Hospital Performance Evaluation Research Articles

Data envelopment analysis based performance evaluation of hospitals – Implementation of novel picture fuzzy BCC model

Data envelopment analysis based performance evaluation of hospitals – Implementation of novel picture fuzzy BCC model

A Kansei engineering-based decision-making method for offline medical service quality evaluation with multidimensional attributes

The evaluation of doctors’ offline medical service quality plays a vital role in evaluating hospital performance. There are two challenges that need to be addressed: (1) Currently, telephone follow-up and questionnaire surveys are the main methods for evaluating the quality of offline (mainly outpatient and inpatient) medical services in hospitals. However, these methods suffer from drawbacks such as low efficiency and resource consumption. (2) There are certain limitations to determining attribute dimensions and weighting patient evaluation attributes based solely on word frequency. To this end, this study proposes a Kansei engineering-based decision-making method for offline medical service quality evaluation with multidimensional attributes. First, this study constructs a medical Kansei lexicon based on speed and cosine similarity. Second, a concept of Kansei utility values is been defined to represent attribute values in medical service quality evaluation. Additionally, this study builds upon the Kano model to obtain the Kano classification of each attribute. On that basis, a novel approach to obtaining the implicit importance of the attributes is proposed. Thus, the weight vector of the attributes can be calculated as the combination of explicit and implicit importance. Finally, the evaluation result is obtained by using the multiattribute decision-making method. The effectiveness of the proposed method is verified through an analysis of online reviews from Haodf.com. The case study reveals that offline patients consider medical ethics and communication skills as the ”must-be” dimension of attributes, medical competence as a one-dimensional attribute, and medical advice and prescriptions as the attractive dimension of attributes. A comparative analysis with the traditional method is conducted to demonstrate the importance of dimensional analysis for offline medical service quality evaluation. The main contributions of this study include the following: (1) A medical Kansei lexicon is constructed based on Kansei engineering, and (2) a novel decision-making framework for medical service evaluation based on both appearance and implicit importance is constructed.

Reducing readmissions: A business case for US hospitals

The 2010 Patient Protection and Affordable Care Act (ACA) linked acute care hospital reimbursements to quality-of-care measures. While the impact of healthcare quality on service provision is well-documented, the effects of 30-day readmissions on hospital financial performance are less understood. This study examines the relationship between 30-day readmission rates and financial performance in U.S. acute care hospitals from 2010 to 2018 using four financial metrics: operating margin, operating revenue, operating cost, and total margin. Data were sourced from the American Hospital Association (AHA) Annual Survey, Centers for Medicare and Medicaid Services (CMS), Area Health Resource Files (AHRF), and CMS cost reports. Univariate analysis of hospital characteristics was followed by an unbalanced panel regression with hospital and year-fixed effects. Inflation adjustments and controls for HRRP, CMI, managed care penetration, payer mix, physician supply, RN and MD staffing, occupancy, and mortality rates were applied. Increased readmission scores for AMI correlated with higher adjusted operating revenues per patient day (β = $26.86, p ≤ .001), but no significant changes in adjusted operating cost, operating margin, or total margin. Following HRRP implementation, adjusted operating revenue decreased for AMI (β = $425.21, p ≤ .019), HF (β = $229.41, p ≤ .023), and PN (β = $229.41, p ≤ .023). HF readmission scores marginally increased the adjusted operating margin (β = $0.0002, p ≤ .064) with no significant impact on other financial metrics. Pneumonia readmission rates marginally increased the adjusted operating margin (β = $0.0002, p ≤ .064) without significantly affecting other financial measures. The study’s findings highlight the potential financial benefits of reducing readmissions for hospitals. However, it also underscores the importance of developing and implementing broader quality metrics that can provide a more comprehensive evaluation of hospital performance, thereby enhancing the audience’s understanding of the need for a comprehensive evaluation of hospital performance.

The Use of a Single Risk Assessment Tool for Mortality and Numerous Hospital-Acquired Conditions.

