Background and aim Benchmarking is a management approach for implementing best medical practices at the lowest cost. The objectives of this study were to set achievable performance benchmarks for individual quality indicators to determine the predicted quality achievement related to better adherence, and to select optimal quality indicators for improving the quality of acute ischemic stroke care. Methods We analyzed data on 500,331 patients diagnosed with acute ischemic stroke who were treated at 518 hospitals in China from January 2011 to May 2017. The primary outcome was independence (modified Rankin Scale score ≤2) at discharge. Data-driven achievable benchmarking used the “pared-mean” approach to set objective performance targets. Hierarchical logistic regression models were employed to evaluate the process–outcome association, as well as the predicted quality improvement if all hospitals were to operate at the benchmark level. Results Of the overall population, 64.01% were independent patients at discharge. The performance benchmarks were >90% for most of the quality indicators. After adjusting for patient-level and hospital-level characteristics and unifying hospital performance to the benchmark level, the quality indicators with high increase in both overall adherence rate and independence rate were thrombolytic therapy, anticoagulant therapy, venous thrombosis prophylaxis. Conclusions Performance targets for three acute treatments, including thrombolytic therapy, anticoagulant therapy, venous thrombosis prophylaxis, could best motivate improvements in both overall adherence rate and independence rate at discharge. The finding suggests that the above three types of acute treatment should be given priority to improve the quality of acute ischemic stroke care.
BackgroundThere are differences in the quality of care among breast cancer patients. Narrowing the quality differences could be achieved by increasing the utilization rate of indicators. Here we explored key indicators that can improve the quality of care and factors that may affect the use of these indicators.MethodsA total of 3669 breast cancer patients were included in our retrospective study. We calculated patient quality-of-care composite score based on patient average method. Patients were divided into high- and low-quality groups according to the mean score. We obtained the indicators with large difference in utilization between the two groups. Multilevel logistic regression model was used to analyze the factors influencing quality of care and use of indicators.ResultsThe mean composite score was 0.802, and the number of patients in the high- and low-quality groups were 1898 and 1771, respectively. Four indicators showed a difference in utilization between the two groups of over 40%. Histological grade, pathological stage, tumor size and insurance type were the factors affecting the quality of care. In single indicator evaluation, besides the above factors, age, patient income and number of comorbidities may also affect the use of these four indicators. Number of comorbidities may have opposite effects on the use of different indicators, as does pathological stage.ConclusionsIdentifying key indicators for enhancing the quality-of-care of breast cancer patients and factors that affect the indicator adherence may provide guides for enhancing the utilization rate of these indicators in clinical practice.
BACKGROUND: Variability in the quality of stroke care is widespread. Identifying performance-based outlier hospitals based on quality indicators (QIs) has become a common practice. OBJECTIVES: To develop a tool for identifying performance-based outlier hospitals based on riskadjusted adherence rates of process indicators. DESIGN: Hospitals were classified into five-level outliers based on the observed-to-expected ratio and P value. The composite quality score was derived by summation of the points for each indicator for each hospital, and associations between outlier status and outcomes were determined.
BackgroundThe admission time of patients with ST-segment elevation myocardial infarction (STEMI) may affect the quality of care they receive. This study aimed to explore the pattern and magnitude of variation in quality of care for patients with STEMI in both the process and outcome domains.MethodsWe performed a retrospective study based on STEMI data from China. We estimated the adjusted ORs of six process indicators and one outcome indicator of STEMI care quality by fitting multilevel multivariable regression models across 42 4hour time periods per week.ResultsThe study cohort comprised 98 628 patients with STEMI. Care quality varied by time of arrival to the emergency department. We identified three main patterns of variation, which were consistent across days of the week. In the first pattern, which applied to electrocardiographic examination within 10 min of arrival and to aspirin or clopidogrel use within 10 min of arrival, quality was lowest for arrivals between 08:00 and 12:00, rose through the day and peaked for arrivals between 24:00 and 04:00. Percutaneous transluminal coronary intervention treatment within 90 min showed the same pattern but with maximal performance for those arriving 20:00–24:00. In the third pattern, applying to lipid function evaluation within 24 hours and beta blocker use within 24 hours, quality was best for arrivals between 04:00–08:00 and 16:00–19:00 and worst for arrivals between 24:00–04:00 and 12:00–16:00.ConclusionsThe quality of care for STEMI shows three patterns of diurnal variation. Detecting the times at which quality is relatively low may lead to quality improvement in healthcare. Quality improvement should focus on reducing the weekend effect and off-hour effect and the diurnal temporal variation.
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