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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.

Introduction Among the healthcare sector, hospitals are the most resource-intensive infrastructures, contributing significantly to environmental degradation. As global sustainability imperatives intensify, there is a critical need to integrate environmental sustainability into hospital performance measurement systems. The study objective is to highlight the most recurrent environmental performance for hospital sustainability from the recent scientific literature and identify key performance indicators. Methods This study employs a scoping review methodology to analyze peer-reviewed recent publications addressing environmental sustainability performance measurement and management in hospitals. The literature search was performed using PubMed, Web of Science and Scopus databases. The search was limited to papers published from 2009. The initial searches resulted in 545 studies. The final sample included 22 papers. Results The review identifies 6 key sustainability domains: energy management, waste management, water consumption, greenhouse gases emissions, transportation and mobility, and site sustainability. The results underscore the multidimensional nature of environmental performance in healthcare facilities and reveal significant variability in the scope and specificity of existing metrics across studies. Discussion The findings are synthesized to propose a unified, indicator-based environmental sustainability assessment framework for hospitals with a set of 18 environmental key performance indicators (EKPIs). The results underscore the multidimensional nature of environmental performance in healthcare facilities and reveal significant variability in the scope of existing metrics across studies. The findings highlight the necessity of integrating standardized indicators into hospital performance assessment frameworks to ensure comparability, track progress, and drive improvements. Additionally, the lack of harmonized measurement systems poses challenges for benchmarking and scaling sustainable practices across diverse healthcare settings. Conclusion This study contributes to the ongoing debate on sustainable healthcare by proposing a structured framework of EKPIs based on the most recent scientific literature and tailored to hospital environments. The framework offers hospital administrators and policymakers actionable tools to monitor and enhance environmental performance.

Background A widespread assumption across health systems suggests that greater clinicians’ involvement in governance and management roles would have wider benefits for the efficiency and effectiveness of healthcare organisations. However, despite growing interest around the topic, it is still poorly understood how managers with a clinical background might specifically affect healthcare performance outcomes. The purpose of this review is, therefore, to map out and critically appraise quantitatively-oriented studies investigating this phenomenon within the acute hospital sector. Methods The review has focused on scientific papers published in English in international journals and conference proceedings. The articles have been extracted through a Boolean search strategy from ISI Web of Science citation and search source. No time constraints were imposed. A manual search by keywords and citation tracking was also conducted concentrating on highly ranked public sector governance and management journals. Nineteen papers were identified as a match for the research criteria and, subsequently, were classified on the basis of six items. Finally, a thematic mapping has been carried out leading to identify three main research sub-streams on the basis of the types of performance outcomes investigated. Results and contribution The analysis of the extant literature has revealed that research focusing on clinicians’ involvement in leadership positions has explored its implications for the management of financial resources, the quality of care offered and the social performance of service providers. In general terms, the findings show a positive impact of clinical leadership on different types of outcome measures, with only a handful of studies highlighting a negative impact on financial and social performance. Therefore, this review lends support to the prevalent move across health systems towards increasing the presence of clinicians in leadership positions in healthcare organisations. Furthermore, we present an explanatory model summarising the reasons offered in the reviewed studies to justify the findings and provide suggestions for future research.

Introduction 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. Methods 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. Results 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]). Conclusion The funnel plot around the median discriminated better between hospitals with significantly worse and better performance than funnel plots for dichotomized %TWL outcomes. Graphical Abstract

This article seeks to review the Australian healthcare system and compare it to similar systems in other countries to highlight the main issues and problems. A literature search for articles relating to the Australian and other developed countries’ healthcare systems was conducted by using Google and the library of Victoria University, Melbourne. Data from the websites of the Commonwealth of Australia, the Australian Institute of Health and Welfare, the Australian Productivity Commission, the Organisation for Economic Co-operation and Development and the World Bank have also been used. Although care within the Australian healthcare system is among the best in the world, there is a need to change the paradigm currently being used to measure the outcomes and allocate resources. The Australian healthcare system is potentially dealing with two main problems: (a) resource allocation, and (b) performance and patient outcomes improvements. An interdisciplinary research approach in the areas of performance measurement, quality and patient outcomes improvement could be adopted to discover new insights, by using the policy implementation error/efficiency and bureaucratic capacity. Hospital managers, executives and healthcare management practitioners could use an interdisciplinary approach to design new performance measurement models, in which financial performance, quality, healthcare and patient outcomes are blended in, for resource allocation and performance improvement. This article recommends that public policy implementation error and the bureaucratic capacity models be applied to healthcare to optimise the outcomes for the healthcare system in Australia. In addition, it highlights the need for evaluation of the current reimbursement method, freedom of choice to patients and a regular scrutiny of the appropriateness of care.

