Hospital Performance Evaluation Research Articles

Patient-level and Physician-level Predictors of Discharge Against Medical Advice: A Multilevel Modeling Approach

Background: Discharge Against Medical Advice (DAMA) is a complex and multifaceted issue in healthcare, often challenging the continuity of care and affecting patient outcomes. Objectives: This study aimed to investigate the predictors of Discharge Against Medical Advice (DAMA) by simultaneously examining patient- and physician-level variables within a unified analytical framework. Methods: This cross-sectional study was conducted in 2023 at one of the largest private hospitals in northwest Iran. The study included all 16,071 patients admitted in 2022 and 137 attending physicians. A multilevel analysis model was employed to examine the influence of variables related to patients and physicians in predicting DAMA. Results: The study involved patients with a mean age of 45.28 ± 19.59 years, with 67.7% being women and 14.8% not having health insurance. Among the physicians studied, the mean age was 56.27 ± 12.29 years, with 67.2% being male and 70.1% being hospital shareholders. Patients with DAMA comprised 6.8% (n = 1094). The null model had a log-likelihood value of -3304.90. When patient-level predictors were added, the value increased to -3041.76, a statistically significant improvement (P < 0.001) based on the chi-square test. Subsequently, incorporating physician-level predictors further increased the log-likelihood value to -2996.16, and this increase was also statistically significant compared to the model with only patient-level predictors (P < 0.001). Physician-level factors, including specialization, sex, and experience, were associated with DAMA. Significant patient-level variables included age, type of insurance, and type of disease (P < 0.05). Conclusions: Utilizing multilevel modeling enables the assessment of the significance of both physician-level and patient-level factors. To avoid conflicting results, it is recommended to evaluate hospital performance based on DAMA by considering both levels.

Hospital performance evaluation indicators: a scoping review.

Hospitals are the biggest consumers of health system budgets and hence measuring hospital performance by quantitative or qualitative accessible and reliable indicators is crucial. This review aimed to categorize and present a set of indicators for evaluating overall hospital performance. We conducted a literature search across three databases, i.e., PubMed, Scopus, and Web of Science, using possible keyword combinations. We included studies that explored hospital performance evaluation indicators from different dimensions. We included 91 English language studies published in the past 10 years. In total, 1161 indicators were extracted from the included studies. We classified the extracted indicators into 3 categories, 14 subcategories, 21 performance dimensions, and 110 main indicators. Finally, we presented a comprehensive set of indicators with regard to different performance dimensions and classified them based on what they indicate in the production process, i.e., input, process, output, outcome and impact. The findings provide a comprehensive set of indicators at different levels that can be used for hospital performance evaluation. Future studies can be conducted to validate and apply these indicators in different contexts. It seems that, depending on the specific conditions of each country, an appropriate set of indicators can be selected from this comprehensive list of indicators for use in the performance evaluation of hospitals in different settings.

A novel risk-adjusted metric to compare hospitals on their antibiotic-prescribing at hospital discharge.

Antibiotic overuse at hospital discharge is common, but there is no metric to evaluate hospital performance at this transition of care. We built a risk-adjusted metric for comparing hospitals on their overall post-discharge antibiotic use. This was a retrospective study across all acute-care admissions within the Veterans Health Administration during 2018-2021. For patients discharged to home, we collected data on antibiotics and relevant covariates. We built a zero-inflated negative binomial mixed-model with two random intercepts for each hospital to predict post-discharge antibiotic exposure and length of therapy (LOT). Data were split into training and testing sets to evaluate model performance using absolute error. Hospital performance was determined by the predicted random intercepts. 1,804,300 patient-admissions across 129 hospitals were included. Antibiotics were prescribed to 41.5% while hospitalized and 19.5% at discharge. Median LOT among those prescribed post-discharge antibiotics was 7 (IQR 4-10). The predictive model detected post-discharge antibiotic use with fidelity, including accurate identification of any exposure (area under the precision-recall curve=0.97) and reliable prediction of post-discharge LOT (mean absolute error = 1.48). Based on this model, 39 (30.2%) hospitals prescribed antibiotics less often than expected at discharge and used shorter LOT than expected. Twenty-eight (21.7%) hospitals prescribed antibiotics more often at discharge and used longer LOT. A model using electronically-available data was able to predict antibiotic use prescribed at hospital discharge and showed that some hospitals were more successful in reducing antibiotic overuse at this transition of care. This metric may help hospitals identify opportunities for improved antibiotic stewardship at discharge.

