Performance analysis of English hospitals during the first and second waves of the coronavirus pandemic.

The coronavirus infection COVID-19 killed millions of people around the world in 2019-2022. Hospitals were in the forefront in the battle against the pandemic. This paper proposes a novel approach to assess the effectiveness of hospitals in saving lives. We empirically estimate the production function of COVID-19 deaths among hospital inpatients, applying Heckman's two-stage approach to correct for the bias caused by a large number of zero-valued observations. We subsequently assess performance of hospitals based on regression residuals, incorporating contextual variables to convex quantile regression. Data of 187 hospitals in England over a 35-week period from April to December 2020 is divided in two sub-periods to compare the structural differences between the first and second waves of the pandemic. The results indicate significant performance improvement during the first wave, however, learning by doing was offset by the new mutated virus straits during the second wave. While the elderly patients were at significantly higher risk during the first wave, their expected mortality rate did not significantly differ from that of the general population during the second wave. Our most important empirical finding concerns large and systematic performance differences between individual hospitals: larger units proved more effective in saving lives, and hospitals in London had a lower mortality rate than the national average.

Operational flexibility impact on hospital performance through the roles of employee engagement and management capability.

BACKGROUND: Very limited empirical research has been done on operational flexibility management in the healthcare industry, especially in hospital settings. This study aimed to propose a model of the effects of operational flexibility on hospital performance through management capability and employee engagement as mediating variables. METHODS: The proposed model is validated through an empirical study among 480 clinical and administrative staff from five hospitals in Jordan. Structural equation modeling and confirmatory factor analysis were the main techniques used to validate the model and examine the hypotheses. RESULTS: Operational flexibility was demonstrated to have a positively significant impact on hospital performance, management capability, and employee engagement. Employee engagement was demonstrated to positively impact hospital performance. Management capability had a significant result on hospital performance without having a clear impact. In addition, management capability and employee engagement played a major role as partial mediating effects between operational flexibility and hospital performance, and there is a role for employee engagement as a partial mediating effect between management capability and hospital performance. CONCLUSION: Significant progress has been achieved in hospital management, especially in terms of operational flexibility, management capability, and staff engagement.

The Effects of Covid-19 on Financial-Economic and Performance Efficiency of Hospitals.

Background: During the pandemic of COVID-19, the function and performance of hospitals have been affected by various economic-financial and management aspects. The aim of the current study was to assess the process of therapeutic care delivery and also the economic-financial functions of the selected hospitals before and after COVID-19. Methods: This research is a descriptive-analytical study and a cross-sectional-comparative study in terms of time, and it was conducted in several selected teaching hospitals of Iran University of Medical Sciences. A purposeful and convenient sampling method was used. The data has been collected using the standard research tool (standard checklist of the Ministry of Health) in the two areas of financial-economic and healthcare performance (such as Data of financial and economic indicators such as direct and indirect costs, liquidity ratio and profitability index as well as key performance indicators of hospitals such as bed occupancy ratio (BOR; %), average length of stay (ALOS), bed turnover rate (BTR), bed turnover distance rate (BTIR) and hospital mortality rate (HMR), physician-to-bed ratio and nurse-to-bed ratio) of hospitals in two times before and after the outbreak of COVID-19 (time period 2018 to 2021). The data was collected from 2018 to 2021. Pearson/Spearman regression was used for the evaluation of the relationship between variables using SPSS 22. Results: This research showed the admission of COVID-19 patients caused a change in the indicators we evaluated. ALOS (-6.6%), BTIR (-40.7%), and discharge against medical advice (-7.0%) decreased from 2018 to 2021. BOR; % (+5.0%), occupy bed days (+6.6%), BTR (+27.5%, HMR (+50%), number of inpatients (+18.8%), number of discharges (+13.1%), number of surgeries (+27.4%), nurse-per-bed ratio (+35.9%), doctor-per-bed ratio (+31.0%) increased in the same period of time. The profitability index was correlated to all of the performance indicators except for the net death rate. Higher length of stay and turnover interval had a negative effect on the profitability index while higher bed turnover rate, bed occupancy ratio, bed day, number of inpatient admission, and number of surgery had a positive effect on the profitability index. Conclusion: It has been shown from the beginning of the COVID-19 pandemic, the performance indicators of the studied hospitals were negatively affected. As a consequence of the COVID-19 epidemic, many hospitals were not able to deal with the negative financial and medical outcomes of this crisis due to a significant decrease in income and a double increase in expenses.

