system that uses a point score based upon initial values of 12 routine physiologic measurements, age, and previous health status, are presented.
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Psychosocial influences on mortality after myocardial infarction.

TL;DR: The increase in risk associated with stress and social isolation applied both to total deaths and to sudden cardiac deaths and was noted among men with both high and low levels of ventricular ectopy during hospitalization for the acute infarction.
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Assessing hospital-associated deaths from discharge data. The role of length of stay and comorbidities.

TL;DR: It is concluded that inpatient death rates depend on length-of-stay patterns and give a biased picture of mortality.
Journal ArticleDOI

Hospital Inpatient Mortality: Is It a Predictor of Quality?

TL;DR: A large number of potential measures of quality of care are being used to differentiate hospitals, and the use of these measures is likely to change in the coming years.
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