PurposeAbundant studies of outpatient visits apply traditional recurrent neural network (RNN) approaches; more recent methods, such as the deep long short-term memory (DLSTM) model, have yet to be implemented in efforts to forecast key hospital data. Therefore, the current study aims to reports on an application of the DLSTM model to forecast multiple streams of healthcare data.Design/methodology/approachAs the most advanced machine learning (ML) method, static and dynamic DLSTM models aim to forecast time-series data, such as daily patient visits. With a comparative analysis conducted in a high-level, urban Chinese hospital, this study tests the proposed DLSTM model against several widely used time-series analyses as reference models.FindingsThe empirical results show that the static DLSTM approach outperforms seasonal autoregressive integrated moving averages (SARIMA), single and multiple RNN, deep gated recurrent units (DGRU), traditional long short-term memory (LSTM) and dynamic DLSTM, with smaller mean absolute, root mean square, mean absolute percentage and root mean square percentage errors (RMSPE). In particular, static DLSTM outperforms all other models for predicting daily patient visits, the number of daily medical examinations and prescriptions.Practical implicationsWith these results, hospitals can achieve more precise predictions of outpatient visits, medical examinations and prescriptions, which can inform hospitals' construction plans and increase the efficiency with which the hospitals manage relevant information.Originality/valueTo address a persistent gap in smart hospital and ML literature, this study offers evidence of the best forecasting models with a comparative analysis. The study extends predictive methods for forecasting patient visits, medical examinations and prescriptions and advances insights into smart hospitals by testing a state-of-the-art, deep learning neural network method.

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.

Purpose The purpose of this paper is to explore the influence of hospital’s visibility for sensing (VFS), learning, coordinating and integrating on hospital-supplier collaboration. Second, it explored the influence of hospital-supplier collaboration on hospital supply chain performance. The author also explored how the technology orientation of the medical chain units influences the above linkages. Design/methodology/approach The study adopted a multi-unit study of different hospital supply chains. Consequently, perceptual data were gathered from seven dominant entities in a typical medical/hospital supply chain: hospitals and clinics, accommodation (i.e. hotels), chemistry and pharmaceutical, marketing/public relations/promotion, medical equipment manufacturers, food and beverage and insurance. The responses were gathered using e-mail survey and were analyzed using structural equation modeling. Findings Based on 192 completed responses, the author found positive influences of VFS, learning and integrating on hospital-supplier collaboration and a positive impact of hospital-supplier collaboration on hospital supply chain performance. An insignificant influence of hospital’s visibility for coordinating was noted on hospital-supplier collaboration. The study argued hospitals to invest more for enriching their dynamic capabilities to diagnose the changes in the environment so as to sustain their collaborative relationships leading to positive performance implications. Originality/value The study is the foremost to investigate the effects of hospital’s dynamic capabilities on its collaborative efforts with its key supplier and their influence on hospital supply chain performance. Also the study is foremost in exploring the importance of technology orientation on hospital dynamic capabilities and hospital-supplier collaboration. An important contribution of the research is the conceptualization of supply chain visibility core components (visibility of sensing, visibility of learning, visibility of coordinating and visibility of integrating) in the context of hospital supply chains.

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.

This study demonstrates that digital maturity contributes to strengthened quality and safety performance outcomes in US hospitals. Advanced digital maturity is associated with more digitally enabled work environments with automated flow of data across information systems to enable clinicians and leaders to track quality and safety outcomes. This research illustrates that an advanced digitally enabled workforce is associated with strong safety leadership and culture and better patient health and safety outcomes. This study aimed to examine the relationship between digital maturity and quality and safety outcomes in US hospitals. The data sources were hospital safety letter grades as well as quality and safety scores on a continuous scale published by The Leapfrog Group. We used the digital maturity level (measured using the Electronic Medical Record Assessment Model [EMRAM]) of 1026 US hospitals. This was a cross-sectional, observational study. Logistic, linear, and Tweedie regression analyses were used to explore the relationships among The Leapfrog Group's Hospital Safety Grades, individual Leapfrog safety scores, and digital maturity levels classified as advanced or fully developed digital maturity (EMRAM levels 6 and 7) or underdeveloped maturity (EMRAM level 0). Digital maturity was a predictor while controlling for hospital characteristics including teaching status, urban or rural location, hospital size measured by number of beds, whether the hospital was a referral center, and type of hospital ownership as confounding variables. Hospitals were divided into the following 2 groups to compare safety and quality outcomes: hospitals that were digitally advanced and hospitals with underdeveloped digital maturity. Data from The Leapfrog Group's Hospital Safety Grades report published in spring 2019 were matched to the hospitals with completed EMRAM assessments in 2019. Hospital characteristics such as number of hospital beds were obtained from the CMS database. The results revealed that the odds of achieving a higher Leapfrog Group Hospital Safety Grade was statistically significantly higher, by 3.25 times, for hospitals with advanced digital maturity (EMRAM maturity of 6 or 7; odds ratio 3.25, 95% CI 2.33-4.55). Hospitals with advanced digital maturity had statistically significantly reduced infection rates, reduced adverse events, and improved surgical safety outcomes. The study findings suggest a significant difference in quality and safety outcomes among hospitals with advanced digital maturity compared with hospitals with underdeveloped digital maturity.