Readmissions performance and penalty experience of safety-net hospitals under Medicare's Hospital Readmissions Reduction Program.

BACKGROUND: The Hospital Readmissions Reduction Program (HRRP), established by the Centers for Medicare and Medicaid Services (CMS) in March 2010, introduced payment-reduction penalties on acute care hospitals with higher-than-expected readmission rates for acute myocardial infarction (AMI), heart failure, and pneumonia. There is concern that hospitals serving large numbers of low-income and uninsured patients (safety-net hospitals) are at greater risk of higher readmissions and penalties, often due to factors that are likely outside the hospital's control. Using publicly reported data, we compared the readmissions performance and penalty experience among safety-net and non-safety-net hospitals. METHODS: We used nationwide hospital level data for 2009-2016 from the Centers for Medicare and Medicaid Services (CMS) Hospital Compare program, CMS Final Impact Rule, and the American Hospital Association Annual Survey. We identified as safety-net hospitals the top quartile of hospitals in terms of the proportion of patients receiving income-based public benefits. Using a quasi-experimental difference-in-differences approach based on the comparison of pre- vs. post-HRRP changes in (risk-adjusted) 30-day readmission rate in safety-net and non-safety-net hospitals, we estimated the change in readmissions rate associated with HRRP. We also compared the penalty frequency among safety-net and non-safety-net hospitals. RESULTS: Our study cohort included 1915 hospitals, of which 479 were safety-net hospitals. At baseline (2009), safety-net hospitals had a slightly higher readmission rate compared to non-safety net hospitals for all three conditions: AMI, 20.3% vs. 19.8% (p value< 0.001); heart failure, 25.2% vs. 24.2% (p-value< 0.001); pneumonia, 18.7% vs. 18.1% (p-value< 0.001). Beginning in 2012, readmission rates declined similarly in both hospital groups for all three cohorts. Based on difference-in-differences analysis, HRRP was associated with similar change in the readmissions rate in safety-net and non-safety-net hospitals for AMI and heart failure. For the pneumonia cohort, we found a larger reduction (0.23%; p < 0.001) in safety-net hospitals. The frequency of readmissions penalty was higher among safety-net hospitals. The proportion of hospitals penalized during all four post-HRRP years was 72% among safety-net and 59% among non-safety-net hospitals. CONCLUSIONS: Our results lend support to the concerns of disproportionately higher risk of performance-based penalty on safety-net hospitals.

Evaluating the comparative efficiency of medical centers in Taiwan: a dynamic data envelopment analysis application.

BACKGROUND: People in Taiwan enjoy comprehensive National Health Insurance coverage. However, under the global budget constraint, hospitals encounter enormous challenges. This study was designed to examine Taiwan medical centers' efficiency and factors that influence it. METHODS: We obtained data from open sources of government routine publications and hospitals disclosed by law to the National Health Insurance Administration, Ministry of Health and Welfare, Taiwan. The dynamic data envelopment analysis (DDEA) model was adopted to estimate all medical centers' efficiencies during 2015-2018. Beta regression models were used to model the efficiency level obtained from the DDEA model. We applied an input-oriented approach under both the constant returns-to-scale (CRS) and variable returns-to-scale (VRS) assumptions to estimate efficiency. RESULTS: The findings indicated that 68.4% (13 of 19) of medical centers were inefficient according to scale efficiency. The mean efficiency scores of all medical centers during 2015-2018 under the CRS, VRS, and Scale were 0.85, 0.930, and 0.95,respectively. Regression results showed that an increase in the population less than 14 years of age, assets, nurse-patient ratio and bed occupancy rate could increase medical centers' efficiency. The rate of emergency return within 3-day and patient self-pay revenues were associated significantly with reduced hospital efficiency (p < 0.05). The result also showed that the foundation owns medical center has the highest efficiency than other ownership hospitals. CONCLUSIONS: The study results provide information for hospital managers to consider ways they could adjust available resources to achieve high efficiency.