Performance Evaluation of Hospitals under Data Uncertainty: An Uncertain Common-Weights Data Envelopment Analysis.

In the context of healthcare systems, the performance evaluation of hospitals plays a crucial role in assessing the quality of healthcare systems and facilitating informed decision-making processes. However, the presence of data uncertainty poses significant challenges to accurate performance measurement. This paper presents a novel uncertain common-weights data envelopment analysis (UCWDEA) approach for evaluating the performance of hospitals under uncertain environments. The proposed UCWDEA approach addresses the limitations of traditional data envelopment analysis (DEA) models by incorporating the uncertainty theory (UT) to model the inherent uncertainty in input and output data. Also, by utilizing a common set of weights (CSW) technique, the UCWDEA method provides a more robust and reliable assessment of hospital performance. The main advantages of the proposed UCWDEA approach can be succinctly summarized as follows. Firstly, it allows for the comparison of all hospitals on a consistent basis to calculate a realistic efficiency score, rather than an overly optimistic efficiency score. Secondly, the uncertain common-weights DEA approach exhibits linearity, enhancing its applicability. Thirdly, it possesses the capability to extend its utility under various other prevalent uncertainty distributions. Moreover, it enhances the discriminatory power of results, facilitates the ranking of hospitals in the presence of data uncertainty, and aids in identifying the sensitivity and stability levels of hospitals towards data uncertainty. Notably, in order to showcase the pragmatic application and efficacy of the uncertain common-weights DEA model, a genuine dataset has been utilized to evaluate the efficiency of 20 public hospitals in Tehran, all of which are affiliated with the Iran University of Medical Sciences. The results of the experiment demonstrate the efficacy of the UCWDEA approach in assessing and ranking hospitals amidst uncertain conditions. In summary, the research outcomes can offer policymakers valuable insights regarding hospital performance amidst data uncertainty. Additionally, it can provide practical recommendations on optimizing resource allocation, benchmarking performance, and formulating effective policies to augment the overall efficiency and effectiveness of healthcare services.

Perspective: Supporting Maternal Efforts to Provide Optimal Infant Nutrition in the Post-Partum Setting

Supporting optimal newborn nutrition and the positive maternal–infant relationship while encouraging safe sleep practices are essential components of maternal and newborn care in the hospital setting following birth. Breastfeeding is widely recognized as the best practice to support the nutritional needs and well-being of the infant, and recommendations have been developed by the WHO, the American Academy of Pediatrics (AAP), and the United States Centers for Disease Control to encourage and successfully support breastfeeding efforts before hospital discharge. The 10 Steps to Successful Breastfeeding, developed and promoted by the WHO, form the basis of the Baby-Friendly Hospital Initiative (BFHI) and have become the international framework for public health initiatives to promote breastfeeding. An evaluation of hospital performance implementing the 10 steps through the process of “Baby-Friendly Designation” (BFD) has been suggested by many breastfeeding advocates as the optimal pathway to attain the goals of the BFHI. However, the WHO has recognized that BFD may not be an appropriate goal in all settings, and indicated, as part of their updated 2018 guidance, that “facilities may make changes in their policies and procedures to obtain the designation, but these changes are not always sustainable, especially when there are no regular monitoring systems in place.” In addition, unintended associated issues regarding newborn safety and maternal dissatisfaction with some of the 10 steps have emerged. This perspective discusses the challenges faced by hospitals attempting to implement the BFHI 10 steps and suggests potential solutions to make progress in those efforts with or without BFD and also the efforts needed to support formula feedings when appropriate.

Application of Failure Mode and Effects Analysis to Improve the Quality of the Front Page of Electronic Medical Records in China: Cross-Sectional Data Mapping Analysis.

