• Humans
• Benchmarking/methods
• Quality of Health Care
• Delivery of Health Care

• The VOICE Project, or 'Value-Based Healthcare for Outcomes in Breast and Lung Cancer in Europe', coordinated by the Kronikgune Institute, has the collaboration of Roche Farma, SA and Roche Diagnostics SLU
• Value-Based Healthcare for Outcomes in Breast and Lung Cancer in Europe
• Kronikgune Institute

• Clinimetric properties
• Comorbidity
• Indices
• Prognosis
• Sensitivity

Most of previous studies aimed to estimate the effect of nurse staffing on quality of acute hospital care have used stochastic methods and their results are mixed.

To measure the magnitude of effect of nurse-staffing level on increasing quality of acute care services in long-run.

The number of practicing nurses’ density per 1000 population as the proxy of nurse-staffing level and three Health Care Quality Indicators (HCQI) included 30-day mortality per 100 patients based on acute myocardial infarction (MORTAMIO), hemorrhagic stroke (MORTHSTO) and ischemic stroke (MORTISTO) were collected as a part of ongoing project by OECD.org in panels of 26 OECD countries over 2005–2015 period.

Panel data analysis.

There were committed relationships from nurse-staffing level to the enhancement of HCQI i.e. 1% increase in nurse-staffing level would reduce the rates of patient mortality based on MORTAMIO, MORTHSTO and MORTISTO by 0.65%, 0.60% and 0.80%, respectively. Furthermore, the role of nurse-staffing level in increasing overall HCQI were simulated at the highest level in Sweden (−3.53), Denmark (−3.31), Canada (−2.59), Netherlands (−2.33), Finland (−2.09), Switzerland (−1.72), Australia (−1.64) and United States (−1.53).

A higher proportion of nurses-staffing level is associated with higher quality of acute care services in OECD countries. Also, the nursing characteristics of Sweden, Denmark, Canada, Netherlands, Finland, Switzerland, Australia and United States would be good patterns for other countries to maximize nursing outcomes in the care of patients with acute and life-threatening conditions by reducing the risk of complication, mortality and adverse clinical outcomes.

• Nurse-staffing level
• OECD countries
• Panel data analysis
• Quality of acute care

• Abrechnungsdaten
• Risikofaktoren
• Risikomodell
• Sepsis
• administrative data
• prediction model
• risikoadjustierte Sterblichkeit
• risk factors
• risk-adjusted mortality
• sepsis

• Diagnosis-Related Groups
• Germany
• Hospital Mortality
• Hospitals
• Humans
• Risk Adjustment
• Sepsis/mortality

To determine whether hospital characteristics predict cirrhosis mortality and how much variation in mortality is attributable to hospital differences.

We used data from the 2005-2011 Nationwide Inpatient Sample and the American Hospital Association Annual survey to identify hospitalizations for decompensated cirrhosis and corresponding facility characteristics. We created hospital-specific risk and reliability-adjusted odds ratios for cirrhosis mortality, and evaluated patient and facility differences based on hospital performance quintiles. We used hierarchical regression models to determine the effect of these factors on mortality.

Seventy-two thousand seven hundred and thirty-three cirrhosis admissions were evaluated in 805 hospitals. Hospital mean cirrhosis annual case volume was 90.4 (range 25-828). Overall hospital cirrhosis mortality rate was 8.00%. Hospital-adjusted odds ratios (aOR) for mortality ranged from 0.48 to 1.89. Patient characteristics varied significantly by hospital aOR for mortality. Length of stay averaged 6.0 ± 1.6 days, and varied significantly by hospital performance (P < 0.001). Facility level predictors of risk-adjusted mortality were higher Medicaid case-mix (OR = 1.00, P = 0.029) and LPN staffing (OR = 1.02, P = 0.015). Higher cirrhosis volume (OR = 0.99, P = 0.025) and liver transplant program status (OR = 0.83, P = 0.026) were significantly associated with survival. After adjusting for patient differences, era, and clustering effects, 15.3% of variation between hospitals was attributable to differences in facility characteristics.

Hospital characteristics account for a significant proportion of variation in cirrhosis mortality. These findings have several implications for patients, providers, and health care delivery in liver disease care and inpatient health care design.