Association between Medicare's Hospital Readmission Reduction Program and readmission rates across hospitals by medicare bed share.

BACKGROUND: Medicare's Hospital Readmissions Reduction Program (HRRP), implemented beginning in 2013, seeks to incentivize Inpatient Prospective Payment System (IPPS) hospitals to reduce 30-day readmissions for selected inpatient cohorts including acute myocardial infarction, heart failure, and pneumonia. Performance-based penalties, which take the form of a percentage reduction in Medicare reimbursement for all inpatient care services, have a risk of unintended financial burden on hospitals that care for a larger proportion of Medicare patients. To examine the role of this unintended risk on 30-day readmissions, we estimated the association between the extent of their Medicare share of total hospital bed days and changes in 30-day readmissions. METHODS: We used publicly available nationwide hospital level data for 2009-2016 from the Centers for Medicare and Medicaid Services (CMS) Hospital Compare program, CMS Final Impact Rule, and the American Hospital Association Annual Survey. Using a quasi-experimental difference-in-differences approach, we compared pre- vs. post-HRRP changes in 30-day readmission rate in hospitals with high and moderate Medicare share of total hospital bed days ("Medicare bed share") vs. low Medicare bed share hospitals. RESULTS: We grouped the 1904 study hospitals into tertiles (low, moderate and high) by Medicare bed share; the average bed share in the three tertile groups was 31.2, 47.8 and 59.9%, respectively. Compared to low Medicare bed share hospitals, high bed share hospitals were more likely to be non-profit, have smaller bed size and less likely to be a teaching hospital. High bed share hospitals were more likely to be in rural and non-large-urban areas, have fewer lower income patients and have a less complex patient case-mix profile. At baseline, the average readmissions rate in the low Medicare bed share (control) hospitals was 20.0% (AMI), 24.7% (HF) and 18.4% (pneumonia). The observed pre- to post-program change in the control hospitals was - 1.35% (AMI), - 1.02% (HF) and - 0.35% (pneumonia). Difference in differences model estimates indicated no differential change in readmissions among moderate and high Medicare bed share hospitals. CONCLUSIONS: HRRP penalties were not associated with any change in readmissions rate. The CMS should consider alternative options - including working collaboratively with hospitals - to reduce readmissions.

Statistical models for analyzing count data: predictors of length of stay among HIV patients in Portugal using a multilevel model.

BACKGROUND: This study offers a comprehensive approach to precisely analyze the complexly distributed length of stay among HIV admissions in Portugal. OBJECTIVE: To provide an illustration of statistical techniques for analysing count data using longitudinal predictors of length of stay among HIV hospitalizations in Portugal. METHOD: Registered discharges in the Portuguese National Health Service (NHS) facilities Between January 2009 and December 2017, a total of 26,505 classified under Major Diagnostic Category (MDC) created for patients with HIV infection, with HIV/AIDS as a main or secondary cause of admission, were used to predict length of stay among HIV hospitalizations in Portugal. Several strategies were applied to select the best count fit model that includes the Poisson regression model, zero-inflated Poisson, the negative binomial regression model, and zero-inflated negative binomial regression model. A random hospital effects term has been incorporated into the negative binomial model to examine the dependence between observations within the same hospital. A multivariable analysis has been performed to assess the effect of covariates on length of stay. RESULTS: The median length of stay in our study was 11 days (interquartile range: 6-22). Statistical comparisons among the count models revealed that the random-effects negative binomial models provided the best fit with observed data. Admissions among males or admissions associated with TB infection, pneumocystis, cytomegalovirus, candidiasis, toxoplasmosis, or mycobacterium disease exhibit a highly significant increase in length of stay. Perfect trends were observed in which a higher number of diagnoses or procedures lead to significantly higher length of stay. The random-effects term included in our model and refers to unexplained factors specific to each hospital revealed obvious differences in quality among the hospitals included in our study. CONCLUSIONS: This study provides a comprehensive approach to address unique problems associated with the prediction of length of stay among HIV patients in Portugal.

Influence of patient and hospital characteristics on inpatient satisfaction in China's tertiary hospitals: A cross-sectional study.