The completeness and accuracy of the front pages of electronic medical records (EMRs) are crucial for evaluating hospital performance and for health insurance payments to inpatients. However, the quality of the first page of EMRs in China's medical system is not satisfactory, which can be partly attributed to deficiencies in the EMR system. Failure mode and effects analysis (FMEA) is a proactive risk management tool that can be used to investigate the potential failure modes in an EMR system and analyze the possible consequences. The purpose of this study was to preemptively identify the potential failures of the EMR system in China and their causes and effects in order to prevent such failures from recurring. Further, we aimed to implement corresponding improvements to minimize system failure modes. From January 1, 2020, to May 31, 2022, 10 experts, including clinicians, engineers, administrators, and medical record coders, in Zhejiang People's Hospital conducted FMEA to improve the quality of the front page of the EMR. The completeness and accuracy of the front page and the risk priority numbers were compared before and after the implementation of specific improvement measures. We identified 2 main processes and 6 subprocesses for improving the EMR system. We found that there were 13 potential failure modes, including data messaging errors, data completion errors, incomplete quality control, and coding errors. A questionnaire survey administered to random physicians and coders showed 7 major causes for these failure modes. Therefore, we established quality control rules for medical records and embedded them in the system. We also integrated the medical insurance system and the front page of the EMR on the same interface and established a set of intelligent front pages in the EMR management system. Further, we revamped the quality management systems such as communicating with physicians regularly and conducting special training seminars. The overall accuracy and integrity rate of the front page (P<.001) of the EMR increased significantly after implementation of the improvement measures, while the risk priority number decreased. In this study, we were able to identify the potential failure modes in the front page of the EMR system by using the FMEA method and implement corresponding improvement measures in order to minimize recurring errors in the health care services in China.

Hospital Performance Evaluation Checklist in Context of COVID-19 Pandemic: Design and Validation.

Around the world, pandemics have been considered among the main hazards in the last 2 decades. Hospitals are 1 of the most important organizations responding to pandemics. The aim of this study was to design and develop a valid checklist for evaluating the hospitals' performance in response to COVID-19 pandemic, for the first time. This study is a mixed method research design that began in February, 2020 and was conducted in 3 phases: Designing a conceptual model, designing a primary checklist structure, and checklist psychometric evaluation. Known-groups method has been used to evaluate construct validity. Two groups of hospitals were compared: group A (COVID-19 Hospitals) and group B (the other hospitals). The checklist's main structure was designed with 6 main domains, 23 sub-domains, and 152 items. The content validity ratio and index were 0.94 and 0.79 respectively. Eleven items were added, 106 items were removed, and 40 items were edited. Independent t-test showed a significant difference between the scores of the 2 groups of hospitals (P < 0.0001). Pearson correlation coefficient test also showed a high correlation between our checklist and the other. The internal consistency of the checklist was 0.98 according to Cronbach's alpha test. Evaluating the hospitals' performance and identifying their strengths and weaknesses, can help health system policymakers and hospital managers, and leads to improved performance in response to COVID-19.

Designing a hospital performance evaluation model based on qualitative components (A case study of Shiraz Hospitals based on professional ethics)

Designing a hospital performance evaluation model based on qualitative components (A case study of Shiraz Hospitals based on professional ethics)

Hospital performance in a statewide commercial insurer episode-based incentive program.

To evaluate hospital performance and behaviors in the first 2 years of a statewide commercial insurance episode-based incentive pay-for-performance (P4P) program. Retrospective cohort study of price- and risk-standardized episode-of-care spending from the Michigan Value Collaborative claims data registry. Changes in hospital-level episode spending between baseline and performance years were estimated during the program years (PYs) 2018 and 2019. The distribution and hospital characteristics associated with P4P points earned were described for both PYs. A difference-in-differences (DID) analysis compared changes in patient-level episode spending associated with program implementation. Hospital-level episode spending for all conditions declined significantly from the baseline year to the performance year in PY 2018 (-$671; 95% CI, -$1113 to -$230) but was not significantly different for PY 2019 ($177; 95% CI, -$412 to $767). Hospitals earned a mean (SD) total of 6.3 (3.1) of 10 points in PY 2018 and 4.5 (2.9) of 10 points in PY 2019, with few significant differences in P4P points across hospital characteristics. The highest-scoring hospitals were more likely to have changes in case mix index and decreases in spending across the entire episode of care compared with the lowest-scoring hospitals. DID analysis revealed no significant changes in patient-level episode spending associated with program implementation. There was little evidence for overall reductions in spending associated with the program, but the performance of the hospitals that achieved greatest savings and incentives provides insights into the ongoing design of hospital P4P metrics.