• Cirrhosis
• Mortality
• Hospital variation
• Resource utilization
• Quality
• Outcomes

• Delivery of Health Care/statistics & numerical data
• Health Resources/statistics & numerical data
• Hospital Mortality
• Hospitals/statistics & numerical data
• Humans
• Inpatients
• Length of Stay
• Liver Cirrhosis/mortality
• Liver Cirrhosis/surgery
• Liver Transplantation/statistics & numerical data
• Odds Ratio
• Risk Factors
• United States/epidemiology

• adult respiratory distress syndrome
• burns
• epidemiology
• risk factors
• smoke inhalation

• Academic Medical Centers
• Acute Lung Injury/mortality
• Adult
• Aged
• Algorithms
• Female
• Hospital Mortality
• Humans
• Male
• Middle Aged
• Respiration, Artificial
• Retrospective Studies
• Risk Factors
• Smoke Inhalation Injury/mortality
• Survival Analysis
• United States/epidemiology

• Adult
• Aged
• Coronary Artery Bypass/mortality
• Coronary Artery Disease/therapy
• Female
• Hospital Mortality
• Hospitals
• Humans
• Korea
• Logistic Models
• Male
• Middle Aged
• Models, Theoretical
• Myocardial Infarction/mortality
• Outcome Assessment, Health Care
• Retrospective Studies
• Risk
• Risk Adjustment
• Risk Factors
• Treatment Outcome

• Aged
• Aged, 80 and over
• Female
• Hospital Mortality
• Humans
• Japan
• Male
• Middle Aged
• Models, Statistical
• Myocardial Infarction/mortality
• Risk Adjustment

• GLMM
• Care group
• Vektis
• bundled payment
• health care costs
• variation

• Patient outcomes
• Quality measurement
• ROC curves
• Simulation and modelling

• econometric methods
• managed care
• performance measures
• profiling
• report cards
• risk adjustment

To determine the rate and risk factors of three operative complications (renal failure, pneumonia, and sternal wound infection) within 30 days after isolated coronary artery bypass surgery.

Medical records of 1,046 consecutive patients between the years 2005 and 2009 were reviewed. Demographic data and peri-operative information were collected and analyzed. Univariate and multivariate analysis between different variables were performed.

Of all patients 3.6% developed pneumonia, 5.9% developed acute renal failure and 8.5% developed sternal wound infection. Independent predictors of acute renal failure were age > 65 years (P = 0.030), pre-operative renal impairment (P < 0.005), peripheral vascular disease (P = 0.005), emergency surgery (P = 0.043), blood transfusion (P = 0.002) mechanical ventilation > 12 hours (P < 0.005) and prolonged inotropic support (P = 0.035). Pneumonia independent predictors were female gender (P < 0.005), diabetes mellitus (P = 0.024), peripheral vascular disease (P = 0.012), emergency surgery (P = 0.007), blood transfusion (P = 0.001), mechanical ventilation > 12 hours (P = 0.005) and prolonged inotropic support (P < 0.005). Sternal wound infection independent predictors were diabetes mellitus (P = 0.017), intra- and post-operative blood transfusion (P < 0.005), and prolonged inotropic support (P = 0.006).

Age, female gender, history of diabetes mellitus, chronic obstructive pulmonary disease, peripheral vascular disease, renal impairment, emergency surgery, per-operative blood transfusion, mechanical ventilation > 12 hours and prolonged inotropic support are associated with the 30-day complication after on-pump isolated coronary artery bypass grafting surgery.

• Bypasses
• Coronary artery
• Factor
• Morbidity
• Risk analysis

Over the last few years, increasing attention has been directed toward the problems inherent to measuring the quality of healthcare and implementing benchmarking strategies. Besides offering accreditation and certification processes, recent approaches measure the performance of healthcare institutions in order to evaluate their effectiveness, defined as the capacity to provide treatment that modifies and improves the patient's state of health. This paper, dealing with hospital effectiveness, focuses on research methods for effectiveness analyses within a strategy comparing different healthcare institutions. The paper, after having introduced readers to the principle debates on benchmarking strategies, which depend on the perspective and type of indicators used, focuses on the methodological problems related to performing consistent benchmarking analyses. Particularly, statistical methods suitable for controlling case-mix, analyzing aggregate data, rare events, and continuous outcomes measured with error are examined. Specific challenges of benchmarking strategies, such as the risk of risk adjustment (case-mix fallacy, underreporting, risk of comparing noncomparable hospitals), selection bias, and possible strategies for the development of consistent benchmarking analyses, are discussed. Finally, to demonstrate the feasibility of the illustrated benchmarking strategies, an application focused on determining regional benchmarks for patient satisfaction (using 2009 Lombardy Region Patient Satisfaction Questionnaire) is proposed.