BACKGROUND: Patient satisfaction has been seen as a key criterion when evaluating hospitals and is one of the main focuses of the current health-care reform in China. This paper aimed to explore patient- and hospital-level factors associated with inpatient satisfaction, which can provide policy implications for the evaluation and development of a patient-oriented health-care system. METHODS: The paper analyses data from the 2017 China National Patient Survey which includes 20 300 inpatients from 131 tertiary hospitals across 31 provinces. Descriptive analysis and multivariable logistic regressions are conducted to identify key factors related to satisfaction. RESULTS: Patient sociodemographic characteristics, including gender, age, income and insurance type, are found to be strongly associated with their satisfaction of inpatient experience. In terms of institutional characteristics, hospital type, size, staffing and financial performance are also significantly correlated with inpatient satisfaction. Patients are more satisfied with specialist hospitals and large hospitals measured by the number of beds and surgeries. Hospitals with higher nurse-to-bed ratio also receive more satisfaction. The financial performance of hospitals, however, is negatively associated with satisfaction. CONCLUSION: Patient satisfaction contains unique information on service quality and thus should be incorporated into the matrix of hospital evaluation. Meanwhile, differences in patient composition must be adjusted to make fair comparisons across hospitals. Moreover, future reform needs to put greater efforts in the design of comprehensive public insurance scheme, efficient hospital structure and an overall well-functioning health-care delivery system in order to better serve patients in China.

Assessing hospital performance indicators. What dimensions? Evidence from an umbrella review.

BACKGROUND: Patients' increasing needs and expectations require an overall assessment of hospital performance. Several international agencies have defined performance indicators sets but there exists no unanimous classification. The Impact HTA Horizon2020 Project wants to address this aspect, developing a toolkit of key indicators to measure hospital performance. The aim of this review is to identify and classify the dimensions of hospital performance indicators in order to develop a common language and identify a shared evidence-based way to frame and address performance assessment. METHODS: Following the PRISMA statement, PubMed, Cochrane Library and Web of Science databases were queried to perform an umbrella review. Reviews focusing on hospital settings, published January 2000-June 2019 were considered. The quality of the studies selected was assessed using the AMSTAR2 tool. RESULTS: Six reviews ranging 2002-2014 were included. The following dimensions were described in at least half of the studies: 6 studies classified efficiency (55 indicators analyzed); 5 studies classified effectiveness (13 indicators), patient centeredness (10 indicators) and safety (8 indicators); 3 studies responsive governance (2 indicators), staff orientation (10 indicators) and timeliness (4 indicators). Three reviews did not specify the indicators related to the dimensions listed, and one article gave a complete definition of the meaning of each dimension and of the related indicators. CONCLUSIONS: The research shows emphasis of the importance of patient centeredness, effectiveness, efficiency, and safety dimensions. Especially, greater attention is given to the dimensions of effectiveness and efficiency. Assessing the overall quality of clinical pathways is key in guaranteeing a truly effective and efficient system but, to date, there still exists a lack of awareness and proactivity in terms of measuring performance of nodes within networks. The effort of classifying and systematizing performance measurement techniques across hospitals is essential at the organizational, regional/national and possibly international levels to deliver top quality care to patients.

Statistical process monitoring to improve quality assurance of inpatient care.

BACKGROUND: Statistical Process Monitoring (SPM) is not typically used in traditional quality assurance of inpatient care. While SPM allows a rapid detection of performance deficits, SPM results strongly depend on characteristics of the evaluated process. When using SPM to monitor inpatient care, in particular the hospital risk profile, hospital volume and properties of each monitored performance indicator (e.g. baseline failure probability) influence the results and must be taken into account to ensure a fair process evaluation. Here we study the use of CUSUM charts constructed for a predefined false alarm probability within a single process, i.e. a given hospital and performance indicator. We furthermore assess different monitoring schemes based on the resulting CUSUM chart and their dependence on the process characteristics. METHODS: We conduct simulation studies in order to investigate alarm characteristics of the Bernoulli log-likelihood CUSUM chart for crude and risk-adjusted performance indicators, and illustrate CUSUM charts on performance data from the external quality assurance of hospitals in Bavaria, Germany. RESULTS: Simulating CUSUM control limits for a false alarm probability allows to control the number of false alarms across different conditions and monitoring schemes. We gained better understanding of the effect of different factors on the alarm rates of CUSUM charts. We propose using simulations to assess the performance of implemented CUSUM charts. CONCLUSIONS: The presented results and example demonstrate the application of CUSUM charts for fair performance evaluation of inpatient care. We propose the simulation of CUSUM control limits while taking into account hospital and process characteristics.