Comparison of Hospital Performance in Acute Ischemic Stroke Based on Mortality and Functional Outcome in South Korea.

Recent evidence suggests a correlation between modified Rankin Scale-based measures, an outcome measure commonly used in acute stroke trials, and mortality-based measures used by health agencies in the evaluation of hospital performance. We aimed to examine whether the 2 types of measures are interchangeable in relation to evaluation of hospital performance in acute ischemic stroke. Five outcome measures, unfavorable functional outcome (3-month modified Rankin Scale score ≥2), death or dependency (3-month modified Rankin Scale score ≥3), 1-month mortality, 3-month mortality, and 1-year mortality, were collected for 8292 individuals who were hospitalized for acute ischemic stroke between January 2014 and May 2015 in 14 hospitals participating in the Clinical Research Collaboration for Stroke in Korea - National Institute of Health registry. Hierarchical regression models were used to calculate per-hospital risk-adjusted outcome rates for each measure. Hospitals were ranked and grouped based on the risk-adjusted outcome rates, and the correlations between the modified Rankin Scale-based and mortality-based ranking and their intermeasure reliability in categorizing hospital performance were analyzed. The comparison between the ranking based on the unfavorable functional outcome and that based on 1-year mortality resulted in a Spearman correlation coefficient of -0.29 and Kendall rank coefficient of -0.23, and the comparison of grouping based on these 2 types of ranks resulted in a weighted kappa of 0.123 for the grouping in the top 33%/middle 33%/bottom 33% and 0.25 for the grouping in the top 20%/middle 60%/bottom 20%, respectively. No significant correlation or similarity in grouping capacities were found between the rankings based on the functional outcome measures and those based on the mortality measures. This study shows that regardless of clinical correlation at an individual patient level, functional outcome-based measures and mortality-based measures are not interchangeable in the evaluation of hospital performance in acute ischemic stroke.

Effect of Medicare Advantage on Hospital Readmission and Mortality Rankings.

Medicare links hospital performance on readmissions and mortality to payment solely on the basis of outcomes among fee-for-service (FFS) beneficiaries. Whether including Medicare Advantage (MA) beneficiaries, who account for nearly half of all Medicare beneficiaries, in the evaluation of hospital performance affects rankings is unknown. To determine if the inclusion of MA beneficiaries in readmission and mortality measures reclassifies hospital performance rankings compared with current measures. Cross-sectional. Population-based. Hospitals participating in the Hospital Readmissions Reduction Program or Hospital Value-Based Purchasing Program. Using the 100% Medicare files for FFS and MA claims, the authors calculated 30-day risk-adjusted readmissions and mortality for acute myocardial infarction, heart failure, chronic obstructive pulmonary disease, and pneumonia on the basis of only FFS beneficiaries and then both FFS and MA beneficiaries. Hospitals were divided into quintiles of performance based on FFS beneficiaries only, and the proportion of hospitals that were reclassified to a different performance group with the inclusion of MA beneficiaries was calculated. Of the hospitals in the top-performing quintile for readmissions and mortality based on FFS beneficiaries, between 21.6% and 30.2% were reclassified to a lower-performing quintile with the inclusion of MA beneficiaries. Similar proportions of hospitals were reclassified from the bottom performance quintile to a higher one across all measures and conditions. Hospitals with a higher proportion of MA beneficiaries were more likely to improve in performance rankings. Hospital performance measurement and risk adjustment differed slightly from those used by Medicare. Approximately 1 in 4 top-performing hospitals is reclassified to a lower performance group when MA beneficiaries are included in the evaluation of hospital readmissions and mortality. These findings suggest that Medicare's current value-based programs provide an incomplete picture of hospital performance. Laura and John Arnold Foundation.

Using Potentially Preventable Severe Maternal Morbidity to Monitor Hospital Performance.