The ratio of observed-to-expected deaths is considered a measure of hospital quality and for this reason will soon become a basis for payment. However, there are drivers of that metric more potent than quality: most important are medical documentation and patient acuity. If hositals underdocument and therefore do not capture the full “expected mortality” they may be tempted to lower their observed/expected ratio by reducing “observed mortality” through limiting access to the very ill. Underdocumentation occurs because hospitals do not recognize, and therefore cannot seek to confirm, specific comorbidities conferring high mortality risk. To help hospitals identify these comorbidities, this paper describes an easily implemented spread-sheet for evaluating comorbid conditions associated, in any particular hospital, with each discharge. This method identifies comorbidities that increase in frequency as mortality risk increases within each diagnostic grouping. The method is inductive and therefore independent of any particular risk-adjustment technique.

Hospitals are increasingly compared based on clinical outcomes adjusted for severity of illness. Multiple methods exist to adjust for differences between patients. The challenge for consumers of this information, both the public and healthcare providers, is interpreting differences in risk adjustment models particularly when models differ in their use of administrative and physiologic data. We set to examine how administrative and physiologic models compare to each when applied to critically ill patients.

We prospectively abstracted variables for a physiologic and administrative model of mortality from two intensive care units in the United States. Predicted mortality was compared through the Pearsons Product coefficient and Bland-Altman analysis. A subgroup of patients admitted directly from the emergency department was analyzed to remove potential confounding changes in condition prior to ICU admission.

We included 556 patients from two academic medical centers in this analysis. The administrative model and physiologic models predicted mortalities for the combined cohort were 15.3% (95% CI 13.7%, 16.8%) and 24.6% (95% CI 22.7%, 26.5%) (t-test p-value<0.001). The r² for these models was 0.297. The Bland-Atlman plot suggests that at low predicted mortality there was good agreement; however, as mortality increased the models diverged. Similar results were found when analyzing a subgroup of patients admitted directly from the emergency department. When comparing the two hospitals, there was a statistical difference when using the administrative model but not the physiologic model. Unexplained mortality, defined as those patients who died who had a predicted mortality less than 10%, was a rare event by either model.

In conclusion, while it has been shown that administrative models provide estimates of mortality that are similar to physiologic models in non-critically ill patients with pneumonia, our results suggest this finding can not be applied globally to patients admitted to intensive care units. As patients and providers increasingly use publicly reported information in making health care decisions and referrals, it is critical that the provided information be understood. Our results suggest that severity of illness may influence the mortality index in administrative models. We suggest that when interpreting “report cards” or metrics, health care providers determine how the risk adjustment was made and compares to other risk adjustment models.

• APACHE
• Academic Medical Centers
• Adolescent
• Adult
• Aged
• Aged, 80 and over
• Algorithms
• Critical Illness/mortality
• Emergency Service, Hospital
• Female
• Hospital Mortality
• Humans
• Intensive Care Units
• Male
• Middle Aged
• Outcome Assessment, Health Care
• Pneumonia/mortality
• Predictive Value of Tests
• Prospective Studies
• Risk Adjustment
• Treatment Outcome
• Virginia

• T32 AI007046 NIAID NIH HHS

• Clinical Competence/standards
• Coronary Artery Bypass/economics
• Coronary Artery Bypass/standards
• Health Care Sector
• Hospital Mortality
• Humans
• Information Services
• Logistic Models
• Pennsylvania
• Quality of Health Care
• Surgery Department, Hospital/economics
• Surgery Department, Hospital/standards
• Surgery Department, Hospital/statistics & numerical data

• Comorbidity
• Episode of Care
• Heart Diseases/mortality
• Hospital Bed Capacity/statistics & numerical data
• Hospital Mortality
• Hospital Records/statistics & numerical data
• Hospitals/classification
• Hospitals/standards
• Hospitals/statistics & numerical data
• Humans
• Least-Squares Analysis
• Linear Models
• Outcome Assessment, Health Care/methods
• Quality Indicators, Health Care
• Risk Adjustment
• Victoria