Organization quality systems and department-level strategies: refinement of the Deepening our Understanding in Quality in Australia (DUQuA) organization and department-level scales.

OBJECTIVE: The aim of this study was to develop and refine indices to measure organization and care pathway-level quality management systems in Australian hospitals. DESIGN: A questionnaire survey and audit tools were derived from instruments validated as part of the Deepening Our Understanding of Quality improvement in Europe (DUQuE) study, adapted for Australian hospitals through expert opinion. Statistical processes were used to explore the factor structure, reliability and non-redundancy and descriptive statistics of the scales. SETTING: Thirty-two large Australian public hospitals. PARTICIPANTS: Audit of quality management processes at organization-level and care pathway processes at department level for three patient conditions (acute myocardial infarction (AMI), hip fracture and stroke) and senior quality manager, at each of the 32 participating hospitals. MAIN OUTCOME MEASURE(S): The degree of quality management evident at organization and care pathway levels. RESULTS: Analysis yielded seven quality systems and strategies scales. The three hospital-level measures were: the Quality Management Systems Index (QMSI), the Quality Management Compliance Index (QMCI) and the Clinical Quality Implementation Index (CQII). The four department-level measures were: Specialised Expertise and Responsibility (SER), Evidence-Based Organisation of Pathways (EBOP), Patient Safety Strategies (PSS) and Clinical Review (CR). For QMCI, and for seven out of eight subscales in QMSI, adequate internal consistency (Cronbach's $\alpha$ >0.8) was achieved. For CQII, lack of variation and ceiling effects in the data resulted in very low internal consistency scores, but items were retained for theoretical reasons. Internal consistency was high for CR (Cronbach's $\alpha$ 0.74-0.88 across the three conditions), and this was supported by all item-total correlations exceeding the desired threshold. For EBOP, Cronbach's $\alpha$ was acceptable for hip fracture (0.80) and stroke (0.76), but only moderate for AMI (0.52). PSS and SER scales were retained for theoretical reasons, although internal consistencies were only moderate (SER) to poor (PSS). CONCLUSIONS: The Deepening our Understanding of Quality in Australia (DUQuA) organization and department scales can be used by Australian hospital managers to assess and measure improvement in quality management at organization and department levels within their hospitals and are readily modifiable for other health systems depending on their needs.

Can benchmarking Australian hospitals for quality identify and improve high and low performers? Disseminating research findings for hospitals.

This paper examines the principles of benchmarking in healthcare and how benchmarking can contribute to practice improvement and improved health outcomes for patients. It uses the Deepening our Understanding of Quality in Australia (DUQuA) study published in this Supplement and DUQuA's predecessor in Europe, the Deepening our Understanding of Quality improvement in Europe (DUQuE) study, as models. Benchmarking is where the performances of institutions or individuals are compared using agreed indicators or standards. The rationale for benchmarking is that institutions will respond positively to being identified as a low outlier or desire to be or stay as a high performer, or both, and patients will be empowered to make choices to seek care at institutions that are high performers. Benchmarking often begins with a conceptual framework that is based on a logic model. Such a framework can drive the selection of indicators to measure performance, rather than their selection being based on what is easy to measure. A Donabedian range of indicators can be chosen, including structure, process and outcomes, created around multiple domains or specialties. Indicators based on continuous variables allow organizations to understand where their performance is within a population, and their interdependencies and associations can be understood. Benchmarking should optimally target providers, in order to drive them towards improvement. The DUQuA and DUQuE studies both incorporated some of these principles into their design, thereby creating a model of how to incorporate robust benchmarking into large-scale health services research.

Deepening our Understanding of Quality in Australia (DUQuA): An overview of a nation-wide, multi-level analysis of relationships between quality management systems and patient factors in 32 hospitals.