The Centers for Disease Control and Prevention (CDC) measure of severe maternal morbidity (SMM) quantifies the burden of SMM but is not restricted to potentially preventable SMM. The authors adapted the CDC SMM measure for this purpose and evaluated it for use as a hospital performance measure. Guidelines for defining performance SMM (pSMM) were (1) exclusion of preexisting conditions from outcome; (2) exclusion of inconsistently documented outcomes; and (3) risk adjustment for conditions that preceded hospitalization. California maternal hospital discharge data from 2016 to 2017 were used for model development, and 2018 data were used for model testing and evaluation of hospital performance. Separate models were developed for hospital types (Community, Teaching, Integrated Delivery System [IDS], and IDS Teaching), generating model-based expected pSMM values. Observed-to-expected (O/E) ratios were calculated for hospitals and used to categorize them as overperforming, average performing, or underperforming using 95% confidence intervals. Performance categories were compared for pSMM vs. CDC SMM (excluding blood transfusion). The overall 2016-2018 pSMM rate was 0.44%. All hospital types had over- and underperformers, and the proportions of Community, Teaching, IDS, and IDS Teaching hospitals whose performance differed from their performance on the CDC SMM measure were 12.1%, 25.0%, 38.9%, and 66.7%, respectively. The rate of potentially preventable SMM as defined by pSMM (0.44%) was less than half the previously published rate of CDC SMM (1.03%). pSMM identified differences in performance across hospitals, and pSMM and CDC SMM classified hospitals' performances differently. pSMM may be suitable for hospital comparisons because it identifies potentially preventable, hospital-acquired SMM that should be responsive to quality improvement activities.

Evaluating hospital performance with plant capacity utilization and machine learning

This study extends the measurement of plant capacity utilization by incorporating undesirable outputs. We select indicators through feature selection in machine learning and also introduce an undesirable output for assessment in these models. By defining and applying four plant capacity concepts, we analyze plant capacity utilization in health institutions in 31 provinces in China over the last 11 years (2009 to 2019). This paper has two main contributions. First, we propose a refined by-production hospital technology by introducing the mortality rate into the performance evaluation of public hospitals. Second, we expand the measures of plant capacity utilization with undesirable outputs. The preliminary results show that after the introduction of the death rate, the long-run output-oriented plant capacity utilization of medical institutions is significantly impacted. Furthermore, we find a high level of long-run input-oriented plant capacity utilization tends to increase mortality.

Factors Associated with Patients' Ratings of Hospitals among Japanese Inpatients: A Cross-sectional Study.

The evaluation of hospital performance often receives great attention. Hospitals refer to patient ratings to undertake quality-improvement activities. However, little is known about the factors that contribute the most to these patient ratings. This study aimed to investigate the association of relevant factors, such as doctors' and nurses' performance, with patients' ratings of hospitals, using the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPSⓇ) questionnaire. A cross-sectional study was conducted among patients who were hospitalized in Japan, from January 2020 to September 2021. Patients' hospital rating scale scores between 0 and 10 were collected and dichotomized. A score of 8 or higher was defined as a high rating. A multivariate logistic regression analysis was conducted to investigate the association between patients' ratings of the hospital and other items in the HCAHPSⓇ questionnaire. The frequency of patients' high and poor hospital ratings were 207 (69%) and 93 (31%), respectively, of 300 respondents. A significant association was observed for the patient's age (adjusted odds ratio (AOR): 1.02; 95% confidence interval (CI): 1.00-1.04), doctor's communication (AOR: 10.47; 95% CI: 3.17-34.58), and discharge planning (AOR: 3.53; 95% CI: 1.96-6.36) with a positive patient rating of the hospital. An emphasis on doctor communication and discharge planning is essential in improving patients' ratings of hospitals. Further research is needed to determine the factors that contribute the most to patients' ratings of hospitals.

Evaluating Risk-Adjusted Hospital Performance Using Large-Scale Data on Mortality Rates of Patients in Intensive Care Units: A Flexible Semi-Nonparametric Modeling Approach.

