ContextA predictive model of mortality in heart failure may be useful for clinicians
to improve communication with and care of hospitalized patients.ObjectivesTo identify predictors of mortality and to develop and to validate a
model using information available at hospital presentation.Design, Setting, and ParticipantsRetrospective study of 4031 community-based patients presenting with
heart failure at multiple hospitals in Ontario, Canada (2624 patients in the
derivation cohort from 1999-2001 and 1407 patients in the validation cohort
from 1997-1999), who had been identitifed as part of the Enhanced Feedback
for Effective Cardiac Treatment (EFFECT) study.Main Outcome MeasuresAll-cause 30-day and 1-year mortality.ResultsThe mortality rates for the derivation cohort and validation cohort,
respectively, were 8.9% and 8.2% in hospital, 10.7% and 10.4% at 30 days,
and 32.9% and 30.5% at 1 year. Multivariable predictors of mortality at both
30 days and 1 year included older age, lower systolic blood pressure, higher
respiratory rate, higher urea nitrogen level (all P<.001),
and hyponatremia (P<.01). Comorbid conditions
associated with mortality included cerebrovascular disease (30-day mortality
odds ratio [OR], 1.43; 95% confidence interval [CI], 1.03-1.98; P = .03), chronic obstructive pulmonary disease (OR, 1.66; 95% CI,
1.22-2.27; P = .002), hepatic cirrhosis (OR, 3.22;
95% CI, 1.08-9.65; P = .04), dementia (OR, 2.54;
95% CI, 1.77-3.65; P<.001), and cancer (OR, 1.86;
95% CI, 1.28-2.70; P = .001). A risk index stratified
the risk of death and identified low- and high-risk individuals. Patients
with very low-risk scores (≤60) had a mortality rate of 0.4% at 30 days
and 7.8% at 1 year. Patients with very high-risk scores (>150) had a mortality
rate of 59.0% at 30 days and 78.8% at 1 year. Patients with higher 1-year
risk scores had reduced survival at all times up to 1 year (log-rank, P<.001). For the derivation cohort, the area under the
receiver operating characteristic curve for the model was 0.80 for 30-day
mortality and 0.77 for 1-year mortality. Predicted mortality rates in the
validation cohort closely matched observed rates across the entire spectrum
of risk.ConclusionsAmong community-based heart failure patients, factors identifiable within
hours of hospital presentation predicted mortality risk at 30 days and 1 year.
The externally validated predictive index may assist clinicians in estimating
heart failure mortality risk and in providing quantitative guidance for decision
making in heart failure care.

Predicting mortality among patients hospitalized for heart failure. Derivation and validation of a clinical model

The National Healthcare Disparities Report summarizes health care quality and access among various racial, ethnic, and income groups and other priority populations, such as residents of rural areas and people with disabilities.

National Healthcare Disparities Report

Purpose This article examines various technical challenges inherent in the design, implementation, and dissemination of health care quality performance measures. Design and methods Using national and state-specific Minimum Data Set data from 1999, we examined sample size, measure stability, creation of ordinal ranks, and risk adjustment as applied to aggregated facility quality indicators. Results Nursing home Quality Indicators now in use are multidimensional and quarterly estimates of incidence-based measures can be relatively unstable, suggesting the need for some averaging of measures over time. Implications Current public reports benchmarking nursing homes' performances may require additional technical modifications to avoid compromising the fairness of comparisons.

The quality of quality measurement in U.S. nursing homes.

We investigated the effects of privatization on hospital efficiency in Germany. To do so, we obtained bootstrapped data envelopment analysis (DEA) efficiency scores in the first stage of our analysis and subsequently employed a difference-in-difference matching approach within a panel regression framework. Our findings show that conversions from public to private for-profit status were associated with an increase in efficiency of between 2.9 and 4.9%. We defined four alternative post-privatization periods and found that the increase in efficiency after a conversion to private for-profit status appeared to be permanent. We also observed an increase in efficiency for the first three years after hospitals were converted to private non-profit status, but our estimations suggest that this effect was rather transitory. Our findings also show that the efficiency gains after a conversion to private for-profit status were achieved through substantial decreases in staffing ratios in all analyzed staff categories with the exception of physicians and administrative staff. It was also striking that the efficiency gains of hospitals converted to for-profit status were significantly lower in the diagnosis-related groups (DRG) era than in the pre-DRG era. Altogether, our results suggest that converting hospitals to private for-profit status may be an effective way to ensure the scarce resources in the hospital sector are used more efficiently.