OBJECTIVE: The Deepening our Understanding of Quality in Australia (DUQuA) project is a multisite, multi-level, cross-sectional study of 32 of the largest hospitals in Australia. This overview examines relationships between (i) organization-level quality management systems and department-level quality management strategies and (ii) patient-level measures (clinical treatment processes, patient-reported perceptions of care and clinical outcomes) within Australian hospitals. DESIGN: We examined hospital quality improvement structures, processes and outcomes, collecting data at organization, department and patient levels for acute myocardial infarction (AMI), hip fracture and stroke. Data sources included surveys of quality managers, clinicians and patients, hospital visits, medical record reviews and national databases. Outcomes data and patient admissions data were analysed. Relationships between measures were evaluated using multi-level models. We based the methods on the Deepening our Understanding of Quality Improvement in Europe (DUQuE) framework, extending that work in parts and customizing the design to Australian circumstances. SETTING, PARTICIPANTS AND OUTCOME MEASURES: The 32 hospitals, containing 119 participating departments, provided wide representation across metropolitan, inner and outer regional Australia. We obtained 31 quality management, 1334 clinician and 857 patient questionnaires, and conducted 2401 medical record reviews and 151 external assessments. External data via a secondary source comprised 14 460 index patient admissions across 14 031 individual patients. Associations between hospital, Emergency Department (ED) and department-level systems and strategies and five patient-level outcomes were assessed: 19 of 165 associations (11.5%) were statistically significant, 12 of 79 positive associations (15.2%) and 7 of 85 negative associations (8.2%). RESULTS: We did not find clear relationships between hospital-level quality management systems, ED or department quality strategies and patient-level outcomes. ED-level clinical reviews were related to adherence to clinical practice guidelines for AMI, hip fracture and stroke, but in different directions. The results, when considered alongside the DUQuE results, are suggestive that front line interventions may be more influential than department-level interventions when shaping quality of care and that multi-pronged strategies are needed. Benchmark reports were sent to each participating hospital, stimulating targeted quality improvement activities. CONCLUSIONS: We found no compelling relationships between the way care is organized and the quality of care across three targeted patient-level outcome conditions. The study was cross-sectional, and thus we recommend that the relationships studied should be assessed for changes across time. Tracking care longitudinally so that quality improvement activities are monitored and fed back to participants is an important initiative that should be given priority as health systems strive to develop their capacity for quality improvement over time.

Quality in perinatal care: applying performance measurement using joint commission on accreditation of healthcare organizations indicators in Italy.

BACKGROUND: Maternal and child health are internationally considered to be among the best measures for assessing health-care quality. The study was carried out with the following aims: 1) to assess the quality of perinatal care (PC) by measuring the frequencies of the five PC indicators developed by the Joint Commission on Accreditation of Healthcare Organizations (JCAHO) and comparing results with international standards; 2) to examine whether maternal, pregnancy care and neonatal characteristics could be factors associated with the quality of perinatal care hospital performance, measured through these indicators. METHODS: We retrospectively reviewed medical charts of women over the age of 18 who experienced delivery in Gynecology/obstetrics wards between January-December 2016, and those of their newborns hospitalized in the Neonatology or Neonatal Intensive Care Unit (NICU) of a public non-teaching hospital in Catanzaro (Italy). Indicators were calculated according to the methodology specified in the manual for JCAHO measures. Univariate and multivariate analyses were performed to test the independent association of maternal, pregnancy care and neonatal characteristics on the adherence to JCAHO PC indicators. RESULTS: The records of 1943 women and 1974 newborns were identified and reviewed in order to be included in at least one of the PC indicators. Elective/early-term delivery, was performed in 27.6% of eligible women, far from the recommended goal (0%); cesarean section in nulliparous women with a term, singleton baby in a vertex position exceeded the suggested target of < 24% and the adherence to antenatal steroids administration was suboptimal (87%). Results of the exclusive breastfeeding indicator achieved a better performance (81%) and compliance with the PC-04 indicator was satisfactory with only 0.4% healthcare-associated bloodstream infection developed in eligible newborns. CONCLUSIONS: This is the first study performed in Italy that has evaluated the quality of PC by using all the five JCAHO indicators. The application of this feasible set of indicators allowed us to measure several aspects of PC for which there is no standardized monitoring system in Italy. Our findings revealed significant deficiencies in the adherence to recommended processes of PC and suggest that there is still substantial work required to improve care.