Significant variability in the quality of healthcare supplied by hospitals is drawing broad attention from the United States Centers for Medicare and Medicaid Services. The primary issue is to evaluate hospital performance based on patient outcomes. Generalized linear random-effects models are a promising analytical tool for evaluating hospital performance. However, hospital compare data often violate the classical assumptions of normality on random effects and linearity representation on transformed conditional mean structures in these models. In this article, we proposed and tested the performance of a class of hospital compare models that embraces nonparametric mean structures with semi-nonparametric hospital random effects. Such models were further improved and integrated into a zero-inflated model. [Formula: see text] programs to implement these newly proposed hospital compare models were thoroughly developed. The [Formula: see text] programs are freely available via a GitHub (https:\\www.GitHub.com) repository. We demonstrate the robustness of the proposed hospital compare models by conducting intensive empirical studies. Flexible semi-nonparametric random effects and functional fixed-effects mean structure were used to analyze patient mortality in a large-scale intensive care unit data set. After applying the proposed models to assess standardized modality rates and address patient-mix variability across hospitals, we detected those underperforming hospitals with higher mortality rates. Our research findings highlight how constructing advanced assessment tools for hospital performance could support better decision-making at the administrative and public levels. The proposed hospital compare models are comprehensive in their capacity to identify patterns of hospital random effects and to convey the variability in healthcare quality with powerful accuracy and interpretability.

An econometric approach to aggregating multiple cardiovascular outcomes in German hospitals

ObjectiveDevelopment of an aggregate quality index to evaluate hospital performance in cardiovascular events treatment.MethodsWe applied a two-stage regression approach using an accelerated failure time model based on variance weights to estimate hospital quality over four cardiovascular interventions: elective coronary bypass graft, elective cardiac resynchronization therapy, and emergency treatment for acute myocardial infarction. Mortality and readmissions were used as outcomes. For the estimation we used data from a statutory health insurer in Germany from 2005 to 2016.ResultsThe precision-based weights calculated in the first stage were higher for mortality than for readmissions. In general, teaching hospitals performed better in our ranking of hospital quality compared to non-teaching hospitals, as did private not-for-profit hospitals compared to hospitals with public or private for-profit ownership.DiscussionThe proposed approach is a new method to aggregate single hospital quality outcomes using objective, precision-based weights. Likelihood-based accelerated failure time models make use of existing data more efficiently compared to widely used models relying on dichotomized data. The main advantage of the variance-based weights approach is that the extent to which an indicator contributes to the aggregate index depends on the amount of its variance.

Dynamic Performance Assessment of Hospitals by Applying Credibility-Based Fuzzy Window Data Envelopment Analysis.

The goal of the current research is to propose the credibility-based fuzzy window data envelopment analysis (CFWDEA) approach as a novel method for the dynamic performance evaluation of hospitals during different periods under data ambiguity and linguistic variables. To reach this goal, a data envelopment analysis (DEA) method, a window analysis technique, a possibilistic programming approach, credibility theory, and chance-constrained programming (CCP) are employed. In addition, the applicability and efficacy of the proposed CFWDEA approach are illustrated utilizing a real data set to evaluate the performance of hospitals in the USA. It should be explained that three inputs including the number of beds, labor-related expenses, patient care supplies, and other expenses as well as three outputs including the number of outpatient department visits, the number of inpatient department admissions, and overall patient satisfaction level, are considered for the dynamic performance appraisal of hospitals. The experimental results show the usefulness of the CFWDEA method for the evaluation and ranking of hospitals in the presence of fuzzy data, linguistic variables, and epistemic uncertainty.

Performance evaluation of general public hospitals in territories of the Former Yugoslavia: An ecological study using the Pabon Lasso model.