• APACHE
• Intensive care unit
• Predicted mortality rate
• Risk adjusted mortality rate

• Black or African American/statistics & numerical data
• Cardiovascular Diseases/ethnology
• Cardiovascular Diseases/mortality
• Health Care Surveys
• Hospital Mortality/trends
• Hospitals/standards
• Humans
• Patient Discharge/statistics & numerical data
• Pilot Projects
• Pneumonia/ethnology
• Pneumonia/mortality
• Poisson Distribution
• Racial Groups
• Risk Assessment
• Risk Factors
• Social Class
• Social Justice
• United States/epidemiology

• Aged
• Clinical Laboratory Information Systems/organization & administration
• Electronic Data Processing/organization & administration
• Female
• Heart Failure/mortality
• Hospital Mortality
• Humans
• Information Systems/organization & administration
• International Classification of Diseases
• Male
• Mental Health
• Middle Aged
• Myocardial Infarction/mortality
• Pneumonia/mortality
• Predictive Value of Tests
• Quality Indicators, Health Care/organization & administration
• Reimbursement, Incentive/organization & administration
• Reproducibility of Results
• Risk Adjustment/organization & administration
• Sepsis/mortality
• Stroke/mortality

• Aged
• Aged, 80 and over
• Female
• Home Care Services/organization & administration
• Home Care Services/standards
• Humans
• Male
• Models, Organizational
• Multivariate Analysis
• Patient Care Team/organization & administration
• Quality of Health Care
• Regression Analysis
• Risk Adjustment
• Risk Management/organization & administration
• United States

• R01 HS11962 AHRQ HHS

Hierarchical modelling represents a statistical method used to analyze nested data, as those concerning patients afferent to different hospitals. Aim of this paper is to build a hierarchical regression model using data from the "Italian CABG outcome study" in order to evaluate the amount of differences in adjusted mortality rates attributable to differences between centres.

The study population consists of all adult patients undergoing an isolated CABG between 2002–2004 in the 64 participating cardiac surgery centres.

A risk adjustment model was developed using a classical single-level regression. In the multilevel approach, the variable "clinical-centre" was employed as a group-level identifier. The intraclass correlation coefficient was used to estimate the proportion of variability in mortality between groups. Group-level residuals were adopted to evaluate the effect of clinical centre on mortality and to compare hospitals performance. Spearman correlation coefficient of ranks (ρ) was used to compare results from classical and hierarchical model.

The study population was made of 34,310 subjects (mortality rate = 2.61%; range 0.33–7.63). The multilevel model estimated that 10.1% of total variability in mortality was explained by differences between centres. The analysis of group-level residuals highlighted 3 centres (VS 8 in the classical methodology) with estimated mortality rates lower than the mean and 11 centres (VS 7) with rates significantly higher. Results from the two methodologies were comparable (ρ = 0.99).

Despite known individual risk-factors were accounted for in the single-level model, the high variability explained by the variable "clinical-centre" states its importance in predicting 30-day mortality after CABG.

• Adolescent
• Adult
• Aged
• Aged, 80 and over
• Cardiac Care Facilities/standards
• Coronary Artery Bypass/mortality
• Cross-Sectional Studies
• Data Interpretation, Statistical
• Female
• Hospital Mortality
• Hospitals, Private/standards
• Hospitals, Public/standards
• Humans
• Italy/epidemiology
• Male
• Middle Aged
• Models, Statistical
• Outcome Assessment, Health Care/methods
• Outcome Assessment, Health Care/statistics & numerical data
• Pilot Projects
• Risk Adjustment
• Risk Assessment
• Risk Factors
• Surveys and Questionnaires

• Adolescent
• Adult
• Child
• Child, Preschool
• Female
• Health Care Surveys
• Hospital Charges/statistics & numerical data
• Hospital Mortality
• Hospitalization/economics
• Hospitalization/statistics & numerical data
• Hospitals, Pediatric/statistics & numerical data
• Hospitals, Teaching/statistics & numerical data
• Humans
• Infant
• Infant, Newborn
• Length of Stay/statistics & numerical data
• Logistic Models
• Male
• Multivariate Analysis
• Pediatrics/statistics & numerical data
• Resource Allocation
• Retrospective Studies
• Sepsis/classification
• Sepsis/mortality
• Sepsis/therapy
• Severity of Illness Index
• Survival Analysis
• Treatment Outcome
• United States/epidemiology