/pdf/changes-in-hospital-efficiency-after-privatization-xdk7o28lt8.pdf

Changes in hospital efficiency after privatization

The AHRQ QIs are one measure set, based on administrative data that can be used to evaluate the quality of clinical services. Most of the QIs focus on health care outcomes rather than rates of processes of care followed. The measures, their extensive documentation, and associated codes for SAS® and Windows® reside in the public domain and are available for download at no cost to the user. Furthermore, the QIs are maintained by AHRQ, which continues to refine and enhance them. Updates to the modules are done on a yearly basis and are routinely released in the first quarter of the year. AHRQ also provides technical support to users on a wide range of issues, including questions about the software package, clarifications of indicator definitions, theoretical questions on the indicators, and interpretation of performance results. The QI support team can be reached via e-mail at support@qualityindicators.ahrq.gov .Future enhancements to the AHRQ QIs are underway and include the development of indicators specific to neonates, the development of additional indicators in areas such as hospital outpatient care, day surgery, diagnostic procedures, and emergency department care. Other planned improvements include incorporating additional clinical data elements such as lab values and do-not-resuscitate-order flag. Additional research is needed to develop evidence-based outcome measures that are sensitive to nursing practice.“Quality of care is highly variable and delivered by a system that is too often poorly coordinated, driving up costs, and putting patients at risk.” (p. 1). Improving the access to and the performance of our health care system is a matter of national urgency. Yet, defining what quality is in health care is not easy. Quality is a complex, multidimensional concept that suggests different things to different people., Consequently, competing views of quality should be balanced among patients, purchasers, managers, and health care professionals. A widely used definition of quality in health care is “the degree to which health services for individuals and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge.”Regardless of how quality is defined, the only way to know whether the quality of health care is improving is to measure the performance of those that deliver it. Performance measures and performance measurement systems provide a tool to determine if quality exists. Currently there is a proliferation of performance measures, and development of these measures shows no sign of abating. While there is a plethora of measures in areas such as cardiac care, there is a dearth or complete lack of measures in other areas such as mental health and cancer care. Better coordination among measure developers is key to reducing the measurement burden of health care organizations. With the adoption of the electronic health record, performance measurement has the potential to become a by-product of care, instead of a distinct data-gathering activity.The AHRQ QIs are one set of performance measures that cover a broad array of conditions and that use an inexpensive, readily available data source. While these measures do have certain limitations, these measures have been and are being used in a variety of initiatives that have contributed to improved quality of care within the United States.

AHRQ Quality Indicators

OBJECTIVE: To examine the relationship of in-hospital and 30-day mortality rates and the association between in-hospital mortality and hospital discharge practices. DATA SOURCES/STUDY SETTING: A secondary analysis of data for 13,834 patients with congestive heart failure who were admitted to 30 hospitals in northeast Ohio in 1992-1994. DESIGN: A retrospective cohort study was conducted. DATA COLLECTION: Demographic and clinical data were collected from patients' medical records and were used to develop multivariable models that estimated the risk of in-hospital and 30-day (post-admission) mortality. Standardized mortality ratios (SMRs) for in-hospital and 30-day mortality were determined by dividing observed death rates by predicted death rates. PRINCIPAL FINDINGS: In-hospital SMRs ranged from 0.54 to 1.42, and six hospitals were classified as statistical outliers (p <.05); 30-day SMRs ranged from 0.63 to 1.73, and seven hospitals were outliers. Although the correlation between in-hospital SMRs and 30-day SMRs was substantial (R = 0.78, p < .001), outlier status changed for seven of the 30 hospitals. Nonetheless, changes in outlier status reflected relatively small differences between in-hospital and 30-day SMRs. Rates of discharge to nursing homes or other inpatient facilities varied from 5.4 percent to 34.2 percent across hospitals. However, relationships between discharge rates to such facilities and in-hospital SMRs (R = 0.08; p = .65) and early post-discharge mortality rates (R = 0.23; p = .21) were not significant. CONCLUSIONS: SMRs based on in-hospital and 30-day mortality were relatively similar, although classification of hospitals as statistical outliers often differed. However, there was no evidence that in-hospital SMRs were biased by differences in post-discharge mortality or discharge practices.

Relationships between in-hospital and 30-day standardized hospital mortality: implications for profiling hospitals.

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Relationships between in-hospital and 30-day standardized hospital mortality: implications for profiling hospitals.