Measuring hospital performance is an area of great importance due to limited resources. Additionally, hospital activities are vital during emergencies providing care under pressure of external factors. This study aimed at analysing performances of general public hospitals from territories of the Former Yugoslavia. This ecological study includes 99 hospitals from 6 territories based on annual reports of institutions in charge of territorial health data. The Pabon Lasso method was used, considering hospitals in the third quadrant of the plot as top-performing in the territory. A particular focus was on performance changes during the pandemic caused by SARS-COV-2 coronavirus. There is considerable heterogeneity in the length of stay, bed occupancy and turnover rate between territories. The proportion of top-performing hospitals varied between 20% and 43%, providing between 16% and 69% of inpatients episodes within the territory. However, countries did not differ in how their hospitals were distributed across the four quadrants of the Pabon Lasso plot. Comparison of Croatian and Kosovar data from 2019 to 2020 suggested a significant decrease in turnover and occupancy rates, but most hospitals retained their plot quartiles from the previous year. This supports the finding that there were no significant changes in hospital performance based on the Pabon Lasso method. The analysis showed significant room for performance improvement and highlighted the ability of the Croatian and Kosovar hospitals and their health systems to maintain efficiency during emergencies.

Performance of Management Strategies With Class I Recommendations Among Patients Hospitalized With ST-Segment Elevation Myocardial Infarction in China

Despite advances in the treatment of ST-segment elevation myocardial infarction (STEMI), little is known about how this evolving knowledge is applied in current clinical practice in China. To evaluate hospital performance and temporal trends in the management of STEMI. This study used data from the Improving Care for Cardiovascular Disease in China-Acute Coronary Syndrome Project, a nationwide quality improvement registry, in collaboration with the American Heart Association and the Chinese Society of Cardiology. Participants included patients with STEMI admitted to 143 tertiary hospitals across China from November 2014 to July 2019, and data were analyzed from November 2020 to December 2021. Levels, hospital-level variations, and trends for utilization rates of the 9 management strategies with Class I recommendations in Chinese and US guidelines. A total of 57 560 hospitalizations with STEMI were included. Overall, 20.0% of patients received all the care according to the 9 guideline-recommended strategies. The performance rate of quality measures was low for reperfusion therapy (61.0%, 35 115/57 560 patients), β-blocker at discharge (68.3%, 37 750/55 285 patients), angiotensin-converting enzyme inhibitor or angiotensin receptor blocker at discharge (55.1%, 2524/4578 patients), and smoking cessation counseling (36.5%, 9586/26 265 patients) among those who were eligible. Of 25 563 patients who underwent primary percutaneous coronary intervention (PCI), 66.8% underwent this procedure within 90 minutes of hospital arrival. Of 1128 patients who underwent fibrinolysis therapy, 253 (22.4%) underwent this treatment within 30 minutes of hospital arrival. Measures with high performance rates included receipt of dual antiplatelet therapy within 24 hours (95.5%, 54 263/56 848 patients) and at discharge (91.8%, 51 452/56 019 patients) and receipt of statin at discharge (93.0%, 52 214/56 141 patients) for those eligible. There was significant variation between hospitals in all-or-none score (ranging from 0 to 61.9%) and performance of individual measures. The quality of care improved during the study period, especially for reperfusion therapy, primary PCI within the first 90 minutes of hospital arrival, and smoking cessation counseling. The quality of care for patients hospitalized with STEMI does not meet guideline-recommended strategies in China, with only 1 in 5 patients receiving all the care according to the 9 guideline-recommended strategies. Large disparities in the quality of care exist across hospitals.

Application of Performance Module in Health Service Quality Evaluation: Viewpoints of Beneficiary-A case Study in Al-Salaam General Hospital in Mosul

The issue of health service quality has become a growing global concern in an effort by medical organizations to provide the best health service that achieves maximum satisfaction for the beneficiary. This study represents an attempt to evaluate the performance of Al-Salam Hospital in Mosul from the perspective of the beneficiary, and the subject acquires great importance derived from being one of the contemporary technologies for designing the hospital performance evaluation system, which is one of the strategic management systems. Due to the limited studies that focused on the views of the beneficiary about the level of health service, the study focused on the components of the quality of the health service in order to study the evaluation of the views of the beneficiaries of the dimensions of the quality of the health service provided to them, to know which of the dimensions of health quality achieves the greatest possible satisfaction of the beneficiary for the purpose of activating it in pursuit of achieving The highest possible satisfaction and to improve the reality of the medical service in the organization. اعتماد أنموذج الأداء الفعلی فی تقییم مستوى جودة الخدمة الصحیة من وجهة نظر المستفید – دراسة حالة فی مستشفى السلام العام فی الموصل