Quality assessment organizations leverage numerous patient safety measures to evaluate hospital performance, resulting in significant financial, administrative, and operational burdens on health systems. Low-intensity approaches that allow for reliable risk stratification of patients can reduce the required investment. The Braden score is a routinely performed bedside nursing evaluation validated to assess risk for hospital-acquired pressure injury. We hypothesized that the tool can be used to evaluate risk for other hospital-related adverse outcomes, including mortality, catheter-associated urinary tract infection (CAUTI), and central line-associated blood stream infection (CLABSI). We found that abnormal Braden scores have significant association with numerous adverse outcome measures, including mortality, CLABSI, CAUTI, and iatrogenic hypoglycemia. Because of its frequency of reevaluation, we have found preliminary evidence that leveraging this tool can reduce harm by quickly identifying the most at-risk patients for various types of iatrogenic harm. We conclude that in the face of increasing automation and technical applications, for example, artificial intelligence-driven tools, highly reliable clinician bedside physical examination and evaluation can still have significant, low-cost, and high-value impact in improving patient safety.

Improving Performance in Surgical department through Communicating Information to Decision Makers: Intervention Study in a tertiary Hospital

Hospital performance evaluation is crucial for early detection of pitfalls, and problem-solving. Data shouldn’t be passively collected. They must be measured and understood through an underlying conceptual framework. Purpose: The General Surgical Department in collaboration with the Public Health Department had the objective of developing a module for periodic monitoring and evaluation of the eight general surgical units based on calculated key performance indicators (KPIs), Composite Indices (CIs) and communication of growing out information through policy briefs directed to the heads of units for better resources allocation and evidence based decision making. Methodology: An operations research using pre-post intervention design. The exploratory component was completed in six months in the 8 units of the department. Pretest service statistical data analyzed KPIs derived from hospital bed and surgical room utilization. Data were utilized for the calculation of 5 CIs for each unit. Study intervention included the development of policy briefs (PB) that were submitted to the head of the department. Informed decisions were made guided by the info-graph data in the form of traffic color coded ranking matrix to promote various services and to improve performance in the department units particularly lower ranked units. The post-test included reanalyzing of the service statistical data. Findings: Comparing pre-posttest figures revealed improvement in the calculated KPIs and CIs. Originality and practical implications: Bringing up a novel, feasible monitoring and evaluation model using refitted tools would support surgical unit heads in evidence-based decision-making and eventually improve performance efficiency.

A hybrid multi-criteria decision-making approach for hospital sustainability performance assessment

PurposeNowadays, the sustainability of healthcare services is of increasing importance. In particular, hospitals have ceased to be only treatment-oriented institutions and have begun to operate on the principles of sustainability in their environmental, economic and social dimensions. In this context, a comprehensive method is required to evaluate and improve the performance of hospitals.Design/methodology/approachIn this study, it is recommended to combine D-DEMATEL (D number theory and decision-making trial and evaluation laboratory methods) and objectives matrix (OMAX) methods, which are two important methods in determining hospital performance. D-DEMATEL is a technique used to analyze complex relationships and interactions that reduces subjective judgments because it is based on the opinions of many decision-makers and can be applied even in cases of incomplete information. OMAX, on the other hand, provides a comprehensive framework for measuring performance and allows different performance indicators to be evaluated together.FindingsThe novel performance assessment model is applied to a hospital in real life. Its performance value, according to 36 determined performance indicators, is calculated at 56.91%. The indicators of the hospital that need improvement are defined by the traffic light system method. The performance indicator importance ranking of D-DEMATEL is compared to the ranking obtained by the fuzzy DEMATEL method.Originality/value Important indicators to be used in later sustainable hospital performance evaluation studies were determined. Also, an integrated D-DEMATEL and OMAX method for evaluating sustainable hospital performance is presented.

Optimizing Hospital Performance Evaluation in Total Weight Loss Outcomes After Bariatric Surgery: A Retrospective Analysis to Guide Further Improvement in Dutch Hospitals.