Ranking hospital performance based on individual indicators: can we increase reliability by creating composite indicators?

BACKGROUND: Report cards on the health care system increasingly report provider-specific performance on indicators that measure the quality of health care delivered. A natural reaction to the publishing of hospital-specific performance on a given indicator is to create 'league tables' that rank hospitals according to their performance. However, many indicators have been shown to have low to moderate rankability, meaning that they cannot be used to accurately rank hospitals. Our objective was to define conditions for improving the ability to rank hospitals by combining several binary indicators with low to moderate rankability. METHODS: Monte Carlo simulations to examine the rankability of composite ordinal indicators created by pooling three binary indicators with low to moderate rankability. We considered scenarios in which the prevalences of the three binary indicators were 0.05, 0.10, and 0.25 and the within-hospital correlation between these indicators varied between - 0.25 and 0.90. RESULTS: Creation of an ordinal indicator with high rankability was possible when the three component binary indicators were strongly correlated with one another (the within-hospital correlation in indicators was at least 0.5). When the binary indicators were independent or weakly correlated with one another (the within-hospital correlation in indicators was less than 0.5), the rankability of the composite ordinal indicator was often less than at least one of its binary components. The rankability of the composite indicator was most affected by the rankability of the most prevalent indicator and the magnitude of the within-hospital correlation between the indicators. CONCLUSIONS: Pooling highly-correlated binary indicators can result in a composite ordinal indicator with high rankability. Otherwise, the composite ordinal indicator may have lower rankability than some of its constituent components. It is recommended that binary indicators be combined to increase rankability only if they represent the same concept of quality of care.

Association between degree of exposure to the Hospital Value Based Purchasing Program and 30-day mortality: experience from the first four years of Medicare's pay-for-performance program.

BACKGROUND: The Hospital Value Based Purchasing Program (HVBP) in the United States, announced in 2010 and implemented since 2013 by the Centers for Medicare and Medicaid Services (CMS), introduced payment penalties and bonuses based on hospital performance on patient 30-day mortality and other indicators. Evidence on the impact of this program is limited and reliant on the choice of program-exempt hospitals as controls. As program-exempt hospitals may have systematic differences with program-participating hospitals, in this study we used an alternative approach wherein program-participating hospitals are stratified by their financial exposure to penalty, and examined changes in hospital performance on 30-day mortality between hospitals with high vs. low financial exposure to penalty. METHODS: Our study examined all hospitals reimbursed through the Medicare Inpatient Prospective Payment System (IPPS) - which include most community and tertiary acute care hospitals - from 2009 to 2016. A hospital's financial exposure to HVBP penalties was measured by the share of its annual aggregate inpatient days provided to Medicare patients ("Medicare bed share"). The main outcome measures were annual hospital-level 30-day risk-adjusted mortality rates for acute myocardial infarction (AMI), heart failure (HF) and pneumonia patients. Using difference-in-differences models we estimated the change in the outcomes in high vs. low Medicare bed share hospitals following HVBP. RESULTS: In the study cohort of 1902 US hospitals, average Medicare bed share was 61 and 41% in high (n = 540) and low (n = 1362) Medicare bed share hospitals, respectively. High Medicare bed share hospitals were more likely to have smaller bed size and less likely to be teaching hospitals, but ownership type was similar among both Medicare bed share groups.. Among low Medicare bed share (control) hospitals, baseline (pre-HVBP) 30-day mortality was 16.0% (AMI), 10.9% (HF) and 11.4% (pneumonia). In both high and low Medicare bed share hospitals 30-day mortality experienced a secular decrease for AMI, increase for HF and pneumonia; differences in the pre-post change between the two hospital groups were small (< 0.12%) and not significant across all three conditions. CONCLUSIONS: HVBP was not associated with a meaningful change in 30-day mortality across hospitals with differential exposure to the program penalty.

Hospitals accreditation status in Indonesia: associated with hospital characteristics, market competition intensity, and hospital performance?

BACKGROUND: Hospital accreditation is widely adopted as a visible measure of an organisation's quality and safety management standards compliance. There is still inconsistent evidence regarding the influence of hospital accreditation on hospital performance, with limited studies in developing countries. This study aims to explore the association of hospital characteristics and market competition with hospital accreditation status and to investigate whether accreditation status differentiate hospital performance. METHODS: East Java Province, with a total 346 hospitals was selected for this study. Hospital characteristics (size, specialty, ownership) and performance indicator (bed occupancy rate, turnover interval, average length of stay, gross mortality rate, and net mortality rate) were retrieved from national hospital database while hospital accreditation status were recorded based on hospital accreditation report. Market density, Herfindahl-Hirschman index (HHI), and hospitals relative size as competition indicators were calculated based on the provincial statistical report data. Logistic regression, Mann-Whitney U-test, and one sample t-test were used to analyse the data. RESULTS: A total of 217 (62.7%) hospitals were accredited. Hospital size and ownership were significantly associated with of accreditation status. When compared to government-owned, hospital managed by ministry of defense (B = 1.705, p = 0.012) has higher probability to be accredited. Though not statistically significant, accredited hospitals had higher utility and efficiency indicators, as well as higher mortality. CONCLUSIONS: Hospital with higher size and managed by government have higher probability to be accredited independent to its specialty and the intensity of market competition. Higher utility and mortality in accredited hospitals needs further investigation.

Does the agglomeration effect occur in the hospital sector? The impact of agglomeration economies on the financial performance of hospitals-An evidence from Poland.

The main aim of the article is to analyze the occurrence of agglomeration effect in the hospital sector on the basis of financial performance. The considerations are made on the example of hospitals in Poland-the country that survived the latest economic crisis relatively well, usually generating positive values of GDP, but where still there is an ongoing discussion on the final shape of healthcare financing model. The article is based on the assumption that there occur significant differences in financial performance between hospitals according to their location. The research hypothesis is as follows: Hospitals operating in big cities are featured by better financial condition than their counterparts operating in smaller towns. To verify the hypothesis, the methods of financial analysis and statistical hypothesis testing are used. As it is emphasized in the article, the assumption is true and the hypothesis can be verified positively.

Assessing performance of Taiwan hospitals using data envelopment analysis: In view of ownership.

With rapid economic development in Taiwan, people have greater awareness of health care and are paying more attention to it. From the perspective of hospital management, the scale of hospitals and efficiency improvement are of concern to hospital managers. However, the extent of efficiency will differ between public and private hospitals due to their different ownership and goals. The study aims to evaluate the efficiency of public and private hospitals and to investigate the influence of ownership on efficiency of hospitals. The differences between hospitals can be understood by analyzing the features of the organization of hospitals and their geographic environment. In this way, hospitals with relatively low efficiency will be able to make improvements based on concrete evidence. By means of the two-stage method, the efficiency scores of 182 hospitals in Taiwan are compared. In the first stage, the data envelopment analysis is applied to obtain the efficiency scores of hospitals. The results show that private hospitals are more efficient than public hospitals. In the second stage, Tobit regression is used to investigate the factors influencing efficiency obtained by the data envelopment analysis. The results indicate that there are differences between ownership in market competition and the average length of stay.

The goldilocks effect: the rhythms and pace of hospital life.

BACKGROUND: While we have made gains in understanding cultures in hospitals and their effects on outcomes of care, little work has investigated how the pace of work in hospitals is associated with staff satisfaction and patient outcomes. In an era of efficiency, as speed accelerates, this requires examination. DISCUSSION: Older studies of pace in cities found that faster lifestyles were linked to increased coronary heart disease and smoking rates, yet better subjective well-being. In this debate we propose the Goldilocks hypothesis: acute care workplaces operating at slow speeds are associated with factors such as increased wait lists, poor performance and costly care; those that are too fast risk staff exhaustion, burnout, missed care and patient dissatisfaction. We hypothesise that hospitals are best positioned by being in the Goldilocks zone, the sweet spot of optimal pace. CONCLUSION: Testing this hypothesis requires a careful study of hospitals, comparing their pace in wards and departments with measures of performance and patient outcomes.