Bariatric surgery aims for optimal patient outcomes, often evaluated through the percentage total weight loss (%TWL). Quality registries employ funnel plots for outcome comparisons between hospitals. However, funnel plots are traditionally used for dichotomous outcomes, requiring %TWL to be dichotomized, potentially limiting feedback quality. This study evaluates whether a funnel plot around the median %TWL has better discriminatory performance than binary funnel plots for achieving at least 20% and 25% TWL. All hospitals performing bariatric surgery were included from the Dutch Audit for Treatment of Obesity. A funnel plot around the median was constructed using 5-year %TWL data. Hospitals positioned above the 95% control limit were colored green and those below red. The same hospitals were plotted in the binary funnel plots for 20% and 25% TWL and colored according to their performance in the funnel plot around the median. We explored the hospital's procedural mix in relation to %TWL performance as possible explanatory factors. The median-based funnel plot identified four underperforming and four outperforming hospitals, while only one underperforming and no outperforming hospitals were found with the binary funnel plot for 20% TWL. The 25% TWL binary funnel plot identified two underperforming and three outperforming hospitals. The proportion of sleeve gastrectomies performed per hospital may explain part of these results as it was negatively associated with median %TWL (β = - 0.09, 95% confidence interval [- 0.13 to - 0.04]). The funnel plot around the median discriminated better between hospitals with significantly worse and better performance than funnel plots for dichotomized %TWL outcomes.

Research Status and Visual Analysis of DRGs in China

In this paper, the visualization software CiteSpace is used to analyze 574 core DRGs research literatures published in CNKI database from 2000 to April 2024.Through the co-occurrence map, the research hotspots and development trends of DRGs are displayed, and the research development direction is predicted. Through visual analysis, the following results are obtained: the annual number of publications is generally on the rise; Hospital expenses , performance evaluation and other keywords for this study; there are 11 clusters in this direction, such as hospitalization expenses , payment method and so on. Emergent keywords are roughly divided into three stages. The conclusions are as follows: the number of literature published is gradually increasing, and the cooperation is relatively fixed; the research method is innovative; the research hotspots are persistent and variable.

A Multi-institutional Analysis of a Textbook Outcome Among Patients Undergoing Microvascular Breast Reconstruction.

Individual outcomes may not accurately reflect the quality of perioperative care. Textbook outcomes (TOs) are composite metrics that provide a comprehensive evaluation of hospital performance and surgical quality. This study aimed to investigate the prevalence and predictors of TOs in a multi-institutional cohort of patients who underwent breast reconstruction with deep inferior epigastric artery perforator flaps. For autologous reconstruction, a TO was previously defined as a procedure without intraoperative complications, reoperation, infection requiring intravenous antibiotics, readmission, mortality, systemic complications, operative duration ≤12 hours for bilateral and ≤10 hours for unilateral/stacked reconstruction, and length of stay (LOS) ≤5 days. We investigated associations between patient-level factors and achieving a TO using multivariable regression analysis. Of 1000 patients, most (73.2%) met a TO. The most common reasons for deviation from a TO were reoperation (9.6%), prolonged operative time (9.5%), and prolonged LOS (9.2%). On univariate analysis, tobacco use, obesity, widowed/divorced marital status, and contralateral prophylactic mastectomy or bilateral reconstruction were associated with a lower likelihood of TOs (P < 0.05). After adjustment, bilateral prophylactic mastectomy (odds ratio [OR], 5.71; P = 0.029) and hormonal therapy (OR, 1.53; P = 0.050) were associated with a higher likelihood of TOs; higher body mass index (OR, 0.91; P = <0.001) was associated with a lower likelihood. Approximately 30% of patients did not achieve a TO, and the likelihood of achieving a TO was influenced by patient and procedural factors. Future studies should investigate how this metric may be used to evaluate patient and hospital-level performance to improve the quality of care in reconstructive surgery.

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 &lt; 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 &lt; 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 &lt; 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 2 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